Information and Knowledge Systems. Digital Technologies, Artificial Intelligence and Decision Making: 5th International Conference, ICIKS 2021, ... Notes in Business Information Processing)

Description

This book constitutes the thoroughly refereed proceedings of the 5th International Conference on Information and Knowledge Systems, ICIKS 2021, which was held online during June 22-23, 2021. The International Conference on Information and Knowledge Systems (ICIKS 2021) gathered both researchers and practitioners in the fields of Information Systems, Artificial Intelligence, Knowledge Management and Decision Support. ICIKS seeks to promote discussions on various organizational, technological, and socio-cultural aspects of research in the design and use of information and knowledge systems in organizations. The 10 full and 2 short papers presented in this volume were carefully reviewed and selected from 32 submissions. They were organized in topical sections as follows: knowledge systems and decision making; machine learning, recommender systems, and knowledge systems; and security, artificial intelligence, and information systems. Preface Organization Contents Knowledge Systems and Decision Making Expertise, Knowledge and Decision: Lessons from the Covid-19 Crisis in France 1 Introduction 2 What Expertise is and Who is an Expert? 3 Bias in Expertise 3.1 Overconfidence 3.2 Other Biases 4 Expert Panels 5 Why the Knowledge of Experts is Necessary but not Sufficient for Decision Making 6 Conclusion References E-learning: Factors Affecting Students Online Learning During COVID-19 Quarantine in a Developing Country 1 Introduction 2 Theoretical Background 2.1 Technological Complexity (TC) and Access to Technology 2.2 Facilitating Conditions 2.3 Learning Value 3 Methodology and Results 4 Discussion 5 Limitations and Conclusion References The Importance of Tacit Knowledge When Teaching Suddenly Online 1 Introduction 2 Background Theory and Assumptions 2.1 Crisis and Behavior 2.2 Knowledge Transfer 2.3 The Organization's Information and Knowledge System (OIKS) 3 Modeling Human Responses for Knowledge Transfer 3.1 Face-to-Face Teaching and Online Teaching 3.2 Knowledge Transfer When Teaching 3.3 Discussions, Problems and Limits 4 Conclusions and Perspectives References Decision Support System for Online Recruitment 1 Introduction 2 Related Work 2.1 Job Offer Parsing 2.2 Job and Resume Matching 2.3 Machine Learning Algorithm Applied on Uncertain Environment 3 Proposed Approach 3.1 The Actors 3.2 Interaction Between Actors 3.3 Decision Support System 4 Research Perspectives References Cognitive Effort Reduction Within Group Decision Making Through Aggregation and Disaggregation of Individual Preferences 1 Introduction 2 Background 2.1 Basic Notations 2.2 Principles of Aggregation/Disaggregation Approach 2.3 Inference Procedure 3 Approach Outline 3.1 Individual Classification 3.2 Aggregation 3.3 Conflict Resolving 3.4 Collective Classification 4 Aggregation Procedure 4.1 Definition of Metric P 4.2 Concordance Power 4.3 Assignment Rule 5 Conflict Resolving 6 Illustrative Didactic Example 6.1 Decision Problem and Dataset 6.2 Application and Results 6.3 Evaluation 7 Conclusion References Machine Learning, Recommender Systems, and Knowledge Systems A Dynamic Convolutional Neural Network Approach for Legal Text Classification 1 Introduction 2 Related Work 3 Proposed Model 4 Experiments and Results 4.1 Dataset 4.2 Pre-processing 4.3 Experiments 5 Discussion 6 Conclusion References Trends of Evolutionary Machine Learning to Address Big Data Mining 1 Introduction 2 Some Approaches to Address Big Data Learning Cost 3 Scaling by Processing Manipulation: Horizontal Parallelization 3.1 Spark and MapReduce 3.2 Parallelizing GP over a Distributed Environment 4 Scaling by the Learning Paradigm: Active Learning 4.1 One-Level Active Sampling 4.2 Multi-level Active Sampling 5 Example of Application 5.1 Application Data Bases 5.2 Experimental Settings 5.3 Results and Discussion 6 Conclusion References Analyzing Performances of Three Context-Aware Collaborator Recommendation Algorithms in Terms of Accuracy and Time Efficiency 1 Introduction 2 Related Work 2.1 Context-Free Recommender Systems 2.2 Context-Aware Recommender System 2.3 Discussion 3 Contributions 3.1 Context-Aware Collaborator Recommendation Algorithms 3.2 Experiments 4 Discussion 5 Conclusion and Future Work References Recommender System for Online Teaching 1 Introduction 2 Related Work 3 Towards a Recommender System for a Personalized Information and Knowledge Transfer 3.1 Method of Incremental Prediction of ``Leader Learners'' 3.2 Recommendation Process Based on the ``Leader Learners'' Prediction 3.3 Simplifying Assumptions 3.4 Functional Architecture of the Interactive Decision Making System 4 Experiments and Results 4.1 Run Time Performance of the Recommendation Algorithm 4.2 Evaluation of the Item Space Coverage 5 Conclusion References Security, Artificial Intelligence, and Information Systems Artificial Intelligence & Cybersecurity: A Preliminary Study of Automated Pentesting with Offensive Artificial Intelligence 1 Introduction 2 Background 2.1 Penetration Testing 2.2 Extended Information System 3 Towards a Unified Risk Grid 4 Research Proposal 4.1 Development of a Methodology and a Decision Support Tool 4.2 Design Science Research Framework 4.3 Limitations 5 Conclusions and Perspectives References Archived Processes Integration for Extension of the Reverse Engineering Approach 1 Introduction 2 Related Work 3 Contributions Addressing the Semantic Interoperability Criteria for the EIS 3.1 Extraction of the Relevant Terms Supported by the Filtering Technique 3.2 Analysis of the Filtered Terms 3.3 Construction of Ontology Design Patterns 3.4 Archived Processes 4 Conclusion and Perspectives References QoS Aware Classification of Composite Web Services Using Rough Approximation 1 Introduction 2 Related Work 3 Construction of Composite Web Services 3.1 Service Type and Instance 3.2 Composite Service Type and Instance 3.3 Composition Graph 3.4 Partial Evaluation of Compositions 4 Classification of Composite Web Services 4.1 Learning Set Specification 4.2 Approximation 4.3 Induction, Validation and Exploitation of Decision Rules 5 Implementation and Evaluation 6 Conclusion A Illustration of Partial Evaluation of Compositions References Towards a Semantic Model of the Context of Navigation 1 Introduction 2 Problem Statement 3 Related Works 4 Why to Use a Semantic Model for This Application 5 The Context of Navigation 5.1 Context of Navigation in Service Robots 5.2 Context of Navigation in Autonomous Vehicles 6 Reasoning over the Context of Navigation 7 Interaction with the Control Loop 8 Conclusions and Future Work References Author Index