Note: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 Introduction to Recommender Systems -- 1.2 Formulation of the Recommendation Problem -- 1.2.1 The Input to a Recommender System -- 1.2.2 The Output of a Recommender System -- 1.3 Methods of Collecting Knowledge About User Preferences -- 1.3.1 The Implicit Approach -- 1.3.2 The Explicit Approach -- 1.3.3 The Mixing Approach -- 1.4 Motivation of the Book -- 1.5 Contribution of the Book -- 1.6 Outline of the Book -- References -- 2 Review of Previous Work Related to Recommender Systems -- 2.1 Content-Based Methods -- 2.2 Collaborative Methods -- 2.2.1 User-Based Collaborative Filtering Systems -- 2.2.2 Item-Based Collaborative Filtering Systems -- 2.2.3 Personality Diagnosis -- 2.3 Hybrid Methods -- 2.3.1 Adding Content-Based Characteristics to Collaborative Models -- 2.3.2 Adding Collaborative Characteristics to Content-Based Models -- 2.3.3 A Single Unifying Recommendation Model -- 2.3.4 Other Types of Recommender Systems -- 2.4 Fundamental Problems of Recommender Systems -- References -- 3 The Learning Problem -- 3.1 Introduction -- 3.2 Types of Learning -- 3.3 Statistical Learning -- 3.3.1 Classical Parametric Paradigm -- 3.3.2 General Nonparametric---Predictive Paradigm -- 3.3.3 Transductive Inference Paradigm -- 3.4 Formulation of the Learning Problem -- 3.5 The Problem of Classification -- 3.5.1 Empirical Risk Minimization -- 3.5.2 Structural Risk Minimization -- 3.6 Support Vector Machines -- 3.6.1 Basics of Support Vector Machines -- 3.6.2 Multi-class Classification Based on SVM -- 3.7 One-Class Classification -- 3.7.1 One-Class SVM Classification -- 3.7.2 Recommendation as a One-Class Classification Problem -- References -- 4 Content Description of Multimedia Data -- 4.1 Introduction -- 4.2 MPEG-7 -- 4.2.1 Visual Content Descriptors. , 4.2.2 Audio Content Descriptors -- 4.3 MARSYAS: Audio Content Features -- 4.3.1 Music Surface Features -- 4.3.2 Rhythm Features and Tempo -- 4.3.3 Pitch Features -- References -- 5 Similarity Measures for Recommendations Based on Objective Feature Subset Selection -- 5.1 Introduction -- 5.2 Objective Feature-Based Similarity Measures -- 5.3 Architecture of MUSIPER -- 5.4 Incremental Learning -- 5.5 Realization of MUSIPER -- 5.5.1 Computational Realization of Incremental Learning -- 5.6 MUSIPER Operation Demonstration -- 5.7 MUSIPER Evaluation Process -- 5.8 System Evaluation Results -- References -- 6 Cascade Recommendation Methods -- 6.1 Introduction -- 6.2 Cascade Content-Based Recommendation -- 6.3 Cascade Hybrid Recommendation -- 6.4 Measuring the Efficiency of the Cascade Classification Scheme -- References -- 7 Evaluation of Cascade Recommendation Methods -- 7.1 Introduction -- 7.2 Comparative Study of Recommendation Methods -- 7.3 One-Class SVM---Fraction: Analysis -- 8 Conclusions and Future Work -- 8.1 Summary and Conclusions -- 8.2 Current and Future Work.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Machine Learning Paradigms -- References -- Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation -- 2 Using a Multi Module Model for Learning Analytics to Predict Learners' Cognitive States and Provide Tailored Learning Pathways and Assessment -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Multi Module Model and Logical Architecture of the System -- 2.4 Learners Clustering, Using the K-Means Algorithm, Supporting System's Modules -- 2.5 Evaluation and Discussion of Experimental Results -- 2.6 Ethics and Privacy for Learning Analytics -- 2.7 Conclusions and Future Work -- References -- 3 Analytics for Student Engagement -- 3.1 Effects of Student Engagement -- 3.2 Conceptualizing Student Engagement -- 3.3 Measuring Student Engagement -- 3.4 Analytics for Student Engagement -- 3.4.1 Early Alert Analytics -- 3.4.2 Dashboard Visualization Analytics -- 3.5 Dashboard Visualizations of Student Engagement -- 3.6 Comparative Reference Frame -- 3.7 Challenges and Potential Solutions for Analytics of Student Engagement: -- 3.7.1 Challenge 1: Connecting Engagement Analytics to Recommendations for Improvement -- 3.7.2 Potential Solutions: Using Diverse Metrics of Engagement to Improve Feedback Provided -- 3.7.3 Challenge 2: Quantifying Meaningful Engagement -- 3.7.4 Potential Solutions: Analytics Reflecting Quantity and Quality of Student Engagement -- 3.7.5 Challenge 3: Purposeful Engagement Reflection -- 3.7.6 Potential Solutions: Options for Purposeful Engagement Reflection -- 3.7.7 Challenge 4: Finding an Appropriate Reference Norm -- 3.7.8 Potential Solutions: Alternative Reference Frames -- 3.8 Conclusion -- References -- 4 Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification. , 4.1 Introduction -- 4.2 Math Learning Process -- 4.3 Analyzing Empirical Evidence -- 4.3.1 Observations on a Learning Episode -- 4.3.2 Setting the Task -- 4.3.3 Students and Knowing Math -- 4.4 Math Meaningfulness and Three Modes of Manipulating the Blue Graph -- 4.4.1 The Adaptation Process: Dragging Points and Using Sliders -- 4.4.2 Typing the Parameters Values -- 4.4.3 Perceiving the 'a' Parameter and Its Properties -- 4.4.4 Typing Values Without Immediate Feedback -- 4.5 Discussion -- 4.5.1 Metacognitive Enactivism -- 4.6 As a Conclusion -- 4.6.1 Objectification as a Condition for Academic Knowing -- References -- Learning Analytics to Predict Student Performance -- 5 Learning Feedback Based on Dispositional Learning Analytics -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Educational Context -- 5.2.2 The Crucial Predictive Power of Cognitive Data -- 5.2.3 An Unexpected Source of Variation: National Cultural Values -- 5.2.4 LA, Formative Assessment, Assessment of Learning and Feedback Preferences -- 5.2.5 LA and Learning Emotions -- 5.3 The Current Study -- 5.3.1 Participants -- 5.3.2 E-tutorial Trace Data -- 5.3.3 Performance Data -- 5.3.4 Disposition Data -- 5.3.5 Analyses -- 5.4 Results -- 5.4.1 Performance -- 5.4.2 National Cultural Values -- 5.4.3 Cognitive Learning Processing Strategies -- 5.4.4 Metacognitive Learning Regulation Strategies -- 5.4.5 Attitudes and Beliefs Towards Learning Quantitative Methods -- 5.4.6 Epistemic Learning Emotions -- 5.4.7 Activity Learning Emotions -- 5.4.8 Adaptive Motivation and Engagement -- 5.4.9 Maladaptive Motivation and Engagement -- 5.5 Discussion and Conclusion -- References -- 6 The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 HOU Distance Learning Methodology and Data Description. , 6.4 Interview Based Survey Results -- 6.5 Machine Learning Techniques, Experiments and Results -- 6.5.1 Machine Learning Techniques, Experiments and Results -- 6.5.2 The Experiments -- 6.5.3 Results -- 6.5.4 Student Behavior Tool -- 6.6 Discussion -- 6.7 Conclusion -- Appendix -- References -- Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses -- 7 An Architectural Perspective of Learning Analytics -- 7.1 Introduction -- 7.2 What is an Architectural Perspective? -- 7.3 Functional Viewpoints -- 7.3.1 Knowledge Discovery Functions -- 7.3.2 Analytical Functions -- 7.3.3 Predictive Functions -- 7.3.4 Generative Functions -- 7.4 Quality Attributes -- 7.5 Information Viewpoint -- 7.6 Architectural Patterns and Styles -- 7.6.1 Model-View-Control (MVC) -- 7.6.2 Publisher-Subscriber -- 7.6.3 Microservices -- 7.6.4 An Architecture for Learning Analytics -- 7.7 Discussion -- References -- 8 Multimodal Learning Analytics in a Laboratory Classroom -- 8.1 Introduction -- 8.2 Classroom Research -- 8.3 The Science of Learning Research Classroom -- 8.4 The Social Unit of Learning Project -- 8.5 Conceptualization(s) of Engagement -- 8.6 Multimodal Learning Analytics of Engagement in Classrooms -- 8.7 Observation Data -- 8.8 Features Selection, Extraction and Evaluation -- 8.8.1 Multimodal Behavioral Features -- 8.8.2 Feature Visualization -- 8.8.3 Feature Extraction Conclusions -- 8.9 Illustration of High Level Construct Based on Features Extracted -- 8.9.1 Attention to Teacher Speech -- 8.9.2 Teacher Attention -- 8.9.3 Student Concentration During Individual Task -- 8.9.4 Engagement During Pair and Group Work -- 8.10 Implications -- 8.11 Conclusion -- References -- 9 Dashboards for Computer-Supported Collaborative Learning -- 9.1 The Emergence of Learning Analytics and Dashboards -- 9.2 Collaborative Learning Theories. , 9.2.1 Group Cognition (GC) -- 9.2.2 Shared Mental Models (SMMs) -- 9.2.3 Situational Awareness (SA) -- 9.2.4 Socially Shared Regulation of Learning (SSRL) -- 9.3 Tools for CSCL -- 9.3.1 Group Awareness Tools (GATs) -- 9.3.2 Shared Mirroring Systems -- 9.3.3 Ambient Displays -- 9.4 Learning Dashboards for CSCL -- 9.5 How Can Collaborative Learning Dashboards Be Improved? -- 9.5.1 Principle 1: Adopt Iterative, User-Centred Design -- 9.5.2 Principle 2: Navigate the Theoretical Space -- 9.5.3 Principle 3: Visualize to Support Decision-Making -- References -- Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning -- 10 Learning Analytics in Distance and Mobile Learning for Designing Personalised Software -- 10.1 Introduction -- 10.2 Distance Learning -- 10.3 Mobile Learning and Mobile Learning Analytics -- 10.4 Personalised Learning Software -- 10.5 Data Collection -- 10.5.1 Modalities of Interaction in PCs -- 10.5.2 Modalities of Interaction in Smartphones -- 10.6 Multi-criteria Analysis -- 10.6.1 Combining Modalities of Interaction in HCI -- 10.6.2 Combining Modalities of Interaction in Smartphones -- 10.7 Conclusions -- References -- 11 Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Description of the Student Model -- 11.3.1 Analyzing Data That Have Been Gathered by the Implementation of ELaC -- 11.3.2 The Improved Student Model of ELaCv2 -- 11.4 Description of the Operation of the Student Model -- 11.4.1 Examples of Operation -- 11.5 Evaluation-Results -- 11.6 Conclusion -- References.

Note: Intro -- Series Editor's Foreword -- Preface -- Contents -- 1 Introduction -- 1.1 Subject of the Book -- 1.2 Purpose of the Book and Contribution to Science -- 1.3 Structure of the Book -- Further Reading -- 2 e-Government: The Concept, the Environment and Critical Issues for the Back-Office Systems -- 2.1 Introduction -- 2.2 The Environment of Public Administration -- 2.3 Developing e-Government-Models and Levels of Development -- 2.3.1 The Three Rings Model -- 2.3.2 The Model of Focus and Centrality -- 2.3.3 The 5 Levels of e-Government Development -- 2.3.4 The Model of 13 Levels of Digital Service Integration -- 2.4 Towards an Electronic Public Administration: The Roadmap -- 2.5 Critical Issues for e-Government -- 2.6 Back-Office Systems Development-Critical Issues for the Greek Case -- 2.6.1 e-Administration-The Current Back-Office in Greece -- 2.6.2 The Current Situation for the Personnel of the Public Administration in Greece -- 2.6.3 Description of the Support System -- 2.6.4 Benefits of the System-Perspectives -- 2.6.5 Critical Issues for the System Development -- References -- 3 Semantic Web: The Evolution of the Web and the Opportunities for the e-Government -- 3.1 The Internet as the Foundation for Service Providing -- 3.2 The Web as an Internet Service-Historical and Technological References -- 3.3 From Web to Web 2.0 -- 3.4 The Road to Web 3.0 and Web X.0 -- 3.5 Web 2.0 Versus Web 3.0: A Comparative Analysis -- 3.5.1 Web 2.0 as a Collaborative Interactive Platform -- 3.5.2 Web 2.0 Technologies -- 3.5.3 The Semantic Web: Making Data Meaningful -- 3.5.4 The Levels of the Semantic Web -- 3.5.5 The Basic Technologies Used in the Semantic Web -- 3.5.6 Web 1.0-2.0-3.0: A Comparative Presentation -- 3.6 Semantic Web Applications -- 3.7 Advantages and Challenges for the Semantic Web -- 3.8 Benefits from the Application of the Semantic Web. , 3.9 Opportunities for e-Government from the Implementation of Semantic Web Solutions -- References -- 4 Representation and Knowledge Management for the Benefit of e-Government-Opportunities Through the Tools of the Semantic Web -- 4.1 Knowledge and e-Government-Competitive Advantage for the Public Sector -- 4.1.1 Knowledge as a Concept -- 4.1.2 Knowledge Creation -- 4.1.3 Knowledge Coding -- 4.2 Knowledge Management in Terms of Semantic Web-Critical Issues for Their Application in e-Government -- 4.2.1 The Concept "Knowledge Management" -- 4.2.2 Critical Issues for the Implementation of Knowledge Management in the Public Sector -- 4.2.3 Knowledge Management Procedures -- 4.2.4 Knowledge Management Systems -- 4.3 Semantic Tools for Knowledge Management in the Domain of Public Administration -- 4.3.1 The RDF Data Model -- 4.3.2 RDF Schema Specification Language -- 4.3.3 The URI and URI's Use -- 4.3.4 Web Ontology Language-OWL -- 4.3.5 Reasoning Tools -- 4.4 Modelling and Extraction of Knowledge in the Field of e-Government-Our Proposal as "The e-Government Ontology" -- 4.4.1 The e-Government Ontology Motivation -- 4.4.2 The Ontology Development in Protégé 4.3 -- 4.5 Knowledge Acquisition from "The e-Government Ontology" -- 4.5.1 SPARQL -- 4.5.2 SPARQL-DL in OWL2 Query Tab of Protégé -- 4.5.3 DL Query Tool of Protégé -- 4.6 Evaluation of Ontology -- 4.6.1 Categorization of the Ontology -- 4.6.2 Basic Principles of Design -- 4.6.3 Methodology of the Ontology Development -- 4.7 Semantic Modelling in the Domain of Official Statistics -- 4.7.1 The Official Statistics Domain -- 4.7.2 Developing an Ontology for the Modelling of Knowledge in the Field of Official Statistics of the ELS -- 4.7.3 Results -- 4.7.4 Assessment and Evaluation of Ontology -- 4.8 Knowledge Representation in the Internal Audit Field -- 4.8.1 Introduction and Motivation. , 4.8.2 Presentation of the Audit Field -- 4.8.3 Modelling of Knowledge Within the Auditing Sector -- 4.8.4 Examples of the Application of Restrictions, Rules and Queries in Ontology -- 4.8.5 Evaluation-Assessment of Ontology -- 4.8.6 Conclusions -- References -- 5 Towards Open Data and Open Governance-Representation of Knowledge and Triplification of Data in the Field of the Greek Open Government Data -- 5.1 From e-Government Towards Open Government -- 5.2 Benefits-Perspectives from the Opening of Government Data -- 5.3 The Case of Greek Open Government Data -- 5.4 Critical Issues for Opening Government Data -- 5.4.1 Basic Rules -- 5.4.2 Basic Steps for Opening Government Data -- 5.4.3 A Proposal for Linked Open Government Data -- 5.5 The Open Data Ontology-Our Proposal for the Case of the Greek Open Data Repository -- 5.5.1 Introduction and Motivation -- 5.5.2 The Ontology Implementation in Protége -- 5.5.3 Ontology Evaluation -- 5.6 Open Data Triplification-The Case of the Greek Open Data from the "Diavgeia Program" -- 5.6.1 Introduction -- 5.6.2 Relevant Tools -- 5.6.3 Triplification-The Case of Open Data by the "Diavgeia Program -- 5.6.4 Conclusions -- 5.7 Open Data Repositories-The Case of the e-Government Ontology Publishing as an Open Ontology in CKAN -- 5.7.1 Methodology-Steps for the Development of the CKAN Repository -- 5.7.2 Opening Data Through CKAN: The Case of Publishing-Opening e-Government Ontology -- References -- 6 Production and Publication of Linked Open Data: The Case of Open Ontologies -- 6.1 Linked Open Data -- 6.1.1 Semantic Web, Linked Data and Linked Open Data-The Fundamental Rules -- 6.1.2 The Way That Linked Data Work -- 6.1.3 Linking Information Resources to Other Resources Through Redirection -- 6.1.4 Basic Steps for Publishing Linked Open Data -- 6.2 Linked Open Data Tools -- 6.2.1 Apache Jena and Apache Fuseki Server. , 6.2.2 Eclipse RDF4J Framework -- 6.2.3 Pubby -- 6.2.4 Apache Tomcat -- 6.2.5 D2R Server and D2RQ Mapping Language -- 6.2.6 OpenLink Virtuoso Server and Virtuoso Open Source Server -- 6.2.7 Comparison of the Basic Tools -- 6.3 Triplification-The Case of Production of RDF Triples from Data in Relational Databases in National Municipal Registry -- 6.3.1 Motivation -- 6.3.2 The Case of the National Municipal Registry -- 6.3.3 Triplification-Steps and Methodology -- 6.3.4 Conclusions and Future Work -- 6.4 RDF Serialisation from JSON Data-The Case of JSON Data in Diavgeia.gov.gr -- 6.4.1 Introduction -- 6.4.2 JSON Versus JSON-LD -- 6.4.3 Producing RDF Triples out of JSON Data -- 6.4.4 Conclusion-Future Work -- 6.5 Publication of Linked Data: The Case of the Open Ontology for Open Government -- 6.5.1 Create SparqL EndPoint and Publish Linked Open Data Using Fuseki and Pubby Server -- 6.5.2 Ontology Publish as Linked Open Data -- 6.6 Publication of Open Ontology in the LOD Cloud -- 6.6.1 Create a SparqL Endpoint Through OpenLink Virtuoso -- 6.6.2 Publication of the e-Government Ontology in the LOD Cloud -- 6.6.3 Publication of the Ontology in Linked Open Vocabularies -- 6.7 Conclusions -- 6.7.1 Pubby Operation -- 6.7.2 Benefits of Publishing the Ontology for Open Government as Open Data -- References -- 7 Education and e-Government-The Case of a Moodle Based Platform for the Education and Evaluation of Civil Servants -- 7.1 Introduction -- 7.2 Current Situation in the Education of Civil Servants in Greece -- 7.3 e-Training in e-Government -- 7.4 The Wiki as a Means of Education and Knowledge Management in Public Institutions -- 7.5 Suggested Wiki Implementation Through Moodle -- References -- 8 Conclusions-Future Work -- Appendix -- A.1 Customization of the Configuration CKAN File (production.ini). , A.2 Configuration File of Pubby Server (Access in a SparqL Endpoint in the Same Machine) -- A.3 Configuration File of Pubby Server (Access in a Remote SparqL Endpoint) -- A.4 Summary of Key Open Government Licence Sites.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Intelligent Computing Systems -- 2 Semantic Tools -- Their Use for Knowledge Management in the Public Sector -- Abstract -- 1 Outlines -- 2 Introduction---Presentation of the Field of Interest -- 2.1 E-Government---The Opportunities Through the Semantic Web -- 2.2 Public Open Data for the Transition to `Open Government' -- 3 Related Work -- 4 Semantic Representation of Knowledge -- 4.1 The RDF Data Model -- 4.2 The URI's Use -- 4.3 RDF Schema Specification Language -- 4.4 Web Ontology Language---OWL -- 5 Reasoning Tools -- 5.1 SWRL Rules -- 5.2 The Query Language SQWRL -- 6 Presentation of Our Ontology Through Prot00E9g00E9 -- 6.1 The Ontology Development in Prot00E9g00E9 4.3 -- 6.2 The E-Government Ontology -- 6.2.1 Defining Classes -- 6.2.2 Defining Properties -- 6.3 The Use of RDF, RDFS, OWL and SWRL Through a Case Study -- 7 Data Mining Technology from Ontologies -- 7.1 SPARQL -- 7.2 SPARQL-DL in OWL2 Query Tab of Prot00E9g00E9 -- 7.3 DL Query Tool of Prot00E9g00E9 -- 8 Evaluation of Ontology -- 8.1 Categorization of the Ontology -- 8.2 Basic Principles of Design -- 8.3 Methodology of the Ontology Development -- 9 Conclusions -- References -- 3 From Game Theory to Complexity, Emergence and Agent-Based Modeling in World Politics -- Abstract -- 1 Introduction -- 2 Game Theory in World Politics -- 2.1 A Game Theoretic Approach of Global Environmental Diplomacy -- 3 From Game Theory to Complexity -- 3.1 Emergence in World Politics -- 4 Simulating Complexity with Agent-Based Modeling -- 4.1 Agent-Based Modeling Research in World Politics -- 4.1.1 Political Applications of ABM -- 5 Conclusions -- Acknowledgments -- References -- List of Software Resources -- 4 A Semantic Approach for Representing and Querying Business Processes -- Abstract -- 1 Introduction. , 2 Semantic Web Techniques in Management Information Systems -- 2.1 What's Worth in Combining Management Information Systems with Semantic Web Technologies? -- 2.2 Process Models, Conceptual Models and Ontologies -- 2.3 Querying Business Process Models -- 2.4 Related Work -- 3 A BPMN Semantic Process Model -- 3.1 The Research Methodology -- 3.2 Developing Business Process Models -- 3.3 Developing the Ontology -- 3.3.1 The Scope of the BPMN Elements -- 3.3.2 The Scope of the Generic BPMN Alternative Models -- 3.3.3 The Scope of the Agent or Actor Participating in the Process -- 3.4 Validating the Ontology -- 4 Querying Conventional Databases and Semantic Models -- 5 Conclusions -- References -- 5 Using Conversational Knowledge Management as a Lens for Virtual Collaboration in the Course of Small Group Activities -- Abstract -- 1 Introduction -- 2 Related Work and Motivation -- 2.1 Conversational Patterns -- 2.2 Design Frames and Technologies for CK Management -- 2.3 Consolidation and Research Focus -- 3 Methodology -- 3.1 Data Samples and Analysis -- 3.2 Language-Action Models -- 4 Implementation -- 4.1 Transformable Document Templates -- 4.2 The Portlets -- 5 Concluding Remarks -- Acknowledgment -- References -- 6 Spatial Environments for m-Learning: Review and Potentials -- Abstract -- 1 Introduction -- 2 List of Resources -- 3 Classification Criteria -- 4 Exemplary Environments -- 5 Comparison -- 6 Results -- 7 Conclusions/Future Work -- References -- 7 Science Teachers' Metaphors of Digital Technologies and Social Media in Pedagogy in Finland and in Greece -- Abstract -- 1 Introduction -- 2 Theoretical Background -- 2.1 Approaching Science -- 2.2 The Relationship Between Science and Digital Technology -- 3 The Study -- 3.1 Aims & -- Methods -- 3.2 The Context and the Participants -- 4 Findings -- 4.1 Science as Way of Thinking. , 4.2 Science as Method -- 5 Conclusions -- References -- 8 Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors -- Abstract -- 1 Introduction -- 2 Gaussian Process Kernel Machines -- 3 Methodology -- 4 Application to Monitoring Complex Energy Systems: The Boiling Water Reactor (BWR) Case -- 4.1 Problem Statement -- 4.2 Testing Results -- 5 Conclusions -- References -- 9 A Framework to Assess the Behavior and Performance of a City Towards Energy Optimization -- Abstract -- 1 Introduction -- 2 Policy Context -- 3 Current Relevant Initiatives -- 4 Description of the Framework -- 5 Municipal Building Level SCEAF -- 6 Conclusions -- Acknowledgment -- References -- 10 An Energy Management Platform for Smart Microgrids -- Abstract -- 1 Introduction -- 2 The Smart Polygeneration Microgrid Pilot Plant -- 3 The Energy Management Platform -- 4 The Supervisory, Control and Data Acquisition (SCADA) System -- 5 Results and Discussion -- 6 Conclusions and Future Research Lines -- References -- List of Resources -- 11 Transit Journaling and Traffic Sensitive Routing for a Mixed Mode Public Transportation System -- Abstract -- 1 Introduction -- 1.1 Limited Scope of Data -- 1.2 Formal Route Names Versus Informal Headsigns -- 1.3 Insufficient Stop Descriptions -- 1.4 Traffic Sensitivity in Routing/Trip Planning -- 2 Related Work -- 2.1 Crowdsourced Mapping and Real-time Tracking -- 2.2 Activity Detection -- 2.3 Trip Planning/Routing -- 2.3.1 Dijkstra's Algorithm -- 2.3.2 A* Search -- 2.3.3 Raptor -- 2.4 Trip Planning with Real-time Data -- 3 Methodology/Design -- 3.1 The Server/Back-End -- 3.1.1 GTFS Data Pre-processing -- 3.1.2 Server Design -- 3.1.3 The Modified RAPTOR Search Algorithm -- 3.2 The Mobile App -- 3.2.1 Search -- 3.2.2 Results/Journey Displays -- 3.2.3 Recording. , 3.2.4 Traffic Report -- 3.2.5 Results Display -- 3.2.6 Journey Display -- 3.2.7 Journal -- 3.2.8 Stop Editor -- 3.2.9 Route Editor -- 4 Tests and Results -- 4.1 Basic Routing Capacity -- 4.1.1 Survey -- 4.1.2 Demographics -- 4.1.3 Algorithm Evaluation -- 4.2 Traffic Sensitivity -- 4.3 Journey Recorder -- 5 Future Work -- 5.1 Base Estimate Correction -- 5.2 Preference-Weighing System -- 5.3 Traffic Flow Prediction -- 5.4 Further Evaluation of Mapping Ability -- 6 Conclusion -- References -- 12 Adaptation of Automatic Information Extraction Method for Environmental Heatmaps to U-Matrices of Self Organising Maps -- Abstract -- 1 Introduction -- 2 Problem Formulation -- 3 HInEx---Heatmap Information Extraction -- 3.1 The Idea -- 3.2 Heatmap Area Isolation -- 3.3 Clustering Image Pixels Based on Colors -- 3.4 Generating Tree Description -- 3.5 The Key Search and Its Analysis -- 3.6 The Axis Search and Their Analysis -- 3.7 Complete Heatmap Description -- 4 SOM Cluster Number Extraction Based on U-Matrix -- 4.1 The Idea of HInEx Application to SOM U-Matrix -- 4.2 Clustering -- 4.3 Extracting a U-Matrix Cell Corresponding to a Single Distance Between Neurons -- 4.4 Searching a Color Representing the Minimal Neuron Distance in SOM -- 4.5 Threshold-like Operation -- 4.6 Dilatation and Erosion-like Operations -- 4.7 Searching for the Number of Groups in SOM -- 5 SOM Generator Description -- 6 Experimental Study -- 7 Conclusion -- Acknowledgements -- References -- 13 Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization -- Abstract -- 1 Introduction -- 2 Evolutionary Optimization -- 3 Determination of the Pareto-Optimal Build Orientations in Stereolithography -- 3.1 Orientation Selection in SL -- 3.2 Algorithm Configuration and Implementation -- 3.3 Build Orientation Case Study. , 4 Determination of the Optimum Packing Layout in Stereolithography Machine Workspace -- 4.1 Optimization Scheme -- 4.2 Packing Layout Construction Process -- 4.3 Packing Layout Case Studies -- 5 Concluding Remarks -- References -- 14 Car-Like Mobile Robot Navigation: A Survey -- Abstract -- 1 Introduction -- 2 RRT-Based Methods -- 2.1 Unsafe Path Planning -- 2.2 Safe Path Planning -- 2.3 Rapidly Exploring Random Tree Algorithm on Rough Terrains (RRT-RT) -- 2.4 RRT Motion Planning Subsystem -- 2.5 Partial Motion Planning -- 2.6 Sensor-Based Random Tree (SRT) -- 2.7 RRT* Algorithm -- 2.8 Voronoi Fast Marching (VFM) and Fast Marching (FM2) -- 2.9 SBL Algorithm -- 2.10 Single-Query Motion Planning -- 2.11 Dynamic-Domain RRT -- 2.12 Transition-Based RRT -- 2.13 Parallelizing Rapidly-Exploring Random Tree (RRT) Algorithm on Large-Scale Distributed-Memory Architectures -- 2.14 Obstacle Sensitive Cost Function for Navigating Car-Like Robots -- 3 Methods Based on Fuzzy Logic -- 3.1 Distributed Active-Vision Network-Space System -- 3.2 Internet-Based Smart Space Navigation Using Fuzzy-Neural Adaptive Control -- 4 Sensor-Based Methods -- 4.1 Dynamic Window Approach (DWA) -- 4.2 Generalized Voronoi Graph (GVG) Theory -- 4.3 Navigation in Dynamic Environments Using Trajectory Deformation -- 4.4 Probabilistic Velocity Obstacle (PVO) -- 5 SLAM-Based Methods -- 5.1 On-line Path Following -- 5.2 The CyCab: A Car-Like Robot Navigating Autonomously and Safely Among Pedestrians -- 5.3 V-Slam -- 5.4 SLAM-Based Turning Strategy in Restricted Environments -- 5.5 L-Slam -- 6 Conclusions and Future Work -- 6.1 Future Directions in Autonomous Robot Navigation and Obstacle Perception -- 6.2 Future Directions in Applications of Autonomously-Navigating Robots -- References -- 15 Computing a Similarity Coefficient for Mining Massive Data Sets -- Abstract -- 1 Introduction. , 2 Related Work.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Applications of Learning and Analytics in Intelligent Systems -- References -- Learning and Analytics in Intelligent Medical Systems -- 2 A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure -- 2.1 Introduction -- 2.2 Theoretical Background -- 2.3 Data and Methods -- 2.3.1 Dataset -- 2.3.2 Evaluation Process -- 2.3.3 Weka -- 2.4 Overview of Proposed Systems -- 2.4.1 Logistic Regression -- 2.4.2 Decision Tree -- 2.4.3 Random Forest -- 2.4.4 K-Nearest Neighbor -- 2.4.5 Artificial Neuronal Network -- 2.4.6 SVM -- 2.4.7 Naïve Bayes -- 2.4.8 OneR -- 2.4.9 ZeroR -- 2.4.10 Hybrid -- 2.5 Comparison Results -- 2.6 Conclusions -- References -- 3 Differential Gene Expression Analysis of RNA-seq Data Using Machine Learning for Cancer Research -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 RNAseq -- 3.2.2 Classical Approach -- 3.2.3 Machine Learning -- 3.2.4 Comparative Workflow -- 3.3 Code and Results of an Analysis with Real Data -- 3.3.1 Loading Packages -- 3.3.2 Loading and Searching the Data from TCGA -- 3.3.3 Patient Selection -- 3.3.4 Dependent Variable Definition -- 3.3.5 Biological Gene Filter -- 3.3.6 Graphics -- 3.3.7 Classical Statistical Analysis -- 3.3.8 Machine Learning Analysis -- 3.4 Conclusions -- References -- 4 Machine Learning Approaches for Pap-Smear Diagnosis: An Overview -- 4.1 Introduction -- 4.2 Cervical Cancer and Pap-Test -- 4.3 The Pap-Smear Databases -- 4.3.1 A Basic Data Analysis of New Data -- 4.4 The Used Methodologies -- 4.4.1 Adaptive Network-Based Fuzzy Inference System (ANFIS) -- 4.4.2 Artificial Neural Networks -- 4.4.3 Heuristic Classification -- 4.4.4 Minimum Distance Classification -- 4.4.5 Hard C-Means Clustering -- 4.4.6 Fuzzy C-Means Clustering -- 4.4.7 Gustafson and Kessel Clustering -- 4.4.8 k-Nearest Neighborhood Classification. , 4.4.9 Weighted k-Nearest Neighborhood Classification -- 4.4.10 Tabu Search -- 4.4.11 Genetic Programming -- 4.4.12 Ant Colony -- 4.5 The Pap-Smear Classification Problem -- 4.5.1 Classification with ANFIS -- 4.5.2 Heuristic Classification Based on GP -- 4.5.3 Classification Using Defuzzification Methods -- 4.5.4 Direct and Hierarchical Classification -- 4.5.5 Classification Using Feed-Forward Neural Network -- 4.5.6 Nearest Neighborhood Classification Based on GP Feature Selection -- 4.5.7 Nearest Neighborhood Classification Using Tabu Search for Feature Selection -- 4.5.8 Nearest Neighborhood Classification Using ACO for Feature Selection -- 4.5.9 Minimum Distance Classifier -- 4.6 Conclusion and Future Work -- References -- Learning and Analytics in Intelligent Power Systems -- 5 Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems -- 5.1 Introduction -- 5.2 Background -- 5.2.1 Kernel Machines -- 5.2.2 Gaussian Processes -- 5.3 Multi-kernel Paradigm for Load Analysis -- 5.3.1 Problem Statement -- 5.3.2 Multi-kernel Paradigm -- 5.4 Results -- 5.4.1 Problem Statement -- 5.4.2 Further Results -- 5.5 Conclusion and Future Work -- References -- 6 Conceptualizing and Measuring Energy Security: Geopolitical Dimensions, Data Availability, Quantitative and Qualitative Methods -- 6.1 Preamble -- 6.1.1 Structure of Chapter -- 6.2 Review of Energy Security Literature -- 6.2.1 Brief History of Energy Security -- 6.2.2 Defining Security and Energy Security -- 6.2.3 Energy Security Since the 20th Century -- 6.2.4 Energy Security and Geopolitics -- 6.2.5 Dimensions of Energy Security -- 6.3 Methodology -- 6.3.1 Research Questions -- 6.4 Analyses and Results -- 6.4.1 Milestone Time Periods -- 6.4.2 Data -- 6.4.3 Measuring Energy Security -- 6.4.4 Creating a Geopolitical Energy Security Index. , 6.4.5 Using Cluster Analysis -- 6.4.6 Looking at Case Studies of Key Countries -- 6.4.7 Carrying Out Interviews of Energy Experts -- 6.4.8 Forecasting Energy Security -- 6.5 Closing Comments -- References -- Learning and Analytics in Performance Assessment -- 7 Automated Stock Price Motion Prediction Using Technical Analysis Datasets and Machine Learning -- 7.1 Introduction -- 7.2 Technical Analysis Synopsis -- 7.3 Machine Learning Component -- 7.3.1 SVM Algorithm -- 7.3.2 Adaptive Boost Algorithm -- 7.4 System Implementation -- 7.4.1 System Structure -- 7.4.2 Training Data Set -- 7.4.3 Selection of Machine Learning Algorithm and Implementation -- 7.4.4 Android Client Application -- 7.5 System Evaluation -- 7.6 Conclusions and Future Work -- References -- 8 Airport Data Analysis Using Common Statistical Methods and Knowledge-Based Techniques -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Airport Data Analysis -- 8.3.1 Data Collection and Cleansing -- 8.3.2 Case Study Description and Scope of Current Analysis -- 8.3.3 Demand Seasonality -- 8.3.4 International Passenger Connectivity Matrix -- 8.3.5 Weekly and Daily Airport Operating Patterns -- 8.3.6 Airplane Types and Associated Runway Length Requirements -- 8.4 Conclusions -- References -- 9 A Taxonomy and Review of the Network Data Envelopment Analysis Literature -- 9.1 Introduction -- 9.2 DMU's Internal Network Structures and Assessment Paradigms -- 9.3 Assessment Paradigms -- 9.3.1 Independent Assessments -- 9.3.2 Joint Assessments -- 9.4 Classification of Network DEA Studies -- 9.5 Conclusion -- References -- Learning and Analytics in Intelligent Safety and Emergency Response Systems -- 10 Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams -- 10.1 Introduction -- 10.2 The Data Lifecycle in the Safety Control of Concrete Dams. , 10.2.1 Raw Data Collection -- 10.2.2 Processing and Data Storage -- 10.2.3 Data Quality Assessment and Outlier Detection -- 10.2.4 Data Analysis and Dam Safety Assessment Based on Quantitative Interpretation Models -- 10.2.5 Data Analysis and Dam Safety Assessment Based on Machine Learning Models -- 10.3 Data Analysis and Data Prediction Using Deep Learning Models-An Overview -- 10.4 Adopted Problem Solving Process-The Design Science Research Methodology -- 10.5 Proposed Methodology-Adding Value to the Interpretation of the Monitored Dam Behaviour Through the Use of Deep Learning Models -- 10.6 Demonstration and Evaluation-Assessment and Interpretation of the Monitored Structural Behaviour of a Concrete Dam During Its Operation Phase -- 10.6.1 The Case Study-The Alto Lindoso Dam -- 10.6.2 The Dataset-Horizontal Displacements Measured by the Pendulum Method -- 10.6.3 Main Results and Discussion -- 10.7 Final Remarks -- References -- 11 Analytics and Evolving Landscape of Machine Learning for Emergency Response -- 11.1 Introduction -- 11.1.1 Emergency Management -- 11.1.2 Machine Learning -- 11.1.3 Scope and Organizations -- 11.2 Applications of Machine Learning in Emergency Response -- 11.2.1 Machine Learning Techniques for Emergency Management Cycles -- 11.2.2 Event Prediction -- 11.2.3 Warning Systems -- 11.2.4 Event Detection and Tracking -- 11.2.5 Situational Awareness -- 11.2.6 Emergency Evaluation -- 11.2.7 Crowdsourcing -- 11.3 Analysis of Emergency Data -- 11.3.1 Big Data in Emergency Management -- 11.3.2 Data Collection -- 11.3.3 Information Extraction and Filtering -- 11.3.4 Data Integration -- 11.3.5 Applications for Data Analysis in Emergency -- 11.4 Challenges and Opportunities of Machine Learning in Response -- 11.4.1 Data Collection -- 11.4.2 Information Extraction -- 11.4.3 Data Filtering -- 11.4.4 Data Integration. , 11.5 Crowdsourcing in Emergency Management -- 11.5.1 Crowdsourcing with Machine Learning for Emergency Management -- 11.5.2 Example: Crowdsourcing and Machine Learning for Tracking Emergency -- 11.6 Conclusions -- References -- Learning and Analytics in Intelligent Social Media -- 12 Social Media Analytics, Types and Methodology -- 12.1 Social Networks and Analytics -- 12.1.1 Descriptive Analytics -- 12.1.2 Diagnostic Analytics -- 12.1.3 Predictive Analytics -- 12.1.4 Prescriptive Analytics -- 12.2 Introduction to Social Network Mining -- 12.3 Data Structure -- 12.3.1 Structured Data -- 12.3.2 Semi-structured Data -- 12.3.3 Unstructured Data -- 12.4 Data Quality -- 12.4.1 Noise -- 12.4.2 Outliers -- 12.4.3 Missing Values -- 12.4.4 Duplicate Data -- 12.5 Data Preprocessing -- 12.5.1 Aggregation -- 12.5.2 Discretization -- 12.5.3 Feature Selection -- 12.5.4 Feature Extraction -- 12.5.5 Sampling -- 12.6 Network Modeling -- 12.6.1 Real World Networks -- 12.6.2 Random Graphs -- 12.6.3 Small World Model -- 12.6.4 Preferential Attachment Model -- 12.7 Network Schemas -- 12.7.1 Multi-relational Network with Single Typed Objects -- 12.7.2 Bipartite Network -- 12.7.3 Star-Schema Network -- 12.7.4 Multiple-hub Network -- 12.8 Task Categorization -- 12.9 Machine Learning -- 12.9.1 Supervised Learning -- 12.9.2 Unsupervised Learning -- 12.10 Conclusions -- References -- 13 Machine Learning Methods for Opinion Mining In text: The Past and the Future -- 13.1 Introduction -- 13.2 Terminology -- 13.3 Early Projects -- 13.4 The Fascinating Opportunities that Sentiment Analysis Raises -- 13.5 Natural Language Processing for Sentiment Analysis -- 13.5.1 Affective Information for Sentiment Analysis -- 13.5.2 Corpora Annotated for Sentiment Analysis Tasks -- 13.5.3 Distributional Semantics and Sentiment Analysis. , 13.6 Traditional Models Based on Lexica and Feature Engineering.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Advances in Selected Artificial Intelligence Areas -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- References -- Part I Advances in Artificial Intelligence Paradigms -- 2 Feature Selection: From the Past to the Future -- 2.1 Introduction -- 2.2 The Need for Feature Selection -- 2.3 History of Feature Selection -- 2.4 Feature Selection Techniques -- 2.4.1 Filter Methods -- 2.4.2 Embedded Methods -- 2.4.3 Wrapper Methods -- 2.5 What Next in Feature Selection? -- 2.5.1 Scalability -- 2.5.2 Distributed Feature Selection -- 2.5.3 Ensembles for Feature Selection -- 2.5.4 Visualization and Interpretability -- 2.5.5 Instance-Based Feature Selection -- 2.5.6 Reduced-Precision Feature Selection -- References -- 3 Application of Rough Set-Based Characterisation of Attributes in Feature Selection and Reduction -- 3.1 Introduction -- 3.2 Background and Related Works -- 3.2.1 Estimation of Feature Importance and Feature Selection -- 3.2.2 Rough Sets and Decision Reducts -- 3.2.3 Reduct-Based Feature Characterisation -- 3.2.4 Stylometry as an Application Domain -- 3.2.5 Continuous Versus Nominal Character of Input Features -- 3.3 Setup of Experiments -- 3.3.1 Preparation of Input Data and Datasets -- 3.3.2 Decision Reducts Inferred -- 3.3.3 Rankings of Attributes Based on Reducts -- 3.3.4 Classification Systems Employed -- 3.4 Obtained Results of Feature Reduction -- 3.5 Conclusions -- References -- 4 Advances in Fuzzy Clustering Used in Indicator for Individuality -- 4.1 Introduction -- 4.2 Fuzzy Clustering -- 4.3 Convex Clustering -- 4.4 Indicator of Individuality -- 4.5 Numerical Examples -- 4.6 Conclusions and Future Work -- References -- 5 Pushing the Limits Against the No Free Lunch Theorem: Towards Building General-Purpose (GenP) Classification Systems -- 5.1 Introduction. , 5.2 Multiclassifier/Ensemble Methods -- 5.2.1 Canonical Model of Single Classifier Learning -- 5.2.2 Methods for Building Multiclassifiers -- 5.3 Matrix Representation of the Feature Vector -- 5.4 GenP Systems Based on Deep Learners -- 5.4.1 Deep Learned Features -- 5.4.2 Transfer Learning -- 5.4.3 Multiclassifier System Composed of Different CNN Architectures -- 5.5 Data Augmentation -- 5.6 Dissimilarity Spaces -- 5.7 Conclusion -- References -- 6 Bayesian Networks: Theory and Philosophy -- 6.1 Introduction -- 6.2 Bayesian Networks -- 6.2.1 Bayesian Networks Background -- 6.2.2 Bayesian Networks Defined -- 6.3 Maximizing Entropy for Missing Information -- 6.3.1 Maximum Entropy Formalism -- 6.3.2 Maximum Entropy Method -- 6.3.3 Solving for the Lagrange Multipliers -- 6.3.4 Independence -- 6.3.5 Overview -- 6.4 Philosophical Considerations -- 6.4.1 Thomas Bayes and the Principle of Insufficient Reason -- 6.4.2 Objective Bayesianism -- 6.4.3 Bayesian Networks Versus Artificial Neural Networks -- 6.5 Bayesian Networks in Practice -- References -- Part II Advances in Artificial Intelligence Applications -- 7 Artificial Intelligence in Biometrics: Uncovering Intricacies of Human Body and Mind -- 7.1 Introduction -- 7.2 Background and Literature Review -- 7.2.1 Biometric Systems Overview -- 7.2.2 Classification and Properties of Biometric Traits -- 7.2.3 Unimodal and Multi-modal Biometric Systems -- 7.2.4 Social Behavioral Biometrics and Privacy -- 7.2.5 Deep Learning in Biometrics -- 7.3 Deep Learning in Social Behavioral Biometrics -- 7.3.1 Research Domain Overview of Social Behavioral Biometrics -- 7.3.2 Social Behavioral Biometric Features -- 7.3.3 General Architecture of Social Behavioral Biometrics System -- 7.3.4 Comparison of Rank and Score Level Fusion -- 7.3.5 Deep Learning in Social Behavioral Biometrics -- 7.3.6 Summary and Applications. , 7.4 Deep Learning in Cancelable Biometrics -- 7.4.1 Biometric Privacy and Template Protection -- 7.4.2 Unimodal and Multi-modal Cancelable Biometrics -- 7.4.3 Deep Learning Architectures for Cancelable Multi-modal Biometrics -- 7.4.4 Performance of Cancelable Biometric System -- 7.4.5 Summary and Applications -- 7.5 Applications and Open Problems -- 7.5.1 User Authentication and Anomaly Detection -- 7.5.2 Access Control -- 7.5.3 Robotics -- 7.5.4 Assisted Living -- 7.5.5 Mental Health -- 7.5.6 Education -- 7.6 Summary -- References -- 8 Early Smoke Detection in Outdoor Space: State-of-the-Art, Challenges and Methods -- 8.1 Introduction -- 8.2 Problem Statement and Challenges -- 8.3 Conventional Machine Learning Methods -- 8.4 Deep Learning Methods -- 8.5 Proposed Deep Architecture for Smoke Detection -- 8.6 Datasets -- 8.7 Comparative Experimental Results -- 8.8 Conclusions -- References -- 9 Machine Learning for Identifying Abusive Content in Text Data -- 9.1 Introduction -- 9.2 Abusive Content on Social Media and Their Identification -- 9.3 Identification of Abusive Content with Classic Machine Learning Methods -- 9.3.1 Use of Word Embedding in Data Representation -- 9.3.2 Ensemble Model -- 9.4 Identification of Abusive Content with Deep Learning Models -- 9.4.1 Taxonomy of Deep Learning Models -- 9.4.2 Natural Language Processing with Advanced Deep Learning Models -- 9.5 Applications -- 9.6 Future Direction -- 9.7 Conclusion -- References -- 10 Toward Artifical Intelligence Tools for Solving the Real World Problems: Effective Hybrid Genetic Algorithms Proposal -- 10.1 Introduction -- 10.2 University Course Timetabling UCT -- 10.2.1 Problem Statement and Preliminary Definitions -- 10.2.2 Related Works -- 10.2.3 Problem Modelization and Mathematical Formulation -- 10.2.4 An Interactive Decision Support System (IDSS) for the UCT Problem. , 10.2.5 Empirical Testing -- 10.2.6 Evaluation and Results -- 10.3 Solid Waste Management Problem -- 10.3.1 Related Works -- 10.3.2 The Mathematical Formulation Model -- 10.3.3 A Genetic Algorithm Proposal for the SWM -- 10.3.4 Experimental Study and Results -- 10.4 Conclusion -- References -- 11 Artificial Neural Networks for Precision Medicine in Cancer Detection -- 11.1 Introduction -- 11.2 The fLogSLFN Model -- 11.3 Parallel Versus Cascaded LogSLFN -- 11.4 Adaptive SLFN -- 11.5 Statistical Assessment -- 11.6 Conclusions -- References -- Part III Recent Trends in Artificial Intelligence Areas and Applications -- 12 Towards the Joint Use of Symbolic and Connectionist Approaches for Explainable Artificial Intelligence -- 12.1 Introduction -- 12.2 Literature Review -- 12.2.1 The Explainable Interface -- 12.2.2 The Explainable Model -- 12.3 New Approaches to Explainability -- 12.3.1 Towards a Formal Definition of Explainability -- 12.3.2 Using Ontologies to Design the Deep Architecture -- 12.3.3 Coupling DNN and Learning Classifier Systems -- 12.4 Conclusions -- References -- 13 Linguistic Intelligence As a Root for Computing Reasoning -- 13.1 Introduction -- 13.2 Language as a Tool for Communication -- 13.2.1 MLW -- 13.2.2 Sounds and Utterances Behavior -- 13.2.3 Semantics and Self-expansion -- 13.2.4 Semantic Drifted Off from Verbal Behavior -- 13.2.5 Semantics and Augmented Reality -- 13.3 Language in the Learning Process -- 13.3.1 Modeling Learning Profiles -- 13.3.2 Looking for Additional Teaching Tools in Academy -- 13.3.3 LEARNITRON for Learning Profiles -- 13.3.4 Profiling the Learning Process: Tracking Mouse and Keyboard -- 13.3.5 Profiling the Learning Process: Tracking Eyes -- 13.3.6 STEAM Metrics -- 13.4 Language of Consciousness to Understand Environments -- 13.4.1 COFRAM Framework -- 13.4.2 Bacteria Infecting the Consciousness. , 13.5 Harmonics Systems: A Mimic of Acoustic Language -- 13.5.1 HS for Traffic's Risk Predictions -- 13.5.2 HS Application to Precision Farming -- 13.6 Conclusions and Future Work -- References -- 14 Collaboration in the Machine Age: Trustworthy Human-AI Collaboration -- 14.1 Introduction -- 14.2 Artificial Intelligence: An Overview -- 14.2.1 The Role of AI-Definitions and a Short Historic Overview -- 14.2.2 AI and Agents -- 14.2.3 Beyond Modern AI -- 14.3 The Role of AI for Collaboration -- 14.3.1 Human-Computer Collaboration Where AI is Embedded -- 14.3.2 Human-AI Collaboration (Or Conversational AI) -- 14.3.3 Human-Human Collaboration Where AI Can Intervene -- 14.3.4 Challenges of Using AI: Toward a Trustworthy AI -- 14.4 Conclusion -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Advances in Assistive Technologies -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- References -- Part I Advances in Assistive Technologies in Healthcare -- 2 Applications of AI in Healthcare and Assistive Technologies -- 2.1 Introduction -- 2.2 Healthcare and Biomedical Research -- 2.2.1 Controlled Monitoring Environment -- 2.2.2 Evolving Healthcare Techniques -- 2.2.3 Diagnosis -- 2.3 Assistive Technologies -- 2.3.1 Smart Homes and Cities -- 2.3.2 Assistive Robotics -- 2.4 Analysis and Forecasting -- 2.5 Conclusions -- References -- 3 A Research Agenda for Dementia Care: Prevention, Risk Mitigation and Personalized Interventions -- 3.1 Introduction -- 3.2 Mild Behavioral Impairments (MBI) and Dementia -- 3.3 Biometric Data -- 3.4 Caring for Caregivers -- 3.5 Tests -- 3.6 Conclusions -- References -- 4 Machine Learning and Finite Element Methods in Modeling of COVID-19 Spread -- 4.1 Introduction -- 4.1.1 Physiology of Human Respiratory System -- 4.1.2 Spreading of SARS-CoV-2 Virus Infection -- 4.1.3 Machine Learning for SARS-CoV-2 -- 4.2 Methods -- 4.2.1 Finite Element Method for Airways and Lobes -- 4.2.2 Machine Learning Method -- 4.3 Results -- 4.3.1 Simulation of Virus Spreading by Finite Element Analysis -- 4.3.2 Machine Learning Results -- 4.4 Conclusions -- References -- Part II Advances in Assistive Technologies in Medical Diagnosis -- 5 Towards Personalized Nutrition Applications with Nutritional Biomarkers and Machine Learning -- 5.1 Introduction -- 5.1.1 Summary -- 5.1.2 Chapter Synopsis -- 5.1.3 Goals and Perspective -- 5.2 Basic Concepts -- 5.2.1 Personalized Medicine -- 5.2.2 Next Generation Sequencing -- 5.2.3 Obesity -- 5.2.4 Nutritional Biomarkers -- 5.3 Neural Networks, Pattern Recognition and Datasets -- 5.3.1 Neural Network. , 5.3.2 Implementation Environment -- 5.3.3 Proposed System -- 5.4 Implementation and Evaluation of the Proposed System -- 5.4.1 Deep Back Propagation Neural Network -- 5.4.2 Standard Biochemistry Profile Neural Network (SBPNN) -- 5.4.3 Neural Network Dietary Profile -- 5.5 Conclusions and Future Research -- 5.5.1 Prevention -- 5.5.2 Modelling -- 5.5.3 Automation -- 5.5.4 Perspective -- 5.5.5 Discussion on Feature Research -- 5.6 Appendix -- References -- 6 Inductive Machine Learning and Feature Selection for Knowledge Extraction from Medical Data: Detection of Breast Lesions in MRI -- 6.1 Introduction -- 6.2 Detailed Literature Review -- 6.3 Presentation of the Data -- 6.3.1 Data Collection -- 6.3.2 Description of Variables -- 6.3.3 Dataset Preprocessing -- 6.4 Methodology -- 6.4.1 Modeling Methodology -- 6.4.2 Feature Selection Process -- 6.4.3 Classification Method -- 6.4.4 Validation Process -- 6.5 Modeling Approaches -- 6.5.1 Experimental Process -- 6.5.2 Experimental Results -- 6.6 Conclusions and Further Search -- Annex 6.1-Abbreviations -- Annex 6.2-Variables Frequency Charts (Original Dataset) -- Annex 6.3-Variables' Values Range -- Annex 6.4-Classification Tree (Benign or Malignant) -- References -- 7 Learning Paradigms for Neural Networks for Automated Medical Diagnosis -- 7.1 Introduction -- 7.2 Classical Artificial Neural Networks -- 7.3 Learning Paradigms -- 7.3.1 Evolutionary Computation Learning Paradigm -- 7.3.2 Bayesian Learning Paradigm -- 7.3.3 Markovian Stimulus-Sampling Learning Paradigm -- 7.3.4 Logistic Regression Paradigm -- 7.3.5 Ant Colony Optimization Learning Paradigm -- 7.4 Conclusions and Future Outlook -- References -- Part III Advances in Assistive Technologies in Mobility and Navigation -- 8 Smart Shoes for Assisting People: A Short Survey -- 8.1 Smart Shoes for People in Need. , 8.1.1 Smart Shoes for Visually Impaired People [6] -- 8.1.2 Smart Shoes for Blind Individuals [13] -- 8.1.3 IoT Based Wireless Smart Shoes and Energy Harvesting System [7] -- 8.1.4 Smart Shoes for Sensing Force [8] -- 8.1.5 Smart Shoes for Temperature and Pressure [9] -- 8.1.6 Smart Shoes in IoT [10] -- 8.1.7 Smart Shoes for People with Walking Disorders [23] -- 8.2 Special Purpose Smart Shoes -- 8.2.1 Smart Shoes with Triboelectric Nanogenerator [11] -- 8.2.2 Smart Shoes Gait Analysis [12] -- 8.2.3 Smart Shoes for Biomechanical Energy Harvesting [14] -- 8.2.4 Smart Shoes with Embedded Piezoelectric Energy Harvesting [15] -- 8.2.5 Pedestrian Navigation Using Smart Shoes with Markers [16] -- 8.2.6 Smart Shoes with 3D Tracking Capabilities [17] -- 8.2.7 Pedestrian's Safety with Smart Shoes Sensing [18] -- 8.2.8 Smart Shoes Insole Tech for Injury Prevention [19] -- 8.3 Maturity Evaluation of the Smart Shoes -- 8.4 Conclusion -- References -- 9 Re-Examining the Optimal Routing Problem from the Perspective of Mobility Impaired Individuals -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Mobility Aspects for People with Special Needs -- 9.3 Related Work -- 9.3.1 Miller-Tucker-Zemlin Formulation of the Traveling Salesman Problem -- 9.3.2 Dantzig-Fulkerson-Johnson Formulation of the Traveling Salesman Problem -- 9.4 The Optimal Routing Problem from the Perspective of Mobility Impaired Individuals -- 9.4.1 Measuring Route Scores Based on the Degree of Accessibility -- 9.4.2 Problem Statement: The Optimal Routing Problem from the Perspective of Mobility Impaired Individuals -- 9.4.3 The Proposed Solution Approach -- 9.5 The Experimental Results and Discussion -- 9.6 Conclusions -- References -- 10 Human Fall Detection in Depth-Videos Using Temporal Templates and Convolutional Neural Networks -- 10.1 Introduction -- 10.2 Proposed Method. , 10.3 Experiments, Results and Discussion -- 10.3.1 SDU Fall Dataset -- 10.3.2 UP-Fall Detection Dataset -- 10.3.3 UR Fall Detection Dataset -- 10.3.4 MIVIA Action Dataset -- 10.4 Conclusions and Future Work -- 10.5 Compliance with Ethical Standards -- References -- 11 Challenges in Assistive Living Based on Tech Synergies: The Cooperation of a Wheelchair and A Wearable Device -- 11.1 Overall Description of the Challenges -- 11.2 Background and Significance -- 11.3 The Associated Research Challenges -- 11.3.1 Main Innovative Tasks -- 11.4 Discussion -- References -- 12 Human-Machine Requirements' Convergence for the Design of Assistive Navigation Software: Τhe Case of Blind or Visually Impaired People -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Methodology -- 12.3.1 Interviews with BVI People and Requirements Classification -- 12.3.2 Description of the Participants -- 12.3.3 Requirements Classification -- 12.4 Analysis of the Elicited Requirements -- 12.4.1 Elicited Requirements of the BVI -- 12.5 Discussion -- 12.6 Conclusion -- Appendix A -- References -- Part IV Advances in Privacy and Explainability in Assistive Technologies -- 13 Privacy-Preserving Mechanisms with Explainability in Assistive AI Technologies -- 13.1 Introduction -- 13.1.1 Data Ethics -- 13.1.2 Data Privacy -- 13.1.3 Data Security -- 13.2 AI Applications in Assistive Technologies -- 13.2.1 Explainable AI (XAI) -- 13.3 Data Privacy and Ethical Challenges for Assistive Technologies -- 13.3.1 Data Collection and Data Sharing -- 13.3.2 Secure and Responsible Data Sharing Framework -- 13.4 AI Assistive Technologies with Privacy Enhancing -- 13.4.1 Privacy-Preserving Mechanisms for AI Assistive Technologies -- 13.5 Discussions -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Advances in Artificial Intelligence-Based Technologies -- References -- Part I Advances in Artificial Intelligence Tools and Methodologies -- 2 Synthesizing 2D Ground Images for Maps Creation and Detecting Texture Patterns -- 2.1 Introduction -- 2.2 Synthesizing 2D Consecutive Region-Images for Space Map Generation -- 2.3 Texture Paths Detection -- 2.4 Simulated Case Study and Comparison with Other Methods -- 2.5 Discussion -- References -- 3 Affective Computing: An Introduction to the Detection, Measurement, and Current Applications -- 3.1 Introduction -- 3.2 Background -- 3.3 Detection and Measurement Devices for Affective Computing -- 3.3.1 Brain Computer Interfaces (BCIs) -- 3.3.2 Facial Expression and Eye Tracking Technologies -- 3.3.3 Galvanic Skin Response -- 3.3.4 Multimodal Input Devices -- 3.3.5 Emotional Speech Recognition and Natural Language Processing -- 3.4 Application Examples -- 3.4.1 Entertainment -- 3.4.2 Chatbots -- 3.4.3 Medical Applications -- 3.5 Conclusions -- References -- 4 A Database Reconstruction Approach for the Inverse Frequent Itemset Mining Problem -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Problem Definition -- 4.3.1 Frequent Itemset Hiding Problem -- 4.3.2 Inverse Frequent Itemset Hiding Problem -- 4.4 Hiding Approach -- 4.5 Conclusion and Future Steps -- References -- 5 A Rough Inference Software System for Computer-Assisted Reasoning -- 5.1 Introduction -- 5.2 Basic Concepts -- 5.2.1 Rough Sets -- 5.2.2 Information System -- 5.2.3 Decision System -- 5.2.4 Indiscernibility Relation -- 5.3 The Approximate Algorithms for Information Systems -- 5.3.1 The Approximate Algorithm for Attribute Reduction -- 5.3.2 The Algorithm for Approximate Rule Generation -- 5.4 Implementation of the Rough Inference System. , 5.5 An Application in Electrical Engineering-A Case Study -- 5.6 Conclusions -- References -- Part II Advances in Artificial Intelligence-based Applications and Services -- 6 Context Representation and Reasoning in Robotics-An Overview -- 6.1 Introduction -- 6.2 Context -- 6.2.1 Definitions of Context -- 6.2.2 Context Aware Systems -- 6.2.3 Context Representation -- 6.3 Context Reasoning -- 6.3.1 Reasoning Approaches and Techniques -- 6.3.2 Reasoning Tools -- 6.4 Conclusions and Future Work -- References -- 7 Smart Tourism and Artificial Intelligence: Paving the Way to the Post-COVID-19 Era -- 7.1 Introduction -- 7.2 Methodology and Research Approach -- 7.3 Artificial Intelligence and Smart Tourism -- 7.3.1 Artificial Intelligence -- 7.3.2 AI Smart Tourism Recommender Systems -- 7.3.3 Deep Learning -- 7.3.4 Augmented Reality In tourism -- 7.3.5 AI Autonomous Agents -- 7.4 Smart Tourism in COVID-19 Pandemic -- 7.5 Conclusions and Future Directions -- References -- 8 Challenges and AI-Based Solutions for Smart Energy Consumption in Smart Cities -- 8.1 Introduction -- 8.2 Smart Energy in Smart Cities -- 8.3 Energy Consumption Challenges and AI Solutions -- 8.3.1 End-User Consumers in Smart Cities -- 8.3.2 Demand Forecasting -- 8.3.3 Prosumers Management -- 8.3.4 Consumption Privacy -- 8.4 Discussion -- References -- 9 How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics -- 9.1 Introduction -- 9.2 The Development of Log File Analysis and Learning Analytics -- 9.3 Analysing Log File Data in Researching Dynamic Problem-Solving -- 9.4 Extracting, Structuring and Analysing Log File Data to Make Different Thinking Profiles Visible -- 9.4.1 Aims -- 9.4.2 Methods -- 9.5 Participants -- 9.6 Instruments -- 9.7 Procedures -- 9.8 Results -- 9.9 Discussion -- 9.10 Conclusions and Limitations. , References -- 10 AI in Consumer Behavior -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Artificial Intelligence (AI) in Consumer Behavior -- 10.3.1 Artificial Intelligence -- 10.3.2 Consumer Behavior -- 10.3.3 AI in Consumer Behavior -- 10.3.4 AI and Ethics -- 10.4 Conclusion -- References -- Part III Theoretical Advances in Computation and System Modeling -- 11 Coupled Oscillator Networks for von Neumann and Non-von Neumann Computing -- 11.1 Introduction -- 11.2 Basic Unit, Network Architecture and Computational Principle -- 11.3 Nonlinear Oscillator Networks and Phase Equation -- 11.3.1 Example -- 11.4 Oscillator Networks for Boolean Logic -- 11.4.1 Registers -- 11.4.2 Logic Gates -- 11.5 Conclusions -- References -- 12 Design and Implementation in a New Approach of Non-minimal State Space Representation of a MIMO Model Predictive Control Strategy-Case Study and Performance Analysis -- 12.1 Introduction -- 12.2 Centrifugal Chiller-System Decomposition -- 12.2.1 Centrifugal Chiller Dynamic Model Description -- 12.2.2 Centrifugal Chiller Dynamic MIMO ARMAX Model Description -- 12.2.3 Centrifugal Chiller Open Loop MIMO ARMAX Discrete-Time Model -- 12.2.4 Centrifugal Chiller Dynamic MIMO ARMAX Model Nonminimal State Space Description -- 12.3 MISO MPC Strategy Design in a Minimal State Space Realization -- 12.3.1 MIMO MPC Optimization Problem Formulation -- 12.3.2 MIMO MPC Parameters Design -- 12.3.3 MIMO MPC MATLAB SIMULINK Simulation Results -- 12.4 MIMO MPC Strategy Design in a Nonminimal State Space Realization -- 12.5 Conclusions -- References.

Note: Title -- Foreword -- Editors -- Preface -- Table of Contents -- Advances in Multimedia Services in Intelligent Environments - Software Development Challenges and Solutions -- Introduction -- Conclusions -- References and Further Reading -- Evaluating the Generality of a Life-Cycle Framework for Incorporating Clustering Algorithms in Adaptive Systems -- Introduction -- The Software Life Cycle -- Inception -- Defining Requirements for the Prototype System and Analysis and Design of the Prototype Adaptive Recommender System -- Building and Evaluating the Prototype Adaptive Recommender System -- Elaboration -- Computing the Resemblance Coefficients for the Data Set and Developing the Clustering Algorithm -- Execute the Clustering Method for the Prototype and Evaluating the Results of the Clustering Algorithm used in the Prototype -- Construction -- The Most Efficient Algorithm and Designing Stereotypes Based on This Algorithm -- Building the User Modeling Component Based on the Stereotypes and Incorporating Them into the System -- Transition-Dynamically Improving System Performance while Used by Real Users -- Conclusion about the Life Cycle Process -- References -- A Distributed Multimedia Data Management over the Grid -- Introduction -- Related Work -- Overall Framework -- Replicated Multidimensional Index Structure -- Distributed Query Processing -- Automatic Load Balancing -- Empirical Study -- Conclusion and Future Works -- References -- A View of Monitoring and Tracing Techniques and Their Application to Service-Based Environments -- Introduction -- Execution Monitoring and Tracing -- Categories of Execution Monitoring -- What Can Be Traced? -- Monitoring Techniques -- Comparison -- Monitoring Challenges for Multimedia Services -- Behavioral Change -- Information Access -- Timing -- Synchronization of Event Traces -- Distributed Monitoring. , Trace Transmission -- Conclusion -- References -- An Ontological SW Architecture SupportingContribution and Retrieval of Service and Process Models -- Introduction -- An Application in the Healthcare Domain -- Ontological SW Architecture -- Three Tier with Ontologies -- Encoding Procedures through Ontologies -- Enabling Direct Contribution through Ontologies -- Enabling Semantic Queries -- Enabling Adapted Presentation through Ontologies -- Conclusions -- References -- Model-Driven Quality Engineering of Service-Based Systems -- Introduction -- Related Work -- QoS-Enabled WSDL (Q-WSDL) -- QoS Characteristics of Web Services -- Q-WSDL Metamodel -- Composite Web Services -- QoS Prediction of Composite Web Services -- Model-Driven Reliability Prediction -- Example Application -- Conclusions -- References -- Application of Logic Models for Pervasive Computing Environments and Context-Aware Services Support -- Introduction -- Contextual Models Requirements for Pervasive Computing -- The LogiPerSe First Order Logic Model -- Partial Validation and Ambiguity -- Scalability -- Composability -- Pervasive Service Scenario -- Logic-Based System Architecture -- Logic Model - Object-Oriented Model - Ontology Integration -- Conclusions -- References -- Intelligent Host Discovery from Malicious Software -- Introduction -- Instant Message Clients -- Search Engines -- Social Networks -- Torrent Trackers -- Conclusions -- References -- Swarm Intelligence: The Ant Paradigm -- Introduction -- Swarm Intelligence -- Principles of Natural Swarms -- The Biological Origins of Ant Algorithms -- Ant Algorithms of the Ant Colony Optimization Framework -- Ant System -- MAX MIN Ant System -- Ant Colony System -- Conclusion -- References -- Formulating Discrete Geometric Random Sums for Facilitating Intelligent Behaviour of a Complex System under a Major Risk -- Introduction. , Formulation of a Discrete Geometric Random Sum -- Stochastic Derivation of a Discrete Geometric Random Sum -- Risk and Crisis Management Applications and Computational Intelligence Interpretation of a Discrete Geometric Random Sum -- Conclusions -- References -- Incorporating a Discrete Renewal Random Sum in Computational Intelligence and Cindynics -- Introduction -- Formulation of a Discrete Renewal Random Sum -- Stochastic Derivation of a Discrete Renewal Random Sum -- Application in Cindynics and Interpretation in Computational Intelligence of a Discrete Renewal Random Sum -- Conclusions -- References -- Author Index.

Note: Title Page -- Preface -- Organizations -- Contents -- Managing Collaborative Sessions in WSNs -- Introduction -- Related Work -- Collaboration Hierarchy in WSNs -- Types of Collaboration -- Collaboration Hierarchy -- Sessions -- WISE-MANager -- Case Study -- Advantages and Disadvantages -- Conclusions -- References -- OGRE-Coder: An Interoperable Environment for the Automatic Generation of OGRE Virtual Scenarios -- Introduction -- OGRE Markup Language (OGREML) -- OGRE-Coder: Design and Implementation Issues -- Requirements and Use Cases -- Architecture -- Implementation Tools -- OGRE-Coder Functionalities -- Authoring OGRE Virtual Environments -- Generating OGRE Code with OGRE-Coder -- Conclusions -- References -- Natural and Intuitive Video Mediated Collaboration -- Current Systems for Enabling Remote Collaboration -- Videoconferencing and Telepresence Systems -- Desktop Video Conferencing -- Interactive Tables and Smart Whiteboards -- The Importance of Usability -- Building Prototypes -- First Prototype -- Second Prototype -- Our Design Concept -- Permanent Connection, Invisible User Interface -- Natural Collaborative Tools -- Discussion -- Is Permanent Connection Too Limited? -- Where Could This Concept Be Applied? -- Challenges of Permanent Video Connections -- Future Development Challenges -- References -- Evolutionary System Supporting Music Composition -- Introduction -- Evolutionary System Supporting Music Composition -- Architecture of the System -- Genetic Algorithm -- The Process of Music Composition -- Experimental Results -- Summary and Conclusions -- References -- Usability Inspection of Informal Learning Environments: The HOU2LEARN Case -- Introduction -- Literature Review -- The HOU2LEARN Platform -- Usability Evaluation -- General -- The Method Applied -- The Experiment -- Conclusions - Future Goals -- References. , Procedural Modeling of Broad-Leaved Trees under Weather Conditions in 3D Virtual Reality -- Introduction -- Related Work -- Procedural Modeling of Broad-Leaved Trees -- Modeling of Tree under Weather Conditions -- Forest Modeling -- Experimental Program -- Conclusion -- References -- New Method for Adaptive Lossless Compression of Still Images Based on the Histogram Statistics -- Introduction -- Basic Principles of the АRL Coding Method -- Evaluation of the Lossless Coding Method Efficiency -- Experimental Results -- Conclusions -- References -- Scene Categorization with Class Extendibility and Effective Discriminative Ability -- Introduction -- Category-Specific Approach -- Whole-Construction/Whole-Representation Strategy -- Category-Specific-Construction/Whole-Representation Strategy -- Category-Specific-Construction/ Category-Specific-Representation Strategy -- Experimental Results -- Conclusions -- References -- Adaptive Navigation in a Web Educational System Using Fuzzy Techniques -- Introduction -- Adaptive Navigation Support -- The Domain Knowledge -- Student Modeling -- Discussion on the Fuzzy Cognitive Maps and Fuzzy User Modeling Used -- Conclusion -- References -- Visualization Tool for Scientific Gateway -- Introduction -- Visual Representation of Datasets -- VT as a New Discovery for Presenting Academic Research Results -- Architecture of Visualization Tool -- Directly Visual Education Form -- Conclusion -- References -- Digital Text Based Activity: Teaching Geometrical Entities at the Kindergarten -- Introduction -- Review Standards -- Method - Data Collection and Observations -- Digital Based Activities at the Kindergarten -- Using Graphical Programs (Mspaint) -- Using Slide Shows (PowerPoint) -- Using Digital Cameras -- Using Spreadsheets (EXCEL) -- Summary and Conclusions -- References. , Cross Format Embedding of Metadata in Images Using QR Codes -- Introduction -- QRCodes -- Our Proposal -- Results -- Applications -- Conclusions -- References -- An Empirical Study for Integrating Personality Characteristics in Stereotype-Based Student Modelling in a Collaborative Learning Environment for UML -- Introduction -- Personality Related Stereotypes -- Empirical Study for Defining the Triggers -- Implementation of Triggers -- Conclusion -- References -- An Efficient Parallel Architecture for H.264/AVC Fractional Motion Estimation -- Introduction -- H.264/AVC FME Observations -- Encoding with INTER8x8 Mode or above -- Statistic Charactistics of Motion Vectors -- The Proposed Architecture -- Reference Pixel Array -- Integer Pixel Sampler in Reference Array -- 14-Input FME Engine -- Data Processing Order -- 3-Stages Processing -- Simulation Results -- Conclusions -- References -- Fast Two-Stage Global Motion Estimation: A Blocks and Pixels Sampling Approach -- Introduction -- Motion Models -- Global Motion Estimation -- Initial Translation Estimation -- Block Sampling and Limited Block Matching -- Initial Estimation of Perspective Model GM Parameters -- Subsampling Pixels and Levenberg-Marquardt Algorithm -- Simulation -- Conclusion -- References -- Frame Extraction Based on Displacement Amount for Automatic Comic Generation from Metaverse Museum Visit Log -- Introduction -- Comic Generation System -- Evaluation -- Implementation -- Evaluation Outline -- Results and Discussions -- Related Work -- Conclusions and Future Work -- References -- Knowledge-Based Authoring Tool for Tutoring Multiple Languages -- Introduction -- Related Work -- Architecture of Our System -- Description of the System -- Authoring Domain Knowledge -- Authoring Student Model -- Authoring of Teaching Model -- Case Study for the Instructor -- Case Study for the Student. , Student Modeling and Error Diagnosis -- Modeling the System's Authoring Process -- Conclusions -- References -- Evaluating an Affective e-Learning System Using a Fuzzy Decision Making Method -- Introduction -- Fuzzy Simple Additive Weighting -- Overall Description of the System -- Evaluation Experiment -- Results -- Conclusions -- References -- Performance Evaluation of Adaptive Content Selection in AEHS -- Introduction -- Performance Evaluation Metrics for Decision-Based AEHS -- Evaluation Methodology for Decision-Based AEHS -- Setting Up the Experiments -- Designing the Media Space -- Designing the Learner Model -- Simulating the AM of an AEHS -- Experimental Results and Discussion -- Extracting the AM of existing AEHS -- Scaling Up the Experiments -- Conclusions -- References -- AFOL: Towards a New Intelligent Interactive Programming Language for Children -- Introduction -- General Architecture of the AFOL Programming Environment -- Overview of the AFOL Programming Learning System -- AFOL Language Commands and Object Oriented Structure -- Conclusions -- References -- Multimedia Session Reconfiguration for Mobility-Aware QoS Management: Use Cases and the Functional Model -- Introduction -- Session Reconfiguration and Use Cases -- Functional Model -- Performance Evaluation -- Conclusions and Future Work -- References -- LSB Steganographic Detection Using Compressive Sensing -- Introduction -- Steganalysis -- Compressive Sensing and BM3D -- The Proposed Method -- Results -- Conclusions -- References -- Analysis of Histogram Descriptor for Image Retrieval in DCT Domain -- Introduction -- Description of the Method -- Pre-processing -- Construction of the AC-Pattern Histogram -- Construction of DC-Pattern Histogram -- Application to Image Retrieval -- Paramaters of Descriptor -- Performance Analysis -- Application to GTF Database. , Application to ORL Database -- Conclusions -- References -- A Representation Model of Images Based on Graphs and Automatic Instantiation of Its Skeletal Configuration -- Introduction -- Related Works -- A Model for Images -- Instantiating the Model -- Experiments -- Conclusion and Outlook -- References -- Advice Extraction fromWeb for Providing Prior Information Concerning Outdoor Activities -- Introduction -- Characteristics Analysis of Advices -- The Definition of Advices -- Construction of Development Data -- Characteristics of Advices -- Characteristics of Advices Suitable for Situations -- Prior Advice Acquisition -- Preprocessing -- Advice Acquisition -- Situation Classification of Advices -- Experiment -- Evaluation Data -- Experiment for Acquiring Advices -- Experiment for Classifying Situation of Advices -- Conclusion -- References -- Automatic Composition of Presentation Slides, Based on Semantic Relationships among Slide Components -- Introduction -- Approach -- Document Structure -- Processing Flow -- Slide Editing -- Semantic Relationship -- Editing Operation -- Slide Composition -- Grouping of Slide Components -- Template-Based Slide Composition -- Prototype System -- Component Editing Interface -- Display Interface -- Conclusion -- References -- Sustainable Obsolescence Management - A Conceptual Unified Framework to Form Basis of an Interactive Intelligent Multimedia System -- Introduction -- Definitions -- Sustainability / Sustainable Development -- Obsolescence -- Sustainability versus Obsolescence - Built Environment Context -- Social -- Environmental -- Economic -- Holistic Sustainable Obsolescence Management -- Obsolescence Assessment (OA) -- Obsolescence Reduction (OR) -- Concluding Remarks -- References -- Automatic Text Formatting for Social Media Based on Linefeed and Comma Insertion -- Introduction. , Text Formatting by Comma and Linefeed Insertion.