The present data product is going to make an analysis of abstract extracted from 1000 articles of intelligent tutor systems domain and aims to find out 1a. questions.
Web of Science database was selected for the search for desired articles. Keyword employed was “Intelligent Tutor Systems,” and the subject area was refined to “education and educational research”. From this initial search process, 2,827 articles were generated. Considering the content vailidity, the first 1000 articles were finally selected through sorting by relevance. An excel recording content of the selected 1000 articles was exported from the Web of Science database saved in the local computer. The content included author, title, abstract, publication type, publication year, conference name, conference location, etc. For ease of use, the excel was formatted into .csv file.
The {tidyverse} is a coherent system of packages for data manipulation, exploration and visualization that share a common design philosophy. It includes ggplot2 - for data visualisation., dplyr - for data manipulation, tidyr - for data tidying, and so on.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using familiar functions from the {tidyverse} collection of packages.
library(tidytext)The {wordcloud2} is a visual representation of text data. Tags are usually single words, and the importance of each tag is shown with font size or color. It is useful for quickly perceiving the most prominent terms, and widely used in media and well understood by the public.
library(wordcloud2)The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in micro blog-like contexts.
library(vader)Read local .csv file then write into the data folder of the present project for future use.
inttutor_webofsci <- read_csv("/Users/minzhuang/Downloads/intelligenttutor.csv")## Rows: 1000 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (35): Publication Type, Authors, Book Authors, Book Editors, Book Group ...
## dbl (3): Publication Year, Start Page, Pubmed Id
## lgl (34): Book Series Subtitle, Language, Document Type, Author Keywords, Ke...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.write_csv(inttutor_webofsci, "data/intelligenttutor.csv")Since not all the variables are needed for analysis, this part selects the data necessary for further workflow.
inttutor_original <- read_csv("data/intelligenttutor.csv")## Rows: 1000 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (35): Publication Type, Authors, Book Authors, Book Editors, Book Group ...
## dbl (3): Publication Year, Start Page, Pubmed Id
## lgl (34): Book Series Subtitle, Language, Document Type, Author Keywords, Ke...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.inttutor <- inttutor_original %>%
select(`Publication Year`,
`Conference Location`,
`Source Title`,
`Article Title`,
`Abstract`
)
inttutor## # A tibble: 1,000 × 5
## `Publication Year` `Conference Location` `Source Title` Artic…¹ Abstr…²
## <dbl> <chr> <chr> <chr> <chr>
## 1 2008 Zagreb, CROATIA KNOWLEDGE - BASED I… Extend… Develo…
## 2 2017 <NA> JOURNAL OF INTELLIG… Intell… The im…
## 3 2012 Hyderabad, INDIA 2012 IEEE FOURTH IN… Tutori… Intell…
## 4 2019 <NA> INTERNATIONAL JOURN… An age… This p…
## 5 1998 <NA> ARTIFICIAL INTELLIG… Toward… For su…
## 6 2014 Cairo, EGYPT 2013 9TH INTERNATIO… Novel … Exampl…
## 7 2020 <NA> EDUCATION AND INFOR… Design… The ai…
## 8 2007 Beijing, CHINA UNIVERSAL ACCESS IN… Agents… Intell…
## 9 2017 Porto, PORTUGAL PROCEEDINGS OF THE … Knowle… In thi…
## 10 2011 Manchester, ENGLAND AGENT AND MULTI-AGE… Oscar:… This p…
## # … with 990 more rows, and abbreviated variable names ¹`Article Title`,
## # ²AbstractFor this part of workflow, the goal is to transform our
abstract data into a “tidy text” of one-token-per-row
format.
tidy_inttutor_0 <- inttutor %>%
unnest_tokens(output = word,
input = `Abstract`) %>%
relocate(word)
head(tidy_inttutor_0)## # A tibble: 6 × 5
## word `Publication Year` `Conference Location` `Source Title` Artic…¹
## <chr> <dbl> <chr> <chr> <chr>
## 1 developments 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 2 in 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 3 the 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 4 wireless 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 5 infrastructure 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 6 have 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## # … with abbreviated variable name ¹`Article Title`This workflow is to remove stop words that are not useful for the
analysis. The stop_words dataset in the {tidytext} package
contains stop words from three lexicons. In order to remove these stop
words, anti_join() function is used that looks for matching
values in a specific column from two datasets and returns rows from the
original dataset that have no matches.
tidy_inttutor_1 <- anti_join(tidy_inttutor_0,
stop_words,
by = "word")
head(tidy_inttutor_1)## # A tibble: 6 × 5
## word `Publication Year` `Conference Location` `Source Title` Artic…¹
## <chr> <dbl> <chr> <chr> <chr>
## 1 developments 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 2 wireless 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 3 infrastructure 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 4 paved 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 5 learning 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## 6 paradigm 2008 Zagreb, CROATIA KNOWLEDGE - B… Extend…
## # … with abbreviated variable name ¹`Article Title`Use the count() function to take a quick count of the
most common tokens in the data frame to see if the results are
meaningful.
tidy_inttutor_1 %>%
count(word) %>%
arrange(desc(n))## # A tibble: 7,000 × 2
## word n
## <chr> <int>
## 1 learning 2242
## 2 tutoring 1729
## 3 system 1623
## 4 students 1482
## 5 intelligent 1419
## 6 systems 989
## 7 student 893
## 8 based 844
## 9 model 708
## 10 knowledge 695
## # … with 6,990 more rowstidy_inttutor_1## # A tibble: 78,890 × 5
## word `Publication Year` `Conference Location` Source Titl…¹ Artic…²
## <chr> <dbl> <chr> <chr> <chr>
## 1 developments 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 2 wireless 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 3 infrastructure 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 4 paved 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 5 learning 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 6 paradigm 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 7 named 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 8 mobile 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 9 learning 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 10 learning 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## # … with 78,880 more rows, and abbreviated variable names ¹`Source Title`,
## # ²`Article Title`Save the tidied data as a new data frame and also save it as a .csv file in our data folder.
tidy_inttutor <- tidy_inttutor_1
write_csv(tidy_inttutor_1, "data/tidy_inttutor.csv")
tidy_inttutor## # A tibble: 78,890 × 5
## word `Publication Year` `Conference Location` Source Titl…¹ Artic…²
## <chr> <dbl> <chr> <chr> <chr>
## 1 developments 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 2 wireless 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 3 infrastructure 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 4 paved 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 5 learning 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 6 paradigm 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 7 named 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 8 mobile 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 9 learning 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## 10 learning 2008 Zagreb, CROATIA KNOWLEDGE - … Extend…
## # … with 78,880 more rows, and abbreviated variable names ¹`Source Title`,
## # ²`Article Title`The exploratory data analysis focus on top 50 tokens of the tidy_inttutor data frame, and to help illustrate the relative frequency each of these top words occurs, word clouds will be generated using {wordclouds2} package.
Let’s take a look at the top tokens among the abstracts of 1000
articles of intelligent tutor systems by counting the number of times
each word occurs with the top_n() function
from the {dplyr} package.
inttutor_top_tokens <- tidy_inttutor %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by ninttutor_top_tokens## # A tibble: 50 × 2
## word n
## <chr> <int>
## 1 learning 2242
## 2 tutoring 1729
## 3 system 1623
## 4 students 1482
## 5 intelligent 1419
## 6 systems 989
## 7 student 893
## 8 based 844
## 9 model 708
## 10 knowledge 695
## # … with 40 more rowsWord clouds are much maligned and sometimes referred to as the “pie charts of text analysis”, they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret.
This workflow is to use {wordclouds2} package for generating HTML based interactive word clouds.
wordcloud2(inttutor_top_tokens)Not surprisingly, “Intelligent”, “Tutoring”, “Systems” are the top tokens, since we know that even before analyzing, it’s better to exclude these tokens from a final data product for more meaningful insights.
inttutor_top_tokens %>%
filter(word != "intelligent" & word != "tutoring"
& word != "systems" & word != "system"
& word != "students" & word != "student")## # A tibble: 44 × 2
## word n
## <chr> <int>
## 1 learning 2242
## 2 based 844
## 3 model 708
## 4 knowledge 695
## 5 paper 638
## 6 tutor 595
## 7 results 390
## 8 study 368
## 9 research 364
## 10 domain 313
## # … with 34 more rowswordcloud2(inttutor_top_tokens)This workflow is to analyze text using bigrams -
tokens consisting of two words. Use unnest_tokens()
function to tokenize abstract into consecutive sequences of words called
n-grams. By seeing how often word X is followed by word
Y, we could then build a model of the relationships between them. Set
n to 2 to examine pairs of two consecutive words.
inttutor_bigrams <- inttutor %>%
unnest_tokens(bigram,
`Abstract`,
token = "ngrams",
n = 2)Before we move any further let’s take a quick look at the most common bigrams in the data.
inttutor_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 64,073 × 2
## bigram n
## <chr> <int>
## 1 of the 1157
## 2 intelligent tutoring 1074
## 3 in the 692
## 4 tutoring system 644
## 5 tutoring systems 616
## 6 this paper 520
## 7 to the 450
## 8 in this 389
## 9 based on 351
## 10 the system 339
## # … with 64,063 more rowsAs we saw above, a lot of the most common bigrams are pairs of common
(uninteresting) words as well. By using the separate()
function from the tidyr package, which splits a column into
multiple based on a delimiter. This lets us separate it into two
columns, “word1” and “word2”, at which point we can remove cases where
either is a stop-word.
library(tidyr)
bigrams_separated <- inttutor_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
tidy_bigrams <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")Let’s take a look at our bigram counts now.
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 20,982 × 2
## bigram n
## <chr> <int>
## 1 intelligent tutoring 1074
## 2 tutoring system 644
## 3 tutoring systems 616
## 4 web based 113
## 5 student model 106
## 6 learning process 104
## 7 learning environment 92
## 8 systems itss 86
## 9 natural language 81
## 10 artificial intelligence 80
## # … with 20,972 more rowsBetter, but there are still many tokens not especially useful for analysis.
Let’s make a custom custom stop word dictionary for bigrams.
my_words <- c("intelligent", "tutoring", "systems", "system")Now let’s separate, filter, and unite again.
tidy_bigrams <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")Let’s take another quick count of our bigrams and creat a word cloud.
tidy_bigrams %>%
count(bigram, sort = TRUE) %>%
wordcloud2()This part is to measure sentiment of abstract of 1000 articles of intelligent tutor systems domain. As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis.
This workflow use sample_n() to save part of the text as
another data frame object for modeling considering the long processing
time if we include all the text. vader_df() takes the new
data frame as parameter and use the $ operator to include
only the text part of data frame.
inttutor_sample <- read_csv("data/intelligenttutor.csv") %>%
sample_n(500)## Rows: 1000 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (35): Publication Type, Authors, Book Authors, Book Editors, Book Group ...
## dbl (3): Publication Year, Start Page, Pubmed Id
## lgl (34): Book Series Subtitle, Language, Document Type, Author Keywords, Ke...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.inttutor_sample## # A tibble: 500 × 72
## Publication…¹ Authors Book …² Book …³ Book …⁴ Autho…⁵ Book …⁶ Group…⁷ Artic…⁸
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 C Neji, … <NA> Woolf,… <NA> Neji, … <NA> <NA> Agent-…
## 2 C Otsuki… <NA> <NA> IEEE Otsuki… <NA> <NA> An Int…
## 3 J Siemer… <NA> <NA> <NA> Siemer… <NA> <NA> Toward…
## 4 J Kaklau… <NA> <NA> <NA> Kaklau… <NA> <NA> Affect…
## 5 J Singh,… <NA> <NA> <NA> Singh,… <NA> <NA> A Para…
## 6 J Le, HX… <NA> <NA> <NA> Le, Hu… <NA> <NA> Design…
## 7 J Payne,… <NA> <NA> <NA> Payne,… <NA> <NA> Effect…
## 8 C Khodei… <NA> <NA> IEEE Khodei… <NA> <NA> Bayesi…
## 9 C Vail, … <NA> Micare… <NA> Vail, … <NA> <NA> Predic…
## 10 C Cetint… <NA> Dimitr… <NA> Cetint… <NA> <NA> Learni…
## # … with 490 more rows, 63 more variables: `Source Title` <chr>,
## # `Book Series Title` <chr>, `Book Series Subtitle` <lgl>, Language <lgl>,
## # `Document Type` <lgl>, `Conference Title` <chr>, `Conference Date` <chr>,
## # `Conference Location` <chr>, `Conference Sponsor` <chr>,
## # `Conference Host` <chr>, `Author Keywords` <lgl>, `Keywords Plus` <lgl>,
## # Abstract <chr>, Addresses <lgl>, Affiliations <lgl>,
## # `Reprint Addresses` <lgl>, `Email Addresses` <lgl>, …vader_inttutor <- vader_df(inttutor_sample$`Abstract`)
vader_inttutor## text
## 1 Affective Computing is a new Artificial Intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This paper represents a study about the integration of this new area in the intelligent tutoring system. The main goal is to analyses learner facial expressions and show how Affective Computing could contribute for this interaction, being part of the development of computer systems where information about learner' emotion would be helpful.
## 2 A survey says that the success rate of information system development project is about 30%. In order to lead a project to a successful one, many companies performed several kinds of education methods to educate more excellent project managers. One of the education methods is the case-based learning that a learner thinks problems and solutions on the practical case. The tutor that provides advice on how to think about the case plays an important role in the case-based learning. However, tutors are fewer than learners. In order to make the case-based learning available online without the tutor, we propose an intelligent tutoring system for case-based e-Learning on project management. A tutor agent instead of a human tutor provides appropriate advice according to a learner's input to the system. The proposed method compares the learner's input to the answers that the human tutor preliminary decides in order to verify the learner's input. A tutor agent chooses the advice corresponding to the content of the input from an advice list. The system repeats to provide advice to the learner until the learner finishes thinking about sufficient solutions.
## 3 For successful teaching to take place an intelligent tutoring system has to he able to cope with any student errors that may occur during a tutoring interaction. Remedial tutoring is increasingly viewed as a central part of the overall tutoring process, and recent research calls for adaptive remedial tutoring. This paper discusses the issues of remedial tutoring that have been proposed of implemented to support efficient remedial tutoring. These issues serve to uncover any underlying principles of remediation that govern remedial tutoring with intelligent tutoring systems. In order to incorporate these principles of remediation into intelligent tutoring systems development processes this paper continues with the development of a model that can be employed in the development of an intelligent tutoring system that is capable of offering remedial tutoring according to these principles. This model is a formalisation of remedial interventions with intelligent tutoring systems. To demonstrate how the model can be employed in developing an intelligent tutoring system, INTUITION, the implementation of an existing business simulation game, has been developed. This paper concludes with an illustration of how the model for remedial operations provides for remedial tutoring within INTUITION. The evaluation of INTUITION shows that the model for remedial operations is a useful method for providing efficient remedial tutoring.
## 4 There are quite a lot of researche performed in the world, which prove that interest, stress and learning productivity of a learner to a large extent determine study results. Moreover, learning emotionality, pleasurableness and attractiveness, which help to increase its efficiency, are stressed by various researchers. Researchers have noticed, that a student's interest, stress, learning productivity and academic achievement are quite closely related. Experts have noticed that in order to cause interest, increase learning productivity and maintain stress in a rational level of a learner it is necessary to constantly change the learning subject with regard to situational and individual interest. For this reason, an Affective Tutoring System for Built ENvironment Management (ATEN), developed by the authors of the article is very suitable. The ATEN was developed and fine-tuned in the course of the TEMPUS project Reformation of the Curricula on Built Environment in the Eastern Neighbouring Area. One of the ATEN innovations is that the System integrates the self-assessment and self-esteem measurement of students with multimodal biometric and intelligent methodologies and technologies. Affective Tutoring System for Built Environment Management can create a rational version of a learning process tailored to a specific student, taking into account such factors as how much the studies are interesting or difficult and the level of stress (with the help of biometric technologies). The System includes an automatic function that takes module topics and compiles an optimal set of personalised materials for a specific student. The case study submitted in this article partly demonstrates this developed System. (C) 2014 Elsevier Ltd. All rights reserved.
## 5 Face-to-face tutoring offers a learning environment that best suits the learner's preferences (learning styles) and grasping levels (learning levels). This cognitive intelligence has been blended in our proposed intelligent tutoring system christened as Seis Tutor. In this paper, we have detailed the architecture of Seis Tutor system and compared it with other existing traditional tutoring systems. Further, the performance of Seis Tutor has been evaluated in terms of personalization and adaptation through a comparison with some existing tutoring systems, i.e., My Moodle, Course-Builder, and Teachable.
## 6 Purpose In intelligent tutoring systems (ITS), learners were often granted limited authority and are forced to obey the decision of the system which might not satisfy their needs. Failure to grant learners sufficient autonomy could yield unexpected effects that hinder learning, including undermining learners' motivation, priming learners' aversion to the algorithm. On the contrary, granting learners overwhelming autonomy could also be harmful as the absence of learning support would also have a negative impact on learning. As such, this study aims to design and implement an intelligent tutoring system that offers learners proper autonomy. Design/methodology/approach The main learning activity in the system is doing exercises, and by finishing exercises learners could earn virtual coins. Based on item response theory, exercises are administered to learners with proper difficulty. Based on a recommended difficulty parameter predicted by the system, learners could manually modify the difficulty of the exercises, they could earn more credits by finishing more challenging exercises. Meanwhile, a pedagogical agent is embedded. Learners could customize the agent's personality jointly with the system to create the learning context they prefer. Findings A intelligent tutoring system with proper learner autonomy (LA) is designed and implemented. Originality/value Few previous researches have noticed the potentially important role that LA plays in ITS. Learning might be facilitated using such a design.
## 7 Objectives: Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from (earning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on (earning gains when using a tutor with limited enforcement. Methods: Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goat states and solution paths were determined. Results: Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path. Conclusions: Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path. (C) 2009 Published by Elsevier B.V.
## 8 In this paper we present student knowledge modeling algorithm in a probabilistic domain within an intelligent tutoring system. The student answers to questions requiring diagnosing skills are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the student's and model answers. Three different approaches to updating are suggested, namely coarse, refined, and blended updating. In addition, different granularity levels are evaluated by changing the value of the updating step and the output of this parametric study is indicated. Results suggest that the refined model provides better approximation of the student model while utilizing blended model decreases the required trial numbers to model the student knowledge with limited reduction in accuracy.
## 9 Modeling student learning during tutorial interaction is a central problem in intelligent tutoring systems. While many modeling techniques have been developed to address this problem, most of them focus on cognitive models in conjunction with often-complex domain models. This paper presents an analysis suggesting that observing students' multimodal behaviors may provide deep insight into student learning at critical moments in a tutorial session. In particular, this work examines student facial expression, electrodermal activity, posture, and gesture immediately following inference questions posed by human tutors. The findings show that for human-human task-oriented tutorial dialogue, facial expression and skin conductance response following tutor inference questions are highly predictive of student learning gains. These findings suggest that with multimodal behavior data, intelligent tutoring systems can make more informed adaptive decisions to support students effectively.
## 10 The paper proposes a machine learning model that can automatically identify off-task behaviors of students while using an intelligent tutoring system. Only log files that record students' actions with the system are used for the development of the model. The model utilizes a set of time features, performance features and mouse movement features and is compared to i) a model that only utilizes time features, ii) a model that uses time and performance features. In order to address data sparseness problem, a robust Ridge Regression algorithm is designed to estimate model parameters. An extensive set of experiment results demonstrate the power of using multiple types of evidence as well as the robust Ridge Regression algorithm.
## 11 Virtual learning environments (VLEs) are used in distance learning and classroom teaching as teachers and students support tools in the teaching-learning process, where teachers can provide material, activities and assessments for students. However, this process is done in the same way for all the students, regardless of their differences in performance and behavior in the environment. The purpose of this work is to develop an agent-based intelligent learning environment model inspired by intelligent tutoring to provide adaptability to distributed VLEs, using Moodle as a case study and taking into account students' performance on tasks and activities proposed by the teacher, as well as monitoring his/her study material access.
## 12 We implemented and evaluated a collaborative lecture module in an ITS that models the pedagogical and motivational tactics of expert human tutors Inspired by the lecture delivery styles of the expert tutors, the collaborative lectures of the ITS were conversational and Interactive, instead of a polished one-way information delivery from tutor to student We hypothesized that the enhanced interactivity of the expert tutor lectures were linked to efforts to promote student engagement This hypothesis was tested in an experiment that compared the collaborative lecture module (dialogue) to less interactive alternatives such as monologues and vicarious dialogues The results indicated that students in the collaborative lecture condition reported more arousal (a key component of engagement) than the controls and that arousal was positively correlated with learning gains We discuss the implications of our findings for ITSs that aspire to model expert human tutors
## 13 The learning of programming is a field of research with relevant studies and publications for more than 25 years. Since its inception, it has been shown that its difficulty lies in the high level of abstraction required to understand certain programming concepts. However, this level can be reduced by using tools and graphic representations that motivate students and facilitate their understanding, associating real-world elements with specific programming concepts. Thus, this paper proposes the use of an intelligent tutoring system (ITS) that helps during the learning of programming by using a notation based on a metaphor of roads and traffic signs represented by 3D graphics in an augmented reality (AR) environment. These graphic visualizations can be generated automatically from the source code of the programs thanks to the modular and scalable design of the system. Students can use them by leveraging the available feedback system, and teachers can also use them in order to explain programming concepts during the classes. This work highlights the flexibility and extensibility of the proposal through its application in different use cases that we have selected as examples to show how the system could be exploited in a multitude of real learning scenarios.
## 14 This paper presents the Logic-ITA, an Intelligent Teaching Assistant system for the teaching/learning of propositional logic. Intelligent Teaching Assistant Systems are dedicated both to learners and teachers. The system embeds three tools: the Logic Tutor, the LT-Configurator and the LT-Analyser. The Logic Tutor is an intelligent tutoring system destined to the students. The other two tools are dedicated to the teacher. The LT-Configurator manages teaching configuration settings and material. The LT-Analyser is for monitoring the class' progress and collect data.
## 15 The paper proposes a method that is part of a new, extended architecture of our web-based intelligent tutoring system (ITS). It was developed to provide hints to students during learning through the application of educational data mining (EDM). The architecture consists of three modules - a) a communication module that enables seamless communication with data mining tools; b) a clustering module that discovers clusters in student data based on their activity and c) a sequential pattern mining (SPM) module that finds efficient frequent learning patterns of students in each cluster. Finally, the obtained results are used by the tutoring module to provide hints to a student on which item to learn next (or previous to the selected one). To improve the hint selection process we developed a method for cluster grading to determine which cluster represents the group that has been using the ITS in the manner closest to an envisioned optimum. We verify the method on data gathered from two groups of students who used the system to master a knowledge domain, and present the obtained results.
## 16 <NA>
## 17 Tutoring research has been ongoing on since the 1960s and workable computer-based tutoring systems (CBTS) have been around since the early 1980s. Expectations are on the rise for CBTS to be available to the masses in much the same way that human tutoring is available from a variety of sources today. A limiting factor in the widespread use of CBTS is the cost to: author tutoring systems; author/deliver domain-specific instructional content; and assess the effectiveness of CBTS tools and methods. A structural framework to represent knowledge within the CBTS domain would enhance reuse and streamline processes making them easier to author on production line scale, and opening the entry point for CBTS to non-computer scientists. This paper considers the benefits and challenges in developing an ontology for a Generalized Intelligent Framework for Tutors (GIFT) to support the development of authoring, instructional and assessment standards and tools for CBTS.
## 18 Nowadays different approaches are coming forth to tutor students using computers. In this paper, a computer based Intelligent Tutoring System (ITS) is presented. It projects out a new approach dealing with diagnosis in student modeling which emphasizes on Bayesian Networks (for decision making) and Item Response Theory (for adaptive question selection). The advantage of such an approach through Bayesian Networks (Formal framework of Uncertainty) is that this structural model allows substantial simplification when specifying parameters (Conditional Probabilities) which measures student ability at different levels of granularity. In addition, the probabilistic student model is proved to be more quicker, accurate and efficient. Since most of the tutoring systems are static HTML web pages of class textbooks, our intelligent system can help a student navigate through online course materials and recommended learning goals.
## 19 Mind-wandering is a ubiquitous phenomenon that is negatively related to learning. The purpose of the current study is to examine mind-wandering during vicarious learning, where participants observed another student engage in a learning session with an intelligent tutoring system (ITS). Participants (N = 118) watched a prerecorded learning session with GuruTutor, a dialogue-based ITS for biology. The response accuracy of the student interacting with the tutor (i.e., the firsthand student) was manipulated across three conditions: Correct (100% accurate responses), Incorrect (0% accurate), and Mixed (50% accurate). Results indicated that Firsthand Student Expertise influenced the frequency of mind-wandering in the participants who engaged vicariously (secondhand students), such that viewing a moderately-skilled firsthand learner (Mixed correctness) reduced the rate of mind-wandering (M = 25.4%) compared to the Correct (M = 33.9%) and Incorrect conditions (M = 35.6%). Firsthand Student Expertise did not impact learning, and we also found no evidence of an indirect effect of Firsthand Student Expertise on learning through mind-wandering (Firsthand Student Expertise -> Mind-wandering -> Learning). Our findings provide evidence that mind-wandering is a frequent experience during online vicarious learning and offer initial suggestions for the design of vicarious learning experiences that aim to maintain learners' attentional focus.
## 20 This meta-analysis synthesizes research on the effectiveness of intelligent tutoring systems (ITS) for college students. Thirty-five reports were found containing 39 studies assessing the effectiveness of 22 types of ITS in higher education settings. Most frequently studied were AutoTutor, Assessment and Learning in Knowledge Spaces, eXtended Tutor-Expert System, and Web Interface for Statistics Education. Major findings include (a) Overall, ITS had a moderate positive effect on college students' academic learning (g = .32 to g =.37); (b) ITS were less effective than human tutoring, but they outperformed all other instruction methods and learning activities, including traditional classroom instruction, reading printed text or computerized materials, computer-assisted instruction, laboratory or homework assignments, and no-treatment control; (c) ITS's effectiveness did not significantly differ by different ITS, subject domain, or the manner or degree of their involvement in instruction and learning; and (d) effectiveness in earlier studies appeared to be significantly greater than that in more recent studies. In addition, there is some evidence suggesting the importance of teachers and pedagogy in ITS-assisted learning.
## 21 The efficacy of a tutoring system for pre-algebra instruction plus human tutoring was compared to instruction provided to small groups of middle school students by experienced human math tutors, with instructional time held constant. Students completed pre- and post-tests of computation, fractions, algebra and rational numbers skills. Results indicated that students showed significant improvement from pre- to post-test, but there was no difference as a function of type of tutoring. The findings help to establish the efficacy of ITS instruction relative to skilled human tutoring of students in small groups.
## 22 The most important feature of the intelligent tutoring systems (ITS), one of the most popular study topics of recent years, is that it provides an opportunity for individual learning by taking students' individual differences into account. In order to be able to realize this feature, it is necessary that the system recognizes students well. The process of recognizing student is performed as a result of observations which ITS applies on students. A number of uncertainties arise during these observations. In order to minimize learning uncertainties and create a productive and effective ITS, type-2 fuzzy logic, one of the artificial intelligence techniques, is used in the system developed in this study. In order to show the effectiveness of the developed web-based ITS, it is applied to the teaching of a basic Control Course. The educational evaluation of the system is presented in the paper. (c) 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 561-571, 2013
## 23 Intelligent tutoring systems are effective for improving students' learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1-14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94-111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98-129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1-34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.
## 24 In this study, an intelligent agent to guide students throughout the course material in the internet is defined. The agent will help students to study and learn the concepts in the course by giving navigational support according to their knowledge level. Overlay model is used to model the knowledge levels of students.
## 25 The ASSISTment system was used by over 600 students in 2004-05 school year as part of their math class. While in [7] we reported student learning within the ASSISTment system, in this paper we focus on the assessment aspect. Our approach is to use data that the system collected through a year to tracking student learning and thus estimate their performance on a high-stake state test (MCAS) at the end of the year. Because our system is an intelligent tutoring system, we are able to log how much assistance students needed to solve problems (how many hints students requested and how many attempts they had to make). In this paper, our goal is to determine if the models we built by taking the assistance information into account could predict students' test scores better. We present some positive evidence that shows our goal is achieved.
## 26 Intelligent tutoring systems constitute an evolution of computer-aided educational software. We present here the modules of an intelligent tutoring system for Automatic Control, developed in our department. Through the software application developed, students can perform complete automatic control laboratory experiments, either over the departmental local area network or over the Internet. Monitoring of access to the system (local as well as international), along with student performance statistics, has yielded strongly encouraging results (as of fall 2004), despite the advanced technical content of the presented paradigm, thus showing the potential of the system developed for education and for training.
## 27 There are different approaches that drive the development and use of educational software, such as Interactive Geometry Systems - IGS and Intelligent Tutoring Systems - ITS. Considering their benefits to teachers and students, these systems may be used to complement each other. The ongoing development of ITS features in an existing IGS called iGeom is presented. First, the limitations of both approaches are listed, describing possible benefits of using them together. Then, the resulting component architecture of the conducted analysis and software design is outlined. The ITS paradigm chosen was Example-tracing Tutors. The current state of research is the tutoring features implementation and planning for testing in classrooms and in distance learning courses.
## 28 This paper presents a way to predict student actions, by using student logs generated by a 3D virtual environment for procedural training. Each student log is categorized in a cluster based on the number of errors and the total time spent to complete the entire practice. For each cluster an extended automata is created, which allows us to generate more reliable predictions according to each student type. States of this extended automata represent the effect of a student correct or failed action. The most common behaviors can be predicted considering the sequences of more frequent actions. This is useful to anticipate common student errors, and this can help an Intelligent Tutoring System to generate feedback proactively.
## 29 this study, we examined the influence of achievement goals and scaffolding on self-regulated learning (SRL) and achievement within MetaTutor, a multi-agent intelligent tutoring system. Eighty-three (N = 83) undergraduate students were randomly assigned to either a control or prompt and feedback condition and engaged in a 1-h learning session with MetaTutor to learn about the human circulatory system. Process and product data were collected from all participants prior to, during, and following the session. MANCOVA analyses revealed that students in the prompt and feedback condition deployed more SRL strategies and spent more time viewing relevant science material compared to students in the control condition. Results also revealed a significant interaction between achievement goals and condition on achievement outcomes, such that learners adopting a dominant performance-approach demonstrated higher achievement in the prompt and feedback condition. Findings are discussed in relation to the role of motivation in self-regulated learning within computer-based learning environments. Implications for the design of pedagogical agents are also discussed. (C) 2015 Elsevier Ltd. All rights reserved.
## 30 This paper introduces a MOF-based metamodel for pedagogic strategy modeling in Intelligent Tutoring Systems. The metamodel is named METAGOGIC and allows the generation of pedagogical strategies models using endogenous mapping between the abstraction layers of the MOF framework. In METAGOGIC the pedagogical strategies are represent in three main sections named Context, PedagogicalApproach and InstructionalActivity. Consistency of pedagogical strategies model generated from METAGOGIC was validated using the technique of tracing. The validation result showed that the model was consistent with METAGOGIC specifications.
## 31 This study attempted to determine the influence of prior knowledge in mathematics of students on learner-interface interactions in a learning-by-teaching intelligent tutoring system. One hundred thirty-nine high school students answered a pretest (i.e., the prior knowledge in mathematics) and a posttest. In between the pretest and posttest, they used the SimStudent, an intelligent tutoring system that follows a teaching-by-learning paradigm. The intervention period lasted for three consecutive days with 1 hour session each. SimStudent captured learner-interface interactions, such as time spent tutoring, number of quizzes conducted, and number of hints requested. It was disclosed that prior knowledge in term identification was the only skill that had a consistent, positive, and significant influence on learner-interface interaction with a SimStudent. Thus, the null hypothesis stating that prior knowledge in mathematics does not significantly influence interaction of students with a simulated student was partially rejected. It was concluded that the students may demonstrate or omit a skill, depending on their prior knowledge on identifying the terms of equations and the next step in solving equations. Recommendations and directions for future studies were presented.
## 32 This conceptual paper integrates empirical studies and existing conceptual work describing emotion regulation strategies deployed in intelligent tutoring systems and advances an integrated framework for the development and evaluation of emotion-aware systems.
## 33 Oscar is a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student's learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and adapting material to suit an individual's learning style. Prediction of learning style is undertaken through capturing independent variables during the conversation. The variable with the highest value determines the individuals learning style. This paper proposes a new method which uses a fuzzy classification tree to build a fuzzy predictive model using these variables which are captured through natural language dialogue Experiments have been undertaken on two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). Early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables.
## 34 Virtual tutoring is a new area in e-learning focused on using artificial intelligence, virtual reality and psychology; it is trained with capabilities to adapt the knowledge and learning abilities for every student's needs. Virtual tutoring uses only virtual characters for interactions between man and machine. This paper is aimed to present the design for a virtual character replacement (even if it is based on virtual reality or on web technologies) by a real one. A solution could be the use of a robot platform in the e-learning process. This robot is called the substitute robot teacher and has to respond with voice and gestures to the individual needs of the student by processing audio and video information using an intelligent emotional agent.
## 35 Despite many research efforts focused on the development of algebraic reasoning and the resolution of story problems, several investigations have reported that relatively advanced students experience serious difficulties in symbolizing certain meaningful relations by using algebraic equations. In this paper, we describe and justify the Graphical User Interface of an Intelligent Tutoring System that allows learning and practising the procedural aspects involved in translating the information contained in a story problem into a symbolic representation. The application design has been driven by cognitive findings from several previous investigations. First, the process of translating a word problem into an algebraic form has been treated in isolation, and clearly separated from algebraic manipulation. Second, the user interface has been devised to force a systematic approach to problem solving, and also avoid the use of a non-algebraic reasoning. Third, sensor-free affective support has been added by using a machine learning approach that relies on data captured from a series of experimental sessions involving 48 subjects. The evaluation of the resulting application has revealed a positive and significant impact in learning gains.
## 36 A model of intelligent tutoring systems with emotional pedagogical agents is presented in this paper, and the functionalities of the key components of the system are described. To improve the interaction between learners and the system, a kind of emotional pedagogical agents which can deduce users' emotional statues, is introduced in order to improve pedagogical effects. The emotional pedagogical agents produces personalized learning units dynamically based on the information provided by user models and the expression information collected from cameras, in order to improve the self-adaptability and pedagogical effects of the system.
## 37 An Intelligent Tutoring System (ITS) aims to customize teaching processes dynamically according to student's profile and activities by means of artificial intelligence techniques. The architecture of an ITS defines its components where the pedagogical model is crucial, because the ITS complexity will depend on its scope (specific or generic). Our interest is focused on generic ITS that are very complex due to the fact that could be applied to different educational domains. This contribution proposes an architecture for ITS that uses a Competency-based learning pedagogical model, in order to manage the complexity and make them easier to understand, together a diagnosis process for such a type of systems.
## 38 The Java (TM) Intelligent Tutoring System (JITS) research project involves the development of a programming tutor designed for students in their first programming course in Java (TM) at the College and University level. This paper describes recent progress on the work presented at the last WBE IASTED conference. The previous paper, entitled A Prototype for an Intelligent Tutoring System for Students Learning to Program in Java (TM) presented an overview of the architectural design including state-of-the-art web-based distributed architecture, the AI techniques used, and the programmer-optimized user interface. This paper delves further into the mechanism of the Java (TM) Tutor which is responsible for the syntax and semantic analysis of the code that the student submits for a programming problem. The ultimate goal of this inner-component of JITS is to understand the 'intent' of the student by carefully analyzing the student's code.
## 39 Literature shows that Intelligent Tutoring Systems (ITS) are growing in acceptance and popularity because they increase performances of students. leverage cognitive development, but also significantly reduce time to acquire knowledge and competencies We present an ITS offering the opportunity of evaluating various metacognitive indicators and able to share this in with other learning tools Our online tutor is based on an existing ITS authoring tool that we extended to support metacognition and share learners' profiles and activities into a standardized. distributed and open tracking repository
## 40 Gaming the system is a behaviour that must be avoided when interactive learning environments are designed. However, from the point of view of the research in mathematics education, the observation of this behaviour may bring to light the students' knowledge. In this paper, we provide results of a study in which primary education students (10-11 years old), grouped in pairs, solved problems in an arithmetical way using an intelligent tutoring system. We analyse cases in which the students were able to refine a fuzzy idea of how to calculate a quantity based on the belief that they necessarily had to use certain quantities, operations or conceptual schemes. We also provide examples of how sometimes such students' certainties can become obstacles.
## 41 Intelligent Tutoring Systems (ITSs) are intended to help in tutoring students in specific domains, typically by improving their problem solving skills. An important aspect of such ITSs is the ability to solve problems in the same manner that the student would, in addition to interpreting student actions and providing relevant feedback and help in case of any errors. Cognitive models, that mimic the way procedural knowledge is represented in human minds, are excellent means toward achieving this goal. This paper discusses cognitive modeling in the MAth Story problem Tutor (MAST). MAST is a Web-based ITS that can generate probability story problems of different contexts, types and difficulty levels. A major contribution of the paper is the ability of MAST to symbolize the probability word problems and solve them in the same manner that the student would. The paper discusses the model tracing approach of MAST to interpret the student actions in symbolizing the word problems and estimating the required probabilities to provide relevant feedback and help in case of any errors. Evaluation results have shown the ability of MAST to tutor the students and considerably improve their probability story problem solving skills.
## 42 With the rapid development of computers and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Building a web-based learning environment depends on related learning theory and technology support. Previously, many researchers put efforts into e-learning system with emphasizing the application of multimedia elements; they often neglect the importance of three crucial elements-personalization, contextual understanding and platform-independent standardized learning materials, which are rather important for students of diverse disciplines backgrounds and learning abilities. The paper describes the design of WTS, an intelligent tutoring system implemented for web tutoring by combining learning theory and artificial intelligence. The system is composed of four tiers, provides collaborative learning, and personalized learning environments. Evaluation results indicate that applying our web-based tutoring system can efficiently help students increase learning efficiency while receiving traditional classroom instruction.
## 43 Intelligent tutoring systems are computer learning systems which personalise their learning content for an individual, based on learner characteristics such as existing knowledge. A recent extension to ITS is to capture student learning styles using a questionnaire and adapt subject content accordingly, however students do not always take the time to complete questionnaires carefully. This paper describes Oscar, a conversational intelligent tutoring system (CITS) which utilises a conversational agent to conduct the tutoring. The CITS aims to mimic a human tutor by dynamically estimating and adapting to a student's learning style during a tutoring conversation. Oscar also offers intelligent solution analysis and problem support for learners. By implicitly modelling the student's learning style during tutoring, Oscar can personalise tutoring to each individual learner to improve the effectiveness of the tutoring. The paper presents the novel methodology and architecture for constructing a CITS. An initial pilot study has been conducted in the domain of tutoring of undergraduate Science and Engineering students using the Index of Learning Styles ILS) model. The experiments to investigate the estimation of learning style have produced encouraging results in the estimation of learning style through a tutoring conversation.
## 44 In this paper we present a new framework for predicting the proper instructional strategy for a given teaching material based on its attributes. The framework is domain-based in the sense that it is based on the qualitative observation of the teaching materials' attributes stored by the system. The prediction process is based on a machine learning approach using feed forward artificial neural network to generate a model that both fit the input data attributes and predict the proper instructional strategy by extracting knowledge implicit in these attributes. The framework was adapted in an Intelligent Tutoring System (ITS) to leach Modern Standard Arabic language to adult English-speaking learners with no pre-knowledge of Arabic language is required. The learning process will be through the Internet since the online education is better suited to mature individuals who are self-motivated and have a good sense of purpose.
## 45 Research has shown that students' problem-solving actions vary in type and duration. Among other causes, this behavior is a result of strategies that are driven by different goals. We describe a first version of a computational cognitive model that explains the origin of these strategies and identifies the tendencies of students towards different learning goals. Our model takes into account (i) interpersonal differences, (ii) an estimation of the student's knowledge level, and (iii) current feedback from the tutor, in order to predict the next action of the student - a solution, a guess or a help request. Our long-term goal is to use identification of the students' strategies and their efficiency in order to better understand the learning process and to improve the metacognitive learning skills of the students.
## 46 This paper presents an extension of the Additive Factors Model to predict learning for students by accounting for aspects of collaboration. The results indicate that student performance is predicted more accurately when the model includes parameters that capture influences of working collaboratively.
## 47 We describe ail affective behavior model (ABM) for intelligent tutoring systems. The model is a Dynamic Decision Network that selects tutorial actions based on both the current affective and pedagogical state of a student, as well as on the assessment of the expected effect of each available action on the Student. We integrated the ABM with an educational game to learn number factorization, and here we present the preliminary results of a user study to evaluate its effectiveness.
## 48 In this paper we present an intelligent tutoring system with affective learning, which is integrated into a social network for learning mathematics. The system is designed to help students of the second grade of primary education to improve the teaching-learning process. The system evaluates cognitive and affective aspects of the student by using a neural network and a fuzzy expert system to decide the following exercise to be resolved by the student, enabling personalized learning. We evaluate and compare our tutoring system against other well-known tutoring systems.
## 49 Model tracing tutors represent a technology designed to mimic key elements of one-on-one human tutoring. We examine the situations in which such supportive computer technologies may devolve into mindless student work with little conceptual understanding or student development. To analyze the support of student intellectual work in the model tracing tutor case, we adapt a cognitive demand framework that has been previously applied with success to teacher-guided mathematics classrooms. This framework is then tested against think-aloud data from students using a model tracing tutor designed to teach proportional reasoning skills in the context of robotics movement planning problems. Individual tutor tasks are coded for designed level of cognitive demand and compared to students' enacted level of cognitive demand. In general, designed levels predicted how students enacted the tasks. However, just as in classrooms, student enactment was often at lower levels of demand than designed. Several contextual design features were associated with this decline. Implications for intelligent tutoring system design and research are discussed.
## 50 Motivation has an undeniable role in the effectiveness of intelligent tutoring systems. In this research, a model is proposed to integrate students' motivation in intelligent tutoring systems. This model is based on the ARCS Model of Motivational Design and log file analysis to estimate students' motivation. Through expert analysis, it was determined that seven attributes ( task time, grade, task difficulty, student's interest in the subject, accordance between content presentation and student's learning style, student's skill level and previous motivational state) affect motivation directly and must be included in the model. In order to determine how accurately these attributes can assess the motivational state of students, a reading comprehension test environment was created using Moodle. Fourteen users participated in the study. Random Forest algorithm was used to classify the collected data into motivated and unmotivated classes. The correct classification rate was 61%. Although the data set is not big enough, however, this preliminary result show that the model is promising and can be further tested and improved.
## 51 Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II.1.
## 52 The initial vision for intelligent tutoring systems involved powerful, multifaceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development, and discuss the potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence.
## 53 This paper proposes what may be the step in computer based training, a distributed, game-based intelligent tutoring system, and discusses possible complications with creating such a system. Recent research has demonstrated a positive effect on learning by integrating intelligent tutoring systems within virtual worlds and video games as teaching aids. These systems cover many topics including reading and math for elementary students as well as computer programming, physics, and medicine for college level students. As well, there has been work demonstrating the positive outcomes of using web-based tutoring systems. While these systems have shown that computer based training is effective, none have investigated multi-user tutoring systems in which teams of players work together within a virtual world to solve more complex problems such as operating a multi-operator robot. Such a system could increase a students understanding of a subject by allowing them to discuss the problem in real time with the other players on their team.
## 54 This paper proposes an approach to following good-learners' paths in Intelligent Tutoring Systems (ITS). Based on a theory which states that learning can be acquired through imitation of competent models, we observed and analyzed the exercises completed by good learners to predict an appropriate sequence of questions for the students to follow. We applied the Hidden Markov Model to represent students' skills and the generative Markov Decision Process to model the exercise experience of good learners. The resulting model represents recommendations of dynamic sequences of questions. A preliminary study has been conducted to elicit a real-world dataset of a programming course. It aims to measure the reliability of the proposed approach, in comparison with the conventional approach where teachers determined the questions. The approach performance is indicated by increased percentages of good learners. The experiment results show that the proposed approach has a better reliability than the conventional approach.
## 55 The goal of this paper is to propose the concept, structure and implementation of a novel intelligent tutoring system designed for beginners in C language and Python. The system is implemented by adding the functions of code classification, program error repair and personal knowledge tracing to an online programming practice platform. This implementation makes the original platform become more intelligent and help students learn programming better.
## 56 In this paper we introduce WHAT, an intelligent tutor for learning the functional programming language Haskell. WHAT adapts its behavior not only individually for each student but also by considering the performance of similar students. The core of its adaptive part is based on the classification of students into classes (groups of students sharing some attributes). By doing that, the behavior of past students of the same class determines how WHAT interacts, in the future, with students of that class. That is, WHAT learns how to deal with each type-of student. Besides, the general-model of each class is instantiated for each student in order to better fit the particular learning needs.
## 57 In this paper, we present a multiplatform and Intelligent Tutoring System for learning Java (Java Sensei). The learning system combines state-of-the-art action selection, motivation through emotions, a modern recommendation mechanism, and multimodal instructional and selection learning. Java Sensei architecture works with a collection of modules and processes, each with its own effective representations and algorithms. The learning system was implemented under different learning methodologies like problem-solving for the pedagogical module, knowledge space for the expert module, and overlays for the student module. One of the main contributions of this work was the integration of cognitive and affective information in a behavioral graph which is used by a learning companion to show emotions and empathy to the student. Java Sensei was tested with different groups of university students with which we obtained positive results. In addition to providing a detailed description of the implementation and evaluation of Java Sensei, we also provide some proposals of future work in our intelligent tutoring systems.
## 58 In computerized tutoring, the pace of instruction is related to the student's mastery levels of the learning objectives. The observable student's behavior that can be used to measure his knowledge is usually his responses to test items. Unobservable variables that are related to learner's motivation can affect learning but are difficult to quantify. In comparison with other decision-theoretic tutoring systems, the novelties of this research are: (1) the efficiency-centric approach to develop the Bayesian networks; (2) the formulation of utility values for different tutoring outcomes that are independent of past actions and to satisfy the separability condition; (3) the development of a common mea, sure for student's mastery levels and item difficulties; and (4) the generation of optimal policies in polynomial time. A prototype web-based tutoring system, known as iTutor, incorporating the novelties has been developed for engineering mechanics. Formative evaluations of iTutor have shown encouraging results.
## 59 The development of Intelligent Tutoring Systems has been studied for decades and requires multi and interdisciplinary knowledge. In this context, this article presents a hybrid intelligent tutoring system in which the teaching strategies are based on neural networks, Self-Organizing Maps (SOM), and knowledge of a specialist teacher. This model has a reactive and adaptive characteristic that provides a personalized and individual instruction for the student, by promoting the necessary guidance on the transfer of didactic contents. This paper presents the development process of the proposed hybrid model, including the system with specialist guidance that ultimately provides the data used in the training stage of the neural networks. The results show the behavior of the system when using specialist guidance and when using the hybrid decision model. The results indicate that the application of the hybrid intelligent tutoring systems model is feasible because it includes both the teacher's knowledge and the student's behavior for establishing teaching strategies, which allows for a greater proximity of the tutor's and the student's actions. The performance of the proposed model was satisfactory compared to other systems proposed in the literature that use connectionism for establishing the teaching strategy.
## 60 Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching as an alternative to traditional didactic medical education to teach clinical-reasoning skills at the early stages of medical education. While PBL has many strengths, effective PBL requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This work describes intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of this work is the development of general domain-independent individual and collaborative student modeling techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. COMET is designed to provide an experience that emulates that of live human-tutored medical PBL sessions as much as possible while at the same time permitting the students to participate collaboratively from disparate locations. The system combines concepts from intelligent tutoring systems (ITSs) with those from computer-supported collaborative learning (CSCL). Medical PBL is particularly challenging due to the complexity of the knowledge involved, the lack of standard, commonly accepted student clinical-reasoning techniques, and the lack of standards for tutoring. This means that we must first attempt to identify prototypical patterns of clinical reasoning and then formalize them to create the clinical reasoning model. Qualitative analysis of PBL tutorial sessions was performed in order to gain insight into the processes involved in PBL, thereby suggesting a framework for generating tutoring feedback. Generating appropriate tutorial actions in COMET requires a model of the students' clinical reasoning for the problem domain. This modeling task is necessarily wrought with uncertainty since we have only a limited number of observations from which to infer each student's level of understanding. Therefore, the system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. COMET incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. In order to evaluate the appropriateness and quality of the feedback generated by COMET, the tutoring hints generated by the system were compared with those of experienced human tutors. On average, 74.17% of the human tutors used the same hint as COMET. The results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773). The validity of the modeling approach has been tested with student models. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Finally, comparison of learning outcomes shows that student clinical reasoning gains from COMET are significantly higher than those obtained from human tutored sessions (Mann-Whitney, p = 0.011).
## 61 <NA>
## 62 This study examined the proportional learning gains attained by 165 college students as they learned about the human circulatory system over two sessions with the intelligent tutoring system, MetaTutor. Results indicated that learners in the prompt and feedback condition, which were afforded the full capabilities of the four pedagogical agents (PAs), attained significantly greater proportional learning gains than learners in the control condition who did not receive the same scaffolding. In addition, we also found that the amount of time spent with each PA produced different types of impacts on the learners, with Sam the Strategizer having the most influence on proportional learning gains. Lastly, results from the revised Agent Persona Inventory (API), administered following the learning session with MetaTutor, revealed key findings regarding learners' overall retrospective affective reactions towards each individual PA. These results have implications for the design of future PAs capable of offering real-time and adaptive pedagogical instruction within Intelligent Tutoring Systems (ITSs).
## 63 We approach the characterization of believable, higher education-oriented agents for assisting humans immersed in the problem of distance (i.e. virtual) education [Espinosa 94] on a highly interactive course [Espinosa 95]. Intelligent behavior is modeled by temporally describing emotions that affect the outcome of decisions. We assume that introspection leads to better decision making in complex human activities [Gay88] [Allouch68] [Julien92]. A set of Enclosed but Open Worlds [Espinosa 96a] [Espinosa 96d] allows for the specification of a computable structure representing such activities. The introspection mechanism includes temporal, educational, and cognitive modeling of user interface issues, and was first described as a monitor in [Espinosa96b]. We characterize tutoring techniques in terms of multi-modal (i.e. temporal and emotional) operators, and causal relationships, and present a computationally oriented merger of Cognitive and Artificial intelligence theories. It should be viewed as a multidisciplinary effort.
## 64 This paper presents a new approach to the analysis and design of intelligent tutoring systems (ITS), based on reactive principles and cognitive models, this way leading to multiagent architecture. In these kinds of models, the analysis problem is treated bottom-up, as opposed to that of traditional artificial intelligence (AI), i.e., top down. We present one ITS example called Makatsina (meaning tutor in TOTONACA, a Mexican pre-Columbian language), constructed according to this approach, which teaches the skills necessary to solve the truss analysis problem by the method of joints. This learning domain is an integration skill. The classical ITS work is based on explicit goals and an internal representation of the environment. The new approach has reactive agents which have no representation of their environment and act using a stimulus/response behavior type. In this way they can respond to the present state of the environment in which they are embedded. With these elements, errors, and teaching plans, each agent behaves as an expert assistant that is able to handle different teaching methods. Reactive agent programming is found to be simple because agents have simple behaviors. The difficulty lies in the interaction mechanism analysis and design between the environment and the intelligent reactive system.
## 65 Since English has been an international language, how to enhance English levels of people by useful computer assisted learning forms or tools is a critical issue in non-English speaking countries because it definitely affects the overall competition ability of a country. With the rapid growth of wireless and mobile technologies, the mobile learning has been gradually considered as a novel and effective learning form because it inherits all the advantages of e-learning as well as breaks the limitations of learning time and space occurring in the traditional classroom learning. To provide an effective and flexible learning environment for English learning, this study adopts the advantages of the mobile learning to present a personalized intelligent mobile learning system (PIMS) which can appropriately recommend English news articles to learners based on the learners' reading abilities evaluated by the proposed fuzzy Item Response Theory (FIRT). In addition, to promote the reading abilities of English news, the unknown or unfamiliar vocabularies of individual learner can also be automatically discovered and retrieved from the reading English news articles by the PIMS system according to the English vocabulary ability of individual learner for enhancing vocabulary learning. Currently, the PIMS system has been successfully implemented on the personal digital assistant (PDA) to provide personalized mobile learning for promoting the reading ability of English news. Experimental results indicated that the proposed system provides an efficient and effective mobile learning mechanism by adaptively recommending English news articles as well as enhancing unknown or unfamiliar vocabularies' learning for individual learners.
## 66 Deductive logic is essential to a complete understanding of computer science concepts, and is thus fundamental to computer science education. Intelligent tutoring systems with individualized instruction have been shown to increase learning gains. We seek to improve the way deductive logic is taught in computer science by developing an intelligent, data-driven logic tutor. We have augmented Deep Thought, an existing computer-based logic tutor, by adding data-driven methods, specifically; intelligent problem selection based on the student's current proficiency, automatically generated on-demand hints, and determination of student problem solving strategies based on clustering previous students. As a result, student tutor completion ( the amount of the tutor the students completed) steadily improved as data-driven methods were added to Deep Thought, allowing students to be exposed to more logic concepts. We also gained additional insights into the effects of different course work and teaching methods on tutor effectiveness.
## 67 Nowadays e-learning education systems are adopted more and more in advanced schools. However, due to different learners have different prior knowledge, preferences, learning goals and understanding capabilities, no any fixed learning pathway is appropriate for all learners. Thus developing e-learning intelligent tutoring system (ITS) in order to provide customized learning pathway to individual learner is highly demanded. Tackle such a refractory bottleneck problem, the framework of proposed e-learning ITS is researched and designed in this paper, which contains domain knowledge model, student model and tutorial model. Furthermore, e-learning curriculums are developed based on standard shareable content object reference model (SCORM); a fuzzy algorithm is applied to estimate students' cognitive ability level; consequently the case-based reasoning technique combined with item response theory (IRT) is adapted such that to derive adaptive learning courseware for individual student, let the difficulty degree of selected courseware match the cognitive ability level of that typical student. Obviously, such an e-learning ITS can help students improve learning effect and increase learning efficiency dramatically.
## 68 In the present paper we present analysis of gaming actions with MathSpring, an established ITS for mathematics for high school students. Our findings indicate that both student and problem features were similarly predictive of gaming behaviors, as well as that gaming was associated with lower excitement and lower learning gains.
## 69 Intelligent Pedagogical Agents (IPAs) can be thought of as embodied intelligent agents that are designed for pedagogical purposes to support learning. They can be designed in particular for virtual worlds. Virtual worlds are becoming an interesting medium for engineering education for the properties of visual collaboration abilities providing authentic learning experiences and for the opportunity of providing active learning. However, virtual worlds need more educational support to be more inhabited with increased learning services. Incorporating intelligent pedagogical agents into virtual worlds adds such learning support by adding intelligence, improving believability, and the opportunity to increase communication with an artificial educator. However the implementation of intelligent pedagogical agents and adopting them in a virtual world require several efforts with different aspects of implementation. This paper reports our first prototype implementation of an IPA interacting with a learner and a learning object in natural science experiment in a virtual world while providing supporting multi-modal communication abilities. The IPA has features of text chat based on the Artificial Intelligence Markup Language (AIML), a text-to-speech synthesis function, and non-verbal communication abilities through gesture animation. The implementation is presented through explained scenarios of the IPA tutoring an experiment or monitoring a learner avatar interaction with a learning object in a Virtual World. The IPA & the learning scenarios are implemented in the open source of Open Wonderland.
## 70 Certain learners are less sensitive to learning environments and can always learn, while others are more sensitive to variations in learning environments and may fail to learn (Cronbach & Snow, 1977). We refer to the former as high learners and the latter as low learners. One important goal of any learning environment is to bring students up to the same level of mastery. We showed that an intelligent tutoring system (ITS) teaching a domain-independent problem-solving strategy indeed closed the gap between high and low learners, not only in the domain where it was taught (probability) but also in a second domain where it was not taught (physics). The strategy includes two main components: one is solving problems via backward chaining (BC) from goals to givens, called the BC strategy, and the other is drawing students' attention to the characteristics of each individual domain principle, called the principle-emphasis skill. Evidence suggests that the low learners transferred the principle-emphasis skill to physics while the high learners seemingly already had such skill and thus mainly transferred the other skill, the BC strategy. Surprisingly, the low learners learned just as effectively as the high learners in physics. We concluded that the effective element of transfer seemed not to be the BC strategy, but the principle-emphasis skill.
## 71 The traditional approach followed by tutors to assess the students is through a set of questions. The quality of a question bank has an impact on the effectiveness of evaluation in educational institutions. Determining the coverage of these questions with respect to a set of prescribed text/reference books helps in evaluating students efficiently. In this paper, we describe a Tutor Assisting e-Framework (TAeF) that enables the tutors to analyze the quality of a question bank. Initially, it clusters all individual topics of each of the input text/reference books according to their dependencies. Later, the questions are classified into these topics. The result is a set of topics, each containing the topic title and the probability by which the question is related to it. Lower the accuracy of the predicted topics, higher is the quality of the question. In other words, if question contains the topic title unaltered, it has a higher probability of being related to the topic; this degrades the quality of question. Furthermore, the congruence relation between the questions and the set of topics is found. This gives the question coverage of each topic. Finally, with this relation, the percentage of understanding the students have developed in each of these topics is computed. The Tutor Assisting e-Framework (TAeF) helps to improve the quality of a question bank, to check the topics covered by each question and the knowledge gained. (C) 2016 Elsevier Ltd. All rights reserved.
## 72 This research was conducted to determine the effects of the using negative knowledge-based Intelligent Teaching System (ITS) evaluator software in the assessment and evaluation processes of English Teaching as a Foreign language. Experimental design with the pretest-posttest control group was used in this study. The study group consists of 67 students who are studying at the Vocational College of Technical Sciences located in Canakkale/Turkey. In this study, educational evaluator software based on correcting misconceptions developed by the researcher was used within the education process only in the experimental group. At the end of the fifth week following the 4-week experimental procedure, the academic achievement of the students was determined by a posttest. As a result, it was found to be a significant difference [t((33)) = -7.13, p < .05] between the pretest-posttest academic achievement of the experimental group. In addition, there was no significant difference [t((65)) = -1.15, p > .05] between the pretest scores of the experimental and control groups. There was a significant difference [t((65)) = -2.25, p < .05] in posttest scores' of control and experimental group students. As a result, using knowledge-based ITS evaluator software increased academic achievement much more than traditional education.
## 73 Nowadays, adaptive and intelligent tutoring system (AITS) is one of regarded topics. So researchers are trying to optimize and expand its application in the field of education. This paper integrates AITS with expert system technology. It is intelligent because it can interact with the learners and offer them some subjects based on pedagogy view. Learning process in this system is as follows. First, the expert system plans a pre- evaluation and then calculates his score. If the learner gets the required score, the activities will be trained. Then the learner will be evaluated by a post- evaluation. After that, the system offers guidance in learning other activities. For that purpose it takes into account achievements, learning context and skill levels, by analyzing the other activities already carried out. The analysis is based on a set of rules. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem- solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them. It can significantly improve the learning result. In other words, it helps learners to study in the best way.
## 74 This paper describes different design levels in the development of an intelligent tutoring system (ITS. Our ITS design proposal should ease the process of ITS development and make it more efficient. Firstly, we present a direct-mapping from the uppermost abstract level to the structural level. Next, we present a first direct top-down approach to design the ITS from its structural level. Since it has some drawbacks, we then propose a second design approach, using a framework. We thus define what we call an ITS-framework, and we show how such a framework can make the design and the development if an ITS more efficient thanks to reusability, flexibility and extensibility. We end up discussing the advantages and disadvantages of this second, framework-based, approach.
## 75 This paper is about an author tool that can be used to produce neurofuzzy tutoring systems for distance and mobile environments. These tutoring systems recognize and classify learning characteristics of learners by using a neuro-fuzzy system. The author tool has three main components: a content editor for building course structure and learning material; an editor for building fuzzy sets for different linguistic variables; and an XML course interpreter which combines a neuro-fuzzy predictive algorithm to display contents on different learning platforms. The author tool builds learning objects from other learning objects which are exported to SCORM format or to mobile devices.
## 76 A formal model of a web-based intelligent tutoring system composed of a user environment and a pedagogical environment is presented, which represents domain knowledge based on ontologies to improve the sharing and reusing of teaching materials. The system constructs the user environment based on users' knowledge levels, learning styles, psychology characteristics, etc. in order to improve the self-adaptability and pedagogical effects of the system. Furthermore, it distinguishes information about a user and what a pedagogical agent knows about the user. Based on the pedagogical agent's knowledge (represented by its cognitive state) about the user, a teaching process is designed for the user. Finally, the running process of the system is discussed in detail to show that the model is practicable.
## 77 The main learning activity provided by intelligent tutoring systems is problem solving, although several recent projects investigated the effectiveness of combining problem solving with worked examples. Previous research has shown that learning from examples is an effective learning strategy, especially for novice learners. A worked example provides step-by-step explanations of how a problem is solved. Many studies have compared learning from examples to unsupported problem solving, and suggested presenting worked examples to students in the initial stages of learning, followed by problem solving once students have acquired enough knowledge. This paper presents a study in which we compare a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adapts learning tasks to students' needs. The adaptive strategy determines the type of the task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received on the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problem solving. Novices from the adaptive condition learnt faster than novices from the control group, while the advanced students from the adaptive condition learnt more than their peers from the control group.
## 78 Intelligent Tutoring Systems (ITSs) have not yet proved very successful and one major reason seems to be that research on ITSs has largely failed to recognize the role of the teacher in the ITS design process. This paper discusses an undergoing project at Massey University, which is incorporating a 'Human Teacher Model' in an ITS prototype to teach Japanese. The project identifies the teacher attributes and formulates them into a coherent teacher model. They are then applied in the prototype, which offers adaptivity to teacher at two levels: presentation based adaptivity and navigation base adaptivity. We believe this work will substantially improve the applicability of ITSs in real academic environment.
## 79 CoLaB Tutor and AC-ware Tutor are Intelligent Tutoring Systems (ITSs) that are based on concept-based learning and are notable due to the fact they are relatively easy to generalize to multiple knowledge domains. In this research study we investigate the performance of CoLaB Tutor, AC-ware Tutor, and Moodle in a blended learning environment for an introductory computer programming course. In our study, regular face-to-face lectures and laboratory exercises were complemented with online learning at the students' own pace, time and location. Our study revealed that CoLaB Tutor students had moderately higher knowledge gains than those students in the AC-ware and Moodle groups. The prediction of student success (pass/fail) for a basic knowledge post-test revealed an overall classification rate of 73,5% for the CoLaB Tutor group (completed knowledge and online score as predictors), 71,4% for the AC-ware group (completed knowledge as predictor) and 70% for the Moodle group (time spent online as predictor). Additionally, students that used ITSs on average passed through more knowledge online than students that used LMS, while students that used LMS on average spent more time online.
## 80 Purpose - Examine the effectiveness of online tutoring software to ameliorate poor performance in intermediate financial accounting. Methodology/approach - Probit regression analysis comparing users versus nonusers of online accounting tutoring software, as well as analysis of student achievement pre and post-technology adoption over a 10-year period. Findings - We confirm prior research findings that the number of terms that have transpired since a student took introductory financial accounting, whether they took the course at a two-year college, or if they needed to repeat the introductory course, are all negatively associated with performance in intermediate accounting. We find evidence that an online tutoring system, ALEKS r, helps moderate these negative correlations. Results suggest that in upper division courses where student knowledge of underlying basic material is uneven, online tutors are an effective tool in bringing students up to an equal level of competence without sacrificing class time. Practical implications - Provides empirical evidence on the usefulness of online accounting software as a review tool in intermediate accounting. Social implications - Disadvantages experienced by accounting students due to when, where, and how they learned introductory accounting can be overcome quickly. Originality/value - Although vendors of intelligent online tutoring software market their product as a useful review tool for intermediate accounting, academic research has not examined the effectiveness of these products.
## 81 This article traces the history of a class of AI systems known as intelligent tutoring systems (ITSes). An ITS is structured like a tutor: A student engages with the system on some educational task (such as creating a geometry proof) and the system can calculate the student's learning and provide interventions that help the student develop a certain skill. The article shows that ITS researchers have been able to navigate the twists and turns in AI patronage as the field fell into its booms and busts. They did so by carefully crafting new identities-most notably as learning scientists-while also holding on to their AI credentials. To successfully commercialize ITSes for adoption in schools, they had to reconceive the systems themselves: from standalone aids for students to learning aids for both teachers and students.
## 82 Intelligent tutors have the potential to be used in supporting learning from collaboration, but there are few results demonstrating their positive effects in this domain. One of the main challenges in automated support for collaboration is the machine classification of dialogue, giving the system an ability to know when and how to intervene. We have developed an automated detector of conceptual content that is used as a basis for providing adaptive prompts to peer tutors in high-school algebra. We conducted an after-school study with 61 participants where we compared this adaptive support to two nonadaptive support conditions, and found that adaptive prompts significantly increased conceptual help and peer tutor domain learning. The amount of conceptual help students gave, as determined by either human coding or machine classification, was predictive of learning. Thus, machine classification was effective both as a basis for feedback and predictor of success.
## 83 Student modelling is a special type of user modelling which is relevant to the adaptability of intelligent tutoring systems. This paper reviews the basic techniques which have been used in student modelling and discusses issues and approaches of current interest. The role of a student model in a tutoring system and methods for representing information about students are discussed. The paper concludes with an overview of some unresolved issues and problems in student modelling.
## 84 In this paper we present an intelligent and affective tutoring system designed and implemented within a social network. The tutoring system evaluates cognitive and affective aspects and applies fuzzy logic to calculate the exercises that are presented to the student. We are using Kohonen neural networks to recognize emotions through faces and voices and multi-attribute utility theory to encourage positive affective states. The social network and the intelligent tutoring system are integrated into a Web application. We present preliminary results with different groups of students using this software tool.
## 85 In this paper, we introduce a new student responsive model to support students who use an Intelligent Tutoring System (ITS) as an E-Learning tool. We proposed a weighted-based model to estimate and suggest learning materials for students who are pursuing a computer-based course. We have built a brand new ITS called WinITS with our proposed responsive student model and deployed it in Hanoi National University of Education-Vietnam (HNUE) in the second semester of the school year 2019-2020 with a computer science course. To compare the effectiveness of applying ITS to the students, we compare test results and analyze some other aspects related to the course. On the other hand, we conducted a survey between two groups: with and without using WinITS. 63 students are volunteers who participated in the case study. Before learning, 43 students from Group 1 will take a short survey of the Felder-Silverman questionnaire to identify learning styles, after that, they go through all the lessons from the course under the support of WinITS, the lessons will be chosen to satisfy student's need. On another side, 18 students from Group 2 will make the same test to compare the result to Group 1. In the range of research, we illustrate that our implementation shows some encouraging results such as reducing learning time, improving test score by 1.13 standard deviations, and making the lesson more interesting and flexible. The results have revealed some advantages of studying with computer-added compared to the traditional class in various ways and showed the effectiveness of the proposed model in Intelligent Tutoring Systems.
## 86 In our on-going endeavor to teach students better help-seeking skills we designed a three-pronged Help-Seeking Support Environment that includes (a) classroom instruction (b) a Self-Assessment Tutor, to help students evaluate their own need for help, and (c) an updated version of the Help Tutor, which provides feedback with respect to students' help-seeking behavior, as they solve problems with the help of an ITS. In doing so, we attempt to offer a comprehensive help-seeking suite to support the knowledge, skills, and dispositions students need in order to become more effective help seekers. In a classroom evaluation, we found that the Help-Seeking Support Environment was successful in improving students, declarative help-seeking knowledge, but did not improve students' learning at the domain level or their help-seeking behavior in a paper-and-pencil environment. We raise a number of hypotheses in an attempt to explain these results. We question the current focus of metacognitive tutoring, and suggest ways to reexamine the role of help facilities and of metacognitive tutoring within ITSs.
## 87 Despite their overwhelming success, present-day Massive Open Online Courses are far removed from the student modelling capacities displayed by earlier Intelligent Tutoring Systems. Being mere content delivery tools, MOOCs typically lack a thorough assessment module as well as tools for personalising the learner's track. When learning music, particularly, these two properties are indispensable. This chapter surveys suggestions made by experts in the field of AI in education today towards the incorporation of ITS tools and techniques into MOOCs. Yet, more traditional student models and tutoring modules are not without shortcomings themselves and the real challenge lies in making active models of both the tutor and the student, which can be used to predict future learning tracks and set the right challenges. Agent-based tutoring systems offer an attractive framework for building such active tutor/student models. The proposed concepts are illustrated in the domain of music composition. A tutoring system has been implemented to teach students the craft of counterpoint, a commonly used strategy for learning polyphonic music composition. It is based on the theory of flow to keep students motivated and optimize learning.
## 88 This paper presents a project the goal of which is to develop ASPIRE, a complete authoring and deployment environment for constraint-based intelligent tutoring systems (ITSs). ASPIRE is based on our previous work on constraint-based tutors and WETAS, the tutoring shell. ASPIRE consists of the authoring server (ASPIRE-Author), which enables domain experts to easily develop new constraint-base tutors, and a tutoring server (ASPIRE-Tutor), which deploys the developed systems. Preliminary evaluation shows that ASPIRE is successful in producing domain models, but more thorough evaluation is planned.
## 89 This paper describes an investigation centered around modeling students' cognitive-affective factors while learning mathematics with an Intelligent Tutoring System. The proposal focuses on the identification of intrinsic and extrinsic factors exhibited by the students and present during the learning process in association to the representation of quantitative and qualitative variables in the tutoring system. The theoretical foundations as well as the methodology of the investigation are presented.
## 90 In this paper. an ITS-DIITS, which helps student to learn calculus, is introduced as a prototype. The whole tutoring procedure of DIITS is divided into three phases: basic, advanced and exploratory exercise. In different phrases, the system provides problems embodying skills or strategies of different levels and allows operations of corresponding levels for student to use. When the student solves a problem step by step on the exercise interface, the system corrects his errors in intermediate steps in basic exercise phase, or makes advice and comments on his problem solving strategies in advanced phase. The domain knowledge, which is represented in the form of an AND/OR graph of skills, directs the search process of the problem solver, and provides template for student modeling and reference for problem generation.
## 91 Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of learner's multiple intelligences, learner's skill level and learner's feedback when implementing personalized mechanisms. In this paper, the authors propose AITS based not only on the learner's multiple intelligences, but also the changing learning performance of the individual learner during the learning process. Therefore, considering learner's skill level and learner's multiple intelligences can promote personalized learning performance. Learner's skill level is obtained from pre-test result analysis, while learner's multiple intelligences are obtained from the analysis of questionnaire. After computing learning success rate of an activity, the system then modifies the difficulty level or the presentation of the corresponding activity to update courseware material sequencing. Learning process in this system is as follows. First, the system determines learning style and characteristics of the learner by an MI-Test and then makes the model. After that it plans a pre-evaluation and then calculates the score. If the learner gets the required score, the activities will be trained. Then the learner will be evaluated by a post-evaluation. Finally the system offers guidance in learning other activities. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem-solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them. It can significantly improve the learning result. In other words, it helps learners to study in the best way.
## 92 Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students' task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that self-explanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6(th)-grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.
## 93 Detecting and responding to affective states may be more influential than intelligence for tutoring success. This paper presents a software system that recognizes emotions of users using Android Cell Phones. The system software consists of a feature extractor, a neural network, and an intelligent tutoring system. The tutoring system, the neural network, and the emotion recognizer were implemented for running in Android devices. We also incorporate a novel fuzzy system, which is part of the intelligent tutoring system that takes actions depending of pedagogical and emotional states. The recognition rate of the emotion classifier was 96 %.
## 94 Intelligent tutoring systems (ITS) and MOOCs tend to have complementary pedagogical approaches, but their combination is rarely (if ever) seen. A key obstacle may be technical integration. We present a generalizable case study of extending ITS authoring technology to make tutors easily embeddable into a variety of MOOC/e-learning platforms and run on a range of web-enabled devices. We enhanced the domain-independent Cognitive Tutor Authoring Tools (CTAT) to enable integration of CTAT tutors into multiple environments. A salient lesson learned is that use of widely-used web-based technologies (HTML and JavaScript) may be a major factor in ITS uptake. Also, we found that embedding tutors into existing LMS is challenging, but environment- specific changes can be isolated in a generalizable manner.
## 95 If computers are to interact naturally with humans, they must express social competencies and recognize human emotion. This talk describes the role of technology in responding to both affect and cognition and examines research to identify student emotions (frustration, boredom and interest) with around 80% accuracy using hardware sensors and student self-reports. We also discuss caring computers that use animated learning companions to talk about the malleability of intelligence and importance of effort and perseverance. Gender differences were noted in the impact of these companions on student affect as were differences for students with learning disabilities. In both cases, students who used companions showed improved math attitudes, increased motivation and reduced frustration and anxiety over the long term. We also describe social tutors that scaffold collaborative problem solving in ill-defined domains. These tutors use deep domain understanding of students' dialogue to recognize (with over 85% accuracy) students who are engaged in useful learning activities. Finally, we describe tutors that help online participants engaged in situations involving differing opinions, e.g., in online dispute mediation, bargaining, and civic deliberation processes.
## 96 The main goal of the work presented here is to allow for the broader dissemination of intelligent tutoring technology. To accomplish this goal, we have two clear objectives. First, we want to allow different types of people to author model-tracing intelligent tutoring systems (ITSs) than can now do so. Second, we want to enable an author to create a tutor for software that was not initially designed with an ITS in mind. Accomplishing these two objectives should increase the number of such ITSs that are produced. We have created the first iteration of an authoring system that addresses both objectives. Non-cognitive scientists and non-programmers have used the system to create a tutor, and the system can interface with third-party software that was not originally designed with the ITS.
## 97 WEAR is a Web-based authoring tool for the construction of Intelligent Tutoring Systems (ITSs) in Algebra-related domains, such as physics, economics, chemistry, etc. In WEAR's authoring environment instructors are able to construct problems and tests and also build adaptive electronic textbooks. In return, WEAR generates an intelligent learning environment in which students can solve problems and study the topics of the curriculum. WEAR apart from modelling the student which is a common practice in almost all ITSs and ITS authoring tools, deals also with modelling the other class of its users: the instructors. Based on the user models it maintains, WEAR adapts the interaction with both students and instructors and provides them with individualised feedback and help.
## 98 We describe in this paper the use of Pinget's notion of cognitive development [4,5] in the building of pre-tests that would then allow for the improvement of a ruler's reasoning ability. We are interested in developing low-cost computer-based instruments which will detect individual differences that nor only predict a student's overall performance, but that can also be easily applied to actual tutoring decisions Our hypothesis was that students with different levels of cognitive development should behave differently bl the context of our math tutoring system. This is a sufficient reason for children to be taught with different strategies. We thought it was very likely that our population of elementary school students had different cognitive levels, although the range of ages of the students was very smalt (10-11 years old). If this were the case, then cognitive development level would be an important aspect to consider while adapting the behavior of our tutoring system. We have adapted classic Piagetian tasks [8] used to measure cognitive development levels for use on computer: We found that this measure predicts student performance in the amount of time to solve problems and also in the number of problems students need to go through to achieve mastery of a topic. We are interested in how this measure of cognitive development could be usefully applied to enhance the behavior of the tutor for students at different cognitive levels I;Further research will deal with finding out what strategics are appropriate for students with different cognitive levels. We have some ideas with respect to this issue. There is evidence in this paper for students at a particular cognitive level showing improvements ir, performance with the aid of certain hints. Meanwhile, students at other cognitive levels showed no improvement in their performance after seeing the same hints.
## 99 We present a new architecture for the creation of embodied educational games, using wearable devices in the form of 'Wearables' for learning, which enable to do mathematics while being physically engaged with the environment. Wearables act as assistants as students engage in Mathematics Games. Evidence shows that students learn and improve their affect and motivation.
## 100 Course sequencing is one of the vital aspects in an Intelligent Tutoring System (ITS) for e-learning to generate the dynamic and individual learning path for each learner. Many researchers used different methods like Genetic Algorithm, Artificial Neural Network, and TF-IDF (Term Frequency-Inverse Document Frequency) in E-leaning systems to find the adaptive course sequencing by obtaining the relation between the courseware. In this paper, heuristic semantic values are assigned to the keywords in the courseware based on the importance of the keyword. These values are used to find the relationship between courseware based on the different semantic values in them. The dynamic learning path sequencing is then generated. A comparison is made in two other important methods of course sequencing using TF-IDF and Vector Space Model (VSM) respectively, the method produces more or less same sequencing path in comparison to the two other methods. This method has been implemented using Eclipse IDE for java programming, MySQL as database, and Tomcat as web server.
## 101 In this paper, we review tutoring approaches of computer-supported systems for learning programming. From the survey we have learned three lessons. First, various AI-supported tutoring approaches have been developed and most existing systems use a feedback-based tutoring approach for supporting students. Second, the AI techniques deployed to support feedback-based tutoring approaches are able to identify the student's intention, i.e. the solution strategy implemented in the student solution. Third, most reviewed tutoring approaches only support individual learning. In order to fill this research gap, we propose an approach to pair learning which supports two students who solve a programming problem face-to-face.
## 102 Interoperability of systems based on knowledge is a very important element for reducing their development cost and enabling an easy-to-perform service enrichment. Intelligent tutoring systems (ITSs) may be described as distant learning systems, which base their work on the simulation of the real teacher in the learning and teaching process. ITSs base their interoperability on the interchange of domain knowledge, knowledge about learning and teaching process and knowledge about students. This paper describes DiSNeT, a distance learning system we designed based on the intelligent tutoring paradigm, on knowledge presentation using distributed semantic networks and on using agents in the learning and teaching process. We also present a methodology for ensuring interoperability between DiSNeT and other ITSs.
## 103 One of the advantages provided by Intelligent Tutoring Systems (ITS) is it allows a flexible pedagogical approach according to the characteristics of the learner. This flexibility can be enhanced using multiple learning strategies that can be successively triggered depending on the progression of learning. For industrial purposes it is not always necessary to develop all the knowledge-based components of an ITS. In the present paper we describe both the elements of an architecture we have progressively designed and a methodology of development we have used in a cooperative project between universities and industries. Particularly, we highlight positive results but also problems that have to be avoided to maintain a coherent and progressive development approach. After reviewing the needs of the industry in terms of training systems and performance objectives, we describe the most important components of the architecture to retain, and comment on the progression of the project. We detail the different learning strategies that can be deployed within an important new concept of actors, which are intelligent agents able to handle pedagogical strategies. (C) 1998 Published by Elsevier Science B.V. All rights reserved.
## 104 Research suggests that promoting metacognitive awareness can increase performance in, and learning from, intelligent tutoring systems (ITSs). The current work examines the effects of two metacognitive prompts within iSTART, a reading comprehension strategy ITS in which students practice writing quality self-explanations. In addition to comparing iSTART practice to a no-training control, those in the iSTART condition (n = 116) were randomly assigned to a 2 (performance threshold: off, on) x 2(self-assessment: off, on) design. The performance threshold notified students when their average self-explanation score was below an experimenter-set threshold and the self-assessment prompted students to estimate their self-explanation score on the current trial. Students who practiced with iSTART had higher posttest self-explanation scores and inference comprehension scores on a transfer test than students in the no training control, replicating previous benefits for iSTART. However, there were no effects of either metacognitive prompt on these learning outcomes. In-system self-explanation scores indicated that the metacognitive prompts were detrimental to performance relative to standard iSTART practice. This study did not find benefits of metacognitive prompts in enhancing performance during practice or after the completion of training. Such findings support the idea that improving reading comprehension strategies comes from deliberate practice with actionable feedback rather than explicit metacognitive supports.
## 105 Intelligent tutoring systems (ITSs), which provide step-by-step guidance to students in complex problem-solving activities, have been shown to enhance student learning in a range of domains. However, they tend to be difficult to build. Our project investigates whether the process of authoring an ITS can be simplified, while at the same time maintaining the characteristics that make ITS effective, and also maintaining the ability to support large-scale tutor development. Specifically, our project tests whether authoring tools based on programming-by-demonstration techniques (developed in prior research) can support the development of a large-scale, real-world tutor. We are creating an open-access Web site, called Mathtutor (http://webmathtutor.org), where middle school students can solve math problems with step-by-step guidance from ITS. The Mathtutor site fields example-tracing tutors, a novel type of ITS that are built by demonstration, without programming, using the Cognitive Tutor Authoring Tools (CTATs). The project's main contribution will be that it represents a stringent test of large-scale tutor authoring through programming by demonstration. A secondary contribution will be that it tests whether an open-access site (i.e., a site that is widely and freely available) with software tutors for math learning can attract and sustain user interest and learning on a large scale.
## 106 Intelligent Educational Systems (IESs) need large amounts of educational content that is typically not provided by the creators of these systems. In this paper we discuss a new approach for authoring practical IESs where core authoring is done by professional design teams, while the educational content is mainly developed by teachers who use the system in their classes. The major bottleneck of this approach is the lack of intelligent authoring support tools that allow regular teachers to author intelligent content that an IES needs in order to perform its functions. As a contribution to solving this problem, we present our recent work on authoring support for an adaptive vocabulary acquisition system, ELDIT. The paper describes the ELDIT system, the needs and challenges of language content authoring by teachers, and the two authoring support components that we have developed for two essential kinds of language learning content: illustrative examples and educational texts.
## 107 A model of a Web-based Intelligent Tutoring Multi-agent System is presented in this paper, and the functionalities of the key components of the system are described. To improve the interaction between learners and the system, and between the system and Internet, three kinds of agents(user agent, psychological agent and information agent) are introduced in order to improve pedagogical effects; and their interactions are discussed. The user agent makes use of learners' knowledge levels, psychological characteristics and learning styles, etc., to construct and update learner models. The psychological agent produces personalized learning units dynamically based on the information provided by user models in order to improve the self-adaptability and pedagogical effects of the system. And the information agent uses multi-media teaching material getting from Internet to represent and update domain knowledge.
## 108 High-quality code enables sustainable software development, which is a prerequisite of a healthy digital society. To train software engineers to write higher-quality code, we developed an intelligent tutoring system (ITS) grounded in recent advances in ITS design. Its hallmark feature is the refactoring challenge subsystem, which enables engineers to develop procedural knowledge for analyzing code quality and improving it through refactoring. We conducted a focus group discussion with five working software engineers to get feedback for our system. We further conducted a controlled experiment with 51 software engineering learners, where we compared learning outcomes from using our ITS with educational pages offered by a learning management system. We examined the correctness of knowledge, level of knowledge retention after one week, and the learners' perceived engagement. We found no statistically significant difference between the two groups, establishing that our system does not lead to worse learning outcomes. Additionally, instructors can analyze challenge submissions to identify common incorrect coding patterns and unexpected correct solutions to improve the challenges and related hints. We discuss how our instructors benefited from the challenge subsystem, shed light on the need for a specialized ITS design grounded in contemporary theory, and examine the broader educational potential.
## 109 Intelligent Tutoring Systems (ITSs) are one of a wide range of learning environments, where the main activity is problem solving. One of the most successful approaches for implementing ITSs is Constraint Based Modeling (CBM). Constraint-based tutors have been successfully used as drill-and-practice environments for learning. More recently CBM tutors have been complemented with a model derived from the field of Psychometrics. The goal of this synergy is to provide CBM tutors with a data-driven and sound mechanism of assessment, which mainly consists in applying the principles of Item Response Theory (IRT). The result of this synergy is, therefore, a formal approach that allows carrying out assessments of performance on problem solving tasks. Several previous studies were conducted proving the validity and utility of this combined approach with small groups of students, in short periods of time and using systems designed specifically for assessment purposes. In this paper, the approach has been extended and adapted to deal with a large set of students who used an ITS over a long period of time. The main research questions addressed in this paper are: (1) Which IRT models are more suitable to be used in a constrained-based tutor? (2) Can data collected from the ITS be used as a source for calibrating the constraints characteristic curves? (3) Which is the best strategy to assemble data for calibration? To answer these questions, we have analyzed three years of data from SQL-Tutor. (C) 2016 Elsevier B.V. All rights reserved.
## 110 This book chapter presents an affective and intelligent tutoring system called Fermat that integrates emotion or affective states with an Intelligent Learning Environment. The system applies Knowledge Space Theory to implement the knowledge representation in the domain and student modules and Fuzzy Logic to implement a new knowledge tracing algorithm, which is used to track student's pedagogical and affective states. The Intelligent Learning Environment was implemented with two main components: an affective and intelligent tutoring system for elementary mathematics and an educational social network. The tutoring system generates math exercises by using a fuzzy system that is fed with cognitive and effective values. Emotion recognition was implemented by two methods: one for feature extraction of the face and one for feature classification using back-propagation neural networks. In addition to recognizing the emotional state of the user, our system gives emotional support through a pedagogical agent. Furthermore, an architecture of software is presented where the emotion recognizer collaborates with the affective and intelligent tutoring system inside a social network. Finally, we present a real-time evaluation with third-grade students in two different schools.
## 111 AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor's development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.
## 112 For several years Intelligent Tutoring Systems (ITSs) have been developed and shown to lead to impressive improvement in student learning in a range of domains. Some of the most important limitations of ITSs are that their development is very time consuming, and they cannot be reused or imported to different platforms. The main benefits of the Sharable Content Object Reference Model (SCORM) are interoperability and reusability. Based on the SCORM Sequencing and Navigation (SN) specification we have developed an approach for implementing Web-Based SCORM compliant ITSs that are therefore reusable and interoperable. The main objective of this paper is to describe our approach and explain how to implement SCORM compliant ITSs using as an example, a prototype that we built.
## 113 A new intelligent tutoring system is presented for the domain of solving equations. This system is novel, because it is an intelligent equation-solving tutor that combines a cognitive model of the domain with a model of dialog-based tutoring. The tutorial model is based on the observation of an experienced human tutor and captures tutorial strategies specific to the domain of equation-solving. In this context, a tutorial dialog is the equivalent of breaking down problems into simpler steps and asking new questions before proceeding to the next step. The resulting system, named E-tutor, was compared, via a randomized controlled experiment, to a traditional model-tracing tutor that does not engage students in dialog. Preliminary results using a very small sample size showed that E-tutor capabilities performed better than the control. This set of preliminary results, though not statistically significant, shows promising opportunities to improve learning performance by adding tutorial dialog capabilities to ITSs. The system is available at www.wpi.edu/similar toleenar/E-tutor.
## 114 Adaptive web based education is an attractive mode of learning. However, the existing systems require a great deal of effort by the authors for the development of each course. Some effort is also required to adapt to a different curriculum, which has many common topics with the current curriculum or adapt to a changed curriculum. To reduce the effort required in the rebuilding of courses, we propose a model where we separate the course content from course structure. We consider a metadata based open repository for a tutoring system, which integrates learning objects from different sources. To enable reuse of these learning objects for course organization, each learning object is annotated with a set of metadata that describe its educational and pedagogic characteristics that are needed for the tutoring process. To reduce the overhead of the content developer we have implemented a tool for automatic annotation, which extracts metadata automatically from documents.
## 115 Intelligent Tutor Systems (ITS) are programs that use artificial intelligence techniques to support students in their learning. These systems can be useful in different educational areas. Intelligent agent is an artificial intelligence technique that can be applied in ITS in order to support its users in the process of interaction with these systems. The teaching of electronics is considered complex by many students. Thus, this article proposes an ITS model directed to the teaching of Basic Electronics.
## 116 Distributed cognitive learning theory provides a comprehensive model to cognitive interaction and knowledge sharing. If distributed cognitive theory and distributed virtual reality technology are introduced into intelligent tutoring system, the creation of a realistic cognitive context, the sharing of resources, the improvement of personalized learning and the enhancement of teaching efficiency will become possible. This paper discusses distributed cognitive learning theory and its guidance to system design, presents the DITS (Distributed Intelligent Tutoring System, DITS) architecture based on Multi-Agent System (MAS) and distributed cognitive theory, and focuses on the analysis of the learning services core modules of DITS, including organization and management of teaching, learning, teaching experts and collaborative communication.
## 117 Even though Intelligent Tutoring Systems (ITS) have been shown to help students learn, little research has investigated how a dashboard could help teachers help their students. In this paper, we explore how a dashboard prototype designed for an ITS affects teachers' knowledge about their students, their classroom lesson plans and class sessions. We conducted a quasi-experimental classroom study with 5 middle school teachers and 8 classes. We found that the dashboard influences what teachers know about their students, which in turn influences the lesson plans they prepare, which then guides what teachers cover in a class session. We believe this is the first study that explores how a dashboard for an ITS affects teacher's knowledge, decision-making and actions in the classroom.
## 118 Introduction We developed and evaluated a Natural Language Interface (NLI) for an Intelligent Tutoring System (ITS) in Diagnostic Pathology. The system teaches residents to examine pathologic slides and write accurate pathology reports while providing immediate feedback on errors they make in their slide review and diagnostic reports. Residents can ask for help at any point in the case, and will receive context-specific feedback. Research questions We evaluated (1) the performance of our natural language system, (2) the effect of the system on learning (3) the effect of feedback timing on learning gains and (4) the effect of ReportTutor on performance to self-assessment correlations. Methods The study uses a crossover 2 x 2 factorial design. We recruited 20 subjects from 4 academic programs. Subjects were randomly assigned to one of the four conditions-two conditions for the immediate interface, and two for the delayed interface. An expert dermatopathologist created a reference standard and 2 board certified AP/CP pathology fellows manually coded the residents' assessment reports. Subjects were given the opportunity to self grade their performance and we used a survey to determine student response to both interfaces. Results Our results show a highly significant improvement in report writing after one tutoring session with 4-fold increase in the learning gains with both interfaces but no effect of feedback timing on performance gains. Residents who used the immediate feedback interface first experienced a feature learning gain that is correlated with the number of cases they viewed. There was no correlation between performance and self-assessment in either condition.
## 119 We present results from an analysis of students' shallow behaviors, i.e., gaming, during their interaction with an Intelligent Tutoring System (ITS). The analysis is based on six college classes using the Andes ITS for homework and test preparation. Our findings show that student features are a better predictor of gaming than problem features, and that individual differences between students impact where and how students game.
## 120 Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems.
## 121 Intelligent Tutoring Systems are characterised for incorporating Artificial Intelligence techniques into their design and development, acting as assistants in the teaching-learning process. Currently, Intelligent Agents concepts have been applied to these systems as a. way to improve them. DORIS is a pedagogical follow-up agent for Intelligent Tutoring Systems developed to perform tasks such as the following: follow students' interaction with the intelligent tutor system, collect the information required for the modelling of students' profile used to customise the environment assist and guide students during the construction of their learning. This paper reports the characteristics and functionality of this agent.
## 122 The present research investigated whether immediate metacognitive feedback on students' help-seeking errors can help students acquire better help-seeking skills. The Help Tutor, an intelligent tutor agent for help seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study I, with 58 students, found that the real-time assessment of students' help-seeking behavior correlated with other independent measures of help seeking, and that the Help Tutor improved students' help-seeking behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also help-seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved help-seeking skills transferred to learning new domain-level content during the month following the intervention, while the help-seeking support was no longer in effect. Implications for metacognitive tutoring are discussed. (C) 2010 Elsevier Ltd. All rights reserved.
## 123 In this article, we start a design of Intelligent Tutor System (ITS) using a new approach based on adaptive workflow. The main idea is to concentrate our work on self-learning process and consider it as a set of pedagogical activities (tasks). Our model use parameters provided by the adopted learner model especially, student knowledge and preferences, and teaching domain schema to propose the optimal pedagogical workflow for each learner to reach his didactical objective. This pedagogical process will be adapted during its execution in the workflow engine by using some parameters of student interaction with the system. To achieve this work, we propose the integration of pedagogical activity base and a workflow engine in the global architecture of ITS. The system thus defined allows the coexistence of an intelligent tutor to carry out the tasks of teachers in the teaching process and a human tutor who takes part in the monitoring, control and follow-up of the execution of self-learning processes models in order to check out the learner path and to resolve blocking and demotivating situations of the learners
## 124 Problem-based learning (PBL) is becoming increasingly popular in medical education as a means of equipping students with the required clinical reasoning skills. However, faculty time is costly and may not be sufficiently available for PBL sessions that demand greater focus and attention from faculty personnel. Intelligent tutoring systems offer a cost effective and viable alternative in helping to train students in the relevant problem domain. Like other knowledge-based systems, intelligent tutoring systems also suffer from issues such as knowledge acquisition bottleneck, limited scope of problem representation and brittleness in understanding and evaluating system input. The objective of this work is to design a tutoring system for medical PBL, which is less burdensome in acquiring system knowledge, provides students with a broad scope of solution representation and is robust in its evaluation of student solutions. We propose the use of the widely available and broad Unified Medical Language System (UMLS), together with strong rule-based methods and weak inference methods to build a tutoring system prototype.
## 125 A computer software that simulates a human tutor's behavior is called as an Intelligent Tutoring System (ITS). The purpose of ITS is to help users to acquire knowledge and skills in the respective domain. ITS is able to learn as it interprets the responses from students. These student responses are very complex. They are able to identify where the student understanding has deviated from the correct solution and why the student has made a mistake. ITS can offer hints to help the student in understanding the material presented to them. ITS enables the users to practice their skills by solving tasks in a highly interactive learning environment. To provide such capabilities, these systems must be able to represent the knowledge effectively. In ITS, the knowledge is stored in one of its component named as the domain model. Thus, for a designer it is important to select an appropriate technique for knowledge representation to build an effective ITS.
## 126 Three studies were conducted with middle school students to evaluate a web-based intelligent tutoring system (ITS) for arithmetic and fractions. The studies involved pre and post test comparisons, as well as group comparisons to assess the impact of the ITS on students' math problem solving. Results indicated that students improved from pre to post test after working with the ITS, whereas students who simply repeated the tests showed no improvement. Students who had more sessions with the ITS improved more than those with less access to the software. Improvement was greatest for students with the weakest initial math skills, who were also most likely to use the multimedia help resources for learning that were integrated into the software.
## 127 Emotion has been viewed as source of motivational energy, but the traditional intelligent tutoring system can't implement affective tutoring, in order to overcome this problem, this paper proposed the study of learning affective recognition, and built an intelligent affective tutoring system which cm recognize the learning affect, so it can adjust teaching strategy according to the student's affective state. The system commences from the learning affective modelling, building up a four quadrant models that describe the link between study. and affective state, and discusses the learning affective recognition and facial expression recognition. Affective recognition will take an important role in the ITS and achieve individual tutoring according to different students' learning affect and realizing humanoid solicitude and harmonious emotion interaction between human and machine.
## 128 Collaborative learning is a method of gaining knowledge in groups. This method is often used in Intelligent Tutoring Systems (ITS). ITS can adapt learning process to students' abilities, learning styles or preferences. Moreover, ITS allows to create collaborative learning groups of students. Such groups could be homogeneous or heterogeneous. It is often said that heterogeneity in groups improves learning effects [3,8]. In this paper an original algorithm for creating heterogeneous groups is proposed. Results of heterogeneous and homogeneous groups were compared and research has shown that students working in heterogeneous groups achieved better results than students in homogeneous groups. It points out that to suitable assign students to groups is a very important matter.
## 129 Authoring tools have been broadly used to design Intelligent Tutoring Systems (ITS). However, ITS community still lacks a current understanding of how authoring tools are used by non-programmer authors to design ITS. Hence, the objective of this work is to review how authoring tools have been supporting ITS design for non-programmer authors. In order to meet our goal, we conduct a Systematic Literature Review (SLR) to identify the primary studies on the use of ITS authoring tools, following a pre-defined review protocol. Among the 4622 papers retrieved from seven digital libraries published from 2009 to June 2016, 33 papers are finally included after applying our exclusion and inclusion criteria. We then identify the main ITS components authored, the ITS types designed, the features used to facilitate the authoring process, the technologies used to develop authoring tools and the time at which authoring occurs. We also look for evidence of the benefits of ITS authoring tools. In summary, the main findings of this work are: (1) there is empirical evidence of the benefits (i.e., mainly in terms of effectiveness, efficiency, quality of authored artifacts, and usability) of using ITS authoring tools for non-programmer authors, specially to aid authoring of learning content and to support authoring of model-tracing/cognitive and example-tracing tutors; 2) domain and pedagogical models have been much more targeted by authoring tools; (3) several ITS types have been authored, with an emphasis on model-tracing/cognitive and example-tracing tutors; (4) besides providing features for authoring all four ITS components, current authoring tools are also presenting general features (e.g., view learners' statistics and reuse tutor design) to create broader authoring tools; (5) a great diversity of technologies, which include AI techniques, software solutions and distributed technologies, are used to develop ITS authoring tools; and (6) authoring tools have been mainly targeting ITS design before students' instruction, but works are also addressing authoring during and/or post-instruction relying both on human and artificial intelligence. We conclude this work by showing several promising research opportunities that are quite important and interesting but underexplored in current research and practice.
## 130 Intelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to welldefined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme ''Autonomous Learning'', the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.
## 131 The recognition of student's motivational states and the adaptation of instructions to the student's motivations are hot topics in the field of intelligent tutoring systems. In this paper, we describe a prototype of an Intelligent Chess Tutoring System based on a set of motivational strategies borrowed from Dweck's theory. The main objectives of the prototype are to teach some chess tactics to middle-level players and to help them to avoid helpless reactions after their errors. The prototype was implemented using Flash Mx 2004. The graphical user interface encompasses a life-like character functioning as tutor.
## 132 Assessment is one of the hardest tasks an Intelligent Tutoring System has to perform. It involves different and sometimes uncorrelated sub-tasks: building a student model to define her needs, defining toots and procedures to perform tests, understanding students' replies to system prompts, defining suitable procedures to evaluate the correctness of students, replies, and strategies to improve students' abilities after the assessment session. In this work we present an improvement of our system, TutorJ, with particular attention to the assessment phase. Many tutoring systems offer only a limited set of assessment options like multiple-choice questions, fill-in-the-blanks tests or other types of predefined replies obtained through graphical widgets (radio-buttons, text-areas). This limited set of solutions makes interaction poor and unable to satisfy the users' needs. Our interest is to enrich interaction with dialog in natural language. In this respect, the assessment problem is strictly connected to natural language understanding. The preliminary step is indeed to understand questions and replies of the student. We have reviewed the system design in the. framework of a cognitive architecture with the aim to reach a double result: the reduction of the effort for the construction of the knowledge base and the improvement of the system capabilities in the assessment process. To this aim a new common semantic space has been defined and implemented. The entire architecture is oriented to intuitive and natural interaction.
## 133 In this article we have discussed the learning skills that are required to help students gain the full benefits of learning within Intelligent Tutoring Systems (ITS). On the one hand, students need to use metacognitive strategies to manage their learning, particularly when they have freedom to determine how they access and use a variety of on-line resources. On the other hand, ITS must includes relevant metacognitive and support activities by considering students' differences in skills, preferences and metacognitive needs. Finally, we propose a model for the development of metacognitive strategies in ITS. The system provides a set of tools and practices exploiting the Zimmerman cyclic model of self-regulated learning (SRL).
## 134 Presenting feedback to learner is one of the essential elements needed for effective learning. Feedback can be given to learners during learning but also to authors during course development. But producing valuable feedback is often time consuming and makes delays. So with this reason and the others like incomplete and inaccurate feedback generating by human, we think that it's important to generate feedback automatically for both learner and author in an intelligent tutoring system (ITS). In this research we used ontology to create a rich supply of feedback. We designed all components of the ITS like course materials and learner model based on ontology to share common understanding of the structure of information among other software agents and make it easier to analyze the domain knowledge. With ontologies in fact, we specify the knowledge to be learned and how the knowledge should be learned. In this paper we also show a mechanism to make reason from the resources and learner model that it made feedbacks based on learner.
## 135 Previous studies have examined human-to-human dialogue in expert tutoring on the speech act level, but these analyses fail to provide the context necessary for understanding how a series of speech acts relate to each other. This research examined tutorial dialogue in terms of sustained, pedagogically distinct phases, referred to as tutoring modes, which gives context to the finer-grained analysis of moves. Our accomplishments were twofold: we developed a new annotation scheme for tutorial dialogue that takes into account clusters of multiple dialogue moves, and we determined the extent to which these modes occurred in the tutoring sessions. We also examined likely sequences of modes, all of which are important factors when building an ITS that reproduces the efforts of expert human tutors.
## 136 Technological advancement has given education a new definition-parallel intelligent education-resulting in fundamentally new ways of teaching and learning. This article exemplifies an important component of parallel intelligent education-artificial education system in a narrative game environment to offer personalized learning. The system collects data on the player's actions while they play, assessing their concept knowledge via k-nearest-neighbor (kNN) classification, and provides tailored feedback to that student as they play the game. Based on an empirical evaluation, the kNN-based game system is shown to accurately provide players with differentiated instructions to guide them through the learning process based on the estimation of their knowledge levels.
## 137 This paper details the design, development and evaluation of an affective tutoring system (ATS)-an e-learning system that detects and responds to the emotional states of the learner. Research into the development of ATS is an active and relatively new field, with many studies demonstrating promising results. However, there is often no practical way to apply these findings in real-world settings. The ATS described in this paper utilizes a generic affective application model to infer and appropriately respond to the learner's affective state. This approach brings several advantages, notably the potential direct support for re-use and retrospective addition of affect sensing functionality into existing e-learning software. Skin conductivity and heart rate variability measurements were used to infer affective activation and valence. The evaluation involved an experiment in which the effectiveness of the fully functional ATS was compared with that of a nonaffective version, and was conducted with 40 adult participants. The evaluation of the effectiveness of this tutoring system showed that measurable improvements in perceived learning may be obtained with a modest level of software development.
## 138 ISCARE (Information System for Competition based on pRoblem solving in Education) is a new and innovative intelligent tutoring system that we have designed and implemented. This tool allows the competition among students for improving their learning process in a course. The tool takes some ideas from the Swiss-system widely used in chess and adapts them to the educational area. The competition is based on different tournaments and rounds. In each round, students are assigned in pairs of two, which compete one against another, and each pair receives different questions that students have to solve in a limit of time. Students can see their partial ratings after each round and their final rating after a tournament. A lot of knowledge from different disciplines was used to design, and implement this system, as ISCARE includes different functionality such as the students' registration into the system, the creation of tournaments, the registration and assignment of students to tournaments, the management of each tournament life cycle (started, in execution, finished, etc.), the addition of the different exercises to tournaments, the calculation of pairs of students for each round with different algorithms, the assignment of exercises per round and pair, the scorings of the students per round and tournament, the management of the students' ratings, or the visualization of information. This paper presents the ISCARE intelligent tutoring system, describing its different options, menus, or functionality as well as its architecture and the specific modeling to achieve the desired features. (C) 2012 Elsevier Ltd. All rights reserved.
## 139 Knowledge Societies also named Social Learning Networks (SLN) allow interaction and collaboration between individuals (instructors and students), who share their connections under a scheme of learning communities around common learning interest. In this paper, we present Zamna, a Knowledge Society implemented as an adaptive learning social network. A community of Instructors and Learners can create, display, share and assess communities, intelligent tutoring systems or adaptive courses in a collaborative environment. The communities and courses are tailored to the student's learning style according to the learning style model of Felder-Silverman. The identification of community's and student's learning style is performed using self-organizing maps. The main contribution of this paper lies at the integration of Artificial Intelligence with SLN.
## 140 Intelligent tutoring systems (ITS) have been a topic of great interest for about five decades. Over the years, ITS research has leveraged AI advancements, and has also helped push the boundaries of AI capabilities with grounded usage scenarios. Using ITSs along with classroom instruction to augment traditional teaching is a canonical example of how humans and machines can work together to solve problems that are otherwise overwhelming and non-scalable individually. The experiences of personalized learning created by (1) seamless orchestration of human decision-making at few critical points with (2) scalability of cognitive capabilities using AI systems can drive increased student engagement leading to improved learning outcomes. By considering two particular use-cases of early childhood learning and higher education, we discuss the challenges involved in designing these complex human-centric systems. These systems integrate technologies involving interactivity, dialog, automated question generation, and learning analytics.
## 141 Human teachers have capabilities that are still not completely uncovered and reproduced into artificial tutoring systems. Researchers have nevertheless developed many ingenious decision mechanisms which obtain valuable results. Some inroads into natural artificial intelligence have even been made, then abandoned for tutoring systems because of the complexity involved and the computational cost. These efforts toward naturalistic systems are noteworthy and still in general use. In this chapter, we describe how some of this AI is put to work in artificial tutoring systems to reach decisions on when and how to intervene. We then take a particular interest in pursuing the path of natural AI for tutoring systems, using human cognition as a model for artificial general intelligence. One tutoring agent built over a cognitive architecture, CTS, illustrates this direction. The chapter concludes on a brief look into what might be the future for artificial tutoring systems, biologically-inspired cognitive architectures.
## 142 In this paper, we describe a system called C-Tutor, an intelligent tutoring system (ITS) for novice C programmers. A program analyzer is the most important part of the ITS for programming. Our program analyzer is a compound of a reverse engineering system and a didactic system. Since a novice program usually contains many bugs, information about the intentions of the programmer is inevitable to recognize a buggy program. In our approach, the intentions of a programmer are automatically extracted as a problem description from a sample program by a reverse engineering system called GOES (GOal Extraction System). Based on the problem description, students' programs are recognized by a didactic system called ExBug (Execution-guided deBugger). As a learning environment, Curriculum Network constructs the knowledge base as genetic graphs to teach programming. C-Tutor is a complete ITS which provides both a program analyzer and a learning environment. Tested with real students' programs, program analyzer gives acceptable recognition results. Program analyzer and learning environment are closely related so that students can learn C language during programming. New problems can be easily set because GOES automatically generates problem descriptions for program analyzers. This makes C-Tutor a more practical tutoring system for a real C language course. (C) 1997 Elsevier Science Ltd.
## 143 This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students' mathematics performance. We define progress in three areas: improved student cognition, engagement, and affect, and we attribute this improvement to specific components and interventions that are inherently affective, cognitive, and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students' metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and student self-reports, and supported through affective learning companions and progress reports. Collectively, these studies elucidate a suite of effective strategies to support advanced personalized learning via an intelligent adaptive tutor that can be tailored to the individual needs, emotions, cognitive states, and metacognitive skills of learners.
## 144 Intelligent Tutoring Systems (ITS) are interactive learning environments based on instruction assisted by computers. The intelligence of these systems is largely attributed to their ability to adapt to a specific student during the teaching process. In general, the adaptation process can be described by three phases: (i) getting the information about the student, (ii) processing the information to initialize and update a student model, and (iii) using the student model to provide the adaptation. In this paper we studied aspects related with student modeling (SM) in Intelligent Tutoring Systems. First vile make a qualitative comparison of two techniques: Bayesian Networks (BN) and Case-based Reasoning (CBR) for SM. We apply both techniques to a case study concerning the development of an ITS for e-learning in the medical domain. Finally, we discuss the results obtained.
## 145 Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students' SRL while they learn about the human circulatory system. MetaTutor's architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners' cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.
## 146 In recent times, it has been seen that the personalized teaching to each pupil favours learning due to an individualized tracking is done to him/her. The goal of this work is doing a personalized tracking of every pupil. The system presented is able to adapt the educational material to the level of every student. It also fulfils the requirements of c-learning technology and provides various accessing levels (administrator, teacher, student and user). Our system tries to reproduce a real e-learning centre with administrative and learning components. The teaching component cannot be solved in a trivial form, rather it requires the use of knowledge engineering and artificial intelligence techniques.
## 147 This paper proposes a method to couple the domain, student and pedagogical models in an Intelligent Tutoring System. The goal of the proposed method is to produce a personalised version of the domain model with respect to the student model, without burdening the teacher with the task of specifying how this personalisation is to be done. The pedagogical model is defined through Object Petri Net, whose transitions control the interaction decisions, according to conditions that refer to the student model and feedback. All student model management is automatically included in the pedagogical model by the authoring tool.
## 148 We ale developing a general argumentation framework for implementing tutoring feedback in the form of persuasive dialogues The objective is to have an intelligent. tutoring system capable of arguing with the student, to convince him of the rationale of the feedback provided to him The application domain is that of medical diagnosis skill learning
## 149 Purpose Considerable attention has been paid to content adaptation in ITS. However, process-oriented adaptation has been neglected and none of ITS addressed the correlation between the learning and the teaching process. Indeed, uncertainty coming from the dynamic preferences of learners and tutors, the evolutionary cognitive state of the learner and the choice of goals and educational strategies is not well considered in ITS. So, in this paper, a new proposal for guiding and adapting the construction of both learning and pedagogical processes named educational processes in intelligent tutoring system is described and detailed. Method The research strategy followed is based on a dynamic Bayesian network to predict the educational context, a strategic perspective for modeling educational processes, and guidance algorithms for their development and support. This aims to generate an individualized learning process for each learner by selecting the most appropriate pedagogical process according to the actual preferences of the tutor. Results Experimental results are given to illustrate the applicability of the proposed solution. The results show an improvement in guiding the learner and the teacher/tutor by considering the evolving and uncertain educational context. Conclusion Learners will be therefore able to achieve the learning goal more efficiently when the pedagogical process is more adapted to their individual differences. The evaluation of knowledge improvement, the appropriateness of educational recommended context and the prediction effectiveness shows promising results.
## 150 We describe Wayang Outpost, a web-based ITS for the Math section of the Scholastic Aptitude Test (SAT). It has several distinctive features: help with multimedia animations and sound, problems embedded in narrative and fantasy contexts, alternative teaching strategies for students of different mental rotation abilities and memory retrieval speeds. Our work on adding intelligence for adaptivity is described. Evaluations prove that students learn with the tutor, but learning depends on the interaction of teaching strategies and cognitive abilities. A new adaptive tutor is being built based on evaluation results; surveys results and students' log files analyses.
## 151 The Affective Meta-Tutoring system is comprised of (1) a tutor that teaches system dynamics modeling, (2) a meta-tutor that teaches good strategies for learning how to model from the tutor, and (3) an affective learning companion that encourages students to use the learning strategy that the meta-tutor teaches. The affective learning companion's messages are selected by using physiological sensors and log data to determine the student's affective state. Evaluations compared the learning gains of three conditions: the tutor alone, the tutor plus meta-tutor and the tutor, meta-tutor and affective learning companion.
## 152 Originally, the task sequencing in adaptive intelligent tutoring systems needs information gained from expert and domain knowledge as well as information about former performances. In a former work a new efficient task sequencer based on a performance prediction system was presented, which only needs former performance information but not the expensive expert and domain knowledge. This task sequencer uses the output of the performance prediction to sequence the tasks according to the theory of Vygotsky's Zone of Proximal Development. In this paper we aim to support this sequencer by a further automatically to gain information source, namely speech input from the students interacting with the tutoring system. The proposed approach extracts features from students speech data and applies to that features an automatic affect recognition method. The output of the affect recognition method indicates, if the last task was too easy, too hard or appropriate for the student. Hence, as according to Vygotsky's theory the next task should not be too easy or too hard for the student to neither bore nor frustrate him, obviously the output of our proposed affect recognition is suitable to be used as an input for supporting a sequencer based on the theory of Vygotsky's Zone of Proximal Development. Hence, in this paper we (1) propose a new approach for supporting task sequencing by affect recognition, (2) present an analysis of appropriate features for affect recognition extracted from students speech input and (3) show the suitability of the proposed features for affect recognition for supporting task sequencing in adaptive intelligent tutoring systems.
## 153 Intelligent tutoring systems (ITSs) are a promising integrated educational tool for customizing formal education using intelligent instruction or feedback. In recent decades, ITSs have transformed teaching and learning and associated research. This study examined the evolution and future trends of ITS research with scientometric methods. First, a dataset comprising 1173 relevant publications was compiled from the Web of Science Core Collection databases (including the Science Citation Index Expanded and the Social Science Citation Index). Then, the publication distributions by time, author, institution, country/region, and knowledge sources were analyzed to reveal the multidisciplinary integration paths. Dataset co-occurrence and co-citation analyses were conducted to identify the most popular research issues, the research chronology, and the emerging trends. It was found that: (a) ITS research has been growing in recent years. According to the Price literature exponential growth curve, this field is still in its initial stage while has high potential; (b) computer science, education, psychology, and engineering were the main ITS research knowledge sources, with ITS social science publications since 2007 being higher than ITS natural sciences publications; (c) interactive learning environments, student modeling, teaching/learning strategies, and machine learning have been the most popular research foci; and (d) the Coh-Metrix, problem-centered instruction, and STEM are the current research trends.
## 154 Recently Intelligent Tutoring Systems (ITS) and Computer-Supported Collaborative Learning (CSCL) have got much attention in the field of computer science, artificial intelligence, cognitive psychology, and educational technologies. An ITS is a technologically intelligent system that provides an adaptive learning paradigm for an individual learner only, while CSCL is also a technology-driven learning paradigm that supports groups of learners in pertaining knowledge by collaboration. In a multidisciplinary research field-the Learning Sciences, both individual and collaborative learning have their own significance. This research aims to extend ITS for collaborative constructivist view of learning using CSCL. Integrating both design architecture of CSCL and ITS, this research model propose a new conceptual framework underpinning Intelligent Tutoring Supported Collaborative Learning (ITSCL). ITSCL extend ITS by supporting multiple learners interacting system. ITSCL support three different types of interaction levels. The first level of interaction supports individual learning by learner-tutor interaction. The second and third level of interaction support collaborative learning, by learner-learner interaction and tutor-group of collaborative learners' interactions, respectively. To evaluate ITSCL, a prototype model was implemented to conduct few experiments. The statistical results extrapolate the learning gains, measured from Paired T-Test and frequency analysis, contend a significant learning gain and improvement in the learning process with enhanced learning performance.
## 155 A key challenge in ITS research and development is to support tutoring at scale, for example by embedding tutors in MOOCs. An obstacle to at-scale deployment is that ITS architectures tend to be complex, not easily deployed in browsers without significant server-side processing, and not easily embedded in a learning management system (LMS). We present a case study in which a widely used ITS authoring tool suite, CTAT/TutorShop, was modified so that tutors can be embedded in MOOCs. Specifically, the inner loop (the example-tracing tutor engine) was moved to the client by reimplementing it in JavaScript, and the tutors were made compatible with the LTI e-learning standard. The feasibility of this general approach to ITS/MOOC integration was demonstrated with simple tutors in an edX MOOC Data Analytics and Learning.
## 156 Affectivity has influence in learning face-to-face environments and improves some aspects in students, such as motivation. For that reason, it is important to integrate affectivity elements into virtual environments. We propose a conceptual model that suggests which elements of tutor, student and dialogue should be integrated and implemented into learning systems. We design an ontology guided by methontology, and apply a mathematical evaluation (OntoQA) to determine the richness of the proposed model. The mathematical evaluation states that the proposed model has relationship richness and horizontal nature. We developed a software application implementing the conceptual model in order to prove its effectivity to generate students' motivation. The findings suggest that the implemented affective learning ontology impacts positively the motivation in students with low academic performance, in female students and in engineering students.
## 157 Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student's proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.
## 158 The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. This research work presents a set of three different approaches applying intelligent systems for automatic identification of learning styles in order to provide an adapted learning scheme under different software platforms. The first approach uses a neuro-fuzzy network (NFN) to select the best learning style. The second approach combines a NFN to classify learning styles with a genetic algorithm for weight optimization. The learning styles are based on Gardner's Pedagogical Model of Multiple Intelligences. The last approach implements a self-organising feature map (SOM) for identifying learning styles under the Felder-Silverman Model. The three approaches are used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural networks can also be exported to mobile devices. We present results of three different tutoring systems produced by three implemented authoring tools.
## 159 Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables. (C) 2016 Elsevier Ltd. All rights reserved.
## 160 Todays, Intelligent and web-based E-Learning is one of the important area in E-Learning. This paper integrates an intelligent and web-based E-Learning with expert system technology in order to model the learning styles using Jackson's model. It is intelligent because it can interact with the learners and offer them some subjects based on Pedagogy view. Learning process of this system is in the following. First system determines learner's individual characteristics and his learning style based on a questionnaire in Jackson's learning styles profiler. Learning styles profiler is a modern measure of individual differences in learning style. Then learner's model is obtained and an Expert system simulator plans a pre-test and then rates him. The concept would be presented if the learner scores enough. Subsequently, the system evaluates him by a post-test. Finally the learner's model would be updated by the modeler based on try-and-error. The proposed system can be available Every Time and Every Where (ETEW) through the web. It has all good facilities such as hypertext component, adaptive sequencing, problem solving support, intelligent solution analysis and adaptive presentation. It improves the learning performance and has some important advantages such as high speed, simplicity of learning and low cost.
## 161 Teachers are increasingly demanding to be active users of gamilled intelligent tutoring systems (ITS). To effectively help teachers to use such systems, it is needed to provide simple and usable solutions for them without requiring advanced technical skills. In this work, we propose an authoring solution to help teachers in the design of gamified ITS. We evaluate our solution using a controlled experiment varying some features of the authoring proposal (template or scratch) with respect to ten metrics in the context of teachers and lecturers in Brazil. The results of this experiment allowed us to stale that (i) authoring gamified ITS by using template requires much less time than from scratch; and (ii) there is no difference in the use or not of template for authoring gamified ITS with regards to the other metrics (e.g, perceived ease of use and usability, complexity, etc), but teachers have a high acceptance for all these metrics. The results found are of great importance since they might help teachers to design gamified ITS in a few minutes.
## 162 The paper describes design of an intelligent tutoring system called FLUTE. The system has been recently developed in order to help students learn concepts from the domain of formal languages and automata. The basic idea of the FLUTE system is a systematic introduction of students into the system's domain, in accordance with both the logical structure of the domain and individual background knowledge and learning capabilities of each student. The main goal of the paper is to illustrate important design decisions and implementation details that are relevant even beyond the FLUTE system itself. The system is presented in the paper primarily from the pedagogical and design perspectives.
## 163 <NA>
## 164 Educational software has frequently been criticized as it has not been explicitly planned to meet the demands of educational environment. Therefore, there is an increasing demand for an intelligent computer technology to become used in the environment of education. This paper proposes the development of an intelligent tutoring system, which will aid students to learn software development. The idea is to simplify the learning process of computer programming which is a difficult process for novice programmers. The proposed system will be designed to use for either individual learning or group based work. The characteristic of proposed system differentiates itself from other programming tutoring systems as this system will monitor student's progress for the purpose of interfering (interactive feedback) at the needs of student. The interactive feedback has been used as part of the learning process through the methodology of learning by assessment.
## 165 Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and extended its instructional domain. Several evaluation studies performed with KERMIT and EER-Tutor show that they are effective Intelligent Tutoring Systems (ITSs). We also comment on various extensions made to EER-Tutor over the years. There are several contributions of our research, such as developing effective problem-solving support for conceptual database design in terms of interface design. Our database design tutors deal with large solution spaces efficiently by specifying constraints that capture equivalent solution states, and using ideal solutions to capture the semantics of the problem. Instead of requiring a problem solver, the ITS checks whether the student's database schema is correct by matching it to constraints and the ideal solution. Another contribution of our work is in guidelines for developing effective feedback to the student.
## 166 This paper proposes the agent based intelligent, adaptive learning or tutoring system (IATS). The proposed architecture of the agent based system provides the framework for the intelligent tutoring system. We define many agents like tutoring agent. student agent, student clustering agent, resource classification agent. The system model is developed using the O-MaSE development model. The clustering is done by the combination of Evolutionary clustering methods and fuzzy c-means clustering. The classification of the resources is representation of the resources in the tree based hierarchical structure from which the adaptive resources are generated. The system provides the adaptive course materials, adaptive user interface, and adaptive examinations to the learner.
## 167 We report our work in progress in the area of music intelligent tutoring systems (MITS). We discuss the motivation behind the design of GUI that aim to support an interactive learning in the MITS environment. These interactivities include onscreen score editing and capturing student behaviours during tutorial sessions (e.g. feedbacks, queries, stream of action-events). In this report, we discuss our representation, then our design and finally present some interaction examples from our system.
## 168 In this study, we meta-analyzed empirical research of the effectiveness of intelligent tutoring systems (ITS) on K-12 students' mathematical learning. A total of 26 reports containing 34 independent samples met study inclusion criteria. The reports appeared between 1997 and 2010. The majority of included studies compared the effectiveness of ITS with that of regular classroom instruction. A few studies compared ITS with human tutoring or homework practices. Among the major findings are (a) overall, ITS had no negative and perhaps a small positive effect on K-12 students' mathematical learning, as indicated by the average effect sizes ranging from g = 0.01 to g = 0.09, and (b) on the basis of the few studies that compared ITS with homework or human tutoring, the effectiveness of ITS appeared to be small to modest. Moderator analyses revealed 2 findings of practical importance. First, the effects of ITS appeared to be greater when the interventions lasted for less than a school year than when they lasted for 1 school year or longer. Second, the effectiveness of ITS for helping students drawn from the general population was greater than for helping low achievers. This finding draws attentions to the issue of whether computerized learning might contribute to the achievement gap between students with different achievement levels and aptitudes.
## 169 Tutoring is a human activity, which has been applied in several fields. In the last decade, this task has become indispensable, especially in higher education institutions. The principal objective attached to the tutor is to assist and support the learners throughout their learning process. Several researchers have studied the impact of collaboration between the learners on their cognitive levels, but few studies have been carried out on the impact of collaboration among the tutors. In the previous work, we have studied the impact of the collaboration among the learners with a specific collaborative CEHL(Computing Environment for Human Learning) [1]. In this work, we focused on the impact of collaboration among tutors.
## 170 hi this research study we investigate whether AC ware Tutor supported education, along with traditional learning and teaching of high school students, contributes more to the student acquisition of knowledge. AC-ware Tutor is intelligent tutoring system, focused on automatic and dynamic generation of adaptive courseware which takes into account the current level of student's knowledge and cognitive characteristics that determine the complexity and level of presented courseware. The experiment was conducted at the High school of Economics, Split with 108 second-class students, during the Business Psychology class in January, 2017.
## 171 Motivation and metacognition are strongly intertwined, with learners high in self-efficacy more likely to use a variety of self-regulatory learning strategies, as well as to persist longer on challenging tasks. The aim of the research was to improve the learner's focus on the process and experience of problem-solving while using an Intelligent Tutoring System (ITS) and including motivational and metacognitive feedback based on the learner's past states and experiences. An existing ITS, SQL-Tutor, was used with first-year undergraduates studying a database module. The study used two versions of SQL- Tutor: the Control group used a base version providing domain feedback and the Study group used an extended version that also provided motivational and metacognitive feedback. This paper summarises the pre-and post-process results. Comparisons between groups showed some differing trends both in learning outcomes and behaviour in favour of the Study group.
## 172 As a rule, intelligent tutoring systems offer a learner only one problem. solving mode, i.e., feedback is provided after each solution step. Moreover, system's hints are ordered on the basis of a degree of informativeness and are delivered to a learner sequentially from the most general to the most specific. The paper presents an approach which provides greater adaptive abilities of intelligent tutoring systems. It supports two modes of problem-solving and uses a two-layer model of hints. Therefore, the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint. The aforementioned approach is being implemented in the intelligent tutoring system for Minimax algorithm at present.
## 173 Determining how to provide good tutoring functions is an important research direction of intelligent tutoring systems. In this study, the authors develop an intelligent tutoring system with good tutoring functions, called FUDAOWANG. The research domain that FUDAOWANG treats is junior middle school mathematics, which belongs to the objective mature domain. Its characteristic is that the knowledge employed is the mature knowledge accepted by most people. FUDAOWANG uses automatic reasoning technology about objective mature problems to realize its intelligence. Based on the results of the automatic reasoning, FUDAOWANG synthetically applies the problem-based tutoring and advanced education concepts to achieve the tutoring functions of stepwise prompt, detailed answers, rethinking after solution, consolidated exercise, etc. The evaluation of FUDAOWANG shows that it is helpful to students in improving learning achievements and cultivating good learning habits.
## 174 In recent years a great effort has been made in order to create Intelligent Tutoring Systems that get close to human teaching. Some of the handicaps of the systems already created are the impossibility of sharing the courses between different Intelligent Tutoring Systems and the difficulty of creating them. Once the intelligent tutoring system is created, creating a new course is an expensive job that requires the intervention of many people that are expert in different areas. In this paper a generic and extensible authoring tool to create courses for different Intelligent Tutoring Systems is presented. This authoring tool allows the creation of courses for different types of intelligent tutoring systems. Once a course is created it can be exported to another intelligent tutoring system, reusing the domain model that the course represents. The prototype of the authoring tool has been tested with two simple Intelligent Tutoring Systems. (C) 2010 Elsevier B.V. All rights reserved.
## 175 Introductory programming is an essential part of the curriculum in any engineering discipline in universities. However, for many beginning students, it is very difficult to learn. In particular, these students often get stuck and frustrated when attempting to solve programming exercises. One way to assist beginning programmers to overcome difficulties in learning to program is to use intelligent tutoring systems (ITSs) for programming, which can provide students with personalized hints of students' solving process in programming exercises. Currently, mostly these systems manually construct the domain models. They take much time to construct, especially for exercises with very large solution spaces. One of the major challenges associated with handling ITSs for programming comes from the diversity of possible code solutions that a student can write. The use of data-driven approaches to develop these ITSs is just starting to be explored in the field. Given that this is still a relatively new research field, many challenges are still remained unsolved. Our goal in this paper is to review and classify analysis techniques that are requested to generate data-driven hints in ITSs for programming. This work also aims equally to identify the possible future directions in this research field.
## 176 A futuristic Technology Enhanced Learning concept, a conversational Intelligent Tutoring System (ITS) for deployment in an e-learning context, is gradually becoming a reality thanks to the continuous advancement in Artificial Intelligence and to the worldwide increasing demand for online learning, especially during the pandemic. However, we do need to consider whether such technology will support student learning or make learning more difficult. In the absence of a mature conversational Intelligent Tutoring System, this article aims to address this question indirectly through an investigation of how students and tutors in an online learning programme perceive the concept of conversational Intelligent Tutoring Systems, such as a chatbot, for online learning. This is achieved by surveying students who are currently enrolled in an online programme and interviewing the tutors on the same programme. The research concludes that ITS would very likely enhance online learning experience for both students and tutors, but there are various concerns that must be addressed.
## 177 Making effective problem selection decisions is an important yet challenging self-regulated learning (SRL) skill. Although efforts have been made to scaffold students' problem selection in intelligent tutoring systems (ITS), little work has tried to support students' learning of the transferable problem selection skill that can be applied when the scaffolding is not in effect. The current work uses a user-centered design approach to extend an ITS for equation solving, Lynnette, so the new designs may motivate and help students learn to apply a general, transferable rule for effective problem selection, namely, to select problem types that are not fully mastered (Mastery Rule). We conducted user research through classroom experimentation, interviews and storyboards. We found that the presence of an Open Learner Model significantly improves students' problem selection decisions, which has not been empirically established by prior work; also, lack of motivation, especially lack of a mastery-approach orientation, may cause difficulty in applying the Mastery Rule. Based on our user research, we designed prototypes of tutor features that aim to foster a mastery-approach orientation as well as transfer of the learned Mastery Rule when the scaffolding is faded. The work contributes to the research of supporting SRL in ITSs through a motivational design perspective, and lays foundation for future controlled experiments to evaluate the transfer of the problem selection skill in new tutor units where there is no scaffolding.
## 178 Control systems performance is one the major subtopics taught in Automatic Control Systems introductory courses. Example-tracing tutors, a novel type of intelligent tutoring system (ITS), have been created in order to support the learning experience of mechatronics engineering students. A scoring system based on very well supported ITS design guidelines have led to the selection and successful prioritization of a relevant and general set of top ranked problems taken from a large initial problem space that was created by an exhaustive revision on imparted material in previous classes. Experiences on the advanced usage of the authoring tool and discussion about development issues are provided.
## 179 Education is increasingly employing Intelligent Tutoring Systems (ITS) both for modelling instructional and teaching strategies and for empowering educational programs. First part of the paper introduces the basic structure of an ITS as well as the problems within of ITS. Chapter two describes WITNeSS - an original hybrid intelligent system in form of a Fuzzy-Neural Soft Computing System for optimising the presentation of learning material to a student for a particular problem. The experiments being conducted in the third chapter are focussed just on some ITS test problems regarding the virtual students (simulating the human learning behaviour) when WITNeSS was compared to CAPIT, another ITS. The concluding remarks are in chapter five.
## 180 Collaborative activities, like peer tutoring, can be beneficial for student learning, but only when students are supported in interacting effectively. Constructing intelligent tutors for collaborating students may be an improvement over fixed forms of support that do not adapt to student behaviors. We have developed an intelligent tutor to improve the help that peer tutors give to peer tutees by encouraging them to explain tutee errors and to provide more conceptual help. The intelligent tutor must be able to classify the type of peer tutor utterance (is it next step help, error feedback, both, or neither?) and the quality (does it contain conceptual content?). We use two techniques to improve automated classification of student utterances: incorporating domain context, and incorporating students' self-classifications of their chat actions. The domain context and self-classifications together significantly improve classification of student dialogue over a baseline classifier for help type. Using domain features alone significantly improves classification over baseline for conceptual content.
## 181 We tested whether the provision of metacognitive knowledge on how to cope with the complexity of a learning environment improved learning. In an experimental setting, high-school students (N = 60) worked through a computer-based geometry lesson either with or without metacognitive support in the form of a cue card. This cue card encouraged students to use instructional resources in the learning environment (i.e., textual and graphic representations and different help facilities) more strategically. During learning, the learners' gaze and log-file data were recorded. The metacognitive support made learning more efficient (i.e., less learning time without impairing outcomes). In addition, low-prior knowledge students developed deeper conceptual understanding. The effects on learning outcomes were mediated by reducing the non-strategic use of help facilities. Our findings suggest that a lack of metacognitive conditional knowledge (i.e., in which situation to use which help facility) can account for learning difficulty in computer-based learning environments. (C) 2012 Elsevier Ltd. All rights reserved.
## 182 Objective: We investigated adapting the interaction style of intelligent tutoring system (ITS) feedback based on human-automation etiquette strategies. Background: Most ITSs adapt the content difficulty level, adapt the feedback timing, or provide extra content when they detect cognitive or affective decrements. Our previous work demonstrated that changing the interaction style via different feedback etiquette strategies has differential effects on students' motivation, confidence, satisfaction, and performance. The best etiquette strategy was also determined by user frustration. Method: Based on these findings, a rule set was developed that systemically selected the proper etiquette strategy to address one of four learning factors (motivation, confidence, satisfaction, and performance) under two different levels of user frustration. We explored whether etiquette strategy selection based on this rule set (systematic) or random changes in etiquette strategy for a given level of frustration affected the four learning factors. Participants solved mathematics problems under different frustration conditions with feedback that adapted dynamic changes in etiquette strategies either systematically or randomly. Results: The results demonstrated that feedback with etiquette strategies chosen systematically via the rule set could selectively target and improve motivation, confidence, satisfaction, and performance more than changing etiquette strategies randomly. The systematic adaptation was effective no matter the level of frustration for the participant. Conclusion: If computer tutors can vary the interaction style to effectively mitigate negative emotions, then ITS designers would have one more mechanism in which to design affect-aware adaptations that provide the proper responses in situations where human emotions affect the ability to learn.
## 183 We present Newton's Pen, a statics tutor implemented on a pentop computer, a writing instrument with an integrated digitizer and embedded processor. The tutor, intended for undergraduate education, scaffolds students in the construction of free body diagrams and equilibrium equations. This project entailed the development of sketch understanding techniques and user interface principles for creating pedagogically sound instructional tools for pentop computers. Development on the pentop platform presented novel challenges because of limited computational resources and a visually static, ink-on-paper display (the only dynamic output device is an audio speaker). We show that a system architecture based on a finite state machine serves as a convenient means for providing context-sensitive tutorial help. We also demonstrate the effectiveness of an instructional model based on a structured solution process with feedback provided at each problem-solving step. This model is designed to match the pentop's unusual user interface capabilities. Our three user studies suggest that Newton's Pen is an effective teaching tool. (C) 2008 Elsevier Ltd. All rights reserved.
## 184 With the popularity of Internet technology, it becomes more difficult for users to retrieve their needed information from so enormous information space. Thus the issue of information overload formed. To solve the problem, recommender system emerged. This paper presents the personalized intelligent tutoring system based on Content-Based Filter Algorithm. The system will be implemented based on Perl with the advantages of developers paying heed to the program logic without caring for data storage, rule of operation and other details. And the use of open source Mojolicious framework can significantly accelerate the development cycle. The function of personalized information recommendation of an intelligent tutoring system will be achieved and personalized information will be recommended to users to improve their learning efficiency.
## 185 One strength of educational games stems from their potential to increase students' motivation and engagement during educational tasks. However, game features may also detract from principle learning goals and interfere with students' ability to master the target material. To assess the potential impact of game-based learning environments, in this study we examined motivation and learning for 84 high-school students across eight 1-hr sessions comparing 2 versions of a reading strategy tutoring system, an intelligent tutoring system (iSTART) and its game-based version (iSTART-ME). The results demonstrate equivalent target task performance (i.e., learning) across environments at pretest, posttest, and retention, but significantly higher levels of enjoyment and motivation for the game-based system. Analyses of performance across sessions reveal an initial decrease in performance followed by improvement within the game-based training condition. These results suggest possible constraints and benefits of game-based training, including time-scale effects. The findings from this study offer a potential explanation for some of the mixed findings within the literature and support the integration of game-based features within intelligent tutoring environments that require long-term interactions for students to develop skill mastery.
## 186 We report on initial system development of SlideTutor - a web-deployed, image-based, model-tracing Intelligent Tutoring System for teaching microscopic diagnosis. The system is based on our previous work describing the development of expertise in this complex visual diagnostic task. SlideTutor is designed to provide individualized coaching to students as they search, and interpret virtual pathology slides. The system models three important sets of cognitive skills: slide search and lesion detection, visual feature identification, and diagnostic inference. We describe the design and development of the model-tracer for this system, including system architecture, knowledge representation, methods for feedback, and student interface.
## 187 In the teaching and learning processes various problems arise as to the understanding and comprehension of knowledge. These difficulties are mainly in which everyone has a different way of learning and classic teaching methods do not meet your particular needs. The development of technology has led to the creation of tools that provides an efficient solution to this problem: Intelligent Tutoring Systems (ITS). The main objective of this article is to identify the main features of these tutors, emphasizing the benefits and support in the teaching-learning in the educational context. The method used is the descriptive and systemic, which allows you to collect the necessary data. The research brought together the most important aspects of ITS and present them as an excellent tool to perform a learning process.
## 188 In BioWorld, a medical intelligent tutoring system, novice physicians are tasked with solving virtual patient cases. Whilst the importance of modeling and predicting clinical reasoning is recognized, an important aspect of the learner contribution remains unexplored - the written case summary prepared by the learner. The premise of investigating the case summaries is that it captures the thought and process of the learners in solving the cases; since, the case summaries hold important reasoning information, it makes sense to incorporate it as part of the novice-expert overlay model. In this paper, case summaries written by novices and experts were considered as an addendum to the existing novice-expert overlay model in the BioWorld system. Toward this goal, using a promising new classification method called confidence-weighted linear classifiers, this paper proposes a way to augment the novice-expert overlay model in BioWorld.
## 189 Research Methods Tutor (RMT) is a dialogue-based intelligent tutoring system for use in conjunction with undergraduate psychology research methods courses. RMT includes five topics that correspond to the curriculum of introductory research methods courses: ethics, variables, reliability, validity, and experimental design. We evaluated the effectiveness of the RMT system in the classroom using a nonequivalent control group design. Students in three classes (n = 83) used RMT, and students in two classes (n = 53) did not use RMT. Results indicated that the use of RMT yielded strong learning gains of 0.75 standard deviations above classroom instruction alone. Further, the dialogue-based tutoring condition of the system resulted in higher gains than did the textbook-style condition (CAI version) of the system. Future directions for RMT include the addition of new topics and tutoring elements.
## 190 <NA>
## 191 SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve problems. There are two different ways to author an expert model with SimStudent. In the context of Authoring by Tutoring, the author interactively tutors SimStudent by posing problems to SimStudent, providing feedback on the steps performed by SimStudent, and also demonstrating steps as a response to SimStudent's hint requests when SimStudent cannot perform steps correctly. In the context of Authoring by Demonstration, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those worked-out examples. We conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. First, the expert model generated with Authoring by Tutoring is better and has higher accuracy while maintaining the same level of completeness than the one generated with Authoring by Demonstration. The reason for this better accuracy is that the expert model generated by tutoring benefits from negative feedback provided for SimStudent's incorrect production applications. Second, authoring by Tutoring requires less time than Authoring by Demonstration. This enhanced authoring efficiency is partially because (a) when Authoring by Demonstration, the author needs to test the quality of the expert model, whereas the formative assessment of the expert model is done naturally by observing SimStudent's performance when Authoring by Tutoring, and (b) the number of steps that need to be demonstrated during tutoring decreases as learning progresses.
## 192 Virtual reality simulation environments offer exciting opportunities and challenges for Students, immersed in a 3D computer simulation of their work environment, improve their skills through practice on realistic tasks. Computer tutors can inhabit the virtual world along with students, allowing them to physically collaborate with students on tasks, and they can interact and communicate in nonverbal nag's that would be impossible with a traditional disembodied computer tutor. This payer discusses these opportunities and challenges, as well as our progress in addressing them in our pedagogical agent Steve.
## 193 Computer-based education has already been acknowledged as an important asset for medical education. For example, web-based educational systems for medicine and health provide an additional important advantage to remote learners through platform- and time-independence. However, such systems are difficult to create as they require a lot of effort from both medical tutors and software engineers. Therefore, repetition of effort has to be avoided and such systems should be able to be used to their full extent from whoever requires medical and health knowledge on the specific topics. This means that they need to incorporate intelligent techniques in order to be able to adapt dynamically to the needs of individual users rather than have many static educational systems designed solely for different kinds of users. In view of this, in this paper we address the problem of developing an adaptive e-learning system for the medical domain of Atheromatosis. Atheromatosis of the aortic arch has been recognized as an important source of embolism, which is a frequent cause of stroke. This is the main reason that the particular topic is of interest to a wide range of users with different background knowledge and needs (e.g. medical students, nurses, common people interested in maintaining a good health, etc.). The inference mechanism of the system uses a combination of rule-based reasoning and a decision making theory. In order to design the reasoning mechanism of the system, and thus incorporate the decision making theory successfully, we conducted an empirical study. The empirical study involved distribution of questionnaires to several classes of potential users of an e-learning system about Atheromatosis and analysis of the results by computer and medical experts. The results of the empirical study were used for designing the reasoning mechanism of the system.
## 194 In this document a platform for the development of ITSs is presented. The objective of this architecture is to provide a tutoring platform with a modular structure suitable to accommodate different sequencing paradigms through a common functional interface. The platform has been tested with positive results.
## 195 Authoring tools enable the more rapid creation of intelligent tutoring systems. Such tools are essential for tutors to become more widespread. In this study we evaluate WebxPST, a browser-based authoring system that enables non-programmers to create model-tracing-like intelligent tutors. Five authors, two course instructors and three undergraduates, created 74 problems suitable for use in an undergraduate statistics curriculum. A subset of these problems was deployed in a classroom. These authors quickly mastered the authoring interface showing the feasibility of the tool.
## 196 Symbolization is the ability to translate a real world situation into the language of algebra. We believe that symbolization is the single most important skill students learn in high school algebra. We present research on what makes this skill difficult and report the discovery of a hidden skill in symbolization. Contrary to past research that has emphasized that symbolization is difficult due to both comprehension difficulties and the abstract nature of variables, we found that symbolization is difficult because it is the articulation in the foreign language of algebra. We also present Ms. Lindquist, an Intelligent Tutoring System (ITS) designed to carry on a tutorial dialog about symbolization. Ms. Lindquist has a separate tutorial model encoding pedagogical content knowledge in the form of different tutorial strategies, which were partially developed by observing an experienced human tutor. We discuss aspects of this human tutor's method that can be modeled well by Ms. Lindquist. Finally, we present an early formative showing that students can learn from the dialogs Ms. Lindquist is able to engage student in. Ms. Lindquist has tutored over 600 students at www.AlgebraTutor.org.
## 197 Learning to program may be very difficult for students who have no previous programming experience because it requires students to possess a combination of theory, practice and problem solving skills. The topic of this paper is to describe the development of a flexible and interactive learning environment for learning programming based on intelligent agent after comparison of ITS and ILE, called Interactive System for Teaching Programming (ISTP).
## 198 The Intelligent Tutoring Systems (ITS) must deal both with strategy and knowledge in order to help students in the teaching/learning process. The knowledge in generally passed on to the students via teaching/learning activities, which may be generated by an Authoring Tool,instead of being embedded in the tutor project. This way, the tutoring system can be both simpler and more flexible, providing the expert teacher with more freedom to generate new activities with the assistance of computer science professionals. Considering the diverse knowledge domains focused in ITSs, it looks useful the availability of an Authoring Tools Generator (ATG) to cope with all these different areas. An authoring tool generated by the ATG works as the link between the teacher and the ITS. Therefore, it must contain an easy to use and clear interface for the teacher to define the activities and at the same time must have a low level structure that allows a straightforward interpretation process done by a computer program.
## 199 This paper presents the architecture of a web-based intelligent tutoring system that can be applied to a number of different scientific or literature courses in different domains. The proposed architecture takes the advantages of the web based systems generally and many advantages of building tutoring systems that are based on the web. The proposed web based Intelligent Tutoring System (IITS) integrates a domain knowledge base system, database system, a student model, a course model, an instructor model, and a user interface model. The proposed architecture is a multi tiered architecture and it consists of the following main components: the user interface on the client side through the web browser, the user interface manager on the web server, the student model, course model, instructor model on the application server, database management server and knowledge management system server. The proposed architecture is adopting very important concepts in educational systems including applying adaptive hypermedia in student and instructor interface, evaluating the student capabilities and adapting the teaching strategy to the student's level, using different teaching and evaluation strategies, case based learning, an instructor agent and generating multiple tests and other important concepts making this proposed system a compatible, flexible, adaptable intelligent tutoring system.
## 200 Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge), the politeness effect was investigated in the context of web-based intelligent tutoring systems, software that runs on the Internet and employs artificial intelligence and learning science techniques to help students learn. The goal was to pinpoint the appropriate conditions for having the web-based tutors provide polite feedback and hints (e.g., Let's convert the units of the first item) versus direct feedback and hints (e.g., Convert the units of the first item now). In the study presented in this paper, 132 high school students in a classroom setting, grouped as low and high prior knowledge learners according to a pre-intervention knowledge questionnaire, did not benefit more from polite feedback and hints than direct feedback and hints on either an immediate or delayed posttest, both of which contained near transfer and conceptual test items. Of particular interest and contrary to an earlier lab study, low prior knowledge students did not benefit more from using the polite version of a tutor. On the other hand, a politeness effect was observed for the students who made the most errors during the intervention, a different proxy for low prior knowledge, hinting that even in a classroom setting, politeness may be beneficial for more needy students. This article presents and discusses these results, as well as discussing the politeness effect more generally, its theoretical underpinnings, and future directions. (c) 2010 Elsevier Ltd. All rights reserved.
## 201 An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of learning by doing. Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner's incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy.
## 202 Intelligent tutoring systems (ITSs) represent a particular kind of eleaming systems, which base their operation on the simulation of a human teacher in the learning and teaching process. With the advent of the mobile computing paradigm, m-learning systems, as the portable and personal fashion of e-learning, paved the way to the introduction of mobile intelligent tutoring. Mobile intelligent tutoring systems (MITSs) are targeted to fit into a mobile learner's daily routine without disrupting her/his other activities, but conversely enhancing the efficiency and effectiveness of learning in the context of handheld terminals of restricted capabilities. As in the non-portable ITS counterparts, MITSs' tasks are taken over by agents, making them agent-based systems. In this paper we discuss the mobile intelligent tutoring paradigm, as well as the agent types to be used in the m-learning environment along with the presently affordable agent infrastructure enabling MITS implementation, and corroborate this with the description of a mobile intelligent tutoring model we are developing.
## 203 In this work, we started from an existing Intelligent Tutoring System (ITS) called Sistema de Apoyo Generalizado para la Ensenanza individualizada (SAGE), able to supervise student's learning according to the first four levels of Bloom's taxonomy: knowledge, comprehension, application and analysis, and we propose to improve its scope by adding other functions according to Marzano's taxonomy, that preserves the basic aspects of Bloom's taxonomy and adds metacognition and emotional response. SAGE starts with a diagnostic test about the subject of study, to which a cognitive diagnostic test will be added, then the system assigns the lesson that must be completed, according to the previous knowledge of the student. While students navigate throughout the lesson, the system will monitor their emotional response and motivation using a camera, facial recognition and machine learning techniques. To decide which will be the next lesson a personalized advance route will be traced according to a student model, and to make the advance between lessons a shared control between the student and the system will be implemented. Using this methodology, teachers will be able to focus on activity planning and evaluation of assignments related with knowledge utilization, like essays or application projects, and the system will be in charge of the tasks of the remaining levels of Marzano's taxonomy (retrieval, comprehension, analysis, metacognition and self-system).
## 204 AutoTtitor simulates a human tutor by holding a conversation with the learner in natural language. The dialogue is augmented by an animated conversational agent and three-dimensional (3-D) interactive simulations in order to enhance the learner's engagement and the depth of the learning. Grounded in constructivist learning theories and tutoring research, AutoTbtor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition. The computational architecture of the system uses the.NET framework and has simplified deployment for classroom trials.
## 205 To echo the United Nations formulated Sustainable Development Goals (SDGs), SDG 4 is to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Furthermore, high-quality education is the base on which human lives can be improved and sustainable development can be accomplished. Therefore, the affective emotional tutoring system established in this study enables learning via mobile devices, which are indispensable in daily life. The real-time interactive agent in the system guides learners to turn negative emotions into positive ones. We explored the usability of and user satisfaction with the affective emotional tutoring system. Sixty-two students participated in the study which used a quantitative research design to explore a learning situation. The overall usability of the system was evaluated with the System Usability Scale (SUS), and the Questionnaire for User Interaction Satisfaction (QUIS) was used to evaluate user satisfaction with the different elements of the system. The results showed that both the usability of and satisfaction with the affective emotional tutoring system were high. The emotional feedback mechanism of the system can help learners turn negative emotions into positive ones.
## 206 Current e-learning systems are still inadequate to support the level of interaction, personalization and engagement demanded by clinicians, care givers, and the patient themselves. For effective e-learning to be delivered in the health context, collaboration between pedagogy and technology is required. Furthermore, e-learning systems should be flexible enough to be adapted to the students' needs, evaluated regularly, easy to use and maintain and provide students' feedback, guidelines and supporting material in different formats. This paper presents the implementation of an Intelligent Tutoring System (SIAS-ITS), and its evaluation compared to a traditional virtual learning platform (Moodle). The evaluation was carried out as a case study, in which the participants were separated in two groups, each group attending a virtual course on the WHO Integrated Management of Childhood Illness (IMCI) strategy supported by one of the two e-learning platforms. The evaluation demonstrated that the participants' knowledge level, pedagogical strategies used, learning efficiency and systems 'usability were improved using the Intelligent Tutoring System.
## 207 Authoring tools for intelligent tutoring systems (ITSs) are meant to provide environments where instructors may author their own ITSs in varying domains. In this way, painful constructions of ITSs, which are not reusable, may be avoided. However, the construction of an authoring tool is associated with many problems, such as the generality of the techniques incorporated, domain-independence, effectiveness for the prospective authors (instructors), and effectiveness for the students who will use the resulting ITSs. In this paper we will report on an empirical study that we conducted in order to design and develop WEAR, an ITS authoring tool for Algebra-related domains. In the study we investigated several aspects concerning the attitude and behaviour of both students and instructors. The study revealed important issues and was then used for the specification of the design of WEAR. A brief description of the developed system is also included in the paper so that the way that the design specifications were put into practice may be shown. However, a lot of the authoring tool's requirements that came to light could be applicable to other authoring tools as well. The most important requirement of this kind was the need for an instructor modelling component so that adaptivity could be provided to human instructors (authors). The provision, of such facility is a novelty in the area of ITS authoring tools. (C) 2002 Elsevier Science Ltd. All rights reserved.
## 208 This study presents a compilation of techniques for Knowledge Representation (KR) in Intelligent Tutoring System (ITS). Shows pros and cons of each approach in order to use the proper technique according to the needs. Analyses literature related to ITS and KR to find the approaches. Highlights: Fuzzy Cognitive Maps, Bayesian Network, Semantic Networks, Graphs, among other methods. Each approach contributes with elements to model knowledge. We made a comparison of each model with determined factors. Each technique of KR provides his own vision of how the world should look. Besides, it shows what information is necessary to represent and what is important to ignore. Different approaches to intelligent reasoning lead to different goals and definitions of success.
## 209 Providing learners with multiple representations of learning content has been shown to enhance learning outcomes. When multiple representations are presented across consecutive problems, we have to decide in what sequence to present them. Prior research has demonstrated that interleaving tasks types (as opposed to blocking them) can foster learning. Do the same advantages apply to interleaving representations? We addressed this question using a variety of research methods. First, we conducted a classroom experiment with an intelligent tutoring system for fractions. We compared four practice schedules of multiple graphical representations: blocked, fully interleaved, moderately interleaved, and increasingly interleaved. Based on data from 230 4th and 5th-grade students, we found that interleaved practice leads to better learning outcomes than blocked practice on a number of measures. Second, we conducted a think-aloud study to gain insights into the learning mechanisms underlying the advantage of interleaved practice. Results show that students make connections between representations only when explicitly prompted to do so (and not spontaneously). This finding suggests that reactivation, rather than abstraction, is the main mechanism to account for the advantage of interleaved practice. Third, we used methods derived from Bayesian knowledge tracing to analyze tutor log data from the classroom experiment. Modeling latent measures of students' learning rates, we find higher learning rates for interleaved practice than for blocked practice. This finding extends prior research on practice schedules, which shows that interleaved practice (compared to blocked practice) impairs students' problem-solving performance during the practice phase when using raw performance measures such as error rates. Our findings have implications for the design of multi-representational learning materials and for research on adaptive practice schedules in intelligent tutoring systems.
## 210 This paper presents a novel Intelligent Tutoring System based on traditional and connectionist Artificial Intelligence. It is adaptive and reactive and has the ability to offer customized and dynamic teaching. Features of apprentice's psychological profile or learning style are employed as basic elements of customization, and they are complemented by (human) expert rules. These rules are represented by probability distributions. The proposed system is implemented on web environment to take advantages such as wide reach and portability. Three types of navigation (on course contents) are compared based on user performances: free (user has full control), random (user is controlled by chance) and intelligent (navigation is controlled by the proposed system: neural network combined with expert rules). Descriptive and inferential analysis of data indicate that the application of proposed techniques is adequate, based on (significant at 5%) results. The main aspects that have been studied are retention (learning improvement) normalized gain, navigation total user time and number of steps (length of visited content). Both customizations (by psychological profiles and learning styles) have shown good results and no significant difference has been found between them.
## 211 Developments in the wireless infrastructure have paved the way to a new e-learning paradigm named mobile learning (m-learning). M-learning systems aim to improve the quality of learning by providing mobile learners with an easy, contextualized and ubiquitous access to knowledge. Our discussion focuses specifically on mobile intelligent tutoring systems. Based on our previous work in the field of intelligent tutoring systems as well as agent technology we have outlined a multi-agent architecture for our intelligent tutoring system xTEx-Sys to be extended to mobile devices. Given the present absence of relevant literature and referent material we think that this paper provides software developers with some valuable guidelines.
## 212 The need for rapid and cost-effective development Intelligent Tutoring Systems with flexible pedagogical approaches has led to a demand for authoring tools. The authoring systems developed to date provide a range of options and flexibility, such as authoring simulations, or authoring tutoring strategies. This paper describes FlexiTrainer, an authoring framework that enables the rapid creation of pedagogically rich and performance-oriented learning environments with custom content and tutoring strategies. FlexiTrainer provides tools for specifying the domain knowledge and derives its power from a visual behavior editor for specifying the dynamic behavior of tutoring agents that interact to deliver instruction. The FlexiTrainer runtime engine is an agent based system where different instructional agents carry out teaching related actions to achieve instructional goals. FlexiTrainer has been used to develop an, ITS for training helicopter pilots in flying skills.
## 213 In this paper, we provide a model of corrective feedback generation for an intelligent tutoring system for Spanish as a foreign language. We have studied two kind of strategies: (1) Giving-Answer Strategies (GAS), where the teacher directly gives the desired target form or indicates the location of the error and (2) Prompting-Answer Strategies (PAS), where the teacher pushes the student less directly to notice and repair their own error. Based on different experimental settings and comparisons with face-to-face tutoring mode, we propose the design of a component of effective teaching strategies into ITS for Spanish as a foreign language. (C) 2009 Elsevier B.V. All rights reserved.
## 214 Student modeling and cognitive diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (ITS). ITS needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. Traditionally, most assessments treat all questions on the test as sampling a single underlying knowledge component. Can we have our cake and eat it, too? That is, can we have a good overall prediction of a high stakes test, while at the same time be able to tell teachers meaningful information about fine-grained knowledge components? In this paper, we introduce an online intelligent tutoring system that has been widely used. We then present some encouraging results about a fine-grained skill model with the system that is able to predict state test scores. This model allows the system track about 106 knowledge components for eighth grade math. In total, 921 eighth grade students were involved in the study. We show that our fine-grained model could improve prediction compared to other coarser grained models and an IRT-based model. We conclude that this intelligent tutoring system can be a good predictor of performance.
## 215 In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students' pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.
## 216 With the rapid growth of technology, computer learning has become increasingly integrated with artificial intelligence techniques in order to develop more personalized educational systems. These systems are known as Intelligent Tutoring systems (ITSs). This paper focused on the variant characteristics of ITSs developed across different educational fields. The original studies from 2007 to 2017 were extracted from the PubMed, ProQuest, Scopus, Google scholar, Embase, Cochrane, and Web of Science databases. Finally, 53 papers were included in the study based on inclusion criteria. The educational fields in the ITSs were mainly computer sciences (37.73%). Action-condition rule-based reasoning, data mining, and Bayesian network with 33.96%, 22.64%, and 20.75% frequency respectively, were the most frequent artificial intelligent techniques applied in the ITSs. These techniques enable ITSs to deliver adaptive guidance and instruction, evaluate learners, define and update the learner's model, and classify or cluster learners. Specifically, the performance of the system, learner's performance, and experiences were used for evaluation of ITSs. Most ITSs were designed for web user interfaces. Although these systems could facilitate reasoning in the learning process, these systems have rarely been applied in experimental courses including problem-solving, decision-making in physics, chemistry, and clinical fields. Due to the important role of a cell phone in facilitating personalized learning and given the low rate of using mobile-based ITSs, this study has recommended the development and evaluation of mobile-based ITSs.
## 217 We designed and implemented the ISCARE tutor which enables competition one against one solving a collection of exercises in a limited amount of time, with a double adaptation: adaptation of matches so that students with similar knowledge levels are paired; and adaptation of exercises. This study proves that a competition system with the characteristics of ISCARE can be an effective tool for learning, producing important learning gains during the learning process.
## 218 Having improved emotional (affective) state may have several benefits on learners, such as promoting higher cognitive flexibility and opens the learner to discovery of new ideas and possibilities. On other side, negative emotional states like boredom and frustration have been linked with less use of self-regulation and cognitive strategies for learning as well as increases in disengaged and disturbing behavior during learning. In the area of computerised learning, several researchers strongly agree that intelligent tutoring systems (ITSs) would significantly improve its performance if it can adapt to the affective state (emotional state) of the learners. This idea has spawned an important trend in the development of ITSs, which are systems with the ability to regulate a learner's adverse emotions. In the present study, we discuss the existing studies that have implemented different emotion regulation strategies such as coping strategies and implementation of these strategies in the domain of intelligent tutoring system (ITS). The results of the review show that applying emotion regulation strategies during computerised learning may produce more optimistic emotions as well as better learning gain.
## 219 Computer-supported approaches have been widely used for enriching the learning process. The technological advances have led tutoring systems to embody intelligence in their functionalities. However, so far, they fail to adequately incorporate intelligence and adaptivity in their diagnostic and reasoning mechanisms. In view of the above, this paper presents a novel expert system for the instruction of the programming language Java. A multilayer inference engine was developed and used in this system to provide individualized instruction to students according to their needs and preferences. The multilayer inference engine incorporates a set of algorithmic methods in different layers promoting personalization in the tutoring strategies. In particular, an artificial neural network and multi-criteria decision analysis are used in one layer for adapting the learning units based on students' learning style, and a fuzzy logic model is applied in the other layer for defining the granularity of learning units according to students' profile characteristics, such as learning style, knowledge level and misconceptions. The students' learning style is based on the Honey and Mumford model. The evaluation of the system was conducted using an established framework and Student's t test, and the results showed a high level of acceptance of the presented model.
## 220 The goal of this research was to explore the impact of diagram interaction on students' cognitive processes during learning. Using a think-aloud protocol, students' processes were studied as they practiced problem solving with an intelligent tutoring system containing diagrams that were either static or interactive. Diagram interaction resulted in better transfer performance, prompted deeper cognitive processing, and reduced students' reliance on shallow problem-solving processes.
## 221 Concept map model is a method that creates domain model by identifying the relationship between concepts in course contents. This study presents an adaptive intelligent web based learning system called OPCOMITS (Object Oriented Programming Tutor using Concept Map Model). OPCOMITS has a free domain model which can be regulated by an expert for any course. It uses concept map model to regulate the topic hierarchy, to measure the student's knowledge about a topic and to stimulate learning. By employing a concept map model, it structures the course and provides an environment in which the lecturer can arrange the chapters, topics, concepts and the prerequisite relationships between the concepts. Thus, it offers an adaptive and effective learning environment by measuring the level of student's knowledge about a topic, offering reinforcing feedback, diagnosing students' weaknesses and directing them to related chapter topic in the domain for revisions. To evaluate the effectiveness of the proposed approach an experiment has been conducted on Computer Programming department in Object Oriented Programming course. From the experimental results, it is found that OPCOMITS has contributed to the academic success of students using it and students have exhibited much better learning than those who have used a conventional e-learning system. (C) 2016 Wiley Periodicals, Inc.
## 222 Intelligent Tutoring Systems (ITS) provide an alternative to the traditional one size fits all approach. Their main aim is to adapt learning content, activities and paths to support learners. Meanwhile, during the last decades, advances in lightweight, portable devices and wireless technologies had drastically impacted Mobile and Ubiquitous environments' development which has driven opportunities towards more personalized, context-aware and dynamic learning processes. Moreover, mobile and hand held devices could be advantageous to incremental learning, based on very short and fine grained activities and resources delivery. However, measuring efficiency and providing the most relevant combination/orchestration of learning activities, resources and paths remains and open and challenging problem especially for enterprises where choices and decisions face several constraints as time, budget, targeted core competencies, etc. This paper, attempts to provide a knapsack based model and solution in order to implement ITS's intelligent decision making about best combination and delivery of e-training activities and resources especially in the context of fast changing Information and Communication Technology (ICT) domain and its required skills. An android and OSGi based prototype is implemented to validate the proposal through some realistic use cases.
## 223 With the popularization of computer and communication technologies, researchers have attempted to develop methods, tools, and environment for computer-assisted learning. Moreover, previous research has addressed the importance of adaptive learning in computer-assisted instruction. In studying the effect of adaptive learning, previous research mainly focused on improving student learning performance based only on a single source of personalized information, such as learning performance (including diagnosing student learning problems), learning style, or cognitive style of individual students, to determine the difficulty levels, learning paths, or presentation styles of subject materials, while the integration among multiple sources of personalized information are seldom taken into consideration. To cope with this issue, in this study, an innovative adaptive learning approach is proposed by basing upon two sources of personalized information, which are, learning problems and learning style. To diagnose the students' learning problems, the results of test answers are analyzed. In addition, Felder & Soloman's (1991) Index of Learning Style (ILS) questionnaire is employed for adjusting the presentation style of subject material based on personalized learning style of the students. Based on the innovative approach, an intelligent diagnostic and adaptive tutoring system has been developed. Moreover, a practical application on a mathematics course for high-school students is going to be conducted to demonstrate the effectiveness of this innovative approach.
## 224 Technology-echnology-enhanced learning generally focuses on the cognitive rather than the affective domain of learning. This multi-method evaluation of the INBECOM project (Integrating Behaviourism and Constructivism in Mathematics) was conducted from the point of view of affective learning levels of Krathwohl et al. (1964). The research questions of the study were: (i) to explore the affective learning experiences of the three groups of participants (researchers, teachers and students) during the use of a mobile game UFractions and an intelligent tutoring system Active Math to enhance the learning of fractions in mathematics; and (ii) to determine the significance of the relationships among the affective learning experiences of the three groups of participants (researchers, teachers and students) in the INBECOM project. This research followed a sequential, equal status, multi-mode research design and methodology where the qualitative data were derived from the interviews with researchers, teachers and students, as well as from learning diaries, feelings blogs, and observations (311 documents) across three contexts (South Africa, Finland, and Mozambique). The qualitative data was quantitized (Saldana, 2009), i.e. analysed deductively in an objective and quantifiable way as instances on an Excel (TM) spreadsheet for statistical analyses. All the data was explored from the affective perspective by labelling the feelings participants experienced according to the affective levels of the Krathwohl et al. (1964) framework. The researchers concluded that: (i) the research participants not only received information, but actively participated in the learning process; responded to what they learned; associated value to their acquired knowledge; organised their values; elaborated on their learning; built abstract knowledge; and adopted a belief system and a personal worldview; and (ii) affirmation of affective learning at all five levels was recognised among the three groups of participants. The study raised a number of issues which could be addressed in future, like how affective levels of learning are intertwined with cognitive levels of learning while learning mathematics in a technology-enhanced learning environment; and how pedagogical models which take into account both cognitive and affective aspects of learning support deep learning.
## 225 In the 1970s, research on intelligent tutoring systems started with great expectations. Meanwhile, it has become evident that many of these expectations could not be met. We argue that this is mostly due to three reasons, (a) the divergence in interests between researchers and teachers, (b) the hitherto missing proof of effectiveness, and, most importantly, (c) theoretical and practical difficulties with the student module, that part of an intelligent tutoring system which constitutes its specific knowledge. We suggest to partly or completely abandon the student module because its construction is complicated by numerous theoretical problems and because also experienced human tutors (who should be modeled) often have very scarce or even wrong models of the student's knowledge. Even if a thorough diagnosis of that knowledge were possible, that would often have no impact on which specific pedagogical interventions to choose. Tutoring systems should - as illustrated by an example - be constructed by relying on results from cognitive and instructional psychology. We discuss some promising didactic approaches that could be used in this endeavor.
## 226 The goals of the modern Computer-assisted Instruction systems are ultimately achievable only through the application of artificial intelligence techniques to the implementation of teaching strategies. The author of the traditional Computer-assisted Instruction course must explicitly predict and program all possible paths through the lessons, including backward branches for review or remediation. The use of planning techniques when defining the global teaching strategy allows the creation of large, individualized courses which handle broader subject areas than most Intelligent tutoring systems, but at the same time avoid the rigidity of traditional Computer-assisted Instruction systems. The generalized algorithms of Planning and Executive subsystems are described. The architecture of a knowledge-managed tutoring system which is viewed as the interrelation of three physically independent but logically linked models (the domain, the teaching strategy, and the student) is presented.
## 227 VanLehn argued that an essential feature of many intelligent tutoring systems (ITSs) is that they provide feedback and hints on every step of a multi-step solution. But if step-based feedback and hints alone suffice for strong learning gains, as Anderson et al. conjecture ([1]), then perhaps a lightweight tutoring system that employ only feedback and bottom-out hints would have advantages. This motivates the current project. Using Excel there are some immediately advantages that can be obtained: most people is familiar with its user interface and its notation for mathematical expressions, Excel already contains facilities for solving some systems of equations and it can be easy combined with many other pieces of software, making it easier for instructors to include the tutor in their course activities. Finally, web-based delivery is simple because most students already have and use Excel.
## 228 A student is said to have committed a careless error when a student's answer is wrong despite the fact that he or she knows the answer (Clements, 1982). In this paper, educational data mining techniques are used to analyze log files produced by a cognitive tutor for Scatterplots to derive a model and detector for carelessness. Bayesian Knowledge Tracing and its variant, the Contextual-Slip-and-Guess Estimation, are used to model and predict carelessness behavior in the Scatterplot Tutor. The study examines as well the robustness of this detector to a major difference in the tutor's interface, namely the presence or absence of an embodied conversational agent, as well as robustness to data from a different school setting (USA versus Philippines).
## 229 Students nowadays are hard to be motivated to solve logical problems with traditional teaching methods. Computers, Smartphone's, tablets and other smart devices disturb their attention. But those smart devices can be used as auxiliary tools of modern teaching methods. The flipped classroom is one such innovative method that moves the solving problems outside the classroom via technology and reinforces solving problems inside the classroom via learning activities. In this paper, the authors implement flipped classroom as an element of Internet of Things (JOT) into learning process of mathematical logic course. In the flipped classroom, an Intelligent Tutoring System (ITS) was used to help students work with the problems in the course outside the classroom. This study showed that perceived usefulness, self-efficacy, compatibility, and perceived support for enhancing social ties are important antecedents to continuance intention to use flipped classroom.
## 230 A major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. This research developed both active interventions to combat gaming and passive interventions to prevent gaming. Our passive graphical intervention has been well received by teachers, and our experimental results suggest that using a combination of intervention types is effective at reducing off-task gaming behavior.
## 231 The development of computers and artificial intelligence theory allow their application in the field of education. Intelligent tutoring systems reflect student learning styles and adapt the curriculum according to their individual needs. The building of intelligent tutoring systems requires not only the creation of suitable software, but especially the search and application of the rules enabling ICT to individually adapt the curriculum. The main idea of this paper is to attempt to specify the rules for dividing the students to systematically working students and more practically or pragmatically inclined students. The paper shows that monitoring the work of students in e-learning environment, analysis of various approaches to educational materials and correspondence assignments show different results for the defined groups of students.
## 232 Much research has been done on the development of an intelligent tutoring system (ITS), and small empirical studies have demonstrated the effectiveness of ITS at promoting student learning. However, large-scale implementation of ITS in school settings has not been researched thoroughly. In this paper, we describe an ongoing randomized controlled trial (RCT) to evaluate the efficacy of a web-based tutoring system-the ASSISTments-as support for homework. The program is used in 46 middle schools in the state of Maine, to provide immediate feedback to students, and to provide reports to teachers to support homework review and instruction adaptation. We describe the challenges for the RCT, approaches used to understand implementation of the system, and findings on how the system is being used.
## 233 This paper presents an adaptive online intelligent tutoring system called Oscar which leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by using knowledge of learning styles to adapt its tutoring style and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic and boost confidence. An initial study into the adaptation to learning styles is reported which produced encouraging results and positive test score improvements.
## 234 We studied the effectiveness of a math fact fluency tool integrated with an intelligent tutor as a means to improve student performance in math standardized tests. The study evaluated the impact of Math Facts Retrieval Training (MFRT) on 250 middle school students and analyzed the main effects of the training by itself and also as a supplement to the Wayang Tutoring System on easy and hard items of the test. Efficacy data shows improved student performance on tests and positive impact on mathematics learning. We also report on interaction effects of MFRT with student gender and incoming math ability.
## 235 The development of intelligent tutoring systems is discussed from intelligent agent and knowledge management perspectives. A conceptual model in which both perspectives are integrated is proposed. The model consists from system's layer based on agent paradigm and knowledge worker's layer responsible for personal knowledge management of knowledge worker (teacher and/or student). The implemented prototype of intelligent knowledge assessment system is described.
## 236 Effective mathematics teachers have a large body of professional knowledge, which is largely undocumented and shared by teachers working in a given country's education system. The volume and cultural nature of this knowledge make it particularly challenging to share curricula and instructional methods between countries. Thus, approaches based on knowledge engineering-designing a software system by interviewing human experts to extract their knowledge and heuristics-are particularly promising for cross-cultural curriculum implementations. Reasoning Mind's Genie 2 system demonstrates the viability of such an approach, bringing elements of Russian mathematics education (known for its effectiveness) to the United States. Genie 2 has been adopted on a large scale, with around 67,000 United States students participating in the 2012-2013 school year. Previously published work (some of it in peer reviewed articles and some in non-peer-reviewed independent evaluations) has associated Genie 2 with high student and teacher acceptance, increases in test scores relative to business as usual conditions, high time on task, and a positive affective profile. Here, we describe for the first time the design, function, and use of the Genie 2 system. Based on this work, we suggest general principles-which collectively represent a proposed methodology-for the design of intelligent tutoring systems intended for cross-cultural transfer of curriculum and instructional methods.
## 237 With the growth of computer and information technologies, e-learning educational systems are becoming more and more popular all over the world, it makes the scene become real that anybody could learn at anywhere in anytime. However, without the assistance of e-learning system, the leaning questions students countered cannot be solved in time. Consequently it is necessary to develop e-learning intelligent tutoring system (ITS) in order to provide learning support service for students. In this paper, an e-learning ITS is proposed to recommend feasible learning courseware for individual student. The architecture of this system and the functions of each component are introduced first, and the structure of curriculum and how to design object-oriented (OO) learning courseware based on shareable content object reference model (SCORM) are discussed later. Furthermore, a fuzzy algorithm is proposed to estimate students' cognitive ability level. Finally, the paper elaborates how to apply case-based reasoning methodology to derive adaptive learning courseware for individual student to learn in effective and efficient way.
## 238 Conversational Intelligent Tutoring Systems (CITS) that automatically adapt to learning styles (LS) can improve learning, however current modelling of LS has ignored Neutral learners. This paper presents research examining the ability of data mining algorithms to predict LS dimensions from behaviour captured during natural language tutorials with Oscar CITS. Two datasets, 2ClassBDS and 3ClassBDS, were cleaned and prepared for the data mining task of predicting student LS. Each dataset comprised four sub-datasets representing the four Felder-Silverman LS dimensions. 3ClassBDS included a third Neutral class describing individuals with a balance of LS preferences. Naive Bayes, Decision Trees, Lazy Learning and Neural Networks algorithms were applied to each dataset and parameters adjusted to improve prediction accuracies. The 2ClassBDS dataset results show good prediction, with decision trees (Simple CART) achieving accuracies of 81.33-86.66%. For 3ClassBDS results were mixed, with the J48 algorithm achieving 56-73% accuracy, indicating that further work and data is needed.
## 239 In several countries, private tutoring has developed as a system of education parallel to traditional schooling. Students from wealthy families have greater access to this system, and the advantages they obtain tend to exaggerate socio-economic inequities. Low-cost programmable computing devices have the potential to execute at least some tasks performed by human tutors, and may therefore help economically disadvantaged students lessen this gap. In this paper, we (1) investigate requirements for designing these kinds of software applications (apps), by conducting an ethnographic study of school students and mathematics tutors at a private tuition center, and (2) illustrate these requirements with a prototype Android app for a component of the middle-school mathematics curriculum: linear equations in one variable. Our results suggest that such apps are unlikely to outperform skilled human tutors in the foreseeable future, but they may provide some benefits for students entirely lacking access to tutors. We hope that this research can contribute to the development of low-cost educational apps that explicitly target such benefits.
## 240 Human tutors are still using traditional methods such as books, lecture notes and static websites in teaching programming languages such as Java and C++. These materials are very less interactive and adaptive for students. Teachers need to pay attention towards each and every student individually. A web-based and problem-based reasoning Intelligent Tutoring System for pointers, called Point-Tool, has been developed for teaching. This system will help the students to extend their knowledge on complex topics like C++ pointers by doing intelligent online tutorials from anywhere and anytime. In this paper, along with problem-based learning approach, software design of Point-Tool has been explained.
## 241 e-Learning is the concept of teaching people through the Internet. By definition, an e-Learning session is not limited by any border, meaning that learners implicated in this session could differ by many criteria which include gender, social classes, religion, nationality, occupation... It has been shown that many of those elements can impact on learning and some are already assessed in e-Learning. However, currently, when an e-Learning system receives a connection call from a user, the system will not make any difference whether this user, is French, Chinese, or American... According to cross-cultural studies, culture has a big impact on individuals' cognitive processes and also on how individuals understand and interact with their environment and peers. In this paper, we show that e-Learning systems which want to adapt to their learners and keep them motivated will take huge benefits by considering learners' culture. We present some results from our study which support Hofstede's Individualism/Collectivism scores [Hofstede, 1980]. Based on those results and on cross-cultural studies in management and, psychology, we propose different elements of learners' culture that an e-Learning system should consider.
## 242 Self-regulated learning is of particular importance to computer-based and online instruction, as students need to manage their own time and their interactions with the system. Elements of self-regulatory learning traditionally include the metacognitive strategies of the students (e. g., their knowledge of their planning, and assessment of their own progress), their management of educational goals (e. g., what information is most important to them, and should receive their primary attention), and the strategies that students use in order to study and retain the provided information [1, 2]. By incorporating feedback and guidance within computer-based learning activities it can encourage students to engage in successful self-regulated learning with a better awareness of their own cognition, and strategies. Intelligent tutoring systems can utilize adaptive scaffolding and guidance in order to support self-regulated student learning [3]. The Generalized Intelligent Framework for Tutoring (GIFT) [4] is an open-source adaptive tutoring system framework. The included tools within GIFT can be used to structure courses which guide individuals through the learning environment and are consistent with self-regulated best practices. The current paper includes a brief review of research into self-regulated learning in the context of computer-based and adaptive instruction. Further, the authoring capabilities of GIFT are discussed, and recommendations are given for future feature additions to GIFT which will benefit instructors who wish to develop courses that facilitate self-regulated learning.
## 243 The present study aims to examine the pedagogical effectiveness of a Chinese mathematical dialogue-based intelligent tutoring system used for teaching mathematics. The mathematical unit 'multiplication and division of time expressions' was taught to 134 fifth-grade students in three types of instruction conditions: the intelligent tutoring system (ITS), conventional teacher instruction and material reading. The results show that student performance was comparable between the proposed mathematical ITS and conventional instruction conditions but was significantly poorer when no teaching method was used. The questionnaire survey showed that the ITS method not only improved maths learning, but also increased motivation among the fifth graders. The effectiveness of ITS remedial instruction in mathematics is discussed.
## 244 The constraint-based modeling (CBM) approach for developing intelligent tutoring systems has shown useful in several domains. However, when applying this approach to an exploratory environment where students are allowed to explore a large solution space for problems to be solved, this approach encounters its limitation: It is not well suited to determine the solution variant the student intended. As a consequence, system's corrective feedback might be not in accordance with the student's intention. To address this problem, this paper proposes to adopt a soft computing approach for solving constraint satisfaction problems. The goal of this paper is two-fold. First, we will show that classical CBM is not well-suited for building a. tutoring system for tasks which have a. large solution space. Second, we introduce a weighted constraint-based model for intelligent tutoring systems. An evaluation study shows that a coaching system for logic programming based on the weighted constraint-based model is able to determine the student's intention correctly in 90.3% of 221. student solutions, while a corresponding tutoring system using classical CBM can only hypothesize the student's intention correctly in 35.5% of the same corpus.
## 245 The conventional elementary education system in India is mostly guided by formal content development, focusing on areas like math, language, science and social-science. Children tend to retain very little knowledge about other important areas of learning like heath care, which needs to be developed in their foundation years. The education on oral health is one such example which is not given the focus they ought to be. Considering its importance in early education, we propose a learning environment where children would gain knowledge through constant interaction with an intelligent tutoring system. The system addresses the challenges in developing a learning environment for children by introducing audio-visual effects, 3D animations and customizing the tutoring process to provide user-controlled pace of learning. It also employs the Wii Remote for imparting a tangible hardware interaction with the interface. This paper describes the proposed system and the studies conducted on treatment and control groups to evaluate its efficacy and compare the learning outcome at various domains. Experimental results depict positive effects on learning in the proposed technology-enhanced environment and paves a way for the deployment of more interactive, technology-driven learning process in the elementary education system.
## 246 Assessment of students' self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of help seeking. Automated feedback on help seeking driven by this method led to a lasting improvement in students' help-seeking behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides help seeking.
## 247 Intelligent Tutoring Systems (ITSs) must take advantage of their high computing capabilities and capacity for information retrieval in order to provide the most effective methodologies for improving students' learning. One type of ITS provides assessments to students and some help as a hint, when they do not know how to solve a problem. Our thesis is that the type of hinting techniques used without changing the contents can influence the learning gains and aptitudes of students. We have implemented some hinting techniques as an extension to the X Tutor ITS. We found that some hinting techniques can produce a significant increase in students' knowledge with respect to others, but the improvement and direction of the comparison depended on some other factors such as the topics to which it was applied. We conclude that proper adaptation of hinting techniques based on different information of the systems will imply better student learning gains. In addition, the results of a student survey, which includes the students' ratings of the different hinting features they interacted with, leads to high variances, which reinforce the idea of the importance of adaptation of hinting techniques in these types of systems.
## 248 Students often face difficulties and experience negative emotions toward second language learning. The affective tutoring system (ATS) is a next-generation learning approach that can detect the affective status of learning to increase performance. Therefore, for the purposes of this study, an innovative affective mobile language tutoring system (AMLTS) was designed to support Japanese language learning. The effects of AMLTS, along with asynchronous discussion, that were intended to improve performance, were examined using a triangulation method. To investigate the effect on emotion, the proposed AMLTS provides a virtual emotion agent that can interact with users and record emotional events, learning assessments, and the results of the interaction into a database. Learning effectiveness evaluations were conducted via two experiments: prototype evaluation and final evaluation. Sixty-three students, all beginners, were invited to use the AMLTS to learn Japanese. The research results show that the proposed AMLTS affective interaction design significantly improves learner engagement and performance. In the emotion feedback analysis and learning process, AMLTS helped students deepen their understanding of the content, enabled them to clearly understand the content, and to engage in peer interaction and experience positive emotions. In the evaluation of system usability, AMLTS reveals good usability for foreign language acquisition.
## 249 This paper present provides a broader view on ELM-ART, one of the first Web-based Intelligent Educational systems that offered a creative combination of two different paradigms - Intelligent Tutoring and Adaptive Hypermedia technologies. The unique dual nature of ELM-ART contributed to its long life and research impact and was a result of collaboration of two researchers with complementary ideas supported by talented students and innovative Web software. The authors present a brief account of this collaborative work and its outcomes. We start with explaining the roots of ELM-ART, explain the emergence of the intelligent textbook paradigm behind the system, and discuss the follow-up and the impact of the original project.
## 250 Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom's taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners' thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.
## 251 The application of Artificial Intelligence (AI) planning techniques to the development of Intelligent Tutoring Systems (ITS) has focused mainly on instructional planning, in settings where the initiative is taken primarily by the system. 3D Virtual Environments (VE) have emerged in the last years as a good means to apply a case-based training approach, placing a more active role on the student. Here Al planning turns out to be an interesting solution for the dynamic resolution of the problems (cases) that are posed to the student. These environments allow the students to navigate through and interact with a virtual representation. This paper describes MAEVIF, a platform for the development of intelligent virtual environments for training (IVETs) whose architecture is based on a collection of cooperative software agents. The role of Al planning in the teaching-learning approach followed by MAEVIF is described, with two main planning services: the generation of a plan as an ideal solution to the case, and the evaluation of the effect of the student's actions during their resolution of the case.
## 252 Learning outcomes from intelligent tutoring systems (ITSs) tend to be quite strong, usually in the neighborhood of one standard deviation. However, most ITS designers use the learning outcomes from expert human tutoring as the gold standard (i.e., two standard deviations). What can be done, with the current state of the art, to increase learning from an ITS? One method is to modify the learning situation by asking students to use the ITS in pairs. To enhance performance, we drew upon the beneficial effects of structured peer collaboration. The results suggest that the intervention was successful. Pairs of students solved more problems and requested fewer bottom-out hints than individuals. To test the possibility that the effect was due to the best partner in the group directing the problem solving, a nominal groups analysis was conducted. A nominal group is a statistical pairing of the non-interacting individuals' performance. The results from the nominal groups replicated the same pattern of results, but with a reduced magnitude. This suggests that the best member may have contributed to some of the overall success of the pair, but does not completely explain their performance.
## 253 To implement real intelligence or adaptivity, the models for intelligent tutoring systems should be learnt from data. However, the educational data sets are so small that machine learning methods cannot be applied directly. In this paper, we tackle this problem, and give general outlines for creating accurate classifiers for educational data. We describe our experiment, where we were able to predict course success with more than 80% accuracy in the middle of course, given only hundred rows of data.
## 254 This paper presents an approach to student's errors diagnosis in intelligent tutoring systems and to the remedial instruction to overcome those errors. Our contribution arises from two key components. Firstly, a diagnosis model, which based on the nature of the learner's input, as well as its exercise knowledge model., uses Bayesian induction (a posteriori maximization) to find the most probable causes of a failure Secondly a remedial instruction model which will be the focus of this paper. This model will use the epistemological nature of the faulty skill that was diagnosed.
## 255 A number of Bayesian toolkits have been developed to learn from data through statistical inference. These toolkits are increasingly being applied in Intelligent Tutoring Systems (ITS) whose aim is to assist student learning. In this paper, we introduce a template for a web-based Bayesian driven ITS, which organizes course material, tests student understanding, and evaluates student performance to suggest a further plan of study. In addition, we provide guidelines for constructing a knowledge domain that will include meaningful problems that test all concepts being learned.
## 256 This work describes the development of a student model that is used in a Japanese language intelligent tutoring system to assess a pupil's proficiency at reading one of the distinct orthographies of Japanese, known as katakana. While the effort required to memorize the relatively few katakana symbols and their associated pronunciations is not prohibitive, a major difficulty in reading katakana is associated with the phonetic modifications which occur when English words which are transliterated into katakana are made to conform to the more restrictive rules of Japanese phonology. The algorithms described here are able to automatically acquire a knowledge base of these phonological transformation rules, use them to assess a student's proficiency, and then appropriately individualize the student's instruction.
## 257 After discussing and formalizing domain knowledge representation and user models, a formal model of emotional pedagogical agents in intelligent tutoring systems is presented in this paper, and the functionalities of model are described in detail. This kind of emotional pedagogical agent considers users' emotional statues during the process of producing personalized learning units dynamically based on the information provided by user models and domain knowledge, in order to improve the self-adaptability and pedagogical effects of the system.
## 258 This paper describes an Intelligent Tutoring System (ITS) called SARA. This system is developed using a new student modelling technique based on Case-Based Reasoning(CBR). SARA is organized around two main knowledge bases, the problems base and the cases base. The architecture of the system consists of several components. The functionality of each component and its relationships with the other components will be shown. Two ways of using the system will be presented: (1)as a system for student modelling, and (2) as a server providing information to be used by people testing or by applications using these services. We will also study the process of building the student model with this system. The student model constructed by SARA represents two important aspects of a student, namely the knowledge component and the inferences model.
## 259 Hinting is an important tutoring tactic in one-on-one tutoring, used when the tutor needs to respond to an unexpected answer from the student. To issue a follow-up hint that is pedagogically helpful and conversationally smooth, the tutor needs to suit the hinting strategy to the student's need while making the strategy fit the high level tutoring plan and the tutoring context. This paper describes a study of the hinting strategies in a corpus of human tutoring transcripts and the implementation of these strategies in a dialogue-based intelligent tutoring system, CIRCSIM-Tutor v. 2. We isolated a set of hinting strategies from human tutoring transcripts. We describe our analysis of these strategies and a model for choosing among them based on domain knowledge, the type of error made by the student, the focus of the tutor's question, and the conversational history. We have tested our model with two classes totaling 74 medical students. Use of this extended model of hinting increases the percentage of questions that students are able to answer for themselves rather than needing to be told.
## 260 Many previous studies have highlighted the influence of learners' affective states on learning with tutoring systems. However, the associations between learning and learners' meta-affective capability are still unclear. The goal of this paper is to analyse meta-affective capability and its influence on learning outcomes as well as the dynamics of affect over time. Two criteria, awareness and self-regulation, were employed to define meta-affective capability. An exploratory study (n = 54) was conducted in which students at the secondary level were asked to interact with an intelligent tutoring system for mathematics and to self-report their affect during their interactions with the system. Pre-post learning outcomes were also measured. A post-hoc comparison of learning gains was made between more meta-affectively capable and less meta-affectively capable students. The results provide some empirical evidence to support the hypothesis that having meta-affective capability is positively associated with learning. Students not demonstrating meta-affective capability seemed to transition frequently from boredom to frustration (p = .0284) and from concentration to neutral (p = 0.0017). However, only a small percentage of the sample were classified as having meta-affective capability, indicating that it is important to scaffold students who are not meta-affectively capable.
## 261 Doctor-patient communication is a crucial element in effective medical care, and the striking health disparities evident in patients with Type II Diabetes may in part be caused by physicians' difficulties in establishing effective communication with patients who differ from them racially, culturally, and economically. REPEAT (Realizing Enhanced Patient Encounters through Aiding and Training) is a digital tutor developed to help solve this problem. REPEAT teaches and coaches learners to improve their general and disparities-focused clinical communication skills using simulated encounters with computer-generated Synthetic Standardized Patients (SSPs) and augments experiential learning in virtual encounters by applying customized, context-sensitive, learner-focused scaffolding. REPEAT authoring tools enable rapid development of learning content, allowing economical transferability to other domains. Key human factors challenges and their design solution in REPEAT are discussed.
## 262 Students experience mathematics in their day-to-day lives as they pursue their individual interests in areas like sports or video games. The present study explores how connecting to students' individual interests can be used to personalize learning using an Intelligent Tutoring System (ITS) for algebra. We examine the idea that the effects of personalization may be moderated by students' depth of quantitative engagement with their out-of-school interests. We also examine whether math problems designed to draw upon students' knowledge of their individual interests at a deep level (i.e., actual quantitative experiences) or surface level (i.e., superficial changes to problem topic) have differential effects. Results suggest that connecting math instruction to students' out-of-school interests can be beneficial for learning in an ITS and reduces gaming the system. However, benefits may only be realized when students' degree of quantitative engagement with their out-of-school interests matches the depth at which the personalized problems are written. Students whose quantitative engagement with their interests is minimal may benefit most when problems draw upon superficial aspects of their interest areas. Students who report significant quantitative engagement with their interests may benefit most when individual interests are deeply incorporated into the quantitative structure of math problems. We also find that problems with deeper personalization may spur positive affective states and ward off negative ones for all students. Findings suggest depth is a critical feature of personalized learning with implications for theory and AI instructional design.
## 263 Constructing models of dynamic systems is an important skill in both mathematics and science instruction. However, it has proved difficult to teach. Dragoon is an intelligent tutoring system intended to quickly and effectively teach this important skill. This paper describes Dragoon and an evaluation of it. The evaluation randomly assigned students in a university class to either Dragoon or baseline instruction that used Dragoon as an editor only. Among students who did use their systems, the tutored students scored reliably higher (p < .021, d = 1.06) on the post-test than the students who used only the conventional editor-based instruction.
## 264 Education in computer science brings specific challenges to the teaching-learning process. Students spend a lot of time dealing with the complexity of problems and learning to use existing technologies. Intelligent Tutoring System (ITS) is a technology that can contribute to this scenario, automating and adapting teaching to the student's profile. This work presents a literature review on ITS's for Computer Science Education, focusing on Artificial Intelligence (AI) in this scenario. We analyze the development and use of ITS's in Computer Science Education and assess AI techniques, algorithms, and datasets. The results of this review point to challenges in research on aspects such as the unavailability and difficulty of reproducing datasets, the lack of in-depth explanations about the relationship between AI techniques and these ITS data, the need to deepen these techniques of AI, and the need for more research about software engineering to ITS. This work contributes to providing opportunities to this research area that can help the digital transformation of Computer Science Education.
## 265 Reinforcement Learning (RL) can be used to train an agent to comply with the needs of a student using an intelligent tutoring system. In this paper, we introduce a method of increasing efficiency by way of customization of the hints provided by a tutoring system, by applying techniques from RL to gain knowledge about the usefulness of hints leading to the exclusion or introduction of other helpful hints. Students are clustered into learning levels and can influence the agents method of selecting actions in each state in their cluster of affect. In addition, students can change learning levels based on their performance within the tutoring system and continue to affect the entire student population. The RL agent, AgentX, then uses the cluster information to create one optimal policy for all students in the cluster and begin to customize the help given to the cluster based on that optimal policy.
## 266 We present BEETLE II, a tutorial dialogue system designed to accept unrestricted language input and support experimentation with different tutorial planning and dialogue strategies. Our first system evaluation used two different tutoring policies and demonstrated that BEETLE II can be successfully used as a platform to study the impact of different approaches to tutoring. In the future, the system can also be used to experiment with a variety of parameters that may affect learning in intelligent tutoring systems.
## 267 Intelligent Tutoring Systems (ITS) is an act of impacting knowledge while computer teaches or acts as the tutors which is a supplement to human teachers. The ability to teach each student based on their individual abilities a major advantage posed by ITS and that is why it is being embraced in this work. This work describes the design of an Intelligent Tutoring System that was tagged Scholastic tutor (St*), which has the individual learning and collaborative problem-solving modules. The individual tutoring module was designed to provide appropriate lessons to individuals based on his/her background knowledge level, interest, and learning style and assimilation rate prior to using the tutoring system. A software agent is used to monitor and process these parameters, arrange the learning topic, and exercises, for each individual. The collaborative problem-based tutoring module was designed to present tutorial problems and provides facilities to assist learners with some useful information and advice for problem solving. This is because the present lecturing methodology which is the conventional teaching methodology provides an interactive classroom setting that promotes the open exchange of ideas and allows for the lecturer to communicate directly with the students but has a great disadvantage of not teaching all the students according to their own learning rate and pace. The intelligent tutor solves this problem by providing individualised learning for each student where they can learn according to their own pace and learning abilities it will provide remedy and advice when learners encounter difficulties during learning session. The classical model of ITS architecture has four main modules; domain model, student model, tutoring model and the user interface model.
## 268 Over the years, the traditional computer-assisted teaching can not meet the needs of university teaching, the traditional teaching system software, most of the existence of low intelligence, lack of teaching strategies and other shortcomings.Multi-AGENT technology and intelligent tutoring systems is the current research focus in computer intelligence education. Integrating multi-Agent features and multi-Agent application theories in ITS, this paper proposes a multiple Agent-based intelligent network tutoring system design model, detailedly analyzes the functions of each layer in the system, and presents system database category design and system model features.
## 269 An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of 'Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.
## 270 Framed by the existing theoretical and empirical research on cognitive and intelligent tutoring systems (ITSs), this commentary explores two areas not directly or extensively addressed by Akhras and Self (this issue). The first area focuses on the lack of conceptual clarity of the proposed constructivist stance and its related constructs (e.g., affordances, situations). Specifically, it is argued that a clear conceptualization of the novel constructivist stance needs to be delineated by the authors before an evaluation of their ambitious proposal to model situations computationally in intelligent learning environments (ILEs) can be achieved. The second area of exploration deals with the similarities between the proposed stance and existing approaches documented in the cognitive, educational computing, and AI in education literature. I believe that the authors are at a crossroads, and that their article presents an initial conceptualization of an important issue related to a constructivist-based approach to the computational modeling of situations in ILEs. However, conceptual clarity is definitively required in order for their approach to be adequately evaluated and used to inform the design of ILEs. As such, I invite the authors to re-conceptualize their framework by addressing how their constructivist stance can be used to address a particular research agenda on the use of computers as metacognitive tools to enhance learning.
## 271 In order to solve such problems in undergraduate tutorial system now in China as low teacher-student ratio, less excellent tutor resources, and the fewer chances for students to have a face-to-face discussion with their tutors, the researchers of this paper designed and developed an Object-oriented Virtual Tutor System, by using Moodle (originally an acronym for Modular Object-Oriented Dynamic Learning Environment) as bottom platform, and put it into practical application with sound effect. For students, this model provides a set of tools to help them construct a learning environment that meets their own needs; while for teachers, it enable them to freely and easily arrange time to accomplish effective instructions and guidance in online virtual space.
## 272 Affective Computing is a new Artificial Intelligence area that deals with the possibility of making computers able to recognize human emotions in different ways. This paper represents a study about the integration of this new area in the intelligent tutoring system. We argue that socially appropriate affective behaviors would provide a new dimension for collaborative learning systems. The main goal is to analyses learner facial expressions and show how Affective Computing could contribute for this interaction. being part of the complete student tracking (traceability) to monitor student behaviors during learning sessions. (C) 2009 Elsevier Ltd. All rights reserved.
## 273 E-learning, also known as online learning or technology enhanced learning, needs to provide more heterogeneous learning including not only Web-based courses and virtual classrooms but also remote labs with an affective recognition system. These latter allow students to interact with real experiments conducted from a distance with real-time facial expression recognition using a webcam. However, multiple systems do not include this kind of learning. Thus, students' motivation, interest, and learning might be negatively affected since there can be no emotional interaction without on demand learning. This work analyzes the impact of students' emotions to enhance the learning experience in two aspects: (i) filling the existing gaps of students and (ii) improving the usability and readability of the platform. The overall goal of our research is to build a novel intelligent tutoring system, called LabTutor, adapted to the profile of every student. It aims to serve as an experienced teacher for students who access and perform experiments on real laboratory equipment in the field of engineering education.
## 274 One of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define effective pedagogical policies for tutoring students according to their needs. This paper proposes to use Reinforcement Learning (RL) in the pedagogical module of an educational system so that the system learns automatically which is the best pedagogical policy for teaching students. One of the main characteristics of this approach is its ability to improve the pedagogical policy based only on acquired experience with other students with similar learning characteristics. In this paper we study the learning performance of the educational system through three important issues. Firstly, the learning convergence towards accurate pedagogical policies. Secondly, the role of exploration/exploitation strategies in the application of RL to AIES. Finally, a method for reducing the training phase of the AIES.
## 275 Reading comprehension is a very important life skill, yet millions of Americans are functionally illiterate. Technology can help, but most computer-based training programs for reading skills fall short in their ability to provide self-paced, individualized learning. The Navy funded three innovative intelligent tutors that were developed based on well-recognized cognitive models of reading and learning. In addition, these tutors provide tools for customization of content. This paper reports a large-scale study of these tutors that demonstrates their teaching effectiveness when used individually and in combination. Of particular note is an ordering of the tutors that led to a skill gain of 1.1 grade levels.
## 276 Affect is an important element of the learning process both in the classroom and with educational technology. This paper presents analyses in relation to the identification of Action Units (AUs) related to affective states and their impact on learning with a tutoring system. To assess affect, a tool was devised to identify AUs on pictures of human faces. Action Units are combinations of individual facial muscles or groups of muscles that create facial expressions in association with affect. Pictures from a population of students were taken while using an intelligent tutoring system for mathematics in a secondary school in a suburban school in Veracruz, Mexico. The students were asked to interact with the tutoring system for 40 minutes and they were photographed with the tool at a rate of 1 picture every 5 seconds acquiring a dataset consisting of 16,800 photos. To achieve identification, the software analyzes individual pictures using Principal Component Analyses (PCA) and Euclidian distance. The tool developed to classify affective states shows 88.88% accuracy in the identification of AUs when matching the recognized AU to the Cohn-Kanade AU-Coded facial expression database. The analyses also elicited the most common AUs for the population and their association with learning with the intelligent tutoring system. These preliminary results shed light on the issues of affect in relation to learning mathematics with tutoring systems and pave the way for the implementation of coping strategies based on the automatic recognition of facial expressions.
## 277 Assessing knowledge acquisition by the student is a main task of an Intelligent Tutoring System. Assessment is needed in order to adapt learning materials and activities to students capacities. To evaluate knowledge acquisition, different techniques can be used, such as probabilistic inference. In this paper we present a proposal based on Bayesian Networks to infer the level of knowledge possessed by the student. We implemented a kind of test to know what student knows. During the test, the software system chooses the new questions based on the responses to the previous ones, that is, the software system makes an adaption in real time. To get the inferences, we use a network of concepts, which contains the relationships between those concepts. This work is focused on the design of the Bayesian Network and the algorithm to do inferences about students knowledge.
## 278 The Interactive Virtual Intelligent System for Scientific Inquiry in a Biology Learning Environment (INVISSIBLE) is software environment being developed as a intelligent tutoring system that provides high school biology students a virtual, hands-on, multimedia learning environment. Using interactive, intelligent software, a student is placed in goal driven scenarios that reflect the authentic experiences of a scientist engaged in using scientific inquiry methods. This paper describes the first of three planned modules, one which involves forensic science and the use of DNA evidence in combination with hairs, fibers, and other evidence in solving a crime scene problem. Core objectives of this module are to increase student learning regarding: (a) knowledge acquisition of content, concepts and principles relevant to genetics, forensic science, and evolutionary biology, (b) relevant scientific process skills and knowledge, and (c) knowledge of nature and methods of science. A demonstration of the software will be given.
## 279 Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module. The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor's capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student's aptitude.
## 280 In this paper, we present a Web-based Intelligent Tutoring System (ITS) for distant education of nursing students and other health professionals in fundamental aspects of health care technology. It offers course units covering the needs of users with different knowledge levels and personal characteristics. It tailors the presentation of the educational material to the users' diverse needs by using AI techniques to specify each user's model as well as to make pedagogical decisions. This is achieved via an expert system that uses a hybrid knowledge representation formalisin integrating symbolic rules with neurocomputing. According to our preliminary results the number of the students that were used the system and passed ITSs tests is not significantly different from them that were participated the same course in the traditional classroom. The Web's universality will enable many users to gain access to the system's operations and significant conclusions regarding the system's efficiency will thus be drawn after its' use with numerous and diverse cases.
## 281 It has been found in recent years that many students who use intelligent tutoring systems game the system, attempting to succeed in the educational environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we introduce a system which gives a gaming student supplementary exercises focused on exactly the material the student bypassed by gaming, and which also expresses negative emotion to gaming students through an animated agent. Students using this system engage in less gaming, and students who receive many supplemental exercises have considerably better learning than is associated with gaming in the control condition or prior studies.
## 282 This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent tutoring systems were limited to a single agent that interacted with a student in the role of a teacher or expert. Technological advances have since made possible systems in which multiple agents interact with the learner and each other to model ideal behavior, strategies, reflections, and social interactions. Though still an emerging technology, multi-agent intelligent tutoring systems afford pedagogical benefits that go beyond the capabilities of the single-agent system and have facilitated learning gains on a variety of subject matters and skills, including science, technology, engineering, mathematics, research methods, metacognition, and language comprehension. The present work describes some common multi-agent designs that may be used to achieve a variety of pedagogical goals. We provide examples of how these designs have been implemented in educational or experimental settings and anticipate future use within the field of artificial intelligence.
## 283 Background: Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators. Objective: The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. Methods: We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. Results: We developed a VPS called Hepius that allows students to gather clinical information from the patient's medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance. Conclusions: By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.
## 284 In recent years there has been an upsurge in forms of instruction that envisage a permanent and ongoing involvement in education of novel concepts such as planned and personalised instruction and autonomous learning. A large number of problems that arise in education today may be solved by introducing new technologies into the educational environment, as they allow the form and content of tutoring systems to be tailored to each individual. The application of Artificial Intelligence techniques is helping open up new prospects in the field of teaching and learning. Using Artificial Intelligence techniques in education has the advantage of making it possible to represent expert reasoning and knowledge skills, and to take advantage of this experience in education. This study has involved the development of a tool to generate auto-regulated intelligent tutoring systems based on models. This form of representation makes it possible to break down, organise and represent information so as to enable the easy creation of functional intelligent computerised tutoring systems. Information about the subject in question, about inference mechanisms, and of a pedagogical nature (independent of any one strategy) is all separated. The tool also enables knowledge acquired by a student to be constantly monitored with a view to auto-regulating the course contents.
## 285 When implementing a tutoring system that attempts a deep understanding of students' natural language explanations, there are three basic approaches to choose between; symbolic, in which sentence strings are parsed using a lexicon and grammar; statistical, in which a corpus is used to train a text classifier; and hybrid, in which rich, symbolically produced features supplement statistical training. Because each type of approach requires different amounts of domain knowledge preparation and provides different quality output for the same input, we describe a method for heuristically combining multiple natural language understanding approaches in an attempt to use each to its best advantage. We explore two basic models for combining approaches in the context of a tutoring system; one where heuristics select the first satisficing representation and another in which heuristics select the highest ranked representation.
## 286 The intelligent tutoring systems are complex adaptive systems that model the instructional processes in order to maximize the outputs of the instructional systems, the marks of students. There is used a blend of artificial intelligent techniques in order to obtain intelligent systems: Bayesian networks, intelligent agents, knowledge representation techniques, artificial neural networks, etc. In this paper, there are presented applications of the artificial neural networks in the instructional systems. Artificial neural networks are structures inspired by the biological systems and they used in different domains: forms recognition, images processing, business modelling, robotics, medicine, learning and teaching processes modelling
## 287 Razzaq and Heffernan (2006) showed that scaffolding compared to hints on demand in an intelligent tutoring system could lead to higher averages on a middle school mathematics post-test. There were significant differences in performance by condition on individual items. For an item that proved to be difficult for all of the students on the pretest, an ANOVA showed that scaffolding helped significantly (p < 0.01). We speculated that the scaffolding had a greater positive effect on learning for this item because it was Much more difficult for the students than the other items. We thought that this result warranted a closer look at the link between the difficulty of an item and the effectiveness of scaffolding. In this paper, we report on an experiment that examines the effect of math proficiency and the level of interaction on learning. We found an interesting interaction between the level of interaction and math proficiency where less-proficient students benefited from more tutor interaction and more-proficient students benefited from less interaction.
## 288 We present a feasibility study of an intelligent tutoring system Peoplia in which a socially intelligent tutoring, agent uses common instructional methods that are augmented by social features to help students learn Peoplia features pseudo-tutor assessments, free-text answering. personalized question generation, and adaptive question selection. It allows students to work NO in and collaboratively while the tutoring friend monitors then social behavior and motivates them by socially relevant interventions
## 289 Feedbackmay be an effective interaction provided by the intelligent tutoring system. Nevertheless, the learning feedback is not easily definable, especially in front of learners with their characteristics and preferences. In this work, the authors propose to predict personalized feedback in a programming language learning context that promotes the feedback of the ITS according to the learner preferences and learner style. The recommended method uses a combination of machine learning techniques to suggest the best appropriate feedback according to learner's preferences and characteristics. For that purpose, the predictive personalized feedback method will respect the following phases: collect the learning experience from the learning resources (LR) and learner preferences (LP), generate groups of clusters that contain the common characteristics using the k-means algorithm, and define the association rules between the four categories and their corresponding activity. Finally, generate the personalized feedback and propose the recommendation through the intervention of an expert in the field.
## 290 Visual reasoning is an essential skill for many disciplines in engineering, architecture, and design. The underlying cognitive processes of visual reasoning form a basis in various problem-solving processes. We describe an intelligent tutoring system for visual reasoning that uses the missing view problem. This system, called Intelligent Visual Reasoning Tutor (IVRT), can adaptively support different learners' needs, track learners' progress, and provide active critiquing. IVRT uses a two-level reasoning architecture, combining geometric reasoning and semantic technologies, which enables the development of ITS for 3D geometry domains. We discuss IVRT's system architecture and implementation, which includes a learning contents model based on skills, lessons, and problems, aid a learner model that measures domain competence as a set of skills. Learning contents and pedagogical leaching strategy rules arc stored in standard OWL ontologies, which can be customized by the teacher.
## 291 Equipping an Intelligent Tutoring System (ITS) with the ability to interpret affective signals from students could potentially improve the learning experience of students by enabling the tutor to monitor the students' progress and provide timely interventions as well as present appropriate affective reactions via a virtual tutor. Most ITSs equipped with affect modeling capabilities attempt to predict the emotional state of users. However, the focus in this work is instead on trying to directly predict the learning outcomes of students from a stream of video capturing the students faces as they work on a set of math problems. Using facial features extracted from a video stream, we train classifiers to directly predict the success or failure of a student's attempt to answer a question while the student has just begun to work on the problem. In this work, we first introduce a novel dataset of student interactions with MathSpring, a popular ITS. We provide an exploratory analysis of the different problem outcome classes using typical facial action unit activations. We develop baseline models to predict the problem outcome labels of students solving math problems and discuss how early problem outcome labels can be forecasted and utilized to provide possible interventions.
## 292 Significant progress can be made in the part of elementary school education that relies on intelligent tutoring systems (ITS), if the role of a referee and a peer advisor will be performed by a pedagogical agent that is a computer implementation of a cognitive architecture modeling the process of learning. Recent studies in cognitive architectures funded by the DARPA IPTO BICA Program have identified the key potential of feasible today artificial intelligence as bootstrapped cognitive growth (i.e., gradual acquisition of knowledge and skills using previously acquired knowledge and skills), up to a human level of intelligence in a selected domain. This approach is not limited to laboratory settings and short-term paradigms, it is intended for a long-term, open-ended learning scenario in real-world settings. Several cognitive architectures were designed for this purpose, among which is GMU BICA, a self-aware biologically inspired cognitive architecture. Here we describe a computational model of student learning based on GMU BICA and its use as an ITS called Cognitive Constructor, which has two components called a Science Microworld and a Pedagogical Agent (GMU BICA agent). Results of our analysis show that the system will be useful in elementary school education.
## 293 <NA>
## 294 In this paper we describe factors for designing a usable and eventually useful Intelligent Tutoring System (ITS) for teaching a programming language. Our model is called TIP-4U (Tutoring Intelligence on Programming - 4 components' Usability). We combine the specific knowledge that has been obtained about the Psychology of Programming (PoP) and the results of empirical studies with the principles of Human Computer Interactions (HCI) systems, in order to define usability factors for the four components of an ITS. The factors of our model are attached to the components of an ITS and underline the role of the modules in combination with the particular characteristics of tutoring a programming language.
## 295 Intelligent tutors have been shown to be almost as effective as human tutors in supporting learning in many domains. However, the construction of intelligent tutors can be costly. One way to address this problem is to use previously collected data to generate models to provide intelligent feedback to otherwise non-personalized tutors. In this work, we explore how we can use previously collected data to build models of student dropout over time; we define dropout as ceasing to interact with the tutor before the completion of all required tasks. We use survival analysis, a statistical method of measuring time to event data, to model how long we can expect students to interact with a tutor. Future work will explore ways to use these models to to provide personalized feedback, with the goal of preventing students from dropping out.
## 296 Mobile devices used in education have the potential to provide learners with access to tutoring systems outside of the classroom or computer laboratory setting. To effectively deliver tutors on mobile devices, developers must consider the interface constraints imposed by the devices. The primary restriction is the small display available to users and the large amount of text and diagrams integral to desktop tutors. This paper describes two approaches to creating a mobile tutor interface and discusses the tradeoffs of each approach.
## 297 Standard intelligent tutoring systems give immediate feedback on whether students' answers are correct. This prevents unproductive floundering, but may also prevent students from engaging deeply with their misconceptions. This paper presents a prototype intelligent tutoring system with grounded feedback that supports students in evaluating and correcting their own errors. In a think-aloud study with five fifth-graders, students used the grounded feedback to self-correct, and solved more fraction addition problems with the tutor than with paper and pencil. These preliminary results are encouraging and motivate experimental work in this area.
## 298 Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. Results: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. Conclusions: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.
## 299 In this paper, a new approach of intelligent tutoring systems based on adaptive workflows and serious games is proposed. The objective is to use workflows for learning and evaluation process in the activity-based learning context. We aim to implement a system that allow the coexistence of an intelligent tutor and a human tutor who could control and follow-up the execution of the learning processes and intervene in blocking situations. Serious games will be the pillar of the evaluation process. The purpose is to provide new summative evaluation methods that increase learner's motivation and encourage them to learn.
## 300 This paper reviews recently published works in the emerging field of Collaborative Intelligent Tutoring Systems (CITS). The paper first provides an overview of the fields of Intelligent Tutoring Systems, Computer-Supported Collaborative Learning, and Collaborative Intelligent Tutoring Systems. We systematically search online bibliographic databases, code their research objectives, qualitatively analyze their methodology, and group papers into 3 categories according to our findings. Then we evaluate the associated systems, highlighting their main features and impacts on student learning. Finally, we identify the gaps for possible future research.
## 301 Uncertainties exist in intelligent tutoring. The partially observable Markov decision process (POMDP) model may provide useful tools for handling uncertainties. The model may enable an intelligent tutoring system (ITS) to choose optimal actions when uncertainties occur. A major difficulty in applying the POMDP model to intelligent tutoring is its computational complexity. Typically, when a technique of policy trees is used, in making a decision the number of policy trees to evaluate is exponential, and the cost of evaluating a tree is also exponential. To overcome the difficulty, we develop a new technique of policy trees, based on the features of tutoring processes. The technique can minimize the number of policy trees to evaluate in making a decision, and minimize the costs of evaluating individual trees.
## 302 The C++ Standard Template Library (STL) Intelligent Tutoring System seeks to guide students in applying the STL for problem-solving. It is discovered that the key problem in using the C++ STL lies in the lack of capability in prerequisite concepts. Therefore, the ability to model a cause-effect relationship in Bayesian reasoning using a corresponding set of conditional probability is highly appropriate for the student modeling. To enhance the student model, a stereotype is assigned according to the student's understanding for further assessments. Fuzzy logic technique is capable of providing human-like diagnosis of the student's knowledge. The development applies practices from the eXtreme Programming methodology and J2EE technologies. The evaluation results revealed that the Bayesian Theorem has the capability of modeling the student's prerequisite and directing the student during the tutorial session. The Fuzzy Stereotyping of Students Expert System works well in categorizing the students according to four stereotypes - novice, beginner, intermediate and advanced.
## 303 We propose and implement a novel intelligent tutoring system, called RadarMath, to support intelligent and personalized learning for math education. The system provides the services including automatic grading and personalized learning guidance. Specifically, two automatic grading models are designed to accomplish the tasks for scoring the text-answer and formula-answer questions respectively. An education-oriented knowledge graph with the individual learner's knowledge state is used as the key tool for guiding the personalized learning process. The system demonstrates how the relevant AI techniques could be applied in today's intelligent tutoring systems.
## 304 As computer-based training systems become increasingly integrated into real-world training, tools which rapidly author courses for such systems are emerging. However, inconsistent user interface design and limited support for a variety of domains makes them time consuming and difficult to use. We present a Generalized, Rapid Authoring Tool (GRAT), which simplifies creation of Intelligent Tutoring Systems (ITSs) using a unified web-based wizard-style graphical user interface and programming-by-demonstration approaches to reduce technical knowledge needed to author ITS logic. We implemented a prototype, which authors courses for two kinds of tasks: A network cabling task and a console device configuration task to demonstrate the tool's potential. We describe the limitations of our prototype and present opportunities for evaluating the tool's usability and perceived effectiveness.
## 305 Currently, in the context in which the activities from all domains are in a closed interdependence with the instructional activity and in which the courses can't be anymore carried out exclusively in the traditional ways, the use of the computer has become an essential and mandatory requirement for all levels of education and for all parties involved. This stage of the educational process evolution has been named the age of the Computer Assisted Instruction. Since this age started, many companies, researchers, specialists and teachers have involved themselves in the design, development and implementation of new systems, software, tools, methods and methodologies capable to answer to the highest standards applicable in education of all grades and also, capable, to offer a very friendly environment for instruction satisfying in the same time the effectiveness requirements. One of the goals followed and also, achieved, was the use of the Computer Assisted Instruction within the Intelligent Tutoring Systems (ITS). This paper is presenting an ITS developed by the author and designed using Natural Language Processing technologies, a system which is capable to assist the students who are looking to achieve and understand elements and aspects related to the Computers Programming and C language discipline. Also, in the article are described and exemplified the functionalities of the system proposed and are presented the steps that a student has to follow from the very beginning (enrolling stage) up to the end of the instruction (visualization of performances achieved). Moreover, in this paper are briefly reviewed the facilities offered by the system to the instructor in terms of the students evolution visualization, assistance and follow up during the instrul k
## 306 In developing a tutoring system, one of the most difficult tasks is to collect tutoring knowledge from multiple educators, especially courses in which the contents change frequently, due to the advent of new technologies. In this paper, we propose a web-based intelligent tutoring strategy construction system, which is able to elicit, analyze, and integrate tutoring knowledge from multiple educators systematically. Experiments on two courses have been performed to evaluate the time, completeness, and accuracy improvement in constructing tutoring knowledge bases with our approach. According to the experimental results, it can be inferred that for most cases, our approach achieves desirable performances.
## 307 This paper investigates the effects of training the metacognitive skill of knowledge monitoring when metacognitive instruction is adapted to the characteristics of students in intelligent tutoring systems. An animated pedagogical agent that trains knowledge monitoring was developed and integrated into a step-based tutoring system that helps students in solving algebraic equations. The training provided by the agent encourages learners to reflect on their knowledge and has its content and frequency of intervention adapted to the characteristics of the student. Related work has not adapted the metacognitive instruction to the characteristics of the student, nor has it aimed at investigating the effects of knowledge monitoring training specifically. Results of a classroom study suggest that students who received metacognitive training improved their knowledge monitoring skill and performed better on tests.
## 308 In BioWorld, a medical intelligent tutoring system, novice physicians are tasked with diagnosing virtual patient cases. Although we are often interested in considering whether learners diagnosed the case correctly or not, we cannot discount the actions that learners take to arrive at a final diagnosis. Thus, the consideration of the sequence of actions becomes important. In this preliminary study, we propose a line of research to investigate learner actions involved in diagnosing virtual patient cases using Hidden Markov Models.
## 309 Domain knowledge (DK) is a basic part of an intelligent tutoring system (ITS). DK usually includes information about the concepts the ITS is dealing with and the teaching material itself, which can be considered as a set of learning objects (LOs). LOs are described by a data set called learning object metadata. Concepts are usually organized in a network, called a concept network or map. Each concept is associated with a number of LOs. In this paper, we present a tool for managing both types of information in DM: creating and editing (a) a concept network and (b) learning object metadata. Additionally, the tool can produce corresponding XML descriptions for each learning object metadata. Existing tools do not offer all the above capabilities.
## 310 Adding student collaboration to an intelligent tutoring system could leverage the benefits of both approaches. We have incorporated a mutual peer tutoring script, where students of similar abilities take turns tutoring each other, into the Cognitive Tutor Algebra. In this paper, we identify three design principles for peer tutoring, and discuss how they were realized in our peer tutoring script. We then develop a cognitive model for peer tutoring, and drawing from student data, identify places for an intelligent tutor to provide feedback. Finally, we describe the implementation of the script and our plans for formal evaluation.
## 311 This work addresses the need for learning environments that are easy to customize for different instructional uses and that can address a large number of students. The generation of web-based intelligent tutoring systems for different domains can help alleviate this need. In this report, we describe the development of an architecture for the generation of intelligent tutoring systems for various domains by interfacing with existing expert systems, and reusing the other tutoring components. We also describe how the architecture can be modified to run in a distributed environment, and discuss the benefits and limitations of our approach.
## 312 The World-Wide Web is an inherently distributed environment. As the web becomes a more important medium for education, we need to consider how different educational systems might interact. Adaptive systems are especially promising in this regard, since they offer the potential to customize themselves to suit students who have not yet used the system. In this paper, we describe our efforts to construct an integrated system which incorporates conceptual instruction through InterBook and problem-solving interaction through PAT Online. InterBook and PAT Online are two separate Web-based adaptive tutoring systems. To achieve a better level of adaptivity these systems interact and exchange information about the user's progress.
## 313 Recently, there has been growing emphasis on supporting robust learning within intelligent tutoring systems, assessed by measures such as transfer to related skills, preparation for future learning, and longer term retention. It has been shown that different pedagogical strategies promote robust learning to different degrees. However, the student modeling methods embedded within intelligent tutoring systems remain focused on assessing basic skill learning rather than robust learning. Recent work has proposed models, developed using educational data mining, that infer whether students are acquiring learning that transfers to related skills, and prepares the student for future learning (PFL). In this earlier work, evidence was presented that these models achieve superior prediction of robust learning to what can be achieved by traditional methods for student modeling. However, using these models to drive intervention by educational software depends on evidence that these models remain effective within new populations. To this end, we analyze the degree to which these detectors remain accurate for an entirely new population of high school students. We find limited evidence of degradation for transfer. More degradation is seen for PFL. This degradation appears to occur in part because it is generally more difficult to infer this construct within the new population.
## 314 We compare the affect associated with an intelligent tutoring environment, Aplusix, and a simulations problem solving game, The Incredible Machine, to determine whether students experience significantly better affect in an educational game than in an ITS. We find that affect was, on the whole, better in Aplusix than it was in The Incredible Machine. Students experienced significantly less boredom and frustration and more flow while using Aplusix. This implies that, while aspects unique to games (e.g. fantasy and competition) may make games more fun, the interactivity and challenge common to both games and ITSs may play a larger role in making both types of systems affectively positive learning environments.
## 315 Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice-one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.
## 316 Tailoring material to the individual learner for delivery on handheld computers represents a major challenge. Most of the cur-rent work in mobile intelligent tutoring systems is more related with personalized systems because they do not support any artificial intelligent technique for implementing the adaptive part in the applications. In this paper, we present MLTutor, an author tool to facilitate the creation of adaptive learning material to be used in mobile devices applying an artificial intelligence approach.
## 317 This paper discusses a study conducted in a blended learning environment for an introductory computer programming course. The experimental environment is a type of flipped classroom model, in which students initially learn the basic concepts online and then learn traditional lecture in the subsequent class. During the design phase of a blended learning course, it is important to determine the time frame a teacher expects from students regarding spending time online. Therefore, the aim of this study of AC-ware supported education is to identify online learning time frames. After the students use AC-ware Tutor for four experimental weeks, we will investigate their weekly online learning behavior. The online learning behavior will be represented using the knowledge tracking variables complemented by additional stereotype tracking variables. By using K-means algorithm, we will highlight four groups of specific online learning behaviors. The performance of each student group will be described using weekly paper-based concept map posttests.
## 318 Intelligent Tutoring Systems (ITSs) have been largely used in school settings and considered effective learning tools. However, students' performance might be impaired by their undesired behaviors. An example of these behaviors is gaming the system, which happens when the student tries to mislead the system in order to advance faster in the tasks. Previous works have tried to treat this behavior by blocking student's actions; however, this restrictive approach has proved to be ineffective. We propose the use of animated pedagogical agents in ITSs as a non-restrictive approach to gaming the system. We believe that an animated pedagogical agent can discourage this behavior by taking two actions: (1) showing it is aware of when students are gaming, and (2) educating students about the negative impact of this behavior on their learning. We implemented this approach in a step-based algebra ITS, and a classroom experiment was conducted with 37 students who used the system for 50min on average to solve linear equations. Although, due to the design restrictions of the experiment, we could not statistically demonstrate that the presence of the agent decreased the gaming of the system, descriptive statistics of the tutor log data show evidence of a possible positive effect of an aware animated pedagogical agent on students' behavior, indicating that this approach deserves further investigation.
## 319 Mathematics is highly structured and underpins most of science and engineering. For this reason it has proved a very suitable test domain for intelligent tutoring system (ITS) research with the result that probably more tutoring systems have been structured for the domain than for any other. However, there still exists no consensus on any approach for the design of such systems. Consequently, existing ITSs in the domain suffer from a considerable number of shortcomings which render them 'unintelligent'. This paper after rigorously examining the shortcomings of existing approaches, presents an alternative approach to constructing ITSs in mathematics which has been demonstrated to have improved on at least some of the shortcomings of existing approaches. It also provides a flavour of how this approach has been implemented in the FITS system.
## 320 In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. However, building this understanding into a computer-based intelligent tutor is a time-intensive process usually conducted by subject experts. Much of this time is spent in building production rules that model all the ways a student might solve a problem. In our prior work, we proposed a novel application of Markov decision processes (MDPs) to automatically generate hints for an intelligent tutor that learns. We demonstrate the feasibility of this approach by extracting MDPs from four semesters of student solutions in a logic proof tutor, and calculating the probability that we will be able to generate hints for students at any point in a given problem. Our past results indicated that extracted MDPs and our proposed hint-generating functions will be able to provide hints over 80% of the time. Our results also indicated that we can provide valuable tradeoffs between hint specificity and the amount of data used to create an MDP.
## 321 Many instructional system design models have been proposed to improve learners' knowledge and abilities through learning environments. Most educators agree that problem solving is among the most meaningful and important kinds of learning and thinking [9]. Ill-structured problem learning and higher order skill improvement have not been explored enough by Intelligent Tutoring Systems (ITS) developers. This paper shows some preliminary ideas about how a 1960s pedagogical technique called Structural Communication (SC) could be used in ITS to exercise higher order skills in learners through the solution of ill-structured problems. It presents how the SC sections are similar to ITS structure modules and how SC could be employed to help authors to create their instructional activities.
## 322 This paper presents an Intelligent Tutoring System (ITS) applied to the teaching of anatomy of the female breast, including some types of cancer related to this organ. This ITS is composed of four modules: Student, an Expert System containing a questionnaire for the diagnosis of the learner's profile; Tutor, an Artificial Neural Network Interactive Activation and Competition, for the application of teaching techniques; Domain, some ontologies containing content and related media; and Interface, developed as an Adaptive Hypermedia System. The objective of this work is to lift requirements for the integration of various modeling types that use Artificial Intelligence techniques in the same ITS, even enabling the use of this system in a Medical Simulation Environment. The validation process of this ITS is in progress because the class period has not yet started at three universities: the University of Brasilia (Federal) and the Catholic University of Brasilia (Private), both located in Brasilia, Brazil, and the Universidad de La Frontera (Federal) located in Temuco, Chile.
## 323 To construct dialogue-based Intelligent Tutoring Systems (ITS) with sufficient pedagogical expertise, a trendy research method is to mine large-scale data collected by existing dialogue-based ITS or generated between human tutors and students to discover effective tutoring strategies. However, most of the existing research has mainly focused on the analysis of successful tutorial dialogue. We argue that, to better inform the design of dialogue-based ITS, it is also important to analyse unsuccessful tutorial dialogues and gain a better understanding of the reasons behind those failures. Therefore, our study aimed to identify effective tutoring strategies by mining a large-scale dataset of both successful and unsuccessful human-human online tutorial dialogues, and further used these tutoring strategies for predicting students' problem-solving performance. Specifically, the study adopted a widely-used educational dialogue act scheme to describe the action behind utterances made by a tutor/student in the broader context of a tutorial dialogue (e.g., asking/answering a question, providing hints). Frequent dialogue acts were identified and analysed by taking into account the prior progress that a student had made before the start of a tutorial session and the problem-solving performance the student achieved after the end of the session. Besides, we performed a sequence analysis on the inferred actions to identify prominent patterns that were closely related to students' problem-solving performance. These prominent patterns could shed light on the frequent strategies used by tutors. Lastly, we measured the power of these tutorial actions in predicting students' problem-solving performance by applying a well-established machine learning method, Gradient Tree Boosting (GTB). Through extensive analysis and evaluations, we identified a set of different action patterns that were pertinent to tutors and students across dialogues of different traits, e.g., students without prior progress in solving problems, compared to those with prior progress, were likely to receive more thought-provoking questions from their tutors. More importantly, we demonstrated that the actions taken by students and tutors during a tutorial process could not adequately predict student performance and should be considered together with other relevant factors (e.g., the informativeness of the utterances). (C) 2021 Elsevier B.V. All rights reserved.
## 324 This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing individual and group models of performance, knowledge, and interaction. We adapt several ITS methodologies to model team performance as well as individuals' performance of the team members. We briefly describe the point processes proposed by von Davier and Halpin (2013), and we also introduce the Competency Architecture for Learning in teaMs (CALM) framework, an extension of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg, & Holden, 2012) to be used for team settings.
## 325 The way in which people learn and institutions teach is changing due to the everincreasing impact of technology. People access the Internet anywhere, anytime and request online training. This has brought about the creation of numerous online learning platforms which offer comprehensive and effective educational solutions which are 100% online. These platforms benefit from intelligent tutoring systems that help and guide students through the learning process, emulating the behavior of a human tutor. However, these systems give the student little freedom to experiment with the knowledge of the subject, that is, they do not allow him/her to propose and carry out tasks on his/her own initiative. They are very restricted systems in term of what the student can do, as the tasks are defined in advance. An intelligent tutoring system is proposed in this paper to encourage students to learn through experimentation, proposing tasks on their own initiative, which involves putting into use all the skills, abilities tools and knowledge needed to successfully solve them. This system has been designed developed and applied for learning predictive parsing techniques and has been used by Computer Science students during four academic courses to evaluate its suitability for improving the student's learning process. (C) 2020 Elsevier Inc. All rights reserved.
## 326 This study describes preliminary results of a research related to Intelligent Programming Tutor (IPT) which is derived from Intelligent Tutoring System (ITS). The system architecture consists of four models. However, in this study student model mainly student characteristic was focused. From literature, 44 research articles were identified from a number of digital databases published between 1997 to 2022 base on systematic literature review (SLR) method. The findings show that the majority 48% of IPT implementation focuses on knowledge and skills. While 52% articles focused on a combination of two to three student characteristics where one of the combinations is knowledge and skill. When narrow down, 25% focused on knowledge and skills with errors or misconceptions; 4% focused on knowledge and skill with cognitive features; 5% focused focus on knowledge and skill with affective features; 2% focused on knowledge and skill with motivation; and 9% based on knowledge and skill with learning style and learning preferences as students' characteristics to build their student model. Whereas 5% focused on a combination of three student characters which are knowledge and skill with cognitive and affective features and 2% focused on knowledge and skill with learning styles and learning preferences and motivation as students' characteristics to construct the tutoring system student model. To provide an appropriate tutoring system for the students, students' characteristic needs to decide for the student model before developing the tutoring system. From the findings, it can say that knowledge and skills is an essential students' characteristic used to construct the tutoring system student model. Unfortunately, other students' characteristic is less considered especially students' motivation.
## 327 The application of game elements within learning environments takes many forms, including serious games, interactive virtual environments, and the application of game mechanics within non-gaming contexts. Given the breadth of strategies for implementation game-elements into instructional systems, it is important to recognize that each strategy carries its own potential benefits and risks. The purpose of the current paper is to review the relevant interdisciplinary literature regarding the application of games and game-elements to learning contexts, and identify the factors to consider when developing a game-inspired instructional system. Secondly, the current discussion considers the special case of game technology and game design elements in intelligent tutoring, and identifies future research opportunities to meaningfully integrate such features in adaptive tutoring systems.
## 328 In a shell system for the generation of intelligent tutoring systems, the instructional model that one applies should be variable independent of the content of instruction. In this article, a taxonomy of content elements is presented in order to define a relatively content-independent instructional planner for introductory programming ITS's; the taxonomy is based on the concepts of programming goals and programming plans. Deliveries may be composed by the instantiation of delivery templates with the content elements. Examples from two different instructional models illustrate the flexibility of this approach. All content in the examples is taken from a course in COMAL-80 turtle graphics.
## 329 The paper presents some considerations about the impact of agents' and related technologies on educational software research and its application to designing and to implementing Intelligent Tutoring Systems. It also presents an overview about systems for Educational Software that adopted the Pedagogical Agents to better explore the interaction and dynamic changes in teaching-learning environments. The first result with these technologies and the use of multimedia resources to improve the interface design is also discussed.
## 330 This paper presents our ongoing efforts toward developing an Intelligent Tutoring System (ITS) to improve student learning in a sophomore engineering dynamics course. An ITS module was developed to guide students applying the Principle of Work and Energy to solve particle dynamics problems. Pre-post tests, each including six technical questions, were administrated to 74 engineering undergraduates who took a dynamics course in a recent semester. The assessment results show that the developed ITS module helps students master dynamics concepts and associated calculations.
## 331 Research in the field of intelligent tutoring systems has failed to provide any substantial of viable systems that could be used in real academic environments. This situation appears to be the result of two factors: first, the failure to identify clearly the objectives and the scope of such systems; and second, the continuously shifting technological platform on which such systems are built. This paper examines the possible objectives for the development of tutoring systems and presents an approach adopted by the Byzantium Project, It describes a model for a computer integrated learning environment (CILE) and discusses the role of an intelligent tutoring tool (ITT) within this model. The paper also considers the potential of the Internet for various learning environments. Based on our experience of designing and implementing four ITTs that have the same look and feel (but which address diverse subject areas) the paper suggests a possible extension of the Byzantium approach to the Internet through the conversion of ITTs into intelligent tutoring applets (ITAs).
## 332 In this study, a Concept Question Answering system applied to the Computer Domain (CQACD) for intelligent tutoring is proposed. This system is a dialogue-based Intelligent Tutoring System (ITS) that allows the tutor and student with mixed-initiative and natural language to ask each other questions concerning the basic computer knowledge in the Computer Basics course. CQACD is based on constructivist principles and encourages the learner to construct knowledge rather than merely receiving knowledge, which has the following characteristics: (a) this system employs a domain ontology with rich semantic relationships to model the basic computer knowledge and build up a concept-centric knowledge model, (b) uses a limited number of 80 input templates with description logics to acquire the intention of questions posed by students, (c) a textual entailment algorithm with semantic technologies is proposed to match the input template and assess the student's contribution to improve the flexibility of the system, and (d) an ontology-driven dialogue management mechanism is proposed, which can quickly form the conversational content and conversational sequence. The experimental results show that CQACD can replace the teachers' tutoring in large classes and can promote the learning of poor students in large classes better than teachers can. The paper reveals that the domain ontology with rich semantic relationships plays an important role in the Concept Question Answer System (CQAS). It can model CQAS's discipline knowledge, provide structured domain knowledge for student model, template design and matching, and provide basic architectural architecture for dialogue management.
## 333 Building on previous work in this area, we provide a description and justification for a new way of identifying modes and mode switches in tutorial dialogues, part of a coding scheme involving 16 modes and 125 distinct dialogue acts. We also present preliminary results from an analysis of 1,438 human-annotated transcripts, consisting of more than 90,000 turns. Among other findings, this analysis shows subtle differences in the mode architecture of successful vs. less successful sessions, as judged by expert tutors.
## 334 Intelligent tutoring systems have proven very effective at teaching hard skills such as math and science, but less research has examined how to teach soft skills such as negotiation. In this paper, we introduce an effective approach to teaching negotiation tactics. Prior work showed that students can improve through practice with intelligent negotiation agents. We extend this work by proposing general methods of assessment and feedback that could be applied to a variety of such agents. We evaluate these techniques through a human subject study. Our study demonstrates that personalized feedback improves students' use of several foundational tactics.
## 335 Calls for widespread Computer Science (CS) education have been issued from the White House down and have been met with increased enrollment in CS undergraduate programs. Yet, these programs often suffer from high attrition rates. One successful approach to addressing the problem of low retention has been a focus on group work and collaboration. This paper details the design of a collaborative ITS (CIT) for foundational CS concepts including basic data structures and algorithms. We investigate the benefit of collaboration to student learning while using the CIT. We compare learning gains of our prior work in a non-collaborative system versus two methods of supporting collaboration in the collaborative-ITS. In our study of 60 students, we found significant learning gains for students using both versions. We also discovered notable differences related to student perception of tutor helpfulness which we will investigate in subsequent work.
## 336 The estimation of the difficulty level of exercises is a fundamental aspect of intelligent tutoring systems, and it is necessary in order to achieve better adaptation to the students' needs and maximize learning efficiency. In this article, we present an approach to automatically estimates the difficulty level of exercises in natural language (NL) to first-order of logic (FOL). The estimation of an exercise's difficulty level is based on the complexity of the corresponding answer, that is the FOL formula, as well as the structure and the semantics of the exercise, that is a natural language sentence and it is carried out in two main steps. Initially, a preliminary estimation is performed based on the complexity of the FOL formula. The system takes as input parameters the number, the type and the order of quantifiers, the number of implications, and the number of different connectives. Afterwards, the final estimation is made based on both semantic aspects of the NL sentence and the structure of the FOL formula. An evaluation study was conducted to assess the system's performance, and the results are very encouraging.
## 337 Data-driven intelligent tutoring systems learn to provide feedback based on past student behavior, reducing the effort required for their development. A major obstacle to applying data-driven methods in the programming domain is the lack of meaningful observable actions for describing the students' problem-solving process. We propose rewrite rules as a language-independent formalization of programming actions in terms of code edits. We describe a method for automatically extracting rewrite rules from students' program-writing traces, and a method for debugging new programs using these rules. We used these methods to automatically provide hints in a web application for learning programming. In-class evaluation showed that students receiving automatic feedback solved problems faster and submitted fewer incorrect programs. We believe that rewrite rules provide a good basis for further research into how humans write and debug programs.
## 338 A constant topic in medical education is clinical reasoning: how do learners solve cases? Learner interactions with Intelligent Tutoring Systems yield fine-grained data that are useful in generating meaningful information and illuminating understanding about learner behaviors and outcomes. We examine and analyze the log files generated by BioWorld, an Intelligent Tutoring System for the medical domain. More specifically, to further our understanding of the nature of reasoning employed by learners while solving virtual patient cases in BioWorld, one important step is to examine the initial list of selected diagnostic hypotheses before any other learner action is taken in diagnosing a case. By exploring the link between initial selected hypotheses and final submitted hypothesis, a better understanding of the learners' reasoning might be achieved.
## 339 Intelligent tutoring systems assist medical faculty in training and equipping Students with the required clinical reasoning skills. Plausible student solutions to a given problem are rejected by tutoring systems as being incorrect, if they do not match a specific solution accepted by the tutoring system. This leads to brittleness in evaluating student solutions. In this paper we describe a combination of knowledge base expansion and exploitation of existing knowledge structure to enhance robustness in an intelligent tutoring system for medical problem-based learning using UMLS. We present a tutoring system that enriches the solution space by collating different plausible solutions and exploiting the knowledge structure in UMLS to offer students a broader scope of reasoning.
## 340 A previous study showed that pedagogical agents that offer feedback with appropriate politeness strategies can help students learn better [21]. This work studied the Politeness Effect in a foreign language intelligent tutoring system, and provided further evidence that tutorial feedback with socially intelligent strategies can influence motivation and learning outcomes.
## 341 Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.
## 342 The purpose of this study was to explore the effectiveness of a new feedback mechanism within an intelligent tutoring system called AutoTutor LITE. Participants were randomly assigned to one of three feedback manipulation conditions within the context of complex scientific material: 1) learners' characteristics curves; 2) random; 3) no feedback. Results revealed that the participants receiving the new feedback mechanism (LCC) showed significantly higher learning gains when compared to the random feedback or no feedback manipulations. Additionally, there were no differences discovered between random feedback and no feedback. Interpretation and implications of results are discussed.
## 343 This research seeks to improve learning by integrating intelligent coaching with peer-to-peer collaboration. We combine advanced technologies that support student freedom and exploration, allow for collaboration and peer tutoring, and provide task advice by modeling student work and behavior. We empirically evaluated both the effect of added collaborative features that enable peer-to-peer interaction and the potential to use this collaboration to improve the intelligent coaching features of the system. We found that collaboration enhances student work and that the coaching features provide opportunities to promote collaboration.
## 344 Intelligent Tutoring Systems are systems which provide direct customized instruction to students. An Intelligent Tutoring System consists of four modules. This research concentrates on two of the modules, namely, the Tutoring Module and the Expert Module, analyzes tutoring systems which contribute to the design of these modules. The study identifies the issues which have not been addressed in previous contributions.
## 345 Recent works in Computer Science, Neurosciences, Education, and Psychology have shown that emotions play an important role in learning. Learner's cognitive ability depends on his emotions. We will point out the role of emotions in learning, distinguishing the different types and models of emotions which have been considered until now. We will address an important issue concerning the different means to detect emotions and introduce recent approaches to measure brain activity using Electroencephalograms (EEG). Knowing the influence of emotional events on learning it becomes important to induce specific emotions so that the learner can be in a more adequate state for better learning or memorization. To this end, we will introduce the main components of an emotionally intelligent tutoring system able to recognize, interpret and influence learner's emotions. We will talk about specific virtual agents that can influence learner's emotions to motivate and encourage him and involve a more cooperative work, particularly in narrative learning environments. Pushing further this paradigm, we will present the advantages and perspectives of subliminal learning which intervenes without conscious perception. Finally, we conclude with new directions to emotional learning.
## 346 An intelligent tutoring system (ITS) is a computer system developed to offer adaptive, one-to-one interactive tutoring. The main modules in an ITS include a student model and a tutoring model. The student model represents and tracks the student's knowledge and affective states, and the tutoring model decides tutoring actions based on the student's current states. Partially observable Markov decision process (POMDP) is a useful tool for building ITSs. It allows a system to adaptively teach a student even when the student's states are not completely observable. A core component in a POMDP is a state space that models the student's states. The space is exponential. When the number of state variables is large, the computational costs become a major barrier to applying POMDP in building ITSs. In our research, we develop a new technique for handling the exponential space. In this technique, the pedagogical order of subject concepts is used to partition the space into sub-spaces, and further reduce their sizes. In this paper, we first describe how POMDP can be used for building an ITS and discuss the exponential space problem. We then present our technique in the context of an experimental system. Finally, we present some experimental results.
## 347 Since 1990, the Air Force has been engaged in a long-term research effort, Fundamental Skills Training project (FST), to bring stare-of-the-art intelligent tutoring technology to bear on growing literacy skills problem in areas such as mathematics, writing, and science. Specifically, the FST project was implemented to help school children attain basic literacy skills. To accomplish the goals of the project, three intelligent tutoring systems, the Word Problem Solving Tutor (WPS), MAESTRO, the Writing Tutor, and the science tutor, Instruction in Scientific Inquiry Skills (ISIS) were developed. These three tutoring systems have been evaluated in field studies involving 47 schools and adult educational systems in 7 states across the nation. These studies have involved 40-50 teachers and as many as 3,000 students each year to evaluate the effectiveness of the software in enhancing critical thinking skills. ISIS is a simulation-based cognitive tutoring system constructed to teach junior and senior high school students scientific inquiry skills and substantive knowledge in ecology. ISIS has been designed to allow students to explore knowledge and domain space and construct knowledge while the tutor encourages and rewards these actions. Students using ISIS are taught under an instructional system that provides an exploratory environment, authentic data, appropriate and timely feedback, and a series of progressively difficult Skill Instructional Modules (SIMs). The primary goal of the science tutor, consistent with major science initiatives and standards, is to increase the level of scientific functioning. This paper focuses on the design, implementation, and evaluation of ISIS, in a series of large-scale field studies conducted over three years. In addition, this paper discuses the effectiveness of the tutor and the impact of formative and summative evaluations on the design of subsequent versions.
## 348 Objective: This manuscript describes the development of a general intelligent tutoring system for teaching visual classification problem solving. Materials and methods: The approach is informed by cognitive theory, previous empirical work on expertise in diagnostic problem-solving, and our own prior work describing the development of expertise in pathology. The architecture incorporates aspects of cognitive tutoring system and knowledge-based system design within the framework of the unified problem-solving method description language component model. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. Results: In this paper, we outline the empirically derived requirements and design principles, describe the knowledge representation and dynamic solution graph, detail the functioning of the instructional layer, and demonstrate two implemented interfaces to the system. Conclusion: Using the general visual classification tutor, we have created SlideTutor, a tutoring system for microscopic diagnosis of inflammatory diseases of skin. (c) 2005 Elsevier B.V. All rights reserved.
## 349 Authoring of computer-based instruction employing intelligent tutoring (IT) presents many challenges. Chief among them is that for non-trivial domains, the knowledge and skills required to author maximally effective instruction often do not reside in a single individual. More often the best outcome is achieved by collaboration among some combination of instructional designers, subject matter experts, psychologists, traditional educators, and in some cases, software engineers. To meet this challenge, well designed collaborative authoring systems are needed. Cloud computing technologies, such as Platform as a Service (PaaS) and Infrastructure as a Service (IaaS), offer new possibilities for the construction of distributed authoring systems. In this paper we discuss several high-level design considerations for developing collaborative IT authoring systems, focusing on extending the capabilities of the existing Generalized Intelligent Framework for Tutoring (GIFT).
## 350 This paper presents a strategy for a personal tutor based on intelligent agents and embedded systems. An educational tool is proposed to join the teaching and learning processes for a subject area in the teaching of electronics engineering. This tool meets software and hardware features from some learning parameters and the resources of an embedded platform. Technical knowledge, professional skills and collaborative work are considered to design the agents in the software side. Finally, some performance variables for the hardware resources are measured with the agent-based system running on an operating system to know the load level of the designed tool.
## 351 Nowadays, many MOOC platforms have arisen to provide free knowledge. These platforms have a large catalog of courses for different specializations that progressively demand more specific learning resources and assessment methods to evaluate the progression of students. Current MOOC platforms are gradually giving support to these new requirements but with a limited assistance. This paper presents the state of art of the analytical system for three relevant MOOC platforms, one of the main pillars for analyzing the progression of courses. Other initiatives are also reviewed to show that current MOOC analytical systems are not ready to support custom MOOC-aware intelligent tutoring systems (ITSs). Thus, the design of a learning analytics system to assist these tools for MOOC platforms is presented.
## 352 Authoring the domain knowledge of an intelligent tutoring system (ITS) is a well-known problem. and an often-mentioned approach is to use authors who are domain experts Unfortunately, this approach requires that potential authors learn to write and debug knowledge written in a formal knowledge representation language If authors were able to use natural language to represent knowledge it would allow them to add and update knowledge far more easily In this paper. the design of such an authoring system. 'Natural-K' is presented Natural-K is an authoring system in which domain authors including non-programmers are able to add problem statements and background knowledge such as commonsense, in natural language
## 353 Intelligent Tutoring Systems (ITSs) have shown to be almost as effective as one-to-one tutoring. Nonetheless, the students' improper use of the ITS help system and its intelligent assistance, i. e. gaming the system or help refusal, can impair learning. This paper presents the use of gamification elements, more specifically, points and difficulty levels, as an approach to prevent the behaviors of gaming the system (help abuse and trial-and-error) and help refusal. This system was integrated into a step-based algebraic ITS and it was evaluated in an experiment, during six weeks, involving 60 students from three classes of the 7th year of an elementary school. Each class of students was assigned to one of the three groups: fully gamified, partially gamified and non-gamified, being that they differ by the level of gamification implemented. The students in the two gamified groups had a lower rate of trial-and-error behavior than the non-gamified group. However, we haven't found statistically significant difference between the fully and partially gamified groups for the trial and error. Also, no differences were observed between the gamified groups and the non-gamified one for the help refusal and help abuse behaviors. The results of this research confirm previous finding that gamification can be used as a non-restrictive approach for the trial-and-error behavior, a form of gaming On the other hand, we were not able to show that gamification can prevent help refusal and abuse.
## 354 Authoring tools have been shown to decrease the amount of time and resources needed for the development of Intelligent Tutoring Systems (ITSs). Although collaborative learning has been shown to be beneficial to learning, most of the current authoring tools do not support the development of collaborative ITSs. In this paper, we discuss an extension to the Cognitive Tutor Authoring Tools to allow for development of collaborative ITSs through multiple synchronized tutor engines. Using this tool, an author can combine collaboration with the type of problem solving support typically offered by an ITS. Different phases of collaboration scripts can be tied to particular problem states in a flexible, problem-specific way. We illustrate the tool's capabilities by presenting examples of collaborative tutors used in recent studies that showed learning gains. The work is a step forward in blending computer-supported collaborative learning and ITS technologies in an effort to combine their strengths.
## 355 This research examined the longitudinal trends of intelligent tutoring systems (ITS) research using text mining techniques in a more comprehensive manner. Two hundred and thirty-one (231) refereed journal articles were retrieved and analyzed from the Web of Science database from the top six major educational technology-based journals, which are based on the Google Scholar metrics and Baidu Scholar in the period from January 2006 to December 2018. Content analysis was implemented for further analysis based on (a) category of research purpose, (b) disciplines domains, (c) sample group, (d) context utilization, (e) research design, (f) category of learning, (g) learning outcome, (h) periodic journal, (i) country, (j) publisher. This review research of ITS presented findings, which could be a layover platform and guidance for researchers, educators, policymakers or even journal publisher for the future research or reference in the realm of ITS regarding the latest trends.
## 356 We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system tries to propose to the student the activity which makes him progress best. We introduce two algorithms that rely on the empirical estimation of the learning progress, one that uses information about the difficulty of each exercise RiARiT and another that does not use any knowledge about the problem ZPDES. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge.
## 357 Intelligent tutoring systems help students acquire cognitive skills by tracing students' knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty metacognitive behavior: A prescriptive rational model aimed at improving help-seeking behavior, and a descriptive machine-learned model aimed at eliminating attempts to game the tutor. In a comparison between the two models we found that while both successfully identify gaming behavior, one is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty behaviors. An analysis of students' actions in two different tutors suggests that the help-seeking model is domain independent, and that students' behavior is fairly consistent across classrooms, age groups, domains, and task elements.
## 358 Students have been facing problems in learning differentiation. Reports and comments from the Malaysian Examination Board indicated that students faced problems in learning differentiation. Teacher-centered instructional approaches, large class size and lack of individual learning support for students were some of the identified contributing factors. This paper aims to present a prototype of an Intelligent Tutoring System (ITS) for enhancing the learning of differentiation for Form Four Additional Mathematics. The ITS uses mastery learning theory and fuzzy logic to provide self paced learning and one-to-one tutoring in the learning process.
## 359 Intelligent Tutoring Systems are meant to provide individualised tutoring to students by adapting the teaching material to their specific needs and abilities. However, their development is a hard task and the end-result is difficult to evaluate. In this paper we present a novel approach for the evaluation of these systems, which relies on an agent that may be used as a simulated student-user. The evaluation agent incorporates modelling techniques of real users that are based on both cognitive and temperamental data. The cognitive model is based on cognitive psychology and simulates the memorisation and retention capabilities of a student. The temperamental data creates an image of the student concerning the way s/he behaves and the kind of personality s/he has. Developers may evaluate the tutoring systems using the Agent rather than real students. Thus better quality of the end result may be achieved at no cost of the educational process.
## 360 This paper describes Annie, a domain-independent intelligent tutor that can be plugged-in to digital games to guide learners using the core mechanics of the game
## 361 For the last 50 years, intelligent tutoring systems have been developed with the aim to supporting one of the most successful educational forms - individual teaching. Recent research has shown that emotions can influence human behavior and learning abilities, as a result developers of tutoring systems have also started to follow these ideas by creating affective tutoring systems. However, adaptation skills of the mentioned type of systems are still imperfect. The paper presents an analysis of emotion recognition methods used in existing systems to enhance ongoing research on the improvement of tutoring adaptation. Regardless of the method chosen, the achievement of accurate emotion recognition requires collecting ground-truth data. To provide ground-truth data for emotional states, the authors have implemented a self-assessment method based on Self-Assessment Manikin. (C) 2017 The Authors. Published by Elsevier B.V.
## 362 The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59% when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p < 0.05.
## 363 The rapid growth of the Internet and Instant Messaging (IM) offers new opportunities as well as challenges to both educators and students. In this paper, we propose an Intelligent Tutoring Agent (ITA) that uses the ontology, INFOMAP, and question answering techniques through the Instant Messaging platform for the operating system course. The ITA embeds the above techniques in the teaching process and plays the role of a tutoring agent to help a teacher track, record, and understand a student's status. The ITA interface can interpret natural language to facilitate communication between the student and the tutor. Student can query and learn the concept of operating system through MSN Messenger, which ITA adopts as the communication protocol. The proposed ITA is accessible by adding the contact ID: iaslita@hotmail.com to the MSN Messenger contacts list.
## 364 Effects such as student dropout and the non-normal distribution of duration data confound the exploration of tutor efficiency, time-in-tutor vs. tutor performance, in intelligent tutors. We use an accelerated failure time (AFT) model to analyze the effects of using automatically generated hints in Deep Thought, a propositional logic tutor. AFT is a branch of survival analysis, a statistical technique designed for measuring time-to-event data and account for participant attrition. We found that students provided with automatically generated hints were able to complete the tutor in about half the time taken by students who were not provided hints. We compare the results of survival analysis with a standard between-groups mean comparison and show how failing to take student dropout into account could lead to incorrect conclusions. We demonstrate that survival analysis is applicable to duration data collected from intelligent tutors and is particularly useful when a study experiences participant attrition.
## 365 Intelligent tutoring systems (ITSs) are commonly designed to enhance student learning. However, they are not typically designed to meet the needs of teachers who use them in their classrooms. ITSs generate a wealth of analytics about student learning and behavior, opening a rich design space for real-time teacher support tools such as dashboards. Whereas real-time dashboards for teachers have become popular with many learning technologies, we are not aware of projects that have designed dashboards for ITSs based on a broad investigation of teachers' needs. We conducted design interviews with ten middle school math teachers to explore their needs for on-the-spot support during blended class sessions, as a first step in a user-centered design process of a real-time dashboard. Based on multi-methods analyses of this interview data, we identify several opportunities for ITSs to better support teachers' needs, noting that the analytics commonly generated by existing teacher support tools do not strongly align with the analytics teachers expect to be most useful. We highlight key tensions and tradeoffs in the design of such realtime supports for teachers, as revealed by Speed Dating possible futures with teachers. This paper has implications for our ongoing co-design of a real-time dashboard for ITSs, as well as broader implications for the design of ITSs that can effectively collaborate with teachers in classroom settings.
## 366 Intelligent tutoring systems have been in existence for decades, and their characteristics can be beneficially applied in environments utilizing information and communication technology (ICT). The intelligence in these systems is seen through the way these systems adapt themselves to the characteristics of the students, such as speed of learning, specific areas in which the student excels as well as falls behind, and rate of learning as more knowledge is learned. In such intelligent learning environments, the agent or set of agents can be modeled to perform pedagogical tasks. This paper considers the necessary characteristics that constitute a good intelligent tutoring system. This paper introduces a framework incorporating an incremental machine-learning approach to capture 1) the dynamics of knowledge creation in the domain of interest and 2) the learned-knowledge content of the student over time. Some of the components of the proposed system are illustrated using examples from an introductory course on database design.
## 367 International opening of universities and research institutions is essential in the development of their research and innovation activities. Abdelmalek Essaadi University (AEU) attaches crucial importance to partnership and international cooperation, and actively participates in national and international cooperation and exchange programs. In order to manage the monitoring and evaluation of its cooperation activities as it evolves, the University has set up a system of information on the governance of university cooperation for proper management and managing better partnerships. When setting up a new information system, end-user training in this new management tool is a very important part of this process. For this reason, the University has adopted the idea of developing an intelligent tutoring system. This system will be based on the Moodle platform and will be fully automated and adaptable to the needs of each learner. This article presents the basic design of the intelligent tutoring system incorporated in the management information system of university cooperation SIMACoop of our university and shows the feasibility of the intelligent tutoring system around an information system.
## 368 The paper presents an intelligent tutoring system for accounting (ITSA). ITSA generates automatically theory test questions and problem solving exercises from a domain model for financial accounting. ITSA uses the constraint learning theory to check the student answer and to provide feedback. The paper argues that this approach reduces the effort required to generate teaching material, and can be used for both testing the students understanding of the accounting theory as well as their problem solving skills.
## 369 This paper describes Dragoon, a simple intelligent tutoring system which teaches the construction of models of dynamic systems. Modelling is one of seven practices dictated in two new sets of educational standards in the U.S.A., and Dragoon is one of the first systems for teaching model construction for dynamic systems. Dragoon can be classified as a step-based tutoring system that uses example-tracing, an explicit pedagogical policy and an open learner model. Dragoon can also be used for computer-supported collaborative learning, and provides tools for classroom orchestration. This paper describes the features, user interfaces, and architecture of Dragoon; compares and contrasts Dragoon with other intelligent tutoring systems; and presents a brief overview of formative and summative evaluations of Dragoon in both high school and college classes. Of four summative evaluations, three found that students who used Dragoon learned more about the target system than students who did equivalent work without Dragoon.
## 370 In an adaptive and intelligent educational system (AIES), the process of learning pedagogical policies according the students needs fits as a Reinforcement Learning (RL) problem. Previous works have demonstrated that a great amount of experience is needed in order for the system to learn to teach properly, so applying RL to the AIES from scratch is unfeasible. Other works have previously demonstrated in a theoretical way that seeding the AIES with an initial value function learned with simulated students reduce the experience required to learn an accurate pedagogical policy. In this paper we present empirical results demonstrating that a value function learned with simulated students can provide the AIES with a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students. and demonstrates that an efficient and useful guide through the contents of the educational system is obtained. (C) 2009 Elsevier B.V. All rights reserved.
## 371 As one of the essential steps towards the inevitable integration process University of Mostar decided to introduce a unified information system for all members of the University of Mostar. Difficulties and some kind of resistance from the side of some employees appear in the application of this information system. To facilitate learning process, and at the same time to reduce administrative costs and save time on the training, the idea of developing an intelligent system for training the work of teachers on University Information System came up. Intelligent tutoring system provides an advanced student learning environment, tailored to its current level of knowledge. As a platform on which our system will be based, we chose an application for the creation and maintenance of online courses via the Internet, Moodle. Moodle provides teachers with full IT support in the organization and implementation of online courses. A plan is to develop a teaching system for the use of University information system, which will be fully automated and adaptable to the needs and current knowledge of each teacher who is trained on it and that gives this system the principal characteristics of intelligent system. (C) 2014 The Authors. Published by Elsevier Ltd.
## 372 This paper is a detailed case study of building Code Tutor, a Web-based intelligent tutoring system (ITS) in the domain of radio communications. It is ontologically founded and was built using CLIPS and Java-based expert system tools, latest integrated graphical CASE tools for software analysis and design, and Java servlets. In Code Tutor, Apache HTTP Server stores and serves static HTML pages, and Apache JServ Java package enables dynamic interpretation of user defined servlet classes and generation of active HTML pages. XML technology is used to generate files that Code Tutor uses to provide recommendations to the learners. Such a rich palette of integrated advanced technologies has greatly alleviated the system design and implementation, and has also led to interesting solutions of a number of problems common to many ITSs. The paper describes these solutions and useful design decisions, and discusses several practical issues related to architectures of intelligent Web-based applications. (C) 2003 Elsevier Science Ltd. All rights reserved.
## 373 Intelligent tutoring systems have been used as valuable educational tools for their adaptability in the computerized pedagogical process. They can vary tremendously according to the provided functions and architecture. In this paper we propose a generic framework for intelligent tutoring systems that learning profile analysis and personalized learning paths are performed through a community of intelligent agents, The basic categories of agents defined in the framework arc, learner Agents, instructor Agents, and coordinator Agent. The learner agents monitor and evaluate the individual learning processes to generate personalized course paths. The instructor agents provide student profile analysis for teachers to perform necessary course adjustment. A message brokering architecture is utilized in the coordinator agent to provide a better mechanism for dealing with failures of agent communication, The agent modules and the process flow are also designed to accomplish the adaptive learning mechanism.
## 374 In this paper one solution for teaching skills to solve n-power algebraic equation by Lobachevsky-GreffeDandelen method is described. Student's mistakes are discovered and classified. Based on signal-parametric approach to fault diagnosis in dynamic systems mathematical diagnostic models which allow detecting mistake classes by comparing student calculated results and system calculated results are created. Features of proposed diagnostic models application are presented. Intelligent tutor system is developed and used on Automatic Control Theory practical training by third year students of National Aerospace University.
## 375 The plethora of different subfields in intelligent tutoring systems (ITS) are often difficult to integrate theoretically when analyzing how to design an Intelligent tutor. Important principles of design are claimed by many subfields, including but not limited to design, human-computer interaction. perceptual psychology, cognitive psychology, affective and motivation psychology, statistics. artificial intelligence. cognitive neuroscience, constructivist and situated cognition theories Because these theories and methods sometimes address the same grain size and sometimes different gram sizes they may or may not conflict or be compatible and this has implications for ITS design These issues of theoretical synthesis also have implications for the experimentation that is used by our various subfields to establish principles Because our proposal allows the combination of multiple perspectives. it becomes apparent that the current forward selection method of theoretical progress might he limited An alternative backward elimination experimental method is explained Finally. we provide examples to illustrate how to build the bridges we propose.
## 376 The information age causes new challenges for industry and education. The intelligent tutoring systems try to Fill the gap between human teachers and computer-based tutoring systems. The paper presents a novel approach in which ontologies transformed into concept maps are being used for systematic creation of knowledge structure of each individual learner. The motivation of the approach and the implementation of the intelligent knowledge assessment system are described.
## 377 The online homework manager (OHM) and the intelligent tutoring system (ITS) are two supplemental teaching tools available for accounting educators' use in the introductory financial accounting course. While research related to these systems is limited, prior studies find a tenuous performance advantage related to their use. To advance the literature in this area, this paper evaluates the performance benefit related to an OHM and an ITS, each employed independently as an additional study aid during the first course unit in one of two sections of the introductory financial accounting course. A third section used paper-and-pencil only and served as a control group. Results of tests on several performance measures did not identify a learning advantage associated with either the OHM or the ITS. Nor was a learning advantage identified when this study's results were compared to exam results from 14 previous semesters. Implications for accounting educators and future research directions are discussed.
## 378 This article proposes a mathematical model of Intelligent Tutoring Systems (ITS), based on observations of the behaviour of these systems. One of the most important problems of pedagogical software is to establish a common language between the knowledge areas involved in their development, basically pedagogical, computing and domain areas. A mathematical model, like the one proposed here, can facilitate the integration of these different areas, as it defines the elements that constitute the system and defines the technological tools to implement it. The article presents an example demonstrating how the formalization was used to design the adaptive mechanism of an ITS to adapt its Interface Module to some student characteristics. (C) 2005 Elsevier Ltd. All rights reserved.
## 379 Emotions play an important role in cognitive processes and specially in learning tasks. Moreover, there are some evidences that the emotional state of the learner correlated with his performance. Furthermore, it's important that new Intelligent Tutoring Systems involve this emotional aspect; they may be able to recognize the emotional state of the learner, and to change it so as to be in the best conditions for learning. In this paper we describe such an architecture developed in order to determine the optimal emotional state for learning and to induce it. Based on experimentation, we have used the Naive Bayes classifier to predict the optimal emotional state according to the personality and then we induce it using a hybrid technique which combines the guided imagery technique, music and images.
## 380 INTUITEL is a research project aiming to offer a personalized learning environment. The INTUITEL approach includes an Intelligent Tutoring System that gives students recommendations and feedback about what the best learning path is for them according to their profile, learning progress, context and environmental influences. INTUITEL combines efficient pedagogical-based recommendations with freedom of choice and it introduces this tutoring support in different Learning Management Systems. During the INTUITEL project various software and pedagogical testing procedures were defined to provide the development teams with feedback, both summative and formative. The current paper describes the initial user test, which was conducted at the University of Valladolid for the course Network Design. The experiment was focused on real learners' reactions to INTUITEL recommendations received by an INTUITEL-enabled LMS. Nineteen students participated in a two phase testing procedure in order to analyze the learners' behavior with INTUITEL, as well as obtaining information about how learners perceive the influence and usefulness of the tutoring system in online learning courses. Results show that students with INTUITEL follow learning paths that are more suitable for them. Besides, the general satisfaction level of participants is high. Most learners appreciate INTUITEL, would follow its recommendations and consider the messages shown by INTUITEL as useful and caring.
## 381 Visualization of data in learning management systems became essential for easier analysis of learner's behavior and interpretation of results on such system. In this paper, we presented design and development process of dashboard for the prototype of program support-CM Tutor. After analysis of various approaches of designing a learning analytics dashboard, we selected most important items for display on the dashboard, and we created our own. We have designed and built a dashboard for intelligent learning management systems. The dashboard is intended for both students and teachers. The teacher has access to all the functionalities of dashboard, while student approach is limited.
## 382 This paper examines the role of adaptive student modeling in cognitive tutors(TM) are problem solving environments constructed around and dissemination. Cognitive tutors cognitive models of the knowledge students are acquiring. Over the past decade we in the Pittsburgh Advanced Cognitive Tutor (PACT) Center at Carnegie Mellon have been employing a cognitive programming tutor in university-based teaching and research, while simultaneously developing cognitive mathematics tutors that are currently in use in about 150 schools in 14 states. This paper examines adaptive student modeling issues in these two contexts. We examine the role of student modeling in making the transition from the research lab to widespread classroom use, describe our university-based efforts to empirically validate student modeling in the ACT Programming Tutor, and conclude with a description of the key role that student modeling plays in formative evaluations of the Cognitive Algebra II Tutor.
## 383 Intelligent tutoring systems (ITSs) have drawn researchers' attention as a means of providing personalized learning content, adaptive feedback, and instructional strategies based on students' characteristics and learning needs. Few studies, however, have explored how prospective and practicing teachers integrate ITSs into their lessons. This study examines the relationships among prospective teachers' concerns about the ITSs, their positioning of an ITSs when planning a math lesson, and their technological, pedagogical, and content knowledge (TPACK). The results indicate that the prospective teachers were intensively concerned with informational and personal aspects regarding ITSs and somewhat interested in modifying ITS-integrated teaching practices. The prospective teachers tended to assess themselves as having a high level of knowledge related to pedagogy but less knowledge about pedagogy and technology. Furthermore, the prospective teachers were more likely to position the ITSs as a servant than as a partner. When positioning the ITSs as a partner, they tended to plan their lessons based on in-the-moment student data provided by the ITS. In addition, the prospective teachers who positioned the ITS as a partner expressed more concern about modifying the ITSs and recognized that they had higher TPACK than those who positioned it as a servant.
## 384 The first application of Artificial Intelligence (AI) to education has been to build Intelligent Tutoring Systems (ITS). With the development of modern information technologies, ITS has been increasingly applied in education more and more widely. The applications of ITS in education have been changing the traditional instructional model and making learning more effective and meaningful. In this paper, the definition, architecture, and characteristics of ITS, some typical ITS and the current status, as well as its research focus of ITS are presented. Then the current research and applications of ITS in China are introduced. In the end, the paper discusses the future development of ITS.
## 385 Intelligent Tutoring Systems (ITS) offer adaptivity to user abilities, personal character trait, learning styles and preferences. The user modelling is one of the major factors that can influence an adaptivity. The content and structure of user profile should allow to recommend learning material suitable for student's needs. In this work a user profile is designed and a method for predicting learner's abilities is proposed. We use a Naive Bayes classifier in order to predict student's learning results. A prediction of user's abilities could be very useful for determining an initial learning scenario or assigning a student to a suitable collaborative learning group.
## 386 The Java Intelligent Tutoring System (JITS) prototype has recently been completed and is currently undergoing field testing. This tutoring system is uniquely beneficial in several ways. Most ITS require the teacher to author problems with corresponding solutions. JITS, on the other hand, requires the teacher to supply the problem and problem specification only. As a result, JITS intelligently scrutinizes the student's code submission and reasons about its suitability based on the description of the problem and the required output. This component is called JECA - a Java Error Correction Algorithm which is summarized in this paper. Another major benefit of JITS over other. ITS is the user interface. JITS is designed to resemble a professional programmer's environment. This paper discusses the pedagogical design underlying the Java Intelligent Tutoring System.
## 387 Adaptive collaborative learning support systems analyze student collaboration as it occurs and provide targeted assistance to the collaborators. Too little is known about how to design adaptive support to have a positive effect on interaction and learning. We investigated this problem in a reciprocal peer tutoring scenario, where two students take turns tutoring each other, so that both may benefit from giving help. We used a social design process to generate three principles for adaptive collaboration assistance. Following these principles, we designed adaptive assistance for improving peer tutor help-giving, and deployed it in a classroom, comparing it to traditional fixed support. We found that the assistance improved the conceptual content of help and the use of interface features. We qualitatively examined how each design principle contributed to the effect, finding that peer tutors responded best to assistance that made them feel accountable for help they gave.
## 388 The aim of this study was to predict university students' learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.
## 389 Interactive Multimedia Intelligent Tutoring System (IMITS) is designed to assist electrical engineering undergraduate students taking their first circuits courses. The IMITS system places the student in a real-life engineering scenario in which the student is a newly hired engineer within the fictional IMITS Corporation and given real-life problems to solve, corresponding to course material. The office has file cabinets, bookshelves, a printer, and a personal computer. The personal computer allows the student to receive televideo messages, receive e-mail, and send e-mail reports to senior engineers. A feature of IMITS is that the student decides which actions to take and may validate analyses and designs using a virtual laboratory incorporated with the software. A brief historical perspective of intelligent tutoring systems is presented, followed by an explanation of their architecture. Next, a detailed discription of the intelligent tutoring system IMITS is given. Then the results of usability and effectiveness evaluations of the software are given.
## 390 In self-regulated learning 1 concept, Intelligent Tutoring Systems (ITS) can be designed to foster learning behaviors through pedagogical agents (PAs) that are used for interactions and exchange information with the human learner. These agents are intelligent and follow rational behaviors, but in the case of multi-agent environments they need to be systematically and specifically designed, however in order to follow a common goal, different self-regulatory systems have been designed that use pedagogical agents, but they fail to constrain the decision making of the agents and maintain a sequential decision making process during learning interactions with human learners. In this paper, we provide a new theoretical model for agent-learner interactions in MetaTutor, a multi-agent hypermedia learning environment, using Markov decision processes. We theoretically define the agents' Markov decisions and their influence on MetaTutor's performance as a whole. First, we formally define the Markov architecture and its parameters. We then link these characteristics to the pedagogical agents we use in MetaTutor and define different versions of MetaTutor agents equipped with Markov decision mechanism. Furthermore, we explore additional details about agents' sequential decision making and how reward functions influence their acting strategies with learners in the learning environment. We introduce the optimization problem in which we aim to maximize the expected return of the overall agents' acts in a self-regulatory system. What specifically distinguishes this work from the previous proposals in the same domain is its novelty in continuous decision making mechanism investigation and performance analysis that improve the applicability of the proposed adaptive model in a multi-agent ITS like MetaTutor.
## 391 Collaborative and individual learning appear to have complementary strengths; however, the best way to combine these learning methods is still unclear. While previous work has demonstrated the effectiveness of Intelligent Tutoring Systems (ITSs) for individual learning, collaborative learning with ITSs is much less frequent - especially for young students. In this paper, we discuss our prior and future work with elementary school students that aims to investigate how to best combine individual and collaborative learning using their complementary strengths within an ITS. Our previous findings demonstrate that ITSs are able to support collaboration, as well as individual learning, for this population. In addition, we propose future research to understand how to best combine individual and collaborative learning within an ITS.
## 392 Programmable logic controllers (PLC) are used for many industrial process control applications. Learning to write ladder logic programs for PLC control is an important and challenging task. However, the learning of ladder logic is often hindered by limited PLC availability due to expensive lab setup, limited lab time, and high student/instructor ratios. With the help of the internet, teaching is not constrained in the traditional classroom pedagogy; the instructors can put the course material on the website and allow the students go on to the course webpage as an alternative way to learn the domain knowledge. However, there is no interaction between the users and learning materials; so, the learning efficiency is often limited. The problem here is how to design a web-based system that is intelligent and adaptive enough to teach the students domain knowledge in PLC. In this research, we proposed a system architecture which combines the pre-test, cased-based reasoning (i.e., heuristic functions), tutorials and tests of the domain concepts, and post-test (i.e., including pre- and post-exam) to customize students' needs according to their knowledge levels and help them learn the PLC concepts, effectively. We have developed an intelligent tutoring system which is mainly based on the feedback and learning preference of the users' questionnaires. It includes many pictures, colorful diagrams, and interesting animations (i.e., switch control of the user's rung configuration) to attract the users' attention. From the model simulation results, a knowledge proficiency effect occurs on problem solving time. If the students are more knowledgeable about PLC concepts, they will take less time to complete problems than those who are not as proficient. Additionally, from the system experiments, the results indicate that the learning algorithm in this system is robust enough to pinpoint the most accurate error pattern (i.e., almost 90 % accuracy of mapping to the most similar error pattern), and the adaptive system will have a higher accuracy of discerning the error patterns which are close to the answers of the PLC problems when the databases have more built-in error patterns. The participant evaluation indicates that after using this system, the users will learn how to solve the problems and have a much better performance than before.
## 393 This paper presents a cognitive conversational agent for use in teaching and learning processes named THOTH (Training by Highly Ontology-oriented Tutoring Host) that is capable of formulating and enunciating a well-defined set of small talk segments in a Q&A (Question and Answer) interaction. The small talk structures are placed within the tutoring conversation by an agent designed as a cognitive assistant, in order to make communication smoother and less formal, presenting a more concerned behavior. Twelve small talk segments are suggested, included in conversation stages such as opening and closing the conversation, maintaining the rhythm and managing learning. We also explore some branches of the theoretical assumptions and concepts grounding THOTH, such as Dennett's intentional stance, Bloom's taxonomy and microlearning theory. In order to measure the perception and effects of using THOTH, we performed a quantitative and qualitative study with a group of students from a course in Applied Artificial Intelligence over one semester. The outcomes are classified into two main categories of analysis - interactivity and intentionality - informing the discussion on the potential uses of a small talk agent as a valuable resource in tutoring interaction, and also raising some points for improvement. In addition to this study, we also drew a small talk profile for this group of students revealing what structures and topics they use the most, as well as a partial performance analysis that allows identifying some effects on learning.
## 394 In this paper we present our approach in the field of Intelligent Tutoring System (ITS), in fact the risk of dropping out for learners have emerged as crucial issues to be solved. So it is necessary to ensure an individualized and continuous learner's follow-up during learning process. Several research effort has been spent on the development of ITS. However the available literature does not generally concentrate on the individual real-time continuous follow up of learners. Our contribution in this field is to design and implement a computer system able to initiate learning and provide an individualized monitoring of learners. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learners' traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces in the machine. The traces are stored in database, this operation enriches collective past experiences. Via monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject.
## 395 A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students' learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student's learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile.
## 396 With the progress of modern society, human knowledge explodes, which calls for more convenient, faster and more effective methods of education. In this research, we focus on the construction of the computer aided intelligent tutoring system based on a new teaching model, and its influence on teachers' teaching self-efficacy. Firstly, on the basis of the actual demand of ICAI system, the theoretical basis and development principle of intelligent tutoring system were expounded, and the system construction of student model and teacher model was studied. Secondly, the implementation of the quantitative assessment of cognitive ability and the teaching strategy base in DCB-ITS student model were studied and the implementation of the intelligent tutoring system was analyzed. Finally, in order to test the efficiency of the system, we carried through an empirical study. Asurvey to 209 college teacherswas conducted, the results showthat teachers who make use of the intelligent tutoring system during the teaching activities report higher scores of teaching self-efficacy, which indicates that compared with the traditional teaching model, the computer aided intelligent teaching model is of great help for teachers in making them feel more confident about teaching effectively.
## 397 INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the student's profile. (C) 2012 Elsevier Ltd. All rights reserved.
## 398 In this paper we present a framework for selecting the proper instructional strategy for a given teaching material based on its attributes. The new approach is based on a flexible design by means of generic rules. The framework was adapted in an Intelligent Tutoring System to teach Modern Standard Arabic language to adult English-speaking learners with no pre-knowledge of Arabic language is required.
## 399 Nowadays, beside computer has come into our life, learning, independent from time and place, is implemented in an effective structure. Since many studies are consummated education is implemented in a structure which takes into account. Benefits of the qualities include being more effective, qualified and independent from time and place. In order to develop the software's that present students effective instruction methods and provide education with being adapted to students, studies are carried out. Intelligent Tutoring Systems (ITSs) are tutoring systems which form with using artificial intelligence techniques in computer programs to facilitate instruction. These systems are based on cognitive learning theory which is a learning theory interested in how information organizes in human's memory. ITSs are intelligent programs which know what, how and whom they will teach so computers play an important part in education and instruction aims are performed and suggested in this work. In this paper we describe ITSs in educational application and demonstrate used-modules in ITSs. Otherwise, these have been compared with computer-aided learning systems. The results indicate that these systems formed with artificial intelligence techniques omit this incompetence with vast rate and countenance students and teachers to learning in a better manner.
## 400 The diagnostic process of an intelligent tutoring system is the procedure by which the system determines a student's level of performance based upon his or her problem solving behavior. In order for a system to insure the satisfactory performance of this process, three key issues must be addressed: (1) how to assign credit or blame to individual skills, (2) how to handle the problem of noisy data, and (3) how to handle the problem of combinatorial explosion inherent in many Al applications. In this paper we describe an informal reasoning technique, known as attribute pattern matching equations (APMEs), which is used in the diagnostic process of the TAPS tutoring system. This technique addresses all three of the foregoing issues in assessing the performance levels of individual skills in the TAPS system. In addition, it will be demonstrated that APMEs are an effective method for reasoning with uncertainty on three different levels.
## 401 Using mathematics is critical to science inquiry at the high school level and is predictive of students' later success in STEM college majors and careers. Inq-ITS (Inquiry Intelligent Tutoring System) has recently added mathematizing functionalities in order to support students in the mathematical practices needed for scientific inquiry, and our teacher alerting dashboard, Inq-Blotter, is being extended to alert teachers in real-time to students' difficulties with this practice. Mathematizing in science can be challenging as students must attend to multiple sources of information (i.e., graphs, data tables), as well as do graphing and modeling. In the present paper, I describe three studies on the use of Inq-Blotter to support students on mathematizing in which I: (1) explore students' eye-movements and think-aloud protocols while mathematizing in Inq-ITS to identify the proportion of mathematizing difficulties that are related to knowledge acquisition processes versus other mathematical competencies (e.g., graph building and modeling), (2) examine if alerts within Inq-Blotter permit teachers to identify students who need help most (relative to teachers without access to alerts), and (3) identify whether teacher support based on alerts leads to improvements on students' next opportunity to engage in mathematizing and examine the corresponding teacher discourse associated with students' gains. These studies will indicate how to support students on mathematizing with intelligent technologies.
## 402 In this paper we show that the C4.5 machine learning algorithm, applied to a number of syntactic features in transcripts, can be used to accurately differentiate between two expert human tutors. Although these tutors had taught together for years and explicitly discussed their tutoring style with one other, an analysis based on frequency of parts of speech and higher-level syntactic constructs was able to easily separate their productions.
## 403 New approaches in Intelligent Tutoring Systems imply a more active participation of the learner in the learning process. The motivation of the learner can be increased by interaction with a companion who strengthens the knowledge acquisition in a cooperation climate. In this article we introduce a new learning strategy called learning by disturbing intended to improve student self-confidence. We compare it to directive learning and peer learning, discussing the advantage and the inconvenience of each one. We present some experiments realized to show in which condition a strategy can be useful or not. We analyze and discuss results obtained.
## 404 Asynchronous distance education delivery systems do not require real-time student-human teacher interaction thus enabling students to use tutoring resources anytime and anywhere. Among various possibilities for implementing asynchronous distance education delivery computer supported ones are nowadays the most popular. Categories thereof are intelligent tutoring systems that are used for supporting and improving the process of learning and teaching in arbitrary domain knowledge. In such education models the role of human teacher is replaced with a computer tutor basically consisting of a domain knowledge base as well as of both a student module with an appropriate student model and a teacher strategy guiding the learning and teaching process. The paper describes an authoring shell - Distributed Tutor-Expert System (DTEx-Sys) - developed for asynchronous distance education purposes, as a secondary knowledge source for teachers and their students in secondary and primary schools.
## 405 A web-based interactive intelligent tutoring system was developed and assessed in an engineering dynamics course. The system consists of two learning modules to help students learn how to apply the Principle of Work and Energy to solve particle and rigid-body dynamics problems. Student learning gains were compared using a quasi-experimental research design that involved pretests and posttests in both a control semester (n = 62) and a treatment semester (n = 44). It is shown that the ITS modules increased student learning gains by 37-43%.
## 406 With the popularity of Intelligent Tutoring System (ITS), it is important to evaluate its teaching effect, but such kind of evaluation is rarely found in Chinese research community. We studied Lexue 100, a personalized and web-based math-teaching ITS. In the study, we use quasi-experiment method to evaluate students' performances, so the experiment class and control class are set to make internal and external comparison. The examination score analysis shows that the system can significantly improve the math performance of the experiment class in regular school examinations. The collected students' answers are reliable and valid, and their statistical analysis demonstrates that this system's teaching quality is overall good including the interface, content, instant feedback and game reward mechanism, which can be very helpful for the students' math learning. The special word frequency analysis also proves that most students find this system to be positive and beneficial to their learning, but functional improvement is still needed.
## 407 Learner modeling, a challenging and complex endeavor, is an important and oft-studied research theme in computer-supported education. From this perspective, Educational Data Mining (EDM) research has focused on modeling and comprehending various dimensions of learning in computer based learning environments (CBLE). Researchers and designers are actively attempting to improve learning systems by incorporating adaptive mechanisms that respond to the varying needs of learners. Recent advances in data mining techniques provide new possibilities and exciting opportunities for developing adaptive systems to better support learners. This study is situated in the context of clinical reasoning in an Intelligent Tutoring System called BioWorld and it aims to examine the relationship between the lab-tests ordered and misconceptions held by learners. Toward this end, we employ an EDM technique called subgroup discovery to unpack the rules that embody the hypothesized link. Examining such links may have implications for identifying the points along learning trajectories where learners should be provided the requisite scaffolding. This study represents our efforts to evaluate and derive empirically based design prescriptions for improving Intelligent Tutoring Systems. Implications for practice and future research directions are also discussed.
## 408 Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must he completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.
## 409 Student model is a very important and time-consuming process in Intelligent Tutoring System. Furthermore, for many Web based educational systems, student personality is hard to apply explicitly. To address this problem, we proposed an approach for combining fuzzy compositive evaluation with Bayesian network to design an applied student model. This article describes improvements made to the method and its application to make corresponding tutoring strategy by reasoning student action that supports useful tutoring services in a practical tutoring system. The proposed framework has been integrated within the Discrete Mathematics Tutor System. We obtain results of an experiment that shows the benefit of the integration of the way.
## 410 Building a domain model consumes a major portion of the time and effort required for building an Intelligent Tutoring System. Past attempts at reducing the knowledge acquisition bottleneck by automating the knowledge acquisition process have focused on procedural tasks. We present CAS (Constraint Acquisition System), an authoring system for automatically acquiring the domain model for non-procedural as well as procedural constraint-based tutoring systems. CAS follows a four-phase approach: building a domain ontology, acquiring syntax constraint directly from it, generating semantic constraints by learning from examples and validating the generated constraints. This paper describes the knowledge acquisition process and reports on results of a preliminary evaluation. The results have been encouraging and further evaluations are planned.
## 411 Constraint-Based Modelling (CBM) is a student modelling technique for Intelligent Tutoring Systems (ITS) that is especially suited to complex, open-ended domains. It is easier to build tutors in such domains using CBM than other common approaches. We present WETAS (Web-Enabled Tutor Authoring System), a tutoring engine that facilitates the rapid implementation of ITS in new domains using CBM. We describe the architecture Of WETAS and give examples of two domains we have implemented. We also present the results of an evaluation of a tutoring system built using WETAS in a New Zealand school.
## 412 Collaborative work has emerged as a hot research topic in Virtual Learning Communities since it may considerably improve the knowledge quality and experience of students. However, this novel approach makes the assessment process challenging (i.e., interactions between virtual students, their achievements, and their profiles have to be properly addressed). The purpose of this paper is to propose a comprehensive Intelligent Tutoring System for Virtual Learning Communities that relies on artificial intelligence techniques which are able to manage the specificities of the collaborative working groups that arise in this domain. These specificities can be summarized in the following four goals: 1) conduct an individualized tracking of every student upon the collected data from his/her profile and daily work, 2) configure the classroom to maximize the performance of all its members, 3) automatically obtain the teacher's feedback about the class operation and possible anomalies, and 4) monitor the working groups behaviour and achievements automatically to redirect their operation when necessary. The framework of the proposed system is described, a proof of concept is presented, and a new virtual student profile, named as bystander, is identified in preliminary experimentations.
## 413 The main purpose of intelligent tutoring systems is to guarantee an effective learning and offer the optimal learning path for each student. Therefore, determination of learning scenario is a very important task in a teaming process. After a new student is registered in the system, he is classified to the appropriate group. Before he begins to learn an opening scenario is determined based on final scenarios of students who belong to the class of learners similar to the new one. The new student is offered the optimal teaming path suitable for his preferences, teaming styles and personal features. In this paper new knowledge structure, which involves version of lessons, is proposed. For the defined knowledge structure definitions of teaming scenario, distance function and the procedure of the scenario determination are presented.
## 414 The paper describes Personal Access Tutor (PAT), an Intelligent Tutoring System which helps students to learn how to create forms and reports in MS Access. We present the architecture and components of PAT and also the services that PAT provides to the students. Results for an external (system) evaluation of PAT (both qualitative and quantitative data) are presented and discussed.
## 415 In this paper, we present a Web-based system aimed at learning basic mathematics. The Web-based system includes different components like a social network for learning, an intelligent tutoring system and an emotion recognizer. We have developed the system with the goal of being accessed from any kind of computer platform and Android-based mobile device. We have also built a neural-fuzzy system for the identification of student emotions and a fuzzy system for tracking student's pedagogical states. We carried out different experiments with the emotion recognizer where we obtained a success rate of 96%. Furthermore, the system (including the social network and the intelligent tutoring system) was tested with real students and the obtained results were very satisfying.
## 416 The intelligent tutoring systems should guarantee an effective learning. Students who use those systems should achieve better learning results in a shorter time. Our previous research pointed out that the personalization of the learning scenario allows to satisfy the mentioned postulates. In this paper the method for determination of an opening learning scenario is presented. Before a student begins to learn an opening scenario is determined based on information provided during a registration process. User is offered the optimal learning path suitable for his learning styles and a current knowledge level. Worked out method applied the ant colony optimization technique. The effectiveness of the proposed solution was tested in a specially implemented environment. The researches demonstrate that the algorithm gives quite good results, because 66% of the learning material in the determined learning scenario were adapted to student's learning styles.
## 417 This paper focuses on the motivation, design, and initial prototype implementation of Collab-ChiQat. Collab-ChiQat is a collaborative reconceptualization of an existing intelligent tutoring system for Computer Science Education originally intended for one-to-one student-system tutoring. Collab-ChiQat allows students to work as pair programmers as they solve coding problems for linked lists, a foundational and difficult to grasp CS concept. The work is unique in it's comparison of how system structuring of collaboration affects both learning and actual collaboration. In one condition, students are left to themselves with no system feedback regarding their collaborative behavior. While in a second condition, the collaboration is semi-structured, meaning students received a visualization of their participation and other metrics.
## 418 Intelligent tutoring systems have been developed to help students learn independently. However, students who are poor self-regulated learners often struggle to use these systems because they lack the skills necessary to learn independently. The field of psychology has extensively studied self-regulated learning and can provide strategies to improve learning, however few of these include the use of technology. The present proposal reviews three elements of self-regulated learning (motivational beliefs, help-seeking behavior, and meta-cognitive self-monitoring) that are essential to intelligent tutoring systems. Future research is suggested, which address each element in order to develop self-regulated learning strategies in students while they are engaged in learning mathematics within an intelligent tutoring system.
## 419 Recent developments in content processing technology and the widespread diffusion of wired and wireless Internet mean that users can now learn by means of a computer, anytime and anywhere. English learning that involves multimedia content can increase the interest of learners and lead to the development of their communication ability. Although using computers to teach English in a conventional educational environment provides motivation and effective learning on the part of the students, the method still has problems, which include the provision of learning materials without consideration of teaching methods, and evaluation without provision for differences in individual student levels. This paper introduces the Intelligent Tutoring System (ITS) for English learning, using web-based technologies. Using this system, the above problems are solved at the same time as the benefits of computer-based learning are retained. Its design and implementation are based on an intelligent tutoring system that provides content suitable for specific levels of ability. We used the contents of the Korean Elementary school 300-certification program for English conversation and an estimation of students' abilities using Item Response Theory (IRT) to evaluate the proposed system.
## 420 Learning at scale (LAS) systems like Massive Open Online Classes (MOOCs) have hugely expanded access to high quality educational materials however, such materials are frequently time and resource expensive to create. In this work we propose a new approach for automatically and adaptively sequencing practice activities for a particular learner and explore its application for foreign language learning. We evaluate our system through simulation and are in the process of running an experiment. Our simulation results suggest that such an approach may be significantly better than an expert system when there is high variability in the rate of learning among the students and if mastering prerequisites before advancing is important. They also suggest it is likely to be no worse than an expert system if our generated curriculum approximately describes the necessary structure of learning in students.
## 421 The following paper describes the conceptual design of an Intelligent Exploration System (IES) that offers a user-adapted graphical environment of web-based knowledge repositories, to support and optimize the explorative learning. The paper starts with a short definition of learning by exploring and introduces the Intelligent Tutoring System and Semantic Technologies for developing such an Intelligent Exploration System. The IES itself will be described with a short overview of existing learner or user analysis methods, visualization techniques for exploring knowledge with semantics technology and the explanation of the characteristics of adaptation to offer a more efficient learning environment.
## 422 The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. In many implementations of automatic classifiers finding the right student learning style represents the hardest assignment. The reason is that most of the techniques work using expert groups or a set of questionnaires which define how the learning styles are assigned to students. This paper presents a novel approach for automatic learning styles classification using a Kohonen network. The approach is used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the author tool.
## 423 This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner's response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner's following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, the three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items.
## 424 Intelligent Tutoring Systems (ITS) could provide an excellent one-on-one support to improve students' conceptual understanding [14]. The structure of a traditional ITS had four combined modules: the knowledge module, the student's modules, the instructor's module, and the end user module. In this paper, the old structure is modified to improve the concept of understanding the programming. The changes that we include to the traditional structure were the Knowledge Evaluation Module and the Reporting Module [14]. The reporting module is created to understand each student's learning levels from different instructors who can see the result of their knowledge level as the system assesses and tutors every student [13]. According to the use of the knowledge Evaluation module the instructor can add changes in quizzes or lectures content [13]. Those new updated modules are expected to improve the highly perform in the intelligent tutoring systems and are considering being one of the major achievements in the current proposed work [13]. A case study is being implemented to show the study of the systems design for the students' understanding [13]. Several sessions of professional development training are being conducted for faculties whom are interested to improve their students' knowledge level of using the developed tool [13].
## 425 Instruction that adapts to individual learner characteristics is often more effective than instruction that treats all learners as the same. A practical approach to making MOOCs adapt to learners may be by integrating frameworks for intelligent tutoring systems (ITSs). Using the Learning Tools Interoperability standard (LTI), we integrated two intelligent tutoring frameworks (GIFT and CTAT) into edX. We describe our initial explorations of four adaptive instructional patterns in the PennX MOOC Big Data and Education. The work illustrates one route to adaptivity at scale.
## 426 In this paper, we describe an intelligent agent that presents different teaming content such as tutorials, examples, and problems adaptively to individual students and learns from its interaction with the students how to improve its performance. We have built an end-to-end intelligent tutoring system, premised on the above goal, with a graphical user interface (GUI) front-end, an agent powered by case-based reasoning (CBR), and a mySQL database backend. We use a casebase to store the pedagogical strategies, embedded in the individual cases and the similarity retrieval and adaptation heuristics. Each case has situation, solution and outcome parameters. The situation parameters include the students' static and dynamic profiles and the instructional content's characteristics while the solution parameters specify the characteristics of the example or problem to be delivered to the student. We developed a set of CSI content that includes five topics and deployed our system in the laboratories. Our results show that when the machine learning mechanism is activated, our agent is able to learn to tutor students more efficiently.
## 427 Because of interoperability issues, intelligent tutoring systems are difficult to deploy in current educational platforms without additional work. This limitation is significant because tutoring systems require considerable time and resources for their implementation. In addition, because these tutors have a high educational value, it is desirable that they could be shared, used by many stakeholders, and easily loaded onto different platforms. This paper describes a new approach to implementing open-source and interoperable intelligent tutors through standardization. In contrast to other methods, our technique does not require using nonstandardized peripheral systems or databases, which would restrict the interoperability of learning objects. Thus, our approach has the advantage of yielding tutors that are fully conformant to e-learning standards and that are free of external resource dependencies. According to our method, atomic tutoring systems are grouped to create molecular tree structures that cover course modules. In addition, given the interoperability of our technique, tutors can also be combined to create courses that have distinct granularities, topics, and target students. The key to our method is the focus on assuring what defines a tutor in terms of behavior and functionalities (inner loops and outer loops). Our proof of concept was developed using SCORM standards. This paper presents the implementation details of our technique, including the theoretical concepts, technical specifications, and practical examples.
## 428 Computerized tutor for programming learning helps students to understand program constructs, and syntax of target programming language. Also, it helps to improve problem-solving skill, and ability to evaluate program solution. In this paper we propose a model concept and architecture prototype of Tutoring System for Programming. It is based on our age-long research and development of the Tutor-Expert System, a model of hypermedia authoring shell for building intelligent tutoring systems. Paper focuses on student-system dialogue, error classes in student's program and mechanism to detect correctness of student's program.
## 429 In this paper we describe the explicit introduction of goals into the knowledge representation within intelligent tutoring systems. Although they are achieved by means of procedures, our model treats goals as a special case of knowledge that represents intentions behind the cognitive system actions. The energy employed to achieve goals distinguishes them from any simple form of knowledge. This distinction involves a different treatment for goals. We propose to define goals as particular semantic knowledge that describes a state to be reached and which is totally distinct from knowledge representing concepts or procedures. One dynamic aspect of our model lies in the non-predefined combinations between goals and applied procedures which handle them.
## 430 This paper outlines a design framework of an intelligent tutoring system (ITS). ITS focuses on a newer and more comprehensive distance learning (DL) process as compared to the established traditional DL programs practiced today. The DL model presented in this paper (CHARLIE) is a high level software based tutorial that has the ability to encompass a wide variety of current DL technologies in a single DL session. CHARLIE's architecture has four components: Control Component (responsible for the interaction between software agents and the operating system); Instructional Component (concerned with the instructional aspects of an ITS session); Text Analysis Component (analyzes the partial syntax and partial semantics of the text in the session); Student Modeling Component (analyzes a student's progress and determines the best model for learning during a session). Each component is serviced by a set of software agents to accomplish its mission. Three additional entities in CHARLIE are two separate databases and an explanation facility. Six agents have been implemented in CHARLIE to create a DL course in C++ programming. Much of CHARLIE remains to be completed which opens many areas for research.
## 431 The emergence of the accessible knowledge society for all underlines the need for all to acquire the necessary knowledge and skills for inclusion. One way to do so is through e-learning, which itself should be accessible to all intended users. The mere provision of knowledge systems is not enough, since the need is for active and accessible learning that engages the participants effectively. This study explores the hypotheses that the solution is to be found in systems that: (i) support rather than replace the tutor, (ii) are accessible to the intended populations of users, (iii) can be adapted to the needs and individual characteristics of diverse users, (iv) are smart enough to adapt in real time to changing user needs, (v) reflect best practice in learning psychology, (vi) provide a high quality student experience, (vii) provide a high quality tutor experience and (viii) use valid student modeling. These hypotheses are evaluated through the five case studies. Accessibility and student modeling turnout to be the weakest points. All the other features are well represented in the case studies. None placed any kind of substantial emphasis upon accessibility. Only one of the case studies (case study 2; Cognitive Tutor Authoring Tools) makes a substantial effort in user modeling, being based upon the generic, cognitive model ACT. Even then, the focus tends to be on the typical or average user and does not address the problems of user diversity. Clearly, accessibility and user modeling need much more work in e-learning.
## 432 Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in Korbit, a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.
## 433 In the last few years, distant learning is gaining traction as a valid teaching approach taking advantage of the Internet and current multimedia capabilities. Even though thousands of students are enrolling to Massive Open Online Courses, there is still a lack of proper educative programs who account for the individual characteristics of the students. In particular, most e-learning courses tend to be mere repositories of contents, very teacher-centric and lacking the necessary individual personalisation to account for each student's needs, expectations and paces. In this work, we propose and describe an Intelligent Tutoring System that enables the automatic adaptation of the contents of the course to the particular learners. The systems was tested with a group of students with very positive direct and indirect results.
## 434 Learning-by-doing in MOOCs may be enhanced by embedding intelligent tutoring systems (ITSs). ITSs support learning-by-doing by guiding learners through complex practice problems while adapting to differences among learners. We extended the Cognitive Tutor Authoring Tools (CTAT), a widely-used non programmer tool kit for building intelligent tutors, so that CTAT-built tutors can be embedded in MOOCs and e-learning platforms. We demonstrated the technical feasibility of this integration by adding simple CTAT-built tutors to an edX MOOC, Big Data in Education. To the best of our knowledge, this integration is the first occasion that material created through an open access non-programmer authoring tool for full-fledged ITS has been integrated in a MOOC. The work offers examples of key steps that may be useful in other ITS-MOOC integration efforts, together with reflections on strengths, weaknesses, and future possibilities.
## 435 We hypothesize that when cognitive tutors are integrated into online courseware, the online courseware can provide a new type of adaptive instructions, such as impasse-driven adaptive remediation and need-based assessments. As a proof of concept, we have developed an adaptive online course on the Open Learning Initiative (OLI) platform by integrating four new instances of cognitive tutors into an existing OLI course. Cognitive tutors were created with an innovative cognitive tutor authoring system called WATSON. To evaluate the effectiveness of the adaptive online course, a quasi-experiment was conducted in a gateway course at Carnegie Mellon University. The results show that the proposed adaptive online course technology is robust enough to be used in actual classroom with mixed effect for learning.
## 436 A personal learning tool, Intelligent Tutoring System (ITS), can be made with a variety of technology options and a variety of methods. The main challenge is how ITS can be used for a variety of subjects and how ITS can be developed by teachers to assist their instructional activities. ITS can be used widely in schools if there is cooperation between the programmer and lecturer. A programmer has a responsibility to develop ITS master module, while a teacher plan and prepares what to teach and how to order. Because, ITS will be widely used in schools, the method of its manufacture should consider the habits and skills of teachers in using information technology for teaching. In addition, the technology that used to develop the ITS modules should be cheap and also easy to publish. Power Point and Learning Management System are e-learning tools that are most widely used by teachers around the world. Teachers have experience of how to prepare a good learning material, create questions, sort them in a particular manner, and put them in a Power Point template. Habits and skills of teachers in using information technology tools are the main consideration in developing this approach. This paper describes a method for developing ITS modules that are intended for teachers so that they can make the ITS modules despite having no knowledge of programming. They also will be able to create a module with low cost and a short time. Master ITS modules are created using the Authoring Tools to shorten the creating time; it also uses SCORM (Sharable Content Object Reference Model) standard which make it compatible with many LMSs. The teacher creates ITS module like creating Power Point slides; at the beginning of the project, teachers' success creates English modules that delivered using LMS and tested to the students.
## 437 This paper proposes a methodology for authoring of intelligent tutoring systems using human computation. The methodology embeds authoring tasks in existing educational tasks to avoid the need for monetary authoring incentives. Because not all educational tasks are equally motivating, there is a tension between designing the human computation task to be optimally efficient in the short term and optimally motivating to foster participation in the long term. In order to enhance intrinsic motivation for participation, the methodology proposes designing the interaction to promote user autonomy, competence, and relatedness as defined by Self-Determination Theory. This design has implications for learning during authoring.
## 438 Artificial Intelligence techniques are applied in learning systems to enhance the quality of interaction between the users and the system. E-learning system components, learning services and learning companion services have been implemented in a traditional manner whereby there is little possibility of reuse due to the tight coupling of components and lack of reuse for learning activities. The scope of our research aims to design a framework for intelligent tutoring in collaborative learning systems using Service-oriented Architecture. We aim to create a better personalized learning environment whereby users can interact with the system according to their needs and levels. Teaching strategy is designed to provide suitable assistance according to different user groups.
## 439 The purpose of this research was to apply machine learning techniques to automate rule generation in the construction of Intelligent Tutoring Systems. By using a pair of somewhat intelligent iterative-deepening, depth-first searches, we were able to generate production rules from a set of marked examples and domain background knowledge. Such production rules required independent searches for both the if and then portion of the rule. This automated rule generation allows generalized rules with a small number of suboperations to be generated in a reasonable amount of time, and provides nonprogrammer domain experts with a tool for developing Intelligent Tutoring Systems.
## 440 This paper presents an intelligent tutoring system (ITS) for the learning of arithmetical problem solving. This is based on an analysis of a) the cognitive processes that take place during problem solving; and b) the usual tasks performed by a human when supervising a student in a one-to-one tutoring situation. The ITS is able to identify the solving strategy that the student is following and offer adaptive feedback that takes into account both the problem's constraints and the decisions previously made by the user. An observational study shows the ITS's accuracy at emulating expert human supervision, and a randomized experiment reveals that the ITS significantly improves students' learning in arithmetical problem solving.
## 441 We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. This model was developed using only log files of system usage (i.e. no screen capture or audio/video data). We show that this model can both accurately identify each student's prevalence of off-task behavior and can distinguish off-task behavior from when the student is talking to the teacher or another student about the subject matter. We use this model in combination with motivational and attitudinal instruments, developing a profile of the attitudes and motivations associated with off-task behavior, and compare this profile to the attitudes and motivations associated with other behaviors in intelligent tutoring systems. We discuss how the model of off-task behavior can be used within interactive learning environments which respond to when students are off-task.
## 442 Web Intelligence is a direction for scientific research that explores practical applications of Artificial Intelligence to the next generation of Web-empowered systems. In this paper we present a Web-based intelligent tutoring system for computer programming. The decision making process conducted in our intelligent system is guided by Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.
## 443 This paper presents an Intel net based intelligent tutoring applications architecture to enable co-operative teaching and learning in numeric disciplines. The architecture allows sharing of resources available world wide and provides customisation Of these resources to suit to local requirements and thereby allows a typical teacher to develop small Intelligent Tutoring Applications (ITA). These ITAs can then be combined in various configurations to create complex Internet Based Intelligent Tutoring Systems.
## 444 We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of an ITS into a single component to allow customization of teaching goals and to simplify improving the ITS. The first agent is responsible for learning a model of how students perform using the tutor in a variety of contexts. The second agent is provided this model of student behavior and a goal specifying the desired educational objective. Reinforcement learning is used by this agent to derive a teaching policy that meets the specified educational goal. Component evaluation studies show each agent performs adequately in isolation. We have also conducted an evaluation with actual students of the complete architecture. Results show ADVISOR was successful in learning a teaching policy that met the educational objective provided. Although this set of machine learning agents has been integrated with a specific intelligent tutor, the general technique could be applied to a broad class of ITS.
## 445 Turbinia-Vyasa is a computer-based instructional system that trains operators to troubleshoot and diagnose faults in marine power plants, The simulator, Turbinia, is based on a hierarchical representation of subsystems, components, and primitives, Vyasa is the computer-based tutor that teaches the troubleshooting task using Trtrbinia, The simulator, an interactive, direct manipulation interface, and the tutor (with its expert, student, and instructional modules) comprise the instructional system, To be effective, knowledge about the system and the troubleshooting task, together with the knowledge to infer student's misconceptions from observed actions and pedagogical knowledge, must be suitably organized and presented to the student, System knowledge is organized in terms of schematics, subsystems, and fluid paths, which are further decomposed into structure, function, and behavior, Information about failures and failure schemas complement the system knowledge, In this paper, we discuss the details of knowledge organization and show how they support the functions of the intelligent tutoring system.
## 446 Many of the intelligent tutoring systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixed-initiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multitum dialogue as complete solutions' or answers evolve. This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps colleges Students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.
## 447 Over the last decades, there have been a rich variety of approaches towards modeling student knowledge and skill within interactive learning environments. There have recently been several empirical comparisons as to which types of student models are better at predicting future performance, both within and outside of the interactive learning environment. However, these comparisons have produced contradictory results. Within this paper, we examine whether ensemble methods, which integrate multiple models, can produce prediction results comparable to or better than the best of nine student modeling frameworks, taken individually. We ensemble model predictions within a Cognitive Tutor for Genetics, at the level of predicting knowledge action-by-action within the tutor. We evaluate the predictions in terms of future performance within the tutor and on a paper post-test. Within this data set, we do not find evidence that ensembles of models are significantly better. Ensembles of models perform comparably to or slightly better than the best individual models, at predicting future performance within the tutor software. However, the ensembles of models perform marginally significantly worse than the best individual models, at predicting post-test performance.
## 448 This paper presents the first statistically reliable empirical evidence from a controlled study for the effect of human-provided emotional scaffolding on student persistence in.an intelligent tutoring system. We describe an experiment that added human-provided emotional scaffolding to an automated Reading Tutor that listens, and discuss the methodology we developed to conduct this experiment. Each student participated in one (experimental) session with emotional scaffolding, and in one (control) session without emotional scaffolding, counterbalanced by order of session. Each session was divided into several portions. After each portion of the session was completed, the Reading Tutor gave the student a choice., continue, or quit. We measured persistence as the number of portions the student completed Human-provided emotional scaffolding added to the automated Reading Tutor resulted in increased student persistence, compared to the Reading Tutor alone. Increased persistence means increased time on task which ought lead to improved learning. If these results for reading turn out to hold for other domains too, the implication for intelligent tutoring systems is that they should respond with not just cognitive support - but emotional scaffolding as well. Furthermore, the general technique of adding human-supplied capabilities to an existing intelligent tutoring system should prove useful for studying other ITSs too.
## 449 This paper addresses the challenge of integrating a dialog system with an ITS created for supporting procedural training in a 3D virtual environment. To this end, we first describe the desired features of the dialog to be provided to students in such system. Then, we explain some technical issues of our proposal such as the architecture; the design of the dialog manager; and the construction of the answers to the students' questions by leveraging an ontology related to the virtual world. Next, we present a pilot study with students to validate our approach. In this pilot study, we utilized a prototype that encompasses a 3D virtual laboratory. In the last seven years, students have used this virtual laboratory to perform a practice guided by an automatic tutor without dialog capacity. So, the evaluated prototype represents an extension of this automatic tutor that aims to give it the dialog capacity in the context of the practice.
## 450 The present work is aimed at guiding the process of development and implementation of a smart module for Moodle to support the learning process of engineering students. There are several methodologies to guide the development of an ITS, each one has its peculiarities, but all agree that an architecture of three modules (Student, Tutor and domain) is enough and appropriate to develop the ITS. Learning Management System (LMS) Moodle was selected as the basis for developing the ITS. The design of the modules: Student, Tutor and Domain was specified to the logical and physical model of the database to be integrated with Moodle database. The proposed methodology was composed of six ordered steps (selection of the LMS platform, integration among the main components, designing the Student Module, designing the Tutor Module, designing the domain module and analysis of coding standards and restrictions of LMS). Its validation by Expert Valuation Method, confirms that it is appropriate and allows guiding the development of a web-based ITS to support the teaching learning process of engineering students.
## 451 This review describes a meta-analysis of findings from 50 controlled evaluations of intelligent computer tutoring systems. The median effect of intelligent tutoring in the 50 evaluations was to raise test scores 0.66 standard deviations over conventional levels, or from the 50th to the 75th percentile. However, the amount of improvement found in an evaluation depended to a great extent on whether improvement was measured on locally developed or standardized tests, suggesting that alignment of test and instructional objectives is a critical determinant of evaluation results. The review also describes findings from two groups of evaluations that did not meet all of the selection requirements for the meta-analysis: six evaluations with nonconventional control groups and four with flawed implementations of intelligent tutoring systems. Intelligent tutoring effects in these evaluations were small, suggesting that evaluation results are also affected by the nature of control treatments and the adequacy of program implementations.
## 452 Our contribution to the Special Issue on the GTE system begins with a response to five major claims made in Van Marcke's paper on GTE. We follow with a description of a system called Eon, a suite of authoring tools for intelligent tutoring systems (ITS), which we are developing in our lab. Next we discuss several general issues in ITS authoring systems as they pertain to GTE, Eon, and other systems, including who the intended audience is, where instructional expertise comes from, tradeoffs between generality and inferencing power, managing complexity for users, and the use of knowledge types. Finally, we suggest several areas for synergy and future work for GTE and Eon.
## 453 To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with different learning outcomes demonstrated distinct learning patterns. Students who failed a problem set exhibited more passive learning behaviors and could hardly learn from practice, while students who mastered a problem set could effectively regulate their learning. Furthermore, we extended the use of TL-HMM to predicting learning outcome from behavior sequences and checked through cross-validation. TL-HMM is demonstrated helpful to gain insight into learners' interactions with online learning environments. In practice, TL-HMM could be embedded in intelligent tutoring systems to monitor learning behaviors and learner status, so as to detect the difficulties of learners and facilitate learning.
## 454 In the paper, the data driven approach for users' modeling in intelligent e-learning system is considered. Individual models are based on preferred learning styles dimensions, according to which students focus on different types of information and show different performances in educational process. Building individual models of learners allows for adjusting teaching paths and materials into their needs. In the presented approach, students are divided into groups by unsupervised classification. Application of two-phase hierarchical clustering algorithm which enables tutors to determine such parameters as maximal number of groups, clustering threshold and weights for different learning style dimensions is described. Experimental results connected with modeling real groups of students are discussed.
## 455 The goal of the Conversational Interfaces project at CSLI is to develop a general purpose architecture which supports multi-modal dialogues with complex devices, services, and applications. We are developing generic dialogue management software which supports collaborative activities between a human and devices. Our systems use a common software base consisting of the Open Agent Architecture, Nuance speech recogniser, Gemini (SRI's parser and generator), Festival speech synthesis, and CSLI's Architecture for Conversational Intelligence (ACI). This chapter focuses on one application of this architecture - an intelligent tutoring system for shipboard damage control. We discuss the benefits of adopting this architecture for intelligent tutoring.
## 456 Students tend to retain naive understandings of concepts such as energy and force even after completing school and entering college. We developed a learning environment called the Virtual Physics System (ViPS) to help students master these concepts in the context of pulleys, a class of simple machines that are difficult to assemble and use in the real world. Several features make the ViPS noteworthy: it combines simulation and tutoring, it customizes tutoring to address common misconceptions, and it employs a pedagogical strategy that identifies student misconceptions and guides students in problem solving through virtual experimentation. This paper presents the ViPS and describes studies in which we evaluated its efficacy and compared learning from the ViPS with learning from constructing and experimenting with real pulleys. Our results indicate that the ViPS is effective in helping students learn and remediate their misconceptions, and that virtual experimentation in the ViPS is more effective than real experimentation with pulleys.
## 457 This research addresses the need for easier, more cost-effective means of developing intelligent tutoring systems (ITSs). A novel and advantageous solution to this problem is the development of a task-specific ITS shell that can generate tutoring systems for different domains within a given class of tasks. Task-specific authoring shells offer an appropriate knowledge representation method to build knowledgeable tutors as well as flexibility for generating ITSs for different domains. In this paper, we describe the development of an architecture that can generate intelligent tutoring systems for different domains by interfacing with existing generic task-based expert systems, and reusing the other tutoring components. The architecture was used to generate an ITS for the domain of composite materials fabrication using an existing expert system.
## 458 Our previous work has demonstrated that the Extensible Problem Specific Tutor (xPST) framework bowels the bar for non-programme's to audior model tracing intelligent tutoring systems (ITSs) on top of existing software and websites In this work we extend xPST to enable authoring of tutors m 3D games This process differs substantially front authoring tutors for traditional CUI software in terms of the inherent domain complexity involved, different types of feedback required and interactions generated by various entities apart from the student, A tutor for a village evacuation task has been constructed in order to demonstrate the capabilities of using the extended x PST system to create a game-based tutor
## 459 This paper describes the results of a pedagogical experiment using an Intelligent Tutoring System that also implements an Animated Pedagogical Agent. The study was performed in Portugal, as an attempt to replicate some experiments performed in the USA, and in order to verify the usefulness of the Pedagogical Agent in a different country with distinct culture. After running the experiment and analyzing the data, we have concluded that in our specific case, we did not find any evidence that the pedagogical agent could boost instructional outcomes of students in Portugal. Our data is showing no statistically significant differences between the control, and experimental groups. Additionally, we have also found that the control group actually performed slightly better than the experimental group. As a result, our study is giving some good evidences that a Pedagogical Agent that was developed, tested and successfully used in one country, may not be successful when used in a different country.
## 460 While intelligent tutoring systems have been designed to teach free-body diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of diagrams.
## 461 We have been creating an authoring tool, the Cognitive Model SDK, which allows non-cognitive scientists and non-programmers to produce a cognitive model for model-tracing tutors [1, 2]. The SDK is in use by developers at Carnegie Learning to produce their commercial Cognitive Tutors for math. However, it has never been evaluated with regards to the strong claim that noncognitive scientists and non-programmers could, without much effort, produce useful cognitive models with it. The research presented here shows that this can be done, using a task that past researchers have used [3]. The models are evaluated across several metrics to see what characteristics of either them or their creators may distinguish better models from worse models. The goal of this work is to establish a baseline for future work examining how cognitive modeling can be opened up to a wider class of people.
## 462 Potential of Game Based learning is huge because people are already engaged in playing entertainment games and multiple intelligent tutors have been designed already for teaching various domains and topics. The Challenge is to develop a model to overcome and to fill the gap between serious game and ITS and through which learning activities would become more organized and meaning full. This research demonstrates a conceptual model which describes how game mechanics used in serious games can be incorporated in intelligent tutoring systems to provide more effective learning. Multiple researches conclude that intelligent tutoring systems are effective but due to boredom factor students don't find it interesting to stay engage with it for a long duration. The purpose of this model is to combine the features of serious games and learning engine of intelligent tutoring systems. A model with combine's features of both fulfils the needs of students by engaging them in game long duration required and to achieve the learning purpose.
## 463 Knowledge societies also named learning social networks allow interaction and collaboration between individuals (instructors and students), who share their connections under a scheme of learning communities around common learning interest. In this paper, we present an affective tutoring system inside a knowledge society implemented in a learning social network. We have designed a new architecture for an entire system that includes the social network with an educational approach, and a set of intelligent tutoring systems for mathematics learning which analyse and evaluate cognitive and affective aspects of the learners. The intelligent tutoring systems were developed based on different theories, concepts and technologies. In this paper, we present an affective tutoring system embedded in a social network which is used to improve poor student results in ENLACE test (National Assessment of Academic Achievement in Schools in Mexico). We also present results when applying the tutoring system to a group of students.
## 464 We present the design of a novel conversational intelligent tutoring system, called DeepTutor. DeepTutor is based on cognitive theories of learning, the framework of Learning Progressions proposed by the science education research community, and deep natural language and dialogue processing techniques and principles. The focus of the paper is on the role of Learning Progressions on the design of DeepTutor. Furthermore, we emphasize the role of Learning Progressions in guiding macro-adaptivity in conversational ITSs. We conducted a large-scale, after-school experiment with hundreds of high-school students using DeepTutor. Importantly, these students interacted with the system totally unsupervised, i.e. without any supervision from an instructor or experimenter. Our work so far validates the Learning Progressions theory.
## 465 Recently, detectors of gaming the system have been developed for several intelligent tutoring systems where the problem-solving process is reified, and gaming consists of systematic guessing and help abuse. Constraint-based tutors differ from the tutors where gaming detectors have previously been developed on several dimensions: in particular, higher-level answers are assessed according to a larger number of finer-grained constraints, and feedback is split into levels rather than an entire help sequence being available at any time. Correspondingly, help abuse behaviors differ, including behaviors such as rapidly repeating the same answer or blank answers to elicit answers. We use text replay labeling in combination with educational data mining methods to create a gaming detector for SQL-Tutor, a popular constraint-based tutor. This detector assesses gaming at the level of multiple-submission sequences and is accurate both at identifying gaming within submission sequences and at identifying how much each student games the system. It achieves only limited success, however, at distinguishing different types of gaming behavior from each other.
## 466 Intelligent Tutoring Systems (ITS) is the interdisciplinary field that investigates how to devise educational systems that provide instruction tailored to the needs of individual learners, as many good teachers do. Research in this field has successfully delivered techniques and systems that provide adaptive support for student problem solving in a variety of domains. There are, however, other educational activities that can benefit from individualized computer-based support, such as studying examples, exploring interactive simulations and playing educational games. Providing individualized support for these activities poses unique challenges, because it requires an ITS that can model and adapt to student behaviors, skills and mental states often not as structured and well-defined as those involved in traditional problem solving. This paper presents a variety of projects that illustrate some of these challenges, our proposed solutions, and future opportunities.
## 467 Intelligent tutoring systems (ITS) are evolving towards a more cooperative relationship between the system and the student. More and more, learning is considered as a constructive process rather than a simple transfer of knowledge. This trend has brought to light new cooperative tutoring strategies. One of these tutoring strategies, the learning companion, designed to overcome some of the limitations of the classical tutoring model, involves a student and two simulated participants: a tutor and another student. Move recently, a new strategy, learning by disturbing, has been proposed. In this strategy, the simulated student is a troublemaker whose role is to deliberately disturb the human student. This article describes the learning by disturbing strategy by contrasting it with the learning companion strategy. In addition, links are drawn between this new strategy and the psychology of learning, in particular the cognitive dissonance theory. An indicator has been developed that measures discord between the ideas, helping to pinpoint the concepts that ave most likely to be misunderstood by the learner. Doing so allows one to plan more efficiently the interventions of the troublemaker.
## 468 Our work takes as a starting point McCalla's proposed ecological approach for the design of peer-based intelligent tutoring systems and proposes three distinct directions for research. The first is to develop an algorithm for selecting appropriate content (learning objects) to present to a student, based on previous learning experiences of like-minded students. The second is to build on this research by also having students leaving explicit annotations on learning objects to convey refinements of their understanding to subsequent students; the challenge is to intelligently match students to those annotations that will be most beneficial for their tutoring. The third is to develop methods for intelligently extracting learning objects from a repository of knowledge, in a manner that may be customized to the needs of specific students. In order to develop our research we are exploring the specific application of assisting health care workers via. peer-based intelligent tutoring.
## 469 E-learning students tend to get jaded and easily dropout from online courses. Enhancing the learners' experience and reducing dropout rates in these e-learning based scenarios is the main purpose of this study. This paper presents the results obtained so far and preliminary conclusions. In a first stage, the objective was to study the background and state of the art of these educational scenarios. In a second phase, identifying key reasons for dropouts, through a survey and interviews, was the aim to understand and detect motives and behavior patterns of students with dropout thoughts. Finally, developing, testing and validating a functional prototype of an Intelligent Tutoring System will allow to evaluate concepts, collect statistical information on its effectiveness, analyze and discover if course completion rates are improved.
## 470 FACT (Formative Assessment with Computational Technology) is an intelligent orchestration system. That is, because it helps the teacher manage the workflow of a complicated set of activities in the classroom, it is an orchestration system. Because it conducts tasks-specific and domain-specific analyses of the students' mathematical products and their group interactions, it is more intelligent than other orchestration systems. From analyzing videos of our iterative development trials, we realized that too many students needed help simultaneously, but the teacher could only visit one group at a time. Thus, we modified FACT to send a few messages to the students directly instead of sending all its advice to the teacher. This paper reports a successful pilot test of auto-sending.
## 471 This paper describes the motivations and goals of the MATHESIS project which concerns the development of an intelligent authoring environment for cognitive math tutors. It also describes the first implemented component of the project, the MATHESIS algebra tutor, a cognitive web-based tutor for algebraic expressions' expanding and factoring. The tutor uses cognitive model tracing by dynamically generating the plausible steps, checking them against student's solution steps and intervening when errors occur. Additionally, the tutor monitors the student's mastery of knowledge from problem to problem, i.e. the various cognitive skills. The tutor will be used as a prototype for the development of an ontology that will contain all of the tutor's knowledge. This ontology will eventually guide the creation of the authoring tools that will make faster and easier the creation of other cognitive tutors.
## 472 In this study we explore how different methods of structuring collaborative interventions affect student learning and interaction in an Intelligent Tutoring System for Computer Science. We compare two methods of structuring collaboration: one condition, unstructured, does not provide students with feedback on their collaboration; whereas the other condition, semistructured, offers a visualization of group performance over time, partner contribution comparison and feedback, and general tips on collaboration. We present a contrastive analysis of student interaction outcomes between conditions, and explore students reported perceptions of both systems. We found that students in both conditions have significant learning gains, equivalent coding efficiency, and limited reliance on system examples. However, unstructured users are more on-topic in their conversational dialogue, whereas semistructured users exhibit better planning skills as problem difficulty increases.
## 473 Intelligent E-learning Systems may become more efficient and attractive to users by the specific support they can provide to both learners and tutors. The paper presents a prototype implementation of an intelligent e-learning system and some of the features it offers to the users.
## 474 In this paper, we present the architecture and describe the functionality of a Web-based Intelligent Tutoring System (ITS), which uses neurules for knowledge representation. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The use of neurules as the knowledge representation basis of the ITS results in a number of advantages. Part of the functionality of the ITS is controlled by a neurule-based inference engine. Apart from that, the system consists of four other components: the domain knowledge, containing the structure of the domain and the educational content, the user modeling component, which records information concerning the user, the pedagogical model, which encompasses knowledge regarding the various pedagogical decisions, and the supervisor unit that controls the functionality of the whole system. The system focuses on teaching Internet technologies.
## 475 This paper proposes a new model for predicting student learning styles for conversational intelligent tutoring systems (CITS). The learning styles are predicted from behavior cues extracted during conversation obtained during automated CITS tutorials. The heart of the model is a fuzzy rule base determined automatically from existing tutorial data with membership function boundaries optimized by a genetic algorithm. The zero-order Sugeno fuzzy inference model is utilized to predict the Felder and Silverman learning styles in two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). This work is motivated by the changing nature of both education and learners and the need to provided personalized tutoring on demand. The model is incorporated into an existing CITS and evaluated using undergraduate University students. The experimental results have shown strong predictive accuracy when compared with existing approaches to delivery of personalized tutorials and have received good student feedback.
## 476 For cross-pollination between ITS authoring tools, it may be useful to know the prevalence of tutoring behaviors crafted with these tools. As a case study, we analyze the problem units of Mathtutor, a web-based intelligent tutor for middle-school mathematics built, as an example-tracing tutor, with the Cognitive Tutor Authoring Tools (CTAT). We focus on tutoring behaviors that are relevant to a wide range of tutoring systems, not just example-tracing tutors, including behaviors not found in VanLehn's (2006) taxonomy of tutor behaviors. Our analysis reveals that several tutor behaviors not typically highlighted in the ITS literature were used extensively, sometimes in unanticipated ways. Others were less prevalent than expected. This novel insight into the prevalence of tutor behaviors may provide practical guidance to ITS authoring tool developers. At a theoretical level, it extends VanLehn's taxonomy of tutor behavior, potentially expanding how the field conceptualizes ITS behavior.
## 477 Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students' transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Implications for accounting instructors and researchers are discussed.
## 478 Educational software applications may be more effective if they can adapt their teaching strategies to the needs of individual students. Individualisation may be achieved through student modelling, which is the main practice for Intelligent Tutoring Systems (ITS). In this paper, we show how principles of cognitive psychology have been adapted and incorporated into the student modelling component of a knowledge-based authoring tool for the generation of ITSs. The cognitive model takes into account the time that has passed since the learning of a fact has occurred and gives the system an insight of what is known and remembered and what needs to be revised and when. This model is individualised by using evidence from each individual student's actions.
## 479 The article presents a method to create pedagogical strategies which are used to build Intelligent Tutoring Systems (ITS) immersed in e-learning content repositories. This solution is designed for a new class of ITS systems which work on multi-topic e-learning content repositories dynamically supplied by many authors. In such systems it is essential to separate pedagogical strategy, or method of conducting teaching, from content that is to be delivered to learners. Thus, by personalizing learning in response to identified needs of learners it is possible to supply them with the most adequate content from among those available in the repository at a given moment. The article presents a solution in which pedagogical strategies are created based on so-called pedagogical patterns which act as templates that are to be used in various educational contexts. Such an approach is designed to conduct learning by means of didactic content (e-learning course), which was given a definite structure by the author, and to which ITS will incorporate new content components that meet the pre-set criteria (subject, level of difficulty, level of interactivity) retrieved from the repository. Sample pedagogical patterns that may be used in the process of personalizing learning include incorporating content retrieved from the repository into a learning pathway realized by the student so that any identified deficiencies may be overcome (Similar Content Strategy Pattern), or supplementing the learning pathway with content from the repository that complements the topics under discussion with a case study illustrating the given topic (Case Study Inclusion Strategy Pattern). Such patterns are of general character and may be used in learning with a variety of e-learning courses. From a technical standpoint the solution is based on Sequencing and Navigation (SN) mechanisms of the SCORM 2004 specification, extended with so-called Triggers and Extension Points that enable activation of the ITS module (ITS Agent), responsible for searching the repository for content that meets preset criteria as well as for selecting the content that best satisfies learner's needs (recommendation). In this approach pedagogical patterns are built as ready-made templates coded in SN. Such templates can be used to build an e-learning course that will make it possible to realize a pre-defined educational strategy (single or multiple). It is also possible to automatically include such patterns in an e-learning course by ITS in the process of personalizing with regard to learner's progress. This solution was verified during construction of the system Edumatic ITS, and then tested on an e-learning repository in the area of protection and management of archaeological heritage which contained env. 4800 SCO's organized in env. 1000 didactically useful components that can be delivered during personalization.
## 480 One important field where mobile technology can make significant contributions is Education. In the fast pace of modern life, students and instructors would appreciate using constructively some spare time that they may have, in order to work on lessons at any place, even when away from offices, classrooms and labs where computers are usually located. In this paper, we describe a mobile authoring tool that we have developed and is called Mobile Author. Mobile Author can be used by human instructors either from a computer or a mobile phone to create their own Intelligent Tutoring Systems (ITSs) and to distribute them to their students. After the ITSs have been created, students can also use any computer or mobile phone to have access to theory and tests. The tutoring systems can assess the students' performance, inform the databases that record the students' progress and provide advice adapted to the needs of individual students. Finally, instructors can monitor their students' progress and communicate with their students during the course. The mobile features of both the authoring tool itself and the resulting ITSs from it have been evaluated by instructors and students, respectively. The results of the evaluation showed that mobile features are indeed considered useful. (C) 2004 Elsevier Ltd. All rights reserved.
## 481 Individuals' achievement goals are known to influence learning behaviors and academic achievement. However, prior research also indicates that undergraduates' achievement goals for psychology course-work vary from assignment to assignment. The effect of stability of achievement goals on learning behaviors and outcomes has yet to be explored. This study examined how adolescents' achievement goals varied over mathematics units completed in an intelligent tutoring system, and whether strength or variability in achievement goals influenced behavior or achievement. At the group level, achievement goals correlated significantly from unit to unit; mean scores were not significantly different over time. However, individuals' goal scores changed reliably across units. No relationships were found between the strength of students' achievement goal scores and learning behaviors or performance. However, students with stable mastery approach goals achieved better grades than those with more variable mastery-approach goals. Students with stable performance-approach goals engaged in fewer help-seeking behaviors than those with variable performance approach goals. (C) 2014 Elsevier Ltd. All rights reserved.
## 482 Intelligent tutors that emulate one-on-one tutoring with a human have been shown to effectively support student learning, but these systems are often challenging to build. Most methods for implementing tutors focus on generating intelligent explanations, rather than generating practice problems and problem progressions. In this work, we explore the possibility of using a single model of a learning domain to support the generation of both practice problems and intelligent explanations. In the domain of algebra, we show how problem generation can be supported by modeling if-then production rules in the logic programming language answer set programming. We also show how this model can be authored such that explanations can be generated directly from the rules, facilitating both worked examples and real-time feedback during independent problem-solving. We evaluate this approach through a proof-of-concept implementation and two formative user studies, showing that our generated content is of appropriate quality. We believe this approach to modeling learning domains has many exciting advantages.
## 483 SimTutor is a multimedia intelligent tutoring system (ITS) for simulation modeling. Multimedia systems are now de facto standard on personal computers and increasing number of intelligent tutoring systems incorporate multimedia systems to enhance interaction with students. SimTutor provides a graphical environment in which the student can practice conceptual model development and interact direct with different simulation modeling software. We used multimedia systems to enhance the pedagogy and for incorporating different strategy into the courseware design. ITS components are accessed through the graphical user interface, allowing them to be developed independently. The modular architecture allows for interoperability of applications with the same event changing protocol. An object-oriented approach is adapted to allow the system to evolve and to flexibly change the data.
## 484 Since the creation of Web-based learning systems researchers have tried to make them adaptive to various characteristics of learners in order to increase the quality of learning. The application of data mining (DM) methods on learning system logs is often used as a basis of intelligent tutoring systems (ITS) that have the ability for automatic adaptation of some aspect of the learning process. Such a system was developed at our institution in the previous years and has been used in a number of courses. To improve our system we added an integration application to create a continuous feedback loop with a DM tool. Our goals, from the student's perspective, are to improve the quality of knowledge acquired by students as well as shorten the learning time by offering recommendations based on mined patterns of learning paths through the knowledge domain. From the perspective of teachers, our goal is to create a rich data visualization system (in the form of a Web application using standard Web technologies to create visualizations) to give them new insight into students' behaviours. In this paper we present the structure of our system and the research design that will be used to verify its results.
## 485 Adaptive Hypermedia and Intelligent Tutoring Systems are both used for computer-based instruction, but their strengths lie in different areas. Adaptive Hypermedia is better suited to the instruction of concepts, while Intelligent Tutoring Systems generally assist in the use of these concepts to solve problems. A general instruction system requires both of these methods of instruction to provide a full learning environment. This paper describes a proposed method of combining Adaptive Hypermedia and Intelligent Tutoring Systems using Knowledge Spaces, a method of mathematically modeling a domain.
## 486 A common problem when trying to apply data mining techniques to improve educational systems is the disconnection between those who have the expertise (e.g. universities) and those who have access to the data (e.g. small companies). Bringing expertise into educational in-production systems is complicated because companies are reluctant to invest a lot of effort into integrating new technology that they do not fully trust, while the technology cannot prove its worth without access to real, valid data. In this paper we explore the requirements that machine learning systems have to be applied to specific learning problems (sequencing and performance prediction), and then propose a minimally invasive protocol for sequencing (based on web services) to easily integrate Learning Analytics Services into e-learning systems.
## 487 In this paper, a kind of Web-based Opening Intelligent Tutoring System (WO-ITS) is proposed. The modal, structure, function and principle of WO-ITS are described. And the main characteristics of WO-ITS that are different from the traditional CAI courseware are discussed.
## 488 In this paper, a formal tutoring process model for case-based Intelligent Tutoring Systems is described. Case-based training necessitates flexibility and adaptability in an Intelligent Tutoring System. Training cases can be developed in different levels of guidance, reflecting the intensity of support given to the learner. The tutoring process model is described as an abstract tutoring process model. Based on the abstract tutoring process model, the basic tutoring process model and the adaptive tutoring process model are realized. The basic tutoring process model contains no learner model and thus is not able to adapt to the learner. It can be used for the design and the steering of simple training cases. The adaptive tutoring process model contains a learner model and adaptation possibilities. Adaptation takes place as adaptation to the learner and adaptation to the training case's development.
## 489 Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem. But does greater freedom mean that students learn more robustly? We developed three versions of the same ITS for solving linear algebraic equations that differed only in the amount of freedom given to students. One condition required students to strictly adhere to a standard strategy, the other two allowed minor and major variations, respectively. We conducted a study in two US middle schools with 57 students in grades 7 and 8. Overall, students' algebra skills improved. Contrary to our hypotheses, the amount of freedom offered by the system did not affect students' learning outcomes, nor did if affect their intrinsic motivation. Students tended to use only the standard strategy and its minor variations. Thus, the study suggests that in the early stages of problem-solving practice within a complex domain, an ITS should allow at least a small amount of freedom, validating, albeit to a limited degree, one source of complexity in ITS architectures. To help students develop strategic flexibility, a desirable outcome in many domains, more is needed than letting students chose their own solution strategy within a given problem. (C) 2012 Elsevier Ltd. All rights reserved.
## 490 Cognitive, affective, metacognitive, and motivational (CAMM) processes are critical components of self-regulated learning (SRL) essential for learning and problem solving. Currently, ITSs are designed to foster cognitive, affective, and metacognitive (CAM) strategies and processes, presenting major gaps in the research since motivation is a key component of SRL and influences the remaining CAM processes. In our study, students interacted with MetaTutor, a hypermedia-based ITS, to investigate how 190 undergraduate students' proportional learning gain (PLG) related to sub-goals set, cognitive strategy use and metacognitive processes differed based on self-reported achievement goal orientation. Results indicated differences between approach, avoidance, and students who adopted both approach and avoidance goal orientations, but no differences between mastery, performance and students who adopted both mastery and performance goal orientations on PLG for content related to sub-goal 1. Conversely, no differences were found between goal orientation groups on PLG for sub-goal 2, revealing possible changes in goal orientation following sub-goal 1. Analyses indicated no differences between goal orientation groups on metacognitive processes and cognitive strategy use. Thus, we suggest turning away from self-report data, where future studies aim to incorporate multi-channel data over durations of tasks as students interact with ITSs to measure motivation and its tendency to fluctuate in real-time. Implications for using multiple data channels to measure motivation could contribute to adaptive ITS design based on all CAMM processes.
## 491 The Extensible Problem Specific Tutor (xPST) allows authors who are not cognitive scientists and not programmers to quickly create an intelligent tutoring system that provides instruction akin to a model-tracing tutor. Furthermore, this instruction is overlaid on existing software, so that the learner's interface does not have to be made from scratch. The xPST architecture allows for extending its capabilities by the addition of plug-ins that communicate with additional third-party software. After reviewing this general architecture, we describe three major implementations that we have created using the xPST system, each using different third-party software as the learner's interface. We have conducted three evaluations of authors using xPST to create tutoring content, and these are considered in turn. These evaluations show that xPST authors can quickly learn the system, and can efficiently produce successful embedded instruction.
## 492 When students are working collaboratively and communicating verbally in a technology-enhanced environment, the system cannot track what collaboration is happening outside of the technology, making it difficult to fully assess the collaboration of the students and adapt accordingly. In this article, we propose using gaze measures as a proxy for cognitive processes to achieve collaboration awareness. Specifically, we use Granger causality to analyse the causal relationships between collaborative and individual gaze measures from students working on a fractions intelligent tutoring system and the influence that the students' dialogue, prior knowledge, or success has on these relationships. We found that collaborative gaze patterns drive the individual focus in the pairs with high posttest scores and when they are engaged in problem-solving dialogues but the opposite with low performing students. Our work adds to the literature by extending the correlational relationships between individual and collaborative gaze measures to causal relationships and suggests indicators that can be used within an adaptive system.
## 493 Programming is a subject that many beginning students find difficult. The PHP Intelligent Tutoring System (PHP ITS) has been designed with the aim of making it easier for novices to learn the PHP language in order to develop dynamic web pages. Programming requires practice. This makes it necessary to include practical exercises in any ITS that supports students learning to program. The PHP ITS works by providing exercises for students to solve and then providing feedback based on their solutions. The major challenge here is to be able to identify many semantically equivalent solutions to a single exercise. The PHP ITS achieves this by using theories of Artificial Intelligence (AI) including first-order predicate logic and classical and hierarchical planning to model the subject matter taught by the system. This paper highlights the approach taken by the PHP ITS to analyse students' programs that include a number of program constructs that are used by beginners of web development. The PHP ITS was built using this model and evaluated in a unit at the Queensland University of Technology. The results showed that it was capable of correctly analysing over 96 % of the solutions to exercises supplied by students.
## 494 This paper presents a pilot study on an intelligent tutoring system for domain-independent argument making. Students' responses to an open- ended question were collected as the instances for supervised text classification based on the grade given by the instructor using structured outcome of the learning observation taxonomy. The responses were processed using Coh-metrix as well as n-gram models to generate attributes for the classification task. The best result of 81.74% in classification correct rate was obtained when all grade classes were used.
## 495 Hypermedia learning environments (HLE) unevenly present new challenges and opportunities to learning processes and outcomes depending on learner characteristics and instructional supports. In this experimental study, we examined how one such HLE-MetaTutor, an intelligent, multi-agent tutoring system designed to scaffold cognitive and metacognitive self-regulated learning (SRL) processes-interacts with learner's prior domain knowledge to affect their note-taking activities and subsequent learning outcomes. Sixty (N = 60) college students studied with MetaTutor for 120 min and took notes on hypermedia content of the human circulatory system. Log-files and screen recordings of learner-system interactions were used to analyze notes for several quantitative and qualitative variables. Results show that most note-taking was a verbatim copy of instructional content, which negatively related to the post-test measure of learning. There was an interaction between prior knowledge and pedagogical agent scaffolding, such that low prior knowledge students took a greater quantity of notes compared to their high prior knowledge counterparts, but only in the absence of MetaTutor SRL scaffolding; when agent SRL scaffolding was present, the note-taking activities of low prior knowledge students were statistically equivalent to the number of notes taken by their high prior knowledge counterparts. Theoretical and instructional design implications are discussed.
## 496 This article is a review of experiments comparing the effectiveness of human tutoring, computer tutoring, and no tutoring. No tutoring refers to instruction that teaches the same content without tutoring. The computer tutoring systems were divided by their granularity of the user interface interaction into answer-based, step-based, and substep-based tutoring systems. Most intelligent tutoring systems have step-based or substep-based granularities of interaction, whereas most other tutoring systems (often called CAI, CBT, or CAL systems) have answer-based user interfaces. It is widely believed as the granularity of tutoring decreases, the effectiveness increases. In particular, when compared to No tutoring, the effect sizes of answer-based tutoring systems, intelligent tutoring systems, and adult human tutors are believed to be d = 0.3, 1.0, and 2.0 respectively. This review did not confirm these beliefs. Instead, it found that the effect size of human tutoring was much lower: d = 0.79. Moreover, the effect size of intelligent tutoring systems was 0.76, so they are nearly as effective as human tutoring.
## 497 In this experimental study, use of Computer Assisted Instruction (CAI) followed by use of an Intelligent Tutoring System (CAI+ITS) was compared to the use of CAI (CAI only) in tutoring students on the topic of Algebraic Expression. Two groups of students participated in the study. One group of 32 students studied algebraic expression in a CAI learning environment, while the other group of 30 students was in a CAI and ITS (CAI+ITS) environment. Before the experimental treatment began, subjects were given a pre-test on algebraic expression. A post-test was also given at the end of the study. The experimental treatment was administered in eight sessions with one hour per session. For the first stage of the study, both groups of subjects studied algebraic expression in a CAI environment. In the second stage, subjects from the CAI group continued with a tutoring session using the drill and practice section of the CAI package, whereas subjects from the CAI+ ITS environment continued their learning using the ITS tutorial. The results of the study showed that there was a significant difference in the students' achievement in algebraic expression between students who learned with CAI+ ITS and who learned with CAI only as the delivery system. The findings of the study indicated that CAI+ ITS was more effective in helping students learn algebraic expression as compared to using CAI alone. This study suggests that educators and software developers should focus on the development of ITS based learning tools or integrate ITS elements in course ware development rather than developing a mere CAI tool.
## 498 The difficulties present in the teaching object-oriented programming and Intelligent Tutoring Systems are exposed in this article. The usefulness of Intelligent Tutoring Systems taking into account technical data mining, among which include artificial neural networks, is explained. The author proposes a model, using data mining techniques, to extract and to analyze information from student interactions inside the platform in the teaching-learning process. The model takes into account the predominant types of intelligence in each student, in order to achieve more individualized solutions. An experiment was employed in order to measure system effectiveness, considering decrease in wrong attempts doing an exercise as a success criterion. The results show a positive impact in solving problems, improving the learning process.
## 499 In this paper we describe Web Passive Voice Tutor (Web PVT), an adaptive web-based Intelligent Computer Assisted Language Learning (ICALL) program that is aimed at teaching non-native speakers the passive voice of the English language. The design of the system has been largely based on the results of an empirical study that was conducted at schools with the collaboration of human teachers. Web PVT incorporates techniques from Intelligent Tutoring Systems (ITS) and Adaptive Hypermedia (AH) technologies to provide students with individualised instruction and feedback. The system uses a combination of stereotypes and the overlay technique for the initialisation of the student model, which is then refined by observing the student while working with the system. The resulting student model is used for the annotation of the links to topics presented to the student. In addition, it is also used in the process of error diagnosis and the adaptation of feedback and advice provided to the student.
## 500 The evaluation of intelligent tutoring systems (ITSs) is an important though often neglected stage of ITS development. There are many evaluation methods available but literature does not provide clear guidelines for the selection of evaluation method(s) to be used in a particular context. This paper describes the evaluation study of DEPTHS, an intelligent tutoring system for learning software design patterns. The study which took place during the spring semester 2006 was aimed at assessing the system's effectiveness and the accuracy of the applied student model. It also targeted the evaluation of the students' subjective experiences with the system.
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## 16 <NA>
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## 35 {0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 1, 0, 0, -0.3, -1.2, 0, 0, 1.1, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 1.7, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0.8, 0, 0, 0, 1.4}
## 36 {0, 0, 0, 2, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.307, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 37 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 39 {0, 0, 0, 1, 0, 0, 0, 0, 0.35, 0, 1, 0, 1.05, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 40 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, -1.7, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, -1.6}
## 41 {2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, -1.7, 1.4, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 1.3, 0, 0.8, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, -1.7, 1.4, 0}
## 42 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 1.7, 0, 1.3, 0, 1.5, 0, 0, 0, 0, 0}
## 43 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1.3, 0, 0, -1.7, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 45 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0}
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## 50 {1.4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 2.1}
## 51 {0, 0, 0, 0, 1, 0, 0.8, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 52 {0, 0, 0.5, 0, 1, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.15, 0, 0, 0, 0, 3.15}
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## 54 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0}
## 55 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 2.293, 0, 1.9637, 0, 0, 0, 1.9}
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## 61 <NA>
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## 100 {0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 101 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0.8, 0, 0, -1.7, 0}
## 102 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0}
## 103 {0, 0, 0, 0.75, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.839175, 1.43185, 0, 0, 0, -2.55, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6395, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 104 {0, 0, 0, 1.5, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.184, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5}
## 105 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 1.3, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0}
## 106 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 2, 0, 1.7, 0, 0, 0.9, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 107 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 108 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, -0.592, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 1.9, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 109 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 3.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 110 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0.6, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 111 {0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 112 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 113 {0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.036, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.592, 0, 1.7, 1.6, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 114 {0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 115 {2, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 116 {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 117 {0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 118 {0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.45, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5465, 1.139175, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0.7, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.6, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0}
## 119 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 120 {0, 2.1, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0}
## 121 {2, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 122 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 1.7, 0, 0, 1.9, 0, 0, 0, 1.7, 0, 0, 2, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 1.7, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.97835, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 123 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, -1.6, 0, 0, 0, 0, 0, 0}
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## 127 {0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0}
## 128 {0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0.37835}
## 129 {0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 1.55, 0, 0, 0, 0, 0, 0, 0, 0, 0.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.15, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 2.4, 0, 0, 0, 1.6395, 0, 2.94555, 0, 0, 0, 0, 0, 0, 0}
## 130 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.233, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 132 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0.7, 0, 0, -2.1, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 2.1, 0, 0, -1.7, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
## 133 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 2.4, 0, 0, 1.6, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 134 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.65, 0, 3.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 135 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 136 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 137 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 138 {0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 1.9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0}
## 139 {0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 1.2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 2.1, 0, 0}
## 140 {2, 0, 0, 0, 0, 0, 0, 0, 0, 3.1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 2, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 141 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 1.5, 0, 2.1, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 142 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 1.4, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 144 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, -3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 145 {0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0.3}
## 146 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.814, 0, 0, -0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0}
## 147 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 1.036, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 148 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1.6, 0, -2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 149 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.814, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.993, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 1.7, 0}
## 150 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 151 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 152 {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 3.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.109, 0, 0, -0.6, 0, 0, 0, 0, 0, 1.11, 0, -1.6428, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 3, 0, 0}
## 153 {2, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 154 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1.3, 0, 0, 0, 0, 0.7, 0, 0, 1.9, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0.2, 0, 0.8, 0, 2.4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0}
## 155 {0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.036, 0, 0, 0, 0, -0.592, 0, 0, 0, 0, -1.036, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 156 {0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 1.4, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 157 {2, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 158 {0, 0, 0, 0, 3.2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 159 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 160 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 1.7, 2, 1.3, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0.8, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0}
## 161 {0, 0, 0, -0.45, 0, 0, 0.85, 0, 0, 0, 1, 0, 0, 0, 0, 0.95, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.35, 0, 0, 0, 0, -0.37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.65, 2.25, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 162 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 163 <NA>
## 164 {0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 165 {0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, -1.7, 0, -1.7, 1.4, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 1.7, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 2.4, 0.7, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0}
## 166 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 167 {0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 168 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.998, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 1.2, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 169 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0}
## 170 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1.6, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 171 {1.4, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0}
## 172 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 1, 0, 2, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0}
## 173 {0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0.8, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, -1.7, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 1.8, 0, 0, 0, 0, 1.9, 0, 0}
## 174 {0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 1.1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 2, 0, 0, 0, 0, -1.4, 0, 1.2, 0, 0, 0, 2, 0, 0, 0, 1, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 175 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -2.4, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.57835, 0, 0, 0, 0, 0, 0.593, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 176 {0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, -0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, -0.8965, 0, 0, 0, 0, 0, 0.9, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 177 {0, 2.1, -1.7, 0, 0, 0, 0, 0.8, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, -1.7, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 1.4, 0, -1.593, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0}
## 178 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.393, 1.57835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 1, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 179 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 180 {0, 0, 0.75, 0, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 2.85, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2.85, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 3.6, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 2.967525, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, -1.5, 0, 2.7, 0, 0, 0, 0, 0, 0}
## 181 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 2.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 182 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 2.3, 1.9, 0, 0, 0, 3.2, 0, 0, 0, 0, 1.4, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 2.3, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, -0.6, 0, 0, 0, 0, 0, 1.1, 0, -1.7, 0, 0, -2.1, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 1.4, 2.3, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, -0.074, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0}
## 183 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0.3, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 184 {0, 0, 2.1, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.628, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 1.9, 0, 0, 1.5}
## 185 {0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0.7, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.9, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 186 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 187 {0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 1.8, 1.3, 0, 0, -1.7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0}
## 188 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 189 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 190 <NA>
## 191 {0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 192 {0, 0, 0, 0, 0, 2.2, 1.6, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0.3, 0, 1.1, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0}
## 193 {0, 0, 0, 0, 0, 0, 0, 0, 0.8, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 194 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0}
## 195 {0, 0, 0, 0, 0, 0, 1.37835, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 196 {0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 197 {0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 198 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.193, 0, 0, 0, 0, 0, 0, 3.493, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 199 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 2, 0, 0}
## 200 {0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, -1.48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.693, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, -1.693, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 201 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0.2, 0, 0.8, 0, 2.4, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9}
## 202 {1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0}
## 203 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 204 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 205 {0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, -2.7, 0, 0, 2.6, 0}
## 206 {0, 0, 0, 0, 0, -1.7, 0, 1.7, 0, 0, 0, 0, 0, 0, 2, -0.9, 0, 0, 2.2, 1.7, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 2.1, 0, 0, 2, 0, 0}
## 207 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 1.093, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 208 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, -1.5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2.7}
## 209 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, -1.1, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0}
## 210 {0, 0, 0, 0, 1.3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2.4, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, -0.592, 0, 0, 0, 0, 0, 0}
## 211 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0}
## 212 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 213 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 214 {0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.9, 0, 0, 0}
## 215 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7}
## 216 {0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 2.2637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0}
## 217 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0.8, 0, 1.4, 0, 0, 0, 0}
## 218 {0, 2.1, 0.6, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 1.5, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0.6, 0, 1.5, -1.3, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 1.5, 0, 2, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.593, 0, 0, 1.1, 0, 1.9, 0, 2.4}
## 219 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, -2.7637, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0}
## 220 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 221 {0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 222 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.0637, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0}
## 223 {0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 224 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0}
## 225 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 1.593, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8363, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 226 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1465, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, -1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 227 {0, -0.75, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 3.45, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3}
## 228 {0, 0, 0, 0, 0, 0, 1.1, 0, -1.5, -1.7, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 229 {0, 0, 0, -0.2, 0, 0, 1, 0, 0.4, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, -0.85, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 2.1, -2.55, 0, 0, 0, 0, 0, 0, 0, 2.1, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 230 {0, 0, 0, 0, 2.2637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 1.407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, -1.4, 0, 0, 0.8, 0, 0, 0.1, 0, 0, 0.8, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0}
## 231 {0, 0, 0, 0, 0, 0, 1.05, 0, 0.45, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, -0.407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 232 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 233 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 2.6, 0, 0, 1.3}
## 234 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3}
## 235 {0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0}
## 236 {2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.893, 0, 1.4637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.993, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 237 {0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3567, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.814, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 1.8, 0}
## 238 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 239 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4}
## 240 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 241 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.666, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1.3, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 242 {0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 1.4, 0, 2.8, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2.633, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.633, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.633, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.633, 0, 0, 2, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0}
## 243 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.25, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.331, 0, 0, 0, 0, 1.65, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 244 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, -1.7, 0, 0, 1.1, 0, 0, 0, 0, -1.2, 0, 0, 0, -0.814, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 1.4, 0, 1.9, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 245 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 1.5, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 246 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0}
## 247 {1, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1965, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0.65, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 248 {0, 0, 0, -1.2, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0}
## 249 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 2.3, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0}
## 250 {1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.95, 0, 0, 0, -1.332, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.6, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0}
## 251 {0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.993, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 1.3, 0, 0, 1.6, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 252 {0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1.2965, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0.55, 0, -0.9965, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, -0.85, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 253 {0, 0, 0, 2.1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 254 {0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0}
## 255 {0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, -1.7, 0, 0, 0, 0, 0, 0}
## 256 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.693, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 257 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.307, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 258 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 259 {0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 264 {0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0}
## 265 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
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## 268 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 2.1, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 269 {0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 270 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 1.6, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 271 {0, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 2.407, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 1.4, 0, 0, 0, 1.8, 2.1, 0, 0, 0, 0, 0, 0, 0}
## 272 {0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 273 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 3, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, -0.666, 0, 0, 0, 0.555, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 274 {0, 0, 0, 0, 1.093, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 275 {0, 0, 0, 0, 0, 0.5465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.6, 0, 0, 0, 0}
## 276 {0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 277 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 278 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, -2.5, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0}
## 279 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.37835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 280 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 281 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 282 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 2, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1}
## 283 {0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 284 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 1.2, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 1.1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 285 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 286 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 287 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 288 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.933, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 289 {0, 0, 0, 2.1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, -1.036, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 290 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, -1.7, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.8, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 291 {0, 0, 2, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 1.4, 0, -1.7, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 292 {0.8, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0.666, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0}
## 293 <NA>
## 294 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 295 {2, 0, 0, 0, 0, 0, 0, 0, 0, 1.82165, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.1, 0, 0, 0, 0}
## 296 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 297 {0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0.15, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.65, 0, 0, 0, -2.94555, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.6, 0, 2.4, 0, 0, 0, 0, 0}
## 298 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 1.4, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0}
## 299 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 1.4, 0, 2.3, 0, 0, 0}
## 300 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 301 {-1.4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.5, 0, 0, -1.4, 0, 0, 0, -1.693, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 302 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, -1.8, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1}
## 303 {0, 0, 0, 0, 0, 1.3, 2, 0, 0, 0, 0, 0, 1.7, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0}
## 304 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 2, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0}
## 305 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 2.493, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 306 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0}
## 307 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 1.9, 0, 0}
## 308 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 309 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 310 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 311 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0}
## 312 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.993, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8}
## 313 {0, 0, 0, 0, 0.7, 0, 0, 1.9, 1.4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, -2.4, 0, 0, 0, -2.693, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0}
## 314 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.007, 0, -1.8363, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.593, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0}
## 315 {0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.05, 0, 0.55, 0, 0, 0, 0, -0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.3, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.5895, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95}
## 316 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.593, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.258, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0}
## 317 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 318 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 319 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0.593, 0, 0, 0, 0, 0, -2, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 320 {0, 0, 2, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0}
## 321 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1.77835, 0, -1.7, 1.4, 0, 0, 0, 0, 1.593, 0, 1.0637, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 1.1, 0, 0, 0}
## 322 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 323 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 2.8, 0, 0, 0, -1.4, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, -1.5, 0, 0, 0, 2.4, 0, 1.9, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.332, 0, 1.4, -1.7, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.593, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 324 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.033, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2.633, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 325 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2.1, 0, 0.7, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.907, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.666, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.893, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2.2, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 326 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 1.67835}
## 327 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 328 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 329 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0}
## 330 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0.8, 0, 1.1, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 1.1, 0, 0, 0, 0}
## 331 {0, 0, 0, 0, 0, 1, 0, 0, 0, -1.15, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.15, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0}
## 332 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, -2.1, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 1, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 333 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 2.507, 0, 0, 0, 0, 0, 0}
## 334 {1, 0, 0, 0, 0, 0, 1.1965, 0, 0, -0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 3, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0}
## 335 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, -1.7, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0}
## 336 {0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.693}
## 337 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 338 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 339 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, -1.7, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0.7, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 340 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.4, 0, 0, 0}
## 341 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.293, 0, 1.6637, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 1.8, 1.8, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 342 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 343 {0, 0, 0, 0, 1.9, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1.7, 0, 3.2, 0, 0.9, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.6, 0}
## 344 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 345 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0.8, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.193, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0}
## 346 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 347 {0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0.7, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 348 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 1.6, 1.3, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 349 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.4637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2.633}
## 350 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 351 {0, 0, 0, 0, 0, 0, 0, 0, 1.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0.85, 0, 0, 0, 0, 0, 0, 0, -1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.665, 0, -1.887, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 352 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.693, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
## 353 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.82165, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 1.7, -3.2, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.592, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 1.7, -3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 1.7, 0, 0, -3.2}
## 354 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7}
## 355 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 356 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0.3, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 357 {2, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 358 {0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 359 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0}
## 360 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 361 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 3.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 362 {0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 1.1, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 2, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 363 {0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.1, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 364 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 1.5, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0}
## 365 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 1.9, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, -0.814, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 1.6637, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0}
## 366 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 1.1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 367 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0}
## 368 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, -1.7, 1.4, 0}
## 369 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 370 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 371 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 372 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0.7, 0, 0, 0.3, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 373 {2, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0}
## 374 {0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 1.6, 0, 0, 0, 0, 0, 0.9, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 375 {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, -0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.999, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 3.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.443, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, -1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 376 {0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0}
## 377 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 378 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, -1.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 379 {0, 1.4, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 2, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 1.5, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 380 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 1.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 2.2}
## 381 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9}
## 382 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 383 {1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7035, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 384 {0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 1.5637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 385 {2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 386 {0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0.7, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, -1.7, 0, -1.7, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0}
## 387 {0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1.4, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 388 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0}
## 389 {0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 390 {0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0}
## 391 {0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 392 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0.296, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7637, 0, 0, -0.9, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, -1.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, -1.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 1.9, 0, 0, 0}
## 393 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.37835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
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## 395 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0.8, 0, 1.4}
## 396 {0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3.1, 1.7, 0, 0, 0, 0, 0, 0, 0, 2.493, 0, 0, 1.9}
## 397 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 398 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0}
## 399 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 2.393, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.67835, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 400 {0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, -1.4, 0, 0, 0, 0}
## 401 {0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 1.8, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 2, 0}
## 402 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0}
## 403 {0, 0, 0, 2, 0, 0, 0, 0, 0, 1.993, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 404 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 2.093, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.9, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 405 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0.8, 0, 0, 0, 1.1, -1.7, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 1.4, 0, 0}
## 406 {0, 0, 1.05, 0, 1, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 0, 4.05, 0, 0, 0, 0, 0, 3.1395, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.9, 0, 2.85, 0, 0, 0, 0, 0, 3, 0, 0, 0}
## 407 {0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 1.6, 0, 0, 0, 0, 0, 1.9, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 408 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.554, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.3, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 409 {0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0}
## 410 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0}
## 411 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 412 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 413 {0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 2.1, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 414 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 415 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.293}
## 416 {0, 2, 0, 0, 0, 1, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0}
## 417 {0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, -1.7, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 418 {2, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 2, 0, 0}
## 419 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 1.1, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 420 {0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 421 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.093, 0, 0}
## 422 {0, 0, 0, 0, 3.2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 423 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0.85, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0}
## 424 {2, 0, 0, 0, 0, 0, 0, 2.7, 0, 1.7, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 425 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2.633, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 426 {0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.993}
## 427 {0, 0, 0, 0, 2, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 1.3, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 428 {0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 1.9, 0, 0, 0, 1.3, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 429 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 430 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 431 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 1, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.507, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.258, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 432 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 1.7637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 2.7, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0.8, 1.3, 0, 0, 0, 0, 0, 0, 0}
## 433 {0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.893, 0, 0, 0, 0}
## 434 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, -1.5, 0, 0, 0}
## 435 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 436 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.888, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 1.5, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 437 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 1.8, 0, 0, 0, 0, 0, 1.3, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 438 {0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 439 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1.707, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0}
## 440 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, -1.7, 1.4}
## 441 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 442 {0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0}
## 443 {0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 2, 0, 0}
## 444 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 445 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 2, 0, 0}
## 446 {0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 1.4, 0, 0, 0, 0, 0, 1.6, 0, 1.4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 1.3, 0, 0, 0, 0}
## 447 {0, 0, 0, 0, 0, 0, 0, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.607, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.82165, 0, 0, 3.2, 0, 0, 0, 0, 0, 0}
## 448 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, -0.222, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, -0.5, 0.55, 0, 0, 0.55, 0, 0, 0, 0, 0, 0, 0, 1.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.629, 0, 0, 0.9, 0, 0, 1.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0}
## 449 {0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 450 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.05, 0, 0, 2.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0}
## 451 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3.1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 452 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 453 {0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, -1.7, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0}
## 454 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 455 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 2, 0}
## 456 {0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 0, 0, 0, 0}
## 457 {0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1.3, 0, 1.5, 1.3, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 458 {0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0}
## 459 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.592, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.607, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, -2.072, 0, 0, 0, 0, 0, 0}
## 460 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 461 {0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 462 {0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0.85, 0, 0.4, 0.9, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.15, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1.1965, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1.05, 0, 0, 0, -1.95, 0, 0, 0, 0, 0, -1.887, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.45, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 463 {0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 464 {0, 0, 0, 0, 0, 0, 1.3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.67835, 0, 0, 0, 0}
## 465 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, -3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 1.7, -3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 466 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0.7, 0, 0, 1.6}
## 467 {2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.993, 0, 0, 0, 0, -2}
## 468 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 2, 0}
## 469 {0, 0, 0, 0, 0, -1.6, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1}
## 470 {0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.2, 0, 0, 0, 0}
## 471 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 1.1, 0, 0, 0, 0}
## 472 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 1.4, 0, 0, 1.5, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, -1.7, -1.4, 0}
## 473 {2, 0, 0, 0, 0, 0, 2.093, 0, 2.1637, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 474 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 475 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0}
## 476 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 477 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 478 {0, 0, 0, 0, 0, 0, 2.393, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 479 {0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 3.2, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0}
## 480 {0, 0.8, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0}
## 481 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 482 {1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.95, 0.85, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, -2.55, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, -2.55, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.55, 0, 0, 0, 1.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.3, 2.25}
## 483 {0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0}
## 484 {0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 485 {0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 0, 0, 0, 0, 2.85, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 486 {0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.91882, 0, 0, 0, 0, 0, 0, -0.666, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0}
## 487 {0, 0, 0, 0, 0, 0, 0, 0, 1.7363, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 488 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 489 {1, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0.85, 0, 0, 1.6, 0, 0, 0, 0.65, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, -0.85, 0, 0, 2.25, 4.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.517525, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.15, 0, 0, 0, 0, 0, 0, 0, 4.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 0, 0, 4.8, 2.1, 0, 0, 0, -1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.55, 0, 0, 0, 2.1, 0, 1.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0}
## 490 {0, 0, 0, 0, 0, 0, 0, 0, -0.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.85, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.85, 0, 0, 0, 0, 0, 0, 0, -0.85, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 491 {0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 2.8, 0, 0}
## 492 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, -1.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 493 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0.593, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0}
## 494 {0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 495 {0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.55, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.65, 0, 0, 0, 0, 0, 0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 496 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.888, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.293, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0}
## 497 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.393, 0, 1.4637, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 498 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, -0.3, 0, 0, 0, 0, 1.2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.97835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 2.6, 0, 0, 1.4, -1.7, 1.8, 0, 0, 0}
## 499 {0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 500 {0, 0, 0, 1, 0, 0, 0, 0, 0, 0.4, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.776, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## compound pos neu neg but_count
## 1 0.836 0.109 0.891 0.000 0
## 2 0.952 0.108 0.878 0.013 0
## 3 0.985 0.152 0.838 0.010 0
## 4 0.972 0.138 0.807 0.054 0
## 5 0.883 0.112 0.888 0.000 0
## 6 0.558 0.137 0.756 0.107 0
## 7 0.966 0.148 0.798 0.054 1
## 8 0.751 0.065 0.920 0.015 0
## 9 0.743 0.097 0.850 0.053 0
## 10 0.735 0.081 0.897 0.022 0
## 11 0.919 0.120 0.880 0.000 0
## 12 0.949 0.115 0.885 0.000 0
## 13 0.751 0.074 0.888 0.039 0
## 14 0.950 0.194 0.806 0.000 0
## 15 0.827 0.047 0.953 0.000 0
## 16 NA NA NA NA NA
## 17 0.933 0.101 0.899 0.000 0
## 18 0.933 0.135 0.848 0.017 0
## 19 0.511 0.039 0.938 0.023 0
## 20 0.893 0.071 0.929 0.000 1
## 21 0.485 0.073 0.898 0.029 1
## 22 0.942 0.132 0.840 0.028 0
## 23 0.989 0.128 0.858 0.015 0
## 24 0.869 0.184 0.816 0.000 0
## 25 0.822 0.074 0.908 0.018 0
## 26 0.897 0.127 0.857 0.016 0
## 27 0.802 0.069 0.931 0.000 0
## 28 0.421 0.093 0.844 0.063 0
## 29 0.836 0.057 0.943 0.000 0
## 30 0.599 0.058 0.942 0.000 0
## 31 0.878 0.086 0.897 0.017 0
## 32 0.459 0.088 0.912 0.000 0
## 33 0.958 0.134 0.866 0.000 0
## 34 0.912 0.112 0.888 0.000 0
## 35 0.735 0.133 0.763 0.103 0
## 36 0.946 0.197 0.803 0.000 0
## 37 0.943 0.143 0.857 0.000 0
## 38 0.875 0.096 0.886 0.018 0
## 39 0.958 0.224 0.776 0.000 1
## 40 0.103 0.070 0.871 0.059 0
## 41 0.869 0.164 0.723 0.113 0
## 42 0.960 0.164 0.817 0.019 0
## 43 0.968 0.127 0.860 0.013 0
## 44 0.898 0.092 0.894 0.014 0
## 45 0.906 0.097 0.903 0.000 0
## 46 0.000 0.000 1.000 0.000 0
## 47 0.791 0.108 0.892 0.000 0
## 48 0.852 0.110 0.890 0.000 0
## 49 0.625 0.083 0.840 0.077 0
## 50 0.975 0.168 0.807 0.025 0
## 51 0.979 0.216 0.784 0.000 0
## 52 0.961 0.243 0.757 0.000 1
## 53 0.959 0.152 0.816 0.032 0
## 54 0.968 0.146 0.854 0.000 0
## 55 0.919 0.214 0.752 0.034 0
## 56 0.915 0.107 0.893 0.000 1
## 57 0.949 0.102 0.898 0.000 0
## 58 0.933 0.121 0.842 0.037 1
## 59 0.938 0.081 0.919 0.000 0
## 60 0.984 0.096 0.872 0.032 0
## 61 NA NA NA NA NA
## 62 0.946 0.109 0.891 0.000 0
## 63 0.799 0.068 0.919 0.013 1
## 64 -0.361 0.058 0.873 0.069 0
## 65 0.994 0.209 0.783 0.008 0
## 66 0.962 0.165 0.795 0.040 0
## 67 0.868 0.110 0.838 0.052 0
## 68 0.153 0.131 0.748 0.121 0
## 69 0.994 0.219 0.781 0.000 0
## 70 0.686 0.081 0.858 0.061 2
## 71 0.881 0.066 0.913 0.021 0
## 72 0.587 0.054 0.921 0.026 0
## 73 0.981 0.160 0.827 0.013 0
## 74 0.944 0.145 0.835 0.020 0
## 75 0.000 0.000 1.000 0.000 0
## 76 0.891 0.091 0.909 0.000 0
## 77 -0.318 0.110 0.779 0.111 0
## 78 0.790 0.086 0.886 0.028 0
## 79 0.900 0.064 0.936 0.000 0
## 80 0.691 0.073 0.886 0.040 0
## 81 0.948 0.135 0.865 0.000 0
## 82 0.994 0.272 0.728 0.000 1
## 83 0.718 0.107 0.860 0.033 0
## 84 0.917 0.132 0.868 0.000 0
## 85 0.987 0.129 0.871 0.000 0
## 86 0.961 0.181 0.792 0.027 1
## 87 0.902 0.111 0.849 0.040 0
## 88 0.625 0.070 0.930 0.000 1
## 89 0.625 0.073 0.927 0.000 0
## 90 -0.700 0.077 0.812 0.110 0
## 91 0.992 0.169 0.813 0.018 1
## 92 0.825 0.082 0.918 0.000 1
## 93 0.959 0.183 0.817 0.000 0
## 94 0.351 0.075 0.877 0.048 2
## 95 0.945 0.157 0.768 0.075 0
## 96 0.968 0.195 0.805 0.000 0
## 97 0.625 0.083 0.875 0.043 0
## 98 0.972 0.114 0.855 0.031 1
## 99 0.889 0.200 0.800 0.000 0
## 100 0.974 0.155 0.845 0.000 0
## 101 0.910 0.154 0.822 0.024 0
## 102 0.848 0.083 0.917 0.000 0
## 103 0.869 0.107 0.860 0.033 1
## 104 0.896 0.084 0.886 0.030 0
## 105 0.969 0.141 0.835 0.023 0
## 106 0.940 0.135 0.835 0.030 0
## 107 0.922 0.102 0.898 0.000 0
## 108 0.954 0.112 0.880 0.007 0
## 109 0.937 0.076 0.903 0.021 0
## 110 0.974 0.135 0.865 0.000 0
## 111 0.940 0.098 0.891 0.011 0
## 112 0.918 0.108 0.892 0.000 0
## 113 0.938 0.124 0.839 0.036 0
## 114 0.791 0.045 0.955 0.000 0
## 115 0.961 0.227 0.773 0.000 0
## 116 0.960 0.172 0.828 0.000 0
## 117 0.878 0.087 0.913 0.000 0
## 118 0.931 0.105 0.863 0.032 1
## 119 0.494 0.084 0.878 0.038 0
## 120 0.984 0.199 0.786 0.015 0
## 121 0.952 0.162 0.838 0.000 0
## 122 0.984 0.200 0.775 0.025 0
## 123 0.822 0.065 0.921 0.014 0
## 124 0.906 0.161 0.740 0.098 0
## 125 0.960 0.137 0.849 0.014 0
## 126 0.934 0.170 0.769 0.061 0
## 127 0.904 0.115 0.859 0.026 1
## 128 0.962 0.185 0.815 0.000 0
## 129 0.981 0.094 0.901 0.004 2
## 130 0.767 0.083 0.883 0.034 0
## 131 0.273 0.083 0.844 0.073 0
## 132 0.973 0.141 0.822 0.038 0
## 133 0.969 0.194 0.784 0.022 0
## 134 0.986 0.173 0.827 0.000 2
## 135 -0.115 0.041 0.922 0.037 1
## 136 0.891 0.114 0.886 0.000 0
## 137 0.883 0.079 0.910 0.012 0
## 138 0.948 0.083 0.906 0.010 0
## 139 0.914 0.135 0.843 0.022 0
## 140 0.947 0.147 0.822 0.031 0
## 141 0.925 0.113 0.868 0.018 0
## 142 -0.252 0.045 0.906 0.049 0
## 143 0.994 0.241 0.759 0.000 0
## 144 0.743 0.079 0.892 0.029 0
## 145 0.742 0.051 0.926 0.023 0
## 146 0.611 0.052 0.922 0.026 0
## 147 0.798 0.080 0.920 0.000 0
## 148 0.557 0.125 0.825 0.050 0
## 149 0.942 0.107 0.853 0.039 0
## 150 0.619 0.078 0.903 0.018 1
## 151 0.807 0.105 0.875 0.020 0
## 152 0.982 0.129 0.840 0.031 1
## 153 0.962 0.098 0.902 0.000 0
## 154 0.991 0.203 0.797 0.000 0
## 155 -0.488 0.030 0.910 0.060 0
## 156 0.955 0.148 0.837 0.014 0
## 157 0.906 0.154 0.846 0.000 0
## 158 0.978 0.142 0.858 0.000 0
## 159 0.919 0.090 0.886 0.023 0
## 160 0.973 0.142 0.836 0.022 0
## 161 0.980 0.177 0.801 0.023 1
## 162 0.758 0.067 0.933 0.000 0
## 163 NA NA NA NA NA
## 164 0.511 0.058 0.899 0.043 0
## 165 0.968 0.185 0.743 0.073 0
## 166 0.818 0.075 0.925 0.000 0
## 167 0.872 0.136 0.864 0.000 0
## 168 0.955 0.094 0.897 0.009 0
## 169 0.643 0.046 0.954 0.000 1
## 170 0.859 0.109 0.891 0.000 0
## 171 0.933 0.113 0.887 0.000 0
## 172 0.937 0.174 0.785 0.041 0
## 173 0.986 0.252 0.731 0.017 0
## 174 0.989 0.242 0.745 0.013 0
## 175 0.245 0.069 0.882 0.050 0
## 176 0.823 0.084 0.897 0.019 1
## 177 0.671 0.131 0.762 0.107 0
## 178 0.940 0.201 0.743 0.056 0
## 179 0.612 0.105 0.831 0.065 0
## 180 0.996 0.309 0.648 0.044 1
## 181 0.911 0.113 0.847 0.040 0
## 182 0.973 0.171 0.738 0.090 0
## 183 0.942 0.111 0.879 0.011 0
## 184 0.839 0.148 0.776 0.076 0
## 185 0.986 0.175 0.825 0.000 1
## 186 0.586 0.047 0.953 0.000 0
## 187 0.913 0.146 0.802 0.052 0
## 188 0.938 0.124 0.876 0.000 0
## 189 0.844 0.079 0.907 0.014 0
## 190 NA NA NA NA NA
## 191 0.953 0.116 0.854 0.031 0
## 192 0.941 0.192 0.808 0.000 0
## 193 0.957 0.094 0.882 0.024 0
## 194 0.557 0.074 0.926 0.000 0
## 195 0.725 0.136 0.805 0.059 0
## 196 0.294 0.061 0.890 0.050 0
## 197 -0.101 0.096 0.803 0.101 0
## 198 0.965 0.132 0.856 0.012 0
## 199 0.952 0.099 0.901 0.000 0
## 200 0.808 0.083 0.852 0.066 0
## 201 0.981 0.126 0.874 0.000 0
## 202 0.946 0.124 0.857 0.019 1
## 203 0.925 0.072 0.928 0.000 0
## 204 0.785 0.087 0.913 0.000 0
## 205 0.985 0.206 0.760 0.034 0
## 206 0.980 0.193 0.768 0.039 0
## 207 0.526 0.051 0.917 0.033 0
## 208 0.908 0.124 0.857 0.019 0
## 209 0.637 0.073 0.872 0.055 0
## 210 0.986 0.191 0.802 0.008 0
## 211 0.958 0.168 0.832 0.000 0
## 212 0.920 0.108 0.882 0.010 0
## 213 0.421 0.072 0.881 0.047 0
## 214 0.984 0.148 0.852 0.000 0
## 215 0.955 0.087 0.913 0.000 0
## 216 0.957 0.094 0.897 0.009 0
## 217 0.778 0.125 0.851 0.024 0
## 218 0.979 0.225 0.705 0.069 0
## 219 0.850 0.066 0.916 0.018 0
## 220 0.681 0.117 0.845 0.038 0
## 221 0.948 0.090 0.898 0.011 0
## 222 0.982 0.166 0.821 0.013 0
## 223 0.886 0.093 0.862 0.045 0
## 224 0.971 0.076 0.924 0.000 1
## 225 0.233 0.089 0.822 0.089 0
## 226 0.140 0.053 0.912 0.035 2
## 227 0.989 0.239 0.735 0.025 1
## 228 -0.778 0.048 0.851 0.100 0
## 229 0.961 0.204 0.706 0.091 1
## 230 0.931 0.208 0.754 0.038 0
## 231 0.624 0.060 0.929 0.011 1
## 232 0.881 0.095 0.905 0.000 0
## 233 0.979 0.253 0.747 0.000 0
## 234 0.947 0.160 0.827 0.013 0
## 235 0.883 0.165 0.835 0.000 0
## 236 0.978 0.125 0.875 0.000 0
## 237 0.961 0.125 0.864 0.010 0
## 238 0.889 0.111 0.847 0.042 0
## 239 0.927 0.100 0.883 0.018 1
## 240 0.889 0.117 0.870 0.013 0
## 241 0.890 0.072 0.928 0.000 0
## 242 0.994 0.204 0.796 0.000 0
## 243 -0.161 0.079 0.845 0.076 2
## 244 0.910 0.129 0.813 0.059 0
## 245 0.967 0.115 0.885 0.000 0
## 246 0.949 0.127 0.873 0.000 0
## 247 0.975 0.150 0.841 0.009 1
## 248 0.968 0.134 0.840 0.026 0
## 249 0.967 0.182 0.818 0.000 0
## 250 0.444 0.134 0.758 0.108 1
## 251 0.980 0.168 0.818 0.014 0
## 252 0.969 0.131 0.851 0.019 2
## 253 0.852 0.135 0.834 0.030 0
## 254 -0.649 0.051 0.846 0.103 0
## 255 0.440 0.073 0.897 0.030 0
## 256 0.079 0.024 0.954 0.022 0
## 257 0.872 0.154 0.846 0.000 0
## 258 0.273 0.035 0.945 0.020 0
## 259 0.625 0.054 0.919 0.027 0
## 260 0.903 0.106 0.849 0.045 0
## 261 0.926 0.144 0.821 0.035 0
## 262 0.980 0.195 0.726 0.079 0
## 263 0.822 0.111 0.866 0.023 0
## 264 0.681 0.079 0.879 0.042 0
## 265 0.961 0.144 0.843 0.013 0
## 266 0.889 0.135 0.865 0.000 0
## 267 0.922 0.105 0.849 0.047 1
## 268 0.832 0.122 0.833 0.044 0
## 269 0.926 0.142 0.858 0.000 0
## 270 0.951 0.108 0.871 0.021 0
## 271 0.949 0.178 0.786 0.036 0
## 272 0.572 0.059 0.918 0.023 0
## 273 0.959 0.119 0.862 0.020 1
## 274 0.954 0.133 0.867 0.000 0
## 275 0.957 0.161 0.839 0.000 1
## 276 0.836 0.040 0.960 0.000 0
## 277 0.681 0.039 0.961 0.000 0
## 278 0.862 0.110 0.852 0.038 0
## 279 0.950 0.103 0.897 0.000 0
## 280 0.936 0.102 0.898 0.000 0
## 281 0.778 0.112 0.836 0.052 0
## 282 0.972 0.133 0.858 0.009 0
## 283 0.886 0.045 0.942 0.013 0
## 284 0.976 0.146 0.842 0.012 0
## 285 0.934 0.112 0.888 0.000 0
## 286 0.935 0.140 0.860 0.000 0
## 287 0.660 0.076 0.873 0.051 0
## 288 0.839 0.151 0.812 0.037 0
## 289 0.939 0.107 0.881 0.012 0
## 290 0.765 0.094 0.856 0.050 0
## 291 0.660 0.113 0.801 0.085 0
## 292 0.969 0.116 0.884 0.000 0
## 293 NA NA NA NA NA
## 294 0.840 0.078 0.922 0.000 0
## 295 0.889 0.099 0.866 0.035 0
## 296 0.402 0.060 0.901 0.040 0
## 297 0.946 0.229 0.707 0.064 1
## 298 0.988 0.129 0.871 0.000 0
## 299 0.893 0.146 0.807 0.048 0
## 300 0.840 0.099 0.901 0.000 0
## 301 0.573 0.119 0.794 0.086 0
## 302 0.226 0.072 0.868 0.060 0
## 303 0.953 0.190 0.810 0.000 0
## 304 0.822 0.098 0.869 0.033 0
## 305 0.967 0.083 0.917 0.000 0
## 306 0.671 0.077 0.897 0.026 0
## 307 0.942 0.122 0.878 0.000 0
## 308 0.758 0.090 0.910 0.000 0
## 309 0.670 0.051 0.949 0.000 0
## 310 0.862 0.102 0.898 0.000 0
## 311 0.926 0.146 0.854 0.000 0
## 312 0.869 0.096 0.904 0.000 0
## 313 0.930 0.138 0.796 0.066 0
## 314 0.927 0.173 0.768 0.059 0
## 315 0.967 0.103 0.877 0.019 2
## 316 0.860 0.150 0.823 0.027 0
## 317 0.494 0.041 0.944 0.015 0
## 318 0.557 0.061 0.893 0.046 0
## 319 -0.341 0.055 0.874 0.072 0
## 320 0.875 0.117 0.829 0.054 0
## 321 0.950 0.203 0.738 0.059 0
## 322 0.542 0.051 0.923 0.025 0
## 323 0.984 0.125 0.835 0.040 0
## 324 0.930 0.140 0.860 0.000 0
## 325 0.983 0.161 0.819 0.020 0
## 326 0.979 0.123 0.860 0.017 0
## 327 0.852 0.099 0.874 0.027 0
## 328 0.660 0.051 0.949 0.000 0
## 329 0.886 0.140 0.860 0.000 0
## 330 0.934 0.218 0.754 0.028 0
## 331 0.714 0.064 0.911 0.025 1
## 332 0.979 0.128 0.853 0.019 0
## 333 0.808 0.086 0.914 0.000 0
## 334 0.967 0.194 0.795 0.011 1
## 335 0.900 0.132 0.817 0.051 0
## 336 0.926 0.129 0.835 0.036 0
## 337 0.363 0.071 0.874 0.054 0
## 338 0.952 0.149 0.851 0.000 0
## 339 0.710 0.137 0.794 0.069 0
## 340 0.919 0.237 0.763 0.000 0
## 341 0.982 0.252 0.748 0.000 0
## 342 -0.557 0.052 0.843 0.105 0
## 343 0.979 0.284 0.716 0.000 0
## 344 0.718 0.094 0.906 0.000 0
## 345 0.974 0.148 0.852 0.000 0
## 346 0.403 0.035 0.943 0.022 0
## 347 0.946 0.094 0.873 0.033 0
## 348 0.961 0.122 0.865 0.013 0
## 349 0.964 0.160 0.840 0.000 0
## 350 0.637 0.050 0.950 0.000 0
## 351 0.140 0.074 0.856 0.070 1
## 352 0.585 0.105 0.831 0.065 0
## 353 0.753 0.123 0.789 0.088 0
## 354 0.882 0.120 0.834 0.046 0
## 355 0.726 0.048 0.952 0.000 0
## 356 0.967 0.147 0.819 0.035 0
## 357 0.585 0.091 0.835 0.074 0
## 358 -0.250 0.058 0.864 0.078 0
## 359 0.735 0.077 0.885 0.038 0
## 360 0.459 0.111 0.889 0.000 0
## 361 0.938 0.128 0.857 0.016 0
## 362 0.944 0.157 0.788 0.055 0
## 363 0.948 0.142 0.858 0.000 0
## 364 0.718 0.084 0.876 0.041 0
## 365 0.988 0.170 0.812 0.018 0
## 366 0.982 0.180 0.820 0.000 0
## 367 0.961 0.114 0.886 0.000 0
## 368 0.226 0.113 0.796 0.091 0
## 369 0.881 0.071 0.929 0.000 0
## 370 0.931 0.105 0.879 0.016 0
## 371 0.955 0.101 0.889 0.010 0
## 372 0.966 0.154 0.831 0.015 0
## 373 0.930 0.115 0.866 0.019 0
## 374 0.612 0.144 0.775 0.081 0
## 375 0.940 0.121 0.855 0.024 1
## 376 0.902 0.185 0.815 0.000 0
## 377 0.844 0.075 0.913 0.012 0
## 378 0.560 0.061 0.914 0.024 0
## 379 0.959 0.183 0.803 0.015 0
## 380 0.989 0.166 0.834 0.000 0
## 381 0.790 0.091 0.892 0.017 0
## 382 0.637 0.060 0.922 0.018 0
## 383 0.403 0.019 0.981 0.000 1
## 384 0.921 0.124 0.876 0.000 0
## 385 0.910 0.147 0.834 0.019 0
## 386 0.764 0.133 0.777 0.090 0
## 387 0.984 0.206 0.778 0.016 0
## 388 0.938 0.113 0.887 0.000 0
## 389 0.862 0.081 0.902 0.016 0
## 390 0.882 0.070 0.900 0.030 2
## 391 0.974 0.186 0.800 0.014 0
## 392 0.515 0.095 0.824 0.081 0
## 393 0.921 0.058 0.942 0.000 0
## 394 0.939 0.097 0.875 0.027 0
## 395 0.361 0.077 0.864 0.059 0
## 396 0.989 0.182 0.811 0.007 0
## 397 0.883 0.074 0.926 0.000 0
## 398 0.402 0.073 0.894 0.033 0
## 399 0.982 0.155 0.830 0.015 0
## 400 0.000 0.081 0.832 0.087 0
## 401 0.982 0.161 0.811 0.028 0
## 402 0.402 0.053 0.947 0.000 0
## 403 0.926 0.183 0.767 0.050 0
## 404 0.935 0.108 0.892 0.000 0
## 405 0.933 0.210 0.762 0.027 0
## 406 0.990 0.213 0.787 0.000 2
## 407 0.981 0.154 0.846 0.000 0
## 408 0.642 0.114 0.798 0.089 1
## 409 0.893 0.133 0.833 0.034 0
## 410 0.910 0.109 0.891 0.000 0
## 411 0.700 0.060 0.940 0.000 0
## 412 0.922 0.078 0.922 0.000 0
## 413 0.964 0.159 0.841 0.000 0
## 414 0.772 0.119 0.881 0.000 0
## 415 0.938 0.122 0.878 0.000 0
## 416 0.982 0.200 0.800 0.000 0
## 417 -0.052 0.059 0.880 0.061 0
## 418 0.862 0.137 0.802 0.061 0
## 419 0.963 0.143 0.830 0.027 0
## 420 0.871 0.082 0.918 0.000 0
## 421 0.963 0.197 0.803 0.000 0
## 422 0.945 0.114 0.886 0.000 0
## 423 0.892 0.077 0.923 0.000 1
## 424 0.979 0.129 0.871 0.000 0
## 425 0.924 0.154 0.846 0.000 0
## 426 0.913 0.103 0.868 0.029 0
## 427 0.959 0.123 0.847 0.030 0
## 428 0.900 0.158 0.815 0.027 0
## 429 0.791 0.098 0.879 0.023 0
## 430 0.963 0.110 0.890 0.000 0
## 431 0.970 0.112 0.876 0.012 0
## 432 0.980 0.143 0.847 0.011 0
## 433 0.855 0.087 0.896 0.017 0
## 434 0.936 0.135 0.831 0.034 0
## 435 0.743 0.059 0.941 0.000 0
## 436 0.977 0.102 0.891 0.006 0
## 437 0.956 0.203 0.759 0.038 0
## 438 0.832 0.092 0.889 0.019 0
## 439 0.840 0.094 0.906 0.000 0
## 440 0.743 0.122 0.814 0.064 0
## 441 0.765 0.063 0.923 0.014 0
## 442 0.964 0.199 0.780 0.020 0
## 443 0.917 0.176 0.824 0.000 0
## 444 0.935 0.105 0.880 0.015 0
## 445 0.103 0.084 0.847 0.069 0
## 446 0.982 0.203 0.781 0.015 0
## 447 0.973 0.135 0.839 0.026 0
## 448 0.959 0.135 0.846 0.020 1
## 449 0.832 0.067 0.933 0.000 0
## 450 0.861 0.064 0.936 0.000 1
## 451 0.946 0.138 0.820 0.042 0
## 452 0.691 0.049 0.951 0.000 0
## 453 0.848 0.122 0.812 0.066 0
## 454 0.511 0.039 0.961 0.000 0
## 455 0.940 0.170 0.803 0.027 0
## 456 0.822 0.094 0.863 0.043 0
## 457 0.940 0.150 0.830 0.020 0
## 458 0.599 0.075 0.902 0.023 0
## 459 0.924 0.112 0.860 0.027 0
## 460 0.772 0.117 0.823 0.060 0
## 461 0.802 0.075 0.905 0.020 0
## 462 0.941 0.156 0.791 0.053 1
## 463 0.898 0.099 0.882 0.019 0
## 464 0.895 0.105 0.895 0.000 0
## 465 0.827 0.113 0.831 0.056 0
## 466 0.973 0.213 0.753 0.034 0
## 467 -0.904 0.041 0.856 0.103 0
## 468 0.956 0.123 0.877 0.000 0
## 469 0.848 0.095 0.886 0.019 0
## 470 0.900 0.103 0.897 0.000 1
## 471 0.718 0.100 0.847 0.053 0
## 472 0.681 0.091 0.859 0.051 0
## 473 0.932 0.277 0.723 0.000 0
## 474 0.700 0.050 0.950 0.000 0
## 475 0.944 0.111 0.879 0.009 0
## 476 0.718 0.064 0.919 0.018 0
## 477 0.836 0.115 0.862 0.023 0
## 478 0.750 0.052 0.948 0.000 0
## 479 0.986 0.087 0.908 0.005 0
## 480 0.965 0.108 0.892 0.000 0
## 481 0.945 0.111 0.876 0.013 0
## 482 0.927 0.157 0.767 0.075 1
## 483 0.859 0.090 0.910 0.000 0
## 484 0.974 0.133 0.867 0.000 0
## 485 0.944 0.194 0.771 0.035 1
## 486 -0.689 0.040 0.869 0.091 0
## 487 0.409 0.066 0.934 0.000 0
## 488 0.836 0.076 0.908 0.015 0
## 489 0.996 0.245 0.723 0.031 1
## 490 -0.421 0.049 0.886 0.065 1
## 491 0.900 0.107 0.874 0.018 0
## 492 0.202 0.038 0.936 0.027 1
## 493 0.958 0.129 0.859 0.012 0
## 494 0.778 0.104 0.869 0.028 0
## 495 0.402 0.049 0.931 0.021 1
## 496 0.906 0.094 0.882 0.025 0
## 497 0.825 0.040 0.953 0.008 0
## 498 0.948 0.185 0.748 0.067 0
## 499 0.710 0.059 0.925 0.017 0
## 500 0.345 0.070 0.883 0.047 1Then the compound score (Hutto, C. & Gilbert, E.
,2014) is used for sentiment analysis by summing the valence scores of
each word in the lexicon, adjusted according to the rules, and then
normalized to be between -1 (most extreme negative) and +1 (most extreme
positive). And typical threshold values are:
positive sentiment: compound score
>= 0.05
neutral sentiment: (compound score
> -0.05) and (compound score < 0.05)
negative sentiment: compound score
<= -0.05
vader_inttutor_summary <- vader_inttutor %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_inttutor_summary## positive negative neutral <NA> ratio
## 1 475 17 3 5 0.03578947This case study focus on the textual data - abstracts of 1000 articles of the intelligent tutor systems domain. Through wrangling the data into a one-token-per-row tidy text format and using simple word counts and word clouds to explore the top tokens; and by summing the valence scores of each word in the lexicon to explore text sentiment.
The most frequent words or phrases used are knowledge based model. Actually it demonstrates that the educational fields in the ITSs are mainly computer sciences. Action-condition rule-based reasoning, data mining, and Bayesian network are the most frequent artificial intelligent techniques applied in the ITSs. These techniques enable ITSs to deliver adaptive guidance and instruction, evaluate learners, define and update the learner’s model, and classify or cluster learners. Specifically, the performance of the system, learner’s performance, and experiences are used for evaluation of ITSs. Also from the word clouds, web and computer are shown up rather than mobile, which demonstrates that most ITSs are designed for web user interfaces. Due to the important role of a cell phone in facilitating personalized learning and given the low rate of using mobile-based ITSs, the development and evaluation of mobile-based ITSs could a be a trend for future, which can be a take away for the researchers who are interested in ITSs domain.
In general, it’s no doubt that the research community are positive towards the intelligent tutor systems. Most interesting here is the negative ones. The articles with neutral or negative attitudes mention that although these ITSs could facilitate reasoning in the learning process, these systems have rarely been applied in experimental courses including problem-solving, decision-making in physics, chemistry, and clinical fields, which provides the gap for future research.
Limitations
This data product only provides a general view of major topics within intelligent tutor systems domain through analyzing the top tokens of abstracts of 1000 articles, but it is limited in providing insights of topic trends and lifestyles, and how the major topics shift along the time.
Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative approach to improving education. Routledge.
Estrellado, R. A., Freer, E., Mostipak, J., Rosenberg, J. M., & Velásquez, I. C. (2020). Data science in education using R. Routledge.
Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 216-225).