Envisioning Information Museum of Cognition

Document structure

Challenge

When doing interdisciplinary research at the intersection of social sciences and AI, taking a computational approach to (some) of the reading feels necessary. Sailors know how to read the weather, use compasses and maps, to get from here to there. My idea of computational reading is in the same spirit: conceptually mapping already published scientific work is as important as ever, but the speed at which new material is getting published raises the bar with regard to the one’s ability to read, map and focus, especially in interdisciplinary contexts.

Having started work on this topic just a few months back SAIL 2.6. Processes of Social Inclusion and Exclusion in Hybrid Teams, I am currently working on understanding the conceptualisations of hybrid intelligence systems. I am also hybrid intelligence system-ing my own workflow (I know, meta- 🐳) by connecting what I already know (a little bit of R, python, visualisation, some statistics, and a lot about data structures) with what a LLM knows (translating code between programming languages, or providing useful code chunks when the human request is clearly specified), in order to facilitate my own science communication flows with human colleagues.

I could, of course, use one of the myriad tools for mapping and visualising literature connections, or just simply text as data, and I often do. Connected Papers, Research Rabbit, Voyant Tools are all part of my stack of text mapping tools on rotation. I find each of them useful in different ways at the beginning of a conceptual exploration, but there comes a point when it’s becoming increasingly important to know what data goes into the prediction, and how exactly the prediction is computed.

Goal: shinyapps.io by Posit (former R) seems to be compatible with building a literature mapping tool for internal use and science communication

Intermediary step: this markdown report illustrating some of the visual elements that could be part of a literature mapping interface later down the line.

Task specification

  • CONCEPT: hybrid intelligence system*
  • DATA: SCOPUS (n=62), WoS (n=35), openALEX(n=28), EBSCO (n=17)

After navigating a series of export and data compatibility issues, the illustrations below are based on the data extracted from SCOPUS.

In the future, as an automatic literature viz and mapping tool, I would see the shiny code connected with the openAlex API, to provide real-time information about a querried concept.

Broad exploration of the bibliographic data using the bibliometrix package

install.packages("bibliometrix", repos=contrib.url("https://cran.r-project.org"))
library(bibliometrix)
## Please note that our software is open source and available for use, distributed under the MIT license.
## When it is used in a publication, we ask that authors properly cite the following reference:
## 
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, 
##                         Journal of Informetrics, 11(4), pp 959-975, Elsevier.
## 
## Failure to properly cite the software is considered a violation of the license.
##                         
## For information and bug reports:
##                         - Take a look at https://www.bibliometrix.org
##                         - Send an email to info@bibliometrix.org   
##                         - Write a post on https://github.com/massimoaria/bibliometrix/issues
##                         
## Help us to keep Bibliometrix and Biblioshiny free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
## 
##                         
## To start with the Biblioshiny app, please digit:
## biblioshiny()
load("~/Desktop/xai_demo/scopus_his.rdata")
## 
## 
## MAIN INFORMATION ABOUT DATA
## 
##  Timespan                              1994 : 2023 
##  Sources (Journals, Books, etc)        48 
##  Documents                             62 
##  Annual Growth Rate %                  3.86 
##  Document Average Age                  6.85 
##  Average citations per doc             16.42 
##  Average citations per year per doc    2.018 
##  References                            2510 
##  
## DOCUMENT TYPES                     
##  article                28 
##  book chapter           2 
##  conference paper       29 
##  conference review      3 
##  
## DOCUMENT CONTENTS
##  Keywords Plus (ID)                    551 
##  Author's Keywords (DE)                207 
##  
## AUTHORS
##  Authors                               172 
##  Author Appearances                    200 
##  Authors of single-authored docs       10 
##  
## AUTHORS COLLABORATION
##  Single-authored docs                  13 
##  Documents per Author                  0.36 
##  Co-Authors per Doc                    3.23 
##  International co-authorships %        19.35 
##  
## 
## Annual Scientific Production
## 
##  Year    Articles
##     1994        1
##     1995        1
##     1996        1
##     2000        1
##     2001        1
##     2005        1
##     2006        1
##     2008        1
##     2009        1
##     2010        1
##     2011        3
##     2012        1
##     2013        2
##     2014        2
##     2015        1
##     2016        2
##     2017        3
##     2018        5
##     2019        3
##     2020       11
##     2021        8
##     2022        8
##     2023        3
## 
## Annual Percentage Growth Rate 3.86 
## 
## 
## Most Productive Authors
## 
##     Authors        Articles    Authors        Articles Fractionalized
## 1  LASECKI WS             3 NA NA                                3.00
## 2  NA NA                  3 EL-BAZ AH                            2.00
## 3  BAHRAMMIRZAEE A        2 BAHRAMMIRZAEE A                      1.25
## 4  BENIAK R               2 ASTANIN SV                           1.00
## 5  BITTNER EAC            2 HERRMANN T                           1.00
## 6  BOUSDEKIS A            2 KAMAR E                              1.00
## 7  CORREIA A              2 MATEJ HRKALOVIC T                    1.00
## 8  EBEL P                 2 REITEMEYER B                         1.00
## 9  EL-BAZ AH              2 VAUGHAN JW                           1.00
## 10 FONSECA B              2 WANG Y                               1.00
## 
## 
## Top manuscripts per citations
## 
##                                                    Paper                                    DOI  TC TCperYear  NTC
## 1  BAHRAMMIRZAEE A, 2010, NEURAL COMPUT APPL                      10.1007/s00521-010-0362-z     308     22.00 1.00
## 2  KAMAR E, 2016, IJCAI INT JOINT CONF ARTIF INTELL               NA                             96     12.00 1.88
## 3  HANRAHAN BV, 2012, PROC ACM CONF COMPUT SUPPORT COOP WORK CSCW 10.1145/2141512.2141550        68      5.67 1.00
## 4  CHANG HC, 2000, COMPUT GEOSCI                                  10.1016/S0098-3004(00)00010-8  67      2.79 1.00
## 5  ARMAGHANI DJ, 2020, APPL SCI                                   10.3390/app10061904            60     15.00 6.73
## 6  DELLERMANN D, 2019, PROC ANNU HAWAII INT CONF SYST SCI         NA                             53     10.60 2.27
## 7  CHENG MY, 2013, AUTOM CONSTR                                   10.1016/j.autcon.2013.05.018   52      4.73 1.96
## 8  VAUGHAN JW, 2018, J MACH LEARN RES                             NA                             49      8.17 2.72
## 9  ATSALAKIS GS, 2018, EUR J OPER RES                             10.1016/j.ejor.2018.01.044     24      4.00 1.33
## 10 KAVITHA CT, 2014, APPL SOFT COMPUT J                           10.1016/j.asoc.2013.10.034     24      2.40 1.60
## 
## 
## Corresponding Author's Countries
## 
##    Country Articles   Freq SCP MCP MCP_Ratio
## 1  INDIA          6 0.1765   6   0     0.000
## 2  GERMANY        3 0.0882   1   2     0.667
## 3  POLAND         3 0.0882   2   1     0.333
## 4  USA            3 0.0882   2   1     0.333
## 5  BRAZIL         2 0.0588   2   0     0.000
## 6  CANADA         2 0.0588   1   1     0.500
## 7  CHINA          2 0.0588   2   0     0.000
## 8  FRANCE         2 0.0588   1   1     0.500
## 9  GREECE         2 0.0588   0   2     1.000
## 10 SPAIN          2 0.0588   1   1     0.500
## 
## 
## SCP: Single Country Publications
## 
## MCP: Multiple Country Publications
## 
## 
## Total Citations per Country
## 
##      Country      Total Citations Average Article Citations
## 1  FRANCE                     332                   166.000
## 2  USA                        184                    61.333
## 3  GREECE                      84                    42.000
## 4  CHINA                       52                    26.000
## 5  INDIA                       39                     6.500
## 6  EGYPT                       23                    23.000
## 7  GERMANY                     19                     6.333
## 8  BRAZIL                      13                     6.500
## 9  SPAIN                       10                     5.000
## 10 UKRAINE                     10                    10.000
## 
## 
## Most Relevant Sources
## 
##                                                                                                                          Sources       
## 1  CEUR WORKSHOP PROCEEDINGS                                                                                                           
## 2  LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
## 3  PROCEEDINGS OF THE ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES                                                        
## 4  NEURAL COMPUTING AND APPLICATIONS                                                                                                   
## 5  PROCEEDINGS OF THE ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK CSCW                                                       
## 6  2020 IEEE PES/IAS POWERAFRICA POWERAFRICA 2020                                                                                      
## 7  ACM INTERNATIONAL CONFERENCE PROCEEDING SERIES                                                                                      
## 8  ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING                                                                                       
## 9  ANNUAL REVIEWS IN CONTROL                                                                                                           
## 10 APPLIED INTELLIGENCE                                                                                                                
##    Articles
## 1         6
## 2         6
## 3         3
## 4         2
## 5         2
## 6         1
## 7         1
## 8         1
## 9         1
## 10        1
## 
## 
## Most Relevant Keywords
## 
##      Author Keywords (DE)      Articles     Keywords-Plus (ID)     Articles
## 1  HYBRID INTELLIGENCE                9 HYBRID INTELLIGENCE              34
## 2  HYBRID INTELLIGENCE SYSTEM         6 ARTIFICIAL INTELLIGENCE          21
## 3  ARTIFICIAL INTELLIGENCE            5 DECISION MAKING                  11
## 4  CROWDSOURCING                      5 INTELLIGENT SYSTEMS              10
## 5  HYBRID INTELLIGENCE SYSTEMS        5 INTELLIGENCE SYSTEMS              9
## 6  NEURAL NETWORK                     4 NEURAL NETWORKS                   7
## 7  AI                                 3 HYBRID INTELLIGENCE SYSTEM        6
## 8  EXPERT SYSTEM                      3 COGNITIVE SYSTEMS                 5
## 9  PREDICTIVE MAINTENANCE             3 CROWDSOURCING                     5
## 10 ARTIFICIAL NEURAL NETWORKS         2 HYBRID SYSTEMS                    5

## Loading required package: sp
## ### Welcome to rworldmap ###
## For a short introduction type :   vignette('rworldmap')
## 17 codes from your data successfully matched countries in the map
## 0 codes from your data failed to match with a country code in the map
## 226 codes from the map weren't represented in your data

## 
## SCOPUS DB: Searching local citations (LCS) by document titles (TI) and DOIs...
## 
## Found 11 documents with no empty Local Citations (LCS)

## 
##  Legend
## 
##                                                       Label
## 1                 BAHRAMMIRZAEE A, 2010, NEURAL COMPUT APPL
## 2          KAMAR E, 2016, IJCAI INT JOINT CONF ARTIF INTELL
## 3    DELLERMANN D, 2019, PROC ANNU HAWAII INT CONF SYST SCI
## 4                        VAUGHAN JW, 2018, J MACH LEARN RES
## 5                        BAHRAMMIRZAEE A, 2011, APPL INTELL
## 6                       EL-BAZ AH, 2015, NEURAL COMPUT APPL
## 7                   HERRMANN T, 2020, LECT NOTES COMPUT SCI
## 8  CORREIA A, 2020, PROC - IEEE INT CONF BIG DATA, BIG DATA
## 9                      WELLSANDT S, 2021, IFAC-PAPERSONLINE
## 10                     POSER M, 2022, LECT NOTES COMPUT SCI
## 11                                GARDECKI A, 2020, SENSORS
## 12                        SHPAK N, 2022, CEUR WORKSHOP PROC
## 13       ELSHAN E, 2021, PROC ANNU HAWAII INT CONF SYST SCI
## 14                        FILL HG, 2020, CEUR WORKSHOP PROC
## 15                   WIETHOF C, 2022, LECT NOTES COMPUT SCI
## 16                       HASOON SO, 2013, J ENG SCI TECHNOL
## 17                        EL-BAZ AH, 2018, APPL MAT INF SCI
## 18 CORREIA A, 2021, PROC - IEEE INT CONF BIG DATA, BIG DATA
## 19                      WELLSANDT S, 2022, ANNU REV CONTROL
## 20          LI MM, 2023, PROC ANNU HAWAII INT CONF SYST SCI
## 21                                GARDECKI A, 2023, SENSORS
##                                                                                                                                                         Author_Keywords
## 1                ARTIFICIAL NEURAL NETWORKS;  CREDIT EVALUATION;  EXPERT SYSTEM;  FINANCIAL PREDICTION AND PLANNING;  HYBRID INTELLIGENT SYSTEMS;  PORTFOLIO MANAGEMENT
## 2                                                                                                                                                                  <NA>
## 3                                                                                                                                                                  <NA>
## 4                                      BEHAVIORAL EXPERIMENTS;  CROWDSOURCING;  DATA GENERATION;  HYBRID INTELLIGENCE;  INCENTIVES;  MECHANICAL TURK;  MODEL EVALUATION
## 5                                                                                           CREDIT RANKING;  EXPERT SYSTEM;  HYBRID INTELLIGENT SYSTEM;  NEURAL NETWORK
## 6                                       BREAST CANCER DIAGNOSIS;  ENSEMBLE CLASSIFIER;  FEATURE SELECTION;  HYBRID INTELLIGENCE SYSTEM;  K-NEAREST NEIGHBOR;  ROUGH SET
## 7                                                                              HYBRID INTELLIGENCE;  MACHINE LEARNING;  PREDICTIVE MAINTENANCE;  SOCIO-TECHNICAL DESIGN
## 8  ARTIFICIAL INTELLIGENCE;  CROWDSOURCING;  HUMAN-AI HYBRID INTERACTION;  HUMAN-MACHINE SYMBIOSIS;  RESEARCH EVALUATION;  SCIENCE MAPPING;  SCIENTOMETRICS;  WORKFLOWS
## 9                             ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE;  HUMAN-AUTOMATION INTEGRATION;  HYBRID INTELLIGENCE SYSTEMS;  PREDICTIVE MAINTENANCE
## 10                                                                                                   CUSTOMER SERVICE;  HYBRID INTELLIGENCE SYSTEM;  REAL-TIME DECISION
## 11                                                       AI SYSTEM;  EXPERIENCE-CENTERED PARADIGM;  HUMAN SUPPORTED SYSTEMS;  PROCESS CONTROL;  USER EXPERIENCE TESTING
## 12                                                      FOREIGN ECONOMIC ACTIVITY;  INTELLIGENT SYSTEMS;  MORPHOLOGICAL ANALYSIS;  PUBLIC-PRIVATE PARTNERSHIP;  SUPPORT
## 13                                                                                                                                                                 <NA>
## 14                                                                                                                                                                 <NA>
## 15                                                                                                                  CUSTOMER SERVICE;  GAMIFICATION;  HUMAN-IN-THE-LOOP
## 16                                                                                                     HYBRID SYSTEM;  NEURAL NETWORK;  RADIAL BASIS FUNCTION;  WINDOWS
## 17                                                                          ENSEMBLE CLASSIFIER;  HEART DISEASE;  HYBRID INTELLIGENCE SYSTEM;  NEURO-FUZZY;  ROUGH SETS
## 18     ARTIFICIAL INTELLIGENCE;  CROWDSOURCING;  HUMAN-AI INTERACTION;  HYBRID COLLECTIVE INTELLIGENCE;  INNOVATIVE SCIENTIFIC DISCOVERY;  TECHNOLOGY ACCEPTANCE MODELS
## 19                            ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE;  HUMAN-AUTOMATION INTEGRATION;  HYBRID INTELLIGENCE SYSTEMS;  PREDICTIVE MAINTENANCE
## 20                                                                                                     AI;  HUMAN-IN-THE-LOOP;  HYBRID INTELLIGENCE;  IT SUPPORT;  ITSM
## 21                               CO-BOTS;  EMPLOYEE TRAINING;  HUMAN RESOURCE;  HUMAN–MACHINE INTERACTION;  INDUSTRY 5.0;  INTERACTION QUALITY SENSOR;  QUALITY OF WORK
##                                                                                                                                                                                                                                                                                                                                                                                                                                                        KeywordsPlus
## 1                                                                                                                                                                    ARTIFICIAL NEURAL NETWORK;  CREDIT EVALUATIONS;  FINANCIAL PREDICTION;  HYBRID INTELLIGENT SYSTEMS;  PORTFOLIO MANAGEMENT; BEHAVIORAL RESEARCH;  EXPERT SYSTEMS;  FINANCIAL DATA PROCESSING;  FORECASTING;  INTELLIGENT SYSTEMS;  INVESTMENTS;  NETWORK MANAGEMENT;  PLANNING; NEURAL NETWORKS
## 2                                                                                                                                                                                                                                                                                                                            HYBRID SYSTEMS; AI SYSTEMS;  HUMAN INTELLIGENCE;  HYBRID INTELLIGENCE;  REASONING METHODS;  RECENT RESEARCHES; ARTIFICIAL INTELLIGENCE
## 3                                                                                                                                                                                                                                   MACHINE LEARNING; ARTIFICIAL GENERAL INTELLIGENCES;  BUSINESS APPLICATIONS;  DEVELOPMENT METHOD;  HYBRID INTELLIGENCE;  INTERDISCIPLINARY RESEARCH;  STRUCTURED DESIGN;  SYSTEM DEVELOPERS;  TECHNOLOGICAL ADVANCES; TAXONOMIES
## 4                                                                                                                                                                                                                         ARTIFICIAL INTELLIGENCE;  COGNITIVE SYSTEMS;  CROWDSOURCING;  LEARNING SYSTEMS;  PROGRAM DEBUGGING; BEHAVIORAL EXPERIMENT;  DATA GENERATION;  HYBRID INTELLIGENCE;  INCENTIVES;  MECHANICAL TURKS;  MODEL EVALUATION; BEHAVIORAL RESEARCH
## 5                           ARTIFICIAL NEURAL NETWORK;  BANKING INDUSTRY;  CENTRAL BANK;  COMMERCIAL BANK;  CREDIT ALLOCATIONS;  CREDIT EVALUATIONS;  CREDIT RANKING;  CREDIT RISKS;  ECONOMIC GROWTHS;  HYBRID INTELLIGENCE;  HYBRID INTELLIGENT SYSTEM;  PRODUCTION COMPANIES;  RANKING MODEL;  TRANSFORMATIONAL MODEL;  UNIQUE FEATURES; ECONOMIC ANALYSIS;  EXPERT SYSTEMS;  HYBRID SYSTEMS;  INDUSTRY;  INTELLIGENT SYSTEMS;  RISK ASSESSMENT; NEURAL NETWORKS
## 6  CLASSIFICATION (OF INFORMATION);  DECISION TABLES;  DECISION THEORY;  DIAGNOSIS;  DISEASES;  EXTRACTION;  FEATURE EXTRACTION;  HYBRID SYSTEMS;  INTELLIGENT SYSTEMS;  MOTION COMPENSATION;  NEAREST NEIGHBOR SEARCH; BREAST CANCER DIAGNOSIS;  CLASSIFICATION AND RECOGNITION;  ENSEMBLE CLASSIFIERS;  HYBRID INTELLIGENCE;  HYBRID INTELLIGENT SYSTEM;  K-NEAREST NEIGHBOR CLASSIFIER;  K-NEAREST NEIGHBORS;  WISCONSIN BREAST CANCER DATASET; ROUGH SET THEORY
## 7                                                                                                                                                                                                            HUMAN COMPUTER INTERACTION;  MACHINE LEARNING;  TAXONOMIES; APPLICATION AREA;  APPROPRIATE DESIGNS;  HYBRID INTELLIGENCE;  INTEGRATED SUPPORTS;  PLANT OPERATORS;  PRODUCTION PLANT;  SOCIO-TECHNICAL DESIGNS;  SOCIOTECHNICAL; PREDICTIVE MAINTENANCE
## 8                                                                                                                                                              ARTIFICIAL INTELLIGENCE;  DATA STREAMS;  DIGITAL STORAGE;  LARGE DATASET; ALGORITHMIC APPROACH;  HETEROGENEOUS DATASETS;  INTERDISCIPLINARY WORK;  MACHINE INTELLIGENCE;  METHODOLOGICAL FRAMEWORKS;  SCIENTIFIC BREAKTHROUGH;  SCIENTOMETRIC ANALYSIS;  SOCIOTECHNICAL SYSTEMS; BEHAVIORAL RESEARCH
## 9                                                                                   ARTIFICIAL INTELLIGENCE;  COGNITIVE SYSTEMS;  DECISION MAKING;  PERSONNEL; AUTOMATION INTEGRATION;  ENGINEERING APPLICATION OF ARTIFICIAL INTELLIGENCE;  ENGINEERING APPLICATIONS;  HUMAN-AUTOMATION INTEGRATION;  HYBRID INTELLIGENCE;  HYBRID INTELLIGENCE SYSTEM;  INTELLIGENCE SYSTEMS;  INTELLIGENT ASSISTANTS;  MAINTENANCE SYSTEMS;  PREDICTIVE MAINTENANCE; MAINTENANCE
## 10                                                                                                                                                                                         ARTIFICIAL INTELLIGENCE;  DECISION MAKING; CUSTOMER-SERVICE;  DESIGN PRINCIPLES;  HYBRID INTELLIGENCE;  HYBRID INTELLIGENCE SYSTEM;  INTELLIGENCE SYSTEMS;  ONLINE CUSTOMERS;  PERSONALIZED SERVICE;  REAL TIME DECISIONS;  SERVICE COMPANIES;  SERVICE ENCOUNTER; SALES
## 11                                                                                                     SOCIAL ROBOTS;  SOFTWARE AGENTS;  STAGES; ASSEMBLY QUALITY;  HYBRID INTELLIGENCE;  INTERACTION SYSTEMS;  PROCESS EVALUATION;  PROCESS STAGES;  RELATIONAL AGENTS;  SELF-OPTIMIZING;  TOWER OF HANOI; USER EXPERIENCE; ARTIFICIAL INTELLIGENCE;  COMPUTER INTERFACE;  HUMAN;  SOFTWARE; ARTIFICIAL INTELLIGENCE;  HUMANS;  SOFTWARE;  USER-COMPUTER INTERFACE
## 12                                                                  ECONOMICS;  EXPERT SYSTEMS;  FUZZY NEURAL NETWORKS;  GENETIC ALGORITHMS;  LINGUISTICS;  MERGERS AND ACQUISITIONS; APPLIED INTELLIGENT SYSTEMS;  ECONOMIC ACTIVITIES;  FOREIGN ECONOMIC ACTIVITY;  HYBRID INTELLIGENCE;  INTELLIGENCE SYSTEMS;  MORPHOLOGICAL ANALYSIS;  NATIONAL ECONOMY;  NEURAL NETWORK EXPERT SYSTEM;  PUBLIC/PRIVATE PARTNERSHIPS;  REGULATORY SUPPORT; INTELLIGENT SYSTEMS
## 13                                                                                                                                                                                                                                                                                                     BLACK BOXES;  BODY OF KNOWLEDGE;  CHANGE OF BEHAVIOR;  CURRENT SITUATION;  DESIGN-SCIENCE RESEARCHES;  HYBRID INTELLIGENCE;  MUSIC INDUSTRY; SYSTEMS SCIENCE
## 14                                                                                                                                                                                                                                                                                                                            DATA HANDLING;  KNOWLEDGE ENGINEERING;  MACHINE LEARNING;  SPRINGS (COMPONENTS); HUMAN INTERVENTION;  HYBRID INTELLIGENCE; BLOCKCHAIN
## 15                                                                                                                                                                                                                                          MOTIVATION; COLLABORATIVE LEARNING;  CUSTOMER-SERVICE;  DESIGN PRINCIPLES;  GAMIFICATION;  HUMAN-IN-THE-LOOP;  HYBRID INTELLIGENCE;  INTELLIGENCE SYSTEMS;  MACHINE-LEARNING;  RESEARCH GAPS;  SERVICE EMPLOYEES; SALES
## 16                                                                                                                                                                                                                                                                                                                                                                                                                                                             <NA>
## 17                                                                                                                                                                                                                                                                                                                                                                                                                                                             <NA>
## 18                                                                                              ARTIFICIAL INTELLIGENCE; COLLECTIVE INTELLIGENCES;  HUMAN-ARTIFICIAL INTELLIGENCE INTERACTION;  HYBRID COLLECTIVE INTELLIGENCE;  INNOVATIVE SCIENTIFIC DISCOVERY;  INTENTIONALITY;  SCIENTIFIC COLLABORATION;  SCIENTIFIC DISCOVERY;  TECHNOLOGY ACCEPTANCE MODEL;  THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY(UTAUT);  THROUGH THE LENS; CROWDSOURCING
## 19                                            ARTIFICIAL INTELLIGENCE;  HUMAN COMPUTER INTERACTION;  NATURAL LANGUAGE PROCESSING SYSTEMS;  PERSONNEL TRAINING; AUTOMATION INTEGRATION;  ENGINEERING APPLICATION OF ARTIFICIAL INTELLIGENCE;  ENGINEERING APPLICATIONS;  HUMAN-AUTOMATION INTEGRATION;  HYBRID INTELLIGENCE;  HYBRID INTELLIGENCE SYSTEM;  INTELLIGENCE SYSTEMS;  INTELLIGENT ASSISTANTS;  MAINTENANCE SYSTEMS;  PREDICTIVE MAINTENANCE; MAINTENANCE
## 20                                                                                                                                                                                                                ARTIFICIAL INTELLIGENCE; FRONTLINE;  HUMAN-IN-THE-LOOP;  HYBRID INTELLIGENCE;  INTELLIGENCE SYSTEMS;  IT SERVICES;  IT SUPPORT;  IT-SERVICE-MANAGEMENT;  LOOP CONFIGURATION;  MANAGEMENT SUPPORT;  SERVICE MANAGEMENT; HUMAN COMPUTER INTERACTION
## 21                                                                                                                                                                              BOTNET;  COMMERCE;  PERSONNEL;  QUALITY CONTROL; CO-BOT;  EMPLOYEE TRAINING;  HUMAN MACHINE INTERACTION;  HYBRID INTELLIGENCE;  INDUSTRY 5.0;  INFORMATION CHANNELS;  INTELLIGENCE SYSTEMS;  INTERACTION QUALITY;  INTERACTION QUALITY SENSOR;  QUALITY OF WORK; PERSONNEL TRAINING
##                                  DOI Year LCS GCS
## 1          10.1007/s00521-010-0362-z 2010   1 308
## 2                                    2016   7  96
## 3                                    2019   5  53
## 4                                    2018   1  49
## 5          10.1007/s10489-009-0177-8 2011   1  24
## 6          10.1007/s00521-014-1731-9 2015   1  23
## 7       10.1007/978-3-030-50334-5_20 2020   1   6
## 8  10.1109/BigData50022.2020.9378096 2020   1   3
## 9       10.1016/j.ifacol.2021.08.005 2021   1   2
## 10      10.1007/978-3-031-06516-3_11 2022   1   1
## 11                 10.3390/s20154074 2020   1   1
## 12                                   2022   0   0
## 13                                   2021   0   3
## 14                                   2020   0   2
## 15       10.1007/978-3-031-05643-7_7 2022   0   0
## 16                                   2013   0   1
## 17              10.18576/amis/120205 2018   0   0
## 18 10.1109/BigData52589.2021.9671358 2021   0   1
## 19   10.1016/j.arcontrol.2022.04.001 2022   0  11
## 20                                   2023   0   0
## 21                 10.3390/s23083826 2023   0   0

Conclusions, limitations, and future steps

  • The bibliometrix package is a rich tool for visualising bibliographies.
  • it could be particularily useful for visualising large volumes of data when mapping a new field

However,

  • a number of visualisations are nested under the same function, which makes bibliometrix as is unsuitable for embedding it into a shiny web app (eg. when a function returns 4 plots, the shiny app only shows the last plot)
  • some of the bibliometrix plots are based on other visualisation packages (eg. ggrepel)

=> so with a bit more digging, the visualisations may be unpacked so as to be able to dashboard a computational lit review in shiny

Bibliography and resources

Package

Aria, M. & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007 Add to Citavi project by DOI (https://doi.org/10.1016/j.joi.2017.08.007 Add to Citavi project by DOI).

Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. ArXiv. https://arxiv.org/abs/2205.01833

Data Appendix

The bibliography that constitutes the basis for the visuals

1st International Conference on Artificial Intelligence in HCI, AI-HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020: Vol. 12217 LNCS. (2020). Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088796298&partnerID=40&md5=a51e492303665890325e3218aeca2675 37th SGAI International Conference on Artificial Intelligence, AI 2017: Vol. 10630 LNAI. (2017). Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037747481&partnerID=40&md5=37238902a4835447287978b11fcb0032 AAAI-MAKE 2020—Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, Volume I. (2020). 2600. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085171933&partnerID=40&md5=449f8858bcd2c4f97705ebce2b628ee2 Al Murayziq, T. S., Kapetanakis, S., & Petridis, M. (2017). Predicting dust storms using hybrid intelligence system: Vol. 10630 LNAI (p. 351). Scopus. https://doi.org/10.1007/978-3-319-71078-5_29 Armaghani, D. J., Asteris, P. G., Fatemi, S. A., Hasanipanah, M., Tarinejad, R., Rashid, A. S. A., & Huynh, V. V. (2020). On the use of neuro-swarm system to forecast the pile settlement. Applied Sciences (Switzerland), 10(6). Scopus. https://doi.org/10.3390/app10061904 Astanin, S. V. (2001). Distinguishing features of analysis and simulation of hybrid intelligence systems. Upravlyayushchie Sistemy i Mashiny, 1, 16–24. Scopus. Atsalakis, G. S., Atsalaki, I. G., & Zopounidis, C. (2018). Forecasting the success of a new tourism service by a neuro-fuzzy technique. European Journal of Operational Research, 268(2), 716–727. Scopus. https://doi.org/10.1016/j.ejor.2018.01.044 Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165–1195. Scopus. https://doi.org/10.1007/s00521-010-0362-z Bahrammirzaee, A., Ghatari, A. R., Ahmadi, P., & Madani, K. (2011). Hybrid credit ranking intelligent system using expert system and artificial neural networks. Applied Intelligence, 34(1), 28–46. Scopus. https://doi.org/10.1007/s10489-009-0177-8 Bodyanskiy, Ye., & Dolotov, A. (2008). Image processing using self-learning fuzzy spiking neural network in the presence of overlapping classes. 213–216. Scopus. https://doi.org/10.1109/BEC.2008.4657517 Chang, H.-C., Kopaska-Merkel, D. C., Chen, H.-C., & Rocky Durrans, S. (2000). Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system. Computers and Geosciences, 26(5), 591–601. Scopus. https://doi.org/10.1016/S0098-3004(00)00010-8 Chen, J.-C., Chang, M., & Heh, J.-S. (2006). Advanced manufacturing process control system for PECVD process utilizing fuzzy logic technique. Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch’eng Hsuebo Pao, 27(6), 633–638. Scopus. Cheng, M.-Y., Hoang, N.-D., & Wu, Y.-W. (2013). Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: A Taiwan case study. Automation in Construction, 35, 306–313. Scopus. https://doi.org/10.1016/j.autcon.2013.05.018 Cios, K. J., Goodenday, L. S., & Sztandera, L. M. (1994). Combining fuzzy generalized operators with decision rules generated by machine learning algorithms. IEEE Engineering in Medicine and Biology Magazine, 13(5), 723–729. Scopus. https://doi.org/10.1109/51.334635 Correia, A., Fonseca, B., Paredes, H., Chaves, R., Schneider, D., & Jameel, S. (2021). Determinants and Predictors of Intentionality and Perceived Reliability in Human-AI Interaction as a Means for Innovative Scientific Discovery. 3681–3684. Scopus. https://doi.org/10.1109/BigData52589.2021.9671358 Correia, A., Jameel, S., Schneider, D., Paredes, H., & Fonseca, B. (2020). A Workflow-Based Methodological Framework for Hybrid Human-AI Enabled Scientometrics. 2876–2883. Scopus. https://doi.org/10.1109/BigData50022.2020.9378096 Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2019). The future of Human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. 2019-January, 274–283. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067273075&partnerID=40&md5=2c95269be5e869e7838e8b521c82f376 Dushkin, R. V., & Andronov, M. G. (2021). The Hybrid Design for Artificial Intelligence Systems: Vol. 1250 AISC (p. 170). Scopus. https://doi.org/10.1007/978-3-030-55180-3_13 El-Assady, M., & Moruzzi, C. (2022). Which Biases and Reasoning Pitfalls Do Explanations Trigger Decomposing Communication Processes in Human-AI Interaction. IEEE Computer Graphics and Applications, 42(6), 11–23. Scopus. https://doi.org/10.1109/MCG.2022.3200328 El-Baz, A. H. (2015). Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis. Neural Computing and Applications, 26(2), 437–446. Scopus. https://doi.org/10.1007/s00521-014-1731-9 El-Baz, A. H. (2018). Neuro-Fuzzy ensemble model-based rough set classifier selection for automatic detection of heart disease. Applied Mathematics and Information Sciences, 12(2), 311–316. Scopus. https://doi.org/10.18576/amis/120205 Elshan, E., Engel, C., & Ebel, P. (2021). Opening the black box of music royalties with the help of hybrid intelligence. 2020-January, 5525–5534. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108322592&partnerID=40&md5=73b415f85f6cbba083a6839d19547582 Fill, H.-G., & Härer, F. (2020). Supporting trust in hybrid intelligence systems using blockchains. 2600. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085214872&partnerID=40&md5=c934c4ceb8f269d688b28b4fa38b2875 Gardecki, A., Podpora, M., Beniak, R., Klin, B., & Pochwała, S. (2020). User experience sensor for man–machine interaction modeled as an analogy to the tower of hanoi. Sensors (Switzerland), 20(15), 1–17. Scopus. https://doi.org/10.3390/s20154074 Gardecki, A., Rut, J., Klin, B., Podpora, M., & Beniak, R. (2023). Implementation of a Hybrid Intelligence System Enabling the Effectiveness Assessment of Interaction Channels Use in HMI. Sensors, 23(8). Scopus. https://doi.org/10.3390/s23083826 Hanrahan, B. V., Convertino, G., & Nelson, L. (2012). Modeling problem difficulty and expertise in StackOverflow. 91–94. Scopus. https://doi.org/10.1145/2141512.2141550 Hasoon, S. O., & Jasim, Y. A. (2013). Diagnosis windows problems based on hybrid intelligence systems. Journal of Engineering Science and Technology, 8(5), 566–577. Scopus. Herrmann, T. (2020). Socio-technical design of hybrid intelligence systems – The case of predictive maintenance: Vol. 12217 LNCS (p. 309). Scopus. https://doi.org/10.1007/978-3-030-50334-5_20 Hingant, J., Zambrano, M., Pérez, F. J., Pérez, I., & Esteve, M. (2018). HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection. Security and Communication Networks, 2018. Scopus. https://doi.org/10.1155/2018/5625860 Ho, L., de Boer, V., van Riemsdijk, M. B., Schlobach, S., & Tielman, M. L. (2022). Abstract Argumentation for Hybrid Intelligence Scenarios. 3209. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138358029&partnerID=40&md5=b5dc9467ae10d62a9320c7002630d9c6 Kamar, E. (2016). Directions in hybrid intelligence: Complementing AI systems with human intelligence. 2016-January, 4070–4073. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006100710&partnerID=40&md5=e4733b434f499c99dfac79ed2a5a4fd2 Kang, M., Weng, Y., Pang, H., Li, L., Fan, X.-R., Hua, J., Chang, F., Wang, X., & Ma, L. (2020). Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning. 7500–7504. Scopus. https://doi.org/10.1109/CAC51589.2020.9326643 Kavitha, C. T., & Chellamuthu, C. (2014). Medical image fusion based on hybrid intelligence. Applied Soft Computing Journal, 20, 83–94. Scopus. https://doi.org/10.1016/j.asoc.2013.10.034 Kisengeu, S. M., Nyakoe, G. N., & Muriithi, C. M. (2020). Under Voltage Load Shedding using Hybrid Metaheuristic Algorithms for Voltage Stability Enhancement: A Review. 2020 IEEE PES/IAS PowerAfrica, PowerAfrica 2020. Scopus. https://doi.org/10.1109/PowerAfrica49420.2020.9219810 Kumar, P., Gupta, P., & Singh, I. (2023). Performance Analysis of Acrylonitrile–Butadiene–Styrene–Polycarbonate Polymer Blend Filament for Fused Deposition Modeling Printing Using Hybrid Artificial Intelligence Algorithms. Journal of Materials Engineering and Performance, 32(4), 1924–1937. Scopus. https://doi.org/10.1007/s11665-022-07243-z Le, H. V., Bui, Q. T., Bui, D. T., Tran, H. H., & Hoang, N. D. (2020). A hybrid intelligence system based on relevance vector machines and imperialist competitive optimization for modelling forest fire danger using GIS. Journal of Environmental Informatics, 36(1), 43–57. Scopus. https://doi.org/10.3808/jei.201800404 Lee, D., Periaux, J., Onate, E., & Gonzalez, L. F. (2011). Advanced Computational Intelligence System for inverse aeronautical design optimisation. 299–304. Scopus. https://doi.org/10.1109/ISPAW.2011.46 Li, M. M., Löfflad, D., Reh, C., & Oeste-Reiß, S. (2023). Towards the Design of Hybrid Intelligence Frontline Service Technologies—A Novel Human-in-the-Loop Configuration for Human-Machine Interactions. 2023-January, 332–341. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152120807&partnerID=40&md5=a0744df92063380d644e6eb749cc2c71 Ludermir, T. B., De Souto, M. C. P., & De Oliveira, W. R. (2009). On a hybrid weightless neural system. International Journal of Bio-Inspired Computation, 1(1–2), 93–104. Scopus. https://doi.org/10.1504/IJBIC.2009.022778 Lundgard, A., Yang, Y., Foster, M. L., & Lasecki, W. S. (2018). Bolt: Instantaneous crowdsourcing via just-in-time training. 2018-April. Scopus. https://doi.org/10.1145/3173574.3174041 Matej Hrkalovic, T. (2022). Designing Hybrid Intelligence Techniques for Facilitating Collaboration Informed by Social Science. 679–684. Scopus. https://doi.org/10.1145/3536221.3557032 Merritt, D., Jones, J., Ackerman, M. S., & Lasecki, W. S. (2017). Kurator: Using the crowd to help families with personal curation tasks. 1835–1849. Scopus. https://doi.org/10.1145/2998181.2998358 Nasir, H. M., Aminuddin, M. M. M., Brahin, N. M. A., & Mispan, M. S. (2021). Hybrid Mean Fuzzy Approach for Attention Detection. International Journal of Online and Biomedical Engineering, 17(6), 58–72. Scopus. https://doi.org/10.3991/ijoe.v17i06.22315 Poser, M., Wiethof, C., Banerjee, D., Shankar Subramanian, V., Paucar, R., & Bittner, E. A. C. (2022). Let’s Team Up with AI! Toward a Hybrid Intelligence System for Online Customer Service: Vol. 13229 LNCS (p. 153). Scopus. https://doi.org/10.1007/978-3-031-06516-3_11 Pramanik, S., Bhowmik, M. K., Bhattacharjee, D., & Nasipuri, M. (2016). Hybrid intelligent techniques for segmentation of breast thermograms. In Hybrid Soft Computing for Image Segmentation (pp. 255–289). Scopus. https://doi.org/10.1007/978-3-319-47223-2_11 Ramazanov, S. K., & Ul’shin, V. A. (1995). Ecological-economic control of the coal drying process under fuzzy information. Problemy Upravleniya I Informatiki (Avtomatika), 2, 108–116. Scopus. Reitemeyer, B. (2020). Automatic Generation of Conceptual Enterprise Models. 2020-October, 74–79. Scopus. https://doi.org/10.1109/EDOCW49879.2020.00022 Sanjay, C., & Prithvi, C. (2014). Hybrid intelligence systems and artificial neural network (ANN) approach for modeling of surface roughness in drilling. Cogent Engineering, 1(1). Scopus. https://doi.org/10.1080/23311916.2014.943935 Sayeekumar, M., Karthik, G. M., & Puhazholi, S. (2019). Hybrid intelligence system using fuzzy inference in cluster architecture for secured group communication. Soft Computing, 23(8), 2727–2734. Scopus. https://doi.org/10.1007/s00500-019-03817-7 Sharma, M., Kochhar, A., Gupta, D., & Zubi, J. A. (2021). Hybrid Intelligent System for Medical Diagnosis in Health Care (Vol. 209, p. 49). Scopus. https://doi.org/10.1007/978-981-16-2972-3_2 Shpak, N., Pyroh, O., Tomych, M., Voronovska, M., & Kovtok, H. (2022). Applied Intelligent Systems of Support for Public-Private Partnership in Foreign Economic Activity. 3171, 1499–1508. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134779629&partnerID=40&md5=1b252610af148069b19dd56a012cb32e Sineglazov, V., & Rjabokonev, A. (2021). Hybrid Intelligence System of Emotional Facial and Speech State Estimation. 3126, 203–208. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128976487&partnerID=40&md5=401218ba59b8f50c70a7efb3db9ef1d9 Thakare, N. M., & Thakare, V. M. (2011). An innovative hybrid approach to construct fuzzy-neural network for 3D face recognition system. 463–467. Scopus. https://doi.org/10.1109/HIS.2011.6122149 Varni, G., Pez, A.-M., & Mancini, M. (2021). Get Together in the Middle-earth: A First Step towards Hybrid Intelligence Systems. 249–253. Scopus. https://doi.org/10.1145/3461615.3485413 Vaughan, J. W. (2018). Making better use of the crowd: How crowdsourcing can advance machine learning research. Journal of Machine Learning Research, 18, 1–46. Scopus. Venda, V. F., & Chachko, S. A. (1996). Ergodynamics and hybrid intelligence systems in the reliability of power plant operators. International Journal of Occupational Safety and Ergonomics, 2(2), 93–108. Scopus. https://doi.org/10.1080/10803548.1996.11076339 Wang, Y. (2022). A Formal Theory of AI Trustworthiness for Evaluating Autonomous AI Systems. 2022-October, 137–142. Scopus. https://doi.org/10.1109/SMC53654.2022.9945351 Wellsandt, S., Klein, K., Hribernik, K., Lewandowski, M., Bousdekis, A., Mentzas, G., & Thoben, K.-D. (2021). Towards using digital intelligent assistants to put humans in the loop of predictive maintenance systems. 54(1), 49–54. Scopus. https://doi.org/10.1016/j.ifacol.2021.08.005 Wellsandt, S., Klein, K., Hribernik, K., Lewandowski, M., Bousdekis, A., Mentzas, G., & Thoben, K.-D. (2022). Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants. Annual Reviews in Control, 53, 382–390. Scopus. https://doi.org/10.1016/j.arcontrol.2022.04.001 Wiethof, C., Roocks, T., & Bittner, E. A. C. (2022). Gamifying the Human-in-the-Loop: Toward Increased Motivation for Training AI in Customer Service: Vol. 13336 LNAI (p. 117). Scopus. https://doi.org/10.1007/978-3-031-05643-7_7 Yang, Y., Kandogan, E., Li, Y., Sen, P., & Lasecki, W. S. (2019). A study on interaction in human-in-the-loop machine learning for text analytics. 2327. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063221560&partnerID=40&md5=d0d314a7a5a0262493ab04c96b0cdea1 Zanchettin, C., & Ludermir, T. B. (2005). Hybrid neural systems for recognition of patterns in artificial noses. Controle y Automacao, 16(2), 159–172. Scopus. https://doi.org/10.1590/s0103-17592005000200005