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Computer Science and Engineering-Theory and Applications

E-BookPDF1 - PDF WatermarkE-Book
282 Seiten
Englisch
Springer International Publishingerschienen am05.02.20181st ed. 2018
This book presents a collection of research findings and proposals on computer science and computer engineering, introducing readers to essential concepts, theories, and applications. It also shares perspectives on how cutting-edge and established methodologies and techniques can be used to obtain new and interesting results. Each chapter focuses on a specific aspect of computer science or computer engineering, such as: software engineering, complex systems, computational intelligence, embedded systems, and systems engineering. As such, the book will bring students and professionals alike up to date on key advances in these areas.mehr
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Produkt

KlappentextThis book presents a collection of research findings and proposals on computer science and computer engineering, introducing readers to essential concepts, theories, and applications. It also shares perspectives on how cutting-edge and established methodologies and techniques can be used to obtain new and interesting results. Each chapter focuses on a specific aspect of computer science or computer engineering, such as: software engineering, complex systems, computational intelligence, embedded systems, and systems engineering. As such, the book will bring students and professionals alike up to date on key advances in these areas.
Details
Weitere ISBN/GTIN9783319740607
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2018
Erscheinungsdatum05.02.2018
Auflage1st ed. 2018
ReiheStudies in Systems, Decision and Control
Reihen-Nr.143
Seiten282 Seiten
SpracheEnglisch
IllustrationenVIII, 282 p. 106 illus., 77 illus. in color.
Artikel-Nr.2612124
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;6
2;Contents;8
3;1 A Comprehensive Context-Aware Recommender System Framework;10
3.1;Abstract;10
3.2;1 Introduction;10
3.3;2 Background and Related Work;12
3.3.1;2.1 Context and Context Awareness;12
3.3.1.1;2.1.1 Context Representation;13
3.3.2;2.2 Frameworks for Context-Aware Recommendations;14
3.3.3;2.3 Requirements for a Context-Aware Recommender System Framework;16
3.4;3 The Comprehensive Context-Aware Recommender System Framework;17
3.4.1;3.1 The Model;18
3.4.1.1;3.1.1 User Aspects;19
3.4.1.2;3.1.2 Context Aspects;19
3.4.1.3;3.1.3 Activity Information;19
3.4.1.4;3.1.4 Items Information;20
3.4.2;3.2 Data Management;20
3.4.2.1;3.2.1 Dataset Generator;21
3.4.3;3.3 Recommendation Algorithms;21
3.4.3.1;3.3.1 Traditional Recommendation Algorithms;22
3.4.3.2;3.3.2 Contextual Recommendation Algorithms;22
3.5;4 Evaluations;24
3.5.1;4.1 Comparative Assessment;24
3.5.2;4.2 Use Cases;25
3.5.2.1;4.2.1 Data Management Feature;26
3.5.2.2;4.2.2 Contextual Recommendation Feature;28
3.6;5 Conclusions and Future Work;29
3.7;Acknowledgements;30
3.8;References;31
4;2 Executive Functions and Their Relationship with Interaction Design;34
4.1;Abstract;34
4.2;1 Introduction;34
4.3;2 User Modelling and Interface Design;36
4.4;3 Executive Functions;38
4.4.1;3.1 Executive Functions and Interaction Design;39
4.4.2;3.2 The Problem of Cognitive Load;40
4.4.3;3.3 Can We Measure Cognitive Load?;41
4.4.3.1;3.3.1 Indirect Measures;41
4.4.3.2;3.3.2 Subjective Measures;41
4.4.3.3;3.3.3 Measurement Through a Secondary Task;42
4.4.3.4;3.3.4 Physiological Measures;42
4.5;4 Research Questions;43
4.6;5 Experimental Design;43
4.6.1;5.1 Objectives;43
4.6.2;5.2 Sample;44
4.6.3;5.3 Structure of the Study;44
4.6.4;5.4 Variables;44
4.6.5;5.5 Instruments;45
4.6.5.1;5.5.1 Nepsy II;45
4.6.5.2;5.5.2 Wisc-IV;46
4.6.5.3;5.5.3 Modified NASA-TLX Test for Children;46
4.6.6;5.6 Methodology;46
4.6.7;5.7 Results;47
4.6.7.1;5.7.1 Data Analysis;47
4.6.7.2;5.7.2 Interaction Rules;48
4.6.7.3;5.7.3 Analysis of Variance;51
4.6.7.4;5.7.4 Regression Analysis;51
4.7;6 Conclusions and Discussion;53
4.8;References;54
5;3 Integrating Learning Styles in an Adaptive Hypermedia System with Adaptive Resources;57
5.1;Abstract;57
5.2;1 Introduction;57
5.3;2 State of the Art;58
5.3.1;2.1 Index of Learning Styles Questionnaire;59
5.3.1.1;2.1.1 Active and Reflective Learners;59
5.3.1.2;2.1.2 Sensing and Intuitive Learners;59
5.3.1.3;2.1.3 Visual and Verbal Learners;59
5.3.1.4;2.1.4 Sequential and Global Learners;60
5.3.2;2.2 Learning Systems;60
5.3.3;2.3 Learning Objects;61
5.3.4;2.4 Simple Sequencing;61
5.4;3 Related Works;62
5.5;4 Arquitecture;63
5.5.1;4.1 System Architecture;65
5.5.2;4.2 User Modeling;66
5.5.3;4.3 Recommendation of Learning Activities;66
5.6;5 The Adaptive Hypermedia System;67
5.6.1;5.1 Welcome Screen;67
5.6.2;5.2 Learning Styles Questionnaire;67
5.6.3;5.3 Creation of Learning Activities;68
5.6.4;5.4 Creation of Courses;68
5.6.5;5.5 Course Presentation Window;69
5.6.6;5.6 Learning Activities;69
5.7;6 Results;70
5.8;7 Conclusion and Future Work;72
5.9;References;73
6;4 On Modeling Tacit Knowledge for Intelligent Systems;76
6.1;Abstract;76
6.2;1 Introduction;76
6.3;2 Tacit Knowledge;77
6.3.1;2.1 Types of Tacit Knowledge;79
6.3.2;2.2 Components of Knowledge;79
6.3.2.1;2.2.1 Particulars;79
6.3.2.2;2.2.2 Pre-concepts;80
6.3.2.3;2.2.3 Concepts;80
6.3.3;2.3 A Further Analysis of Knowledge;81
6.3.3.1;2.3.1 Inarticulable Tacit Knowledge;81
6.3.3.2;2.3.2 Articulable Tacit Knowledge;81
6.3.3.3;2.3.3 Explicit Knowledge;81
6.3.3.4;2.3.4 Relation Between Tacit and Explicit Knowledge;82
6.3.3.5;2.3.5 Know-What and Know-How;83
6.3.3.6;2.3.6 Synthesis of Knowledge;83
6.4;3 Tacit Knowledge s Acquisition Process;84
6.5;4 Tacit Knowledge Acquisition Based on Rule Following;87
6.5.1;4.1 Proposed Interpretation of Rule Following;88
6.5.2;4.2 From Individual Tacit Knowledge to Collective (Social) Tacit Knowledge;89
6.6;5 A Proposal for Artificial Intelligence and Multiagent Systems;91
6.7;6 Conclusions;92
6.8;References;93
7;5 Influence of the Betweenness Centrality to Characterize the Behavior of Communication in a Group;95
7.1;Abstract;95
7.2;1 Introduction;95
7.3;2 Network Models;96
7.4;3 Barbell Graph;98
7.5;4 Types of Centrality Measures;99
7.5.1;4.1 Degree Centrality;99
7.5.2;4.2 Eigenvector Centrality;99
7.5.3;4.3 Closeness Centrality;99
7.5.4;4.4 Betweenness Centrality;100
7.6;5 Agent-Based Modeling;101
7.6.1;5.1 Proposed Agent-Based Model for Rumor Spreading;101
7.7;6 Results;105
7.8;7 Conclusions;106
7.9;8 Future Work;107
7.10;References;107
8;6 Multi-layered Network Modeled with MAS and Network Theory;108
8.1;Abstract;108
8.2;1 Introduction;108
8.3;2 Related Work;110
8.3.1;2.1 Complex Networks;110
8.3.2;2.2 Multilayer Networks;112
8.3.3;2.3 Multi-agent System;114
8.3.4;2.4 Multi-agent System Architectures;115
8.3.5;2.5 Negotiation;116
8.4;3 Proposed Model;118
8.5;4 Case Study;119
8.6;5 Results;126
8.7;6 Conclusion and Future Work;127
8.8;Acknowledgements;127
8.9;References;127
9;7 A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo;131
9.1;Abstract;131
9.2;1 Introduction;132
9.2.1;1.1 Fuzzy Logic as a Methodology;133
9.2.2;1.2 Related Work;134
9.3;2 The JT2FIS NETLOGO Tool-Kit;134
9.3.1;2.1 Develop Mamdani and Takagi-Sugeno Fuzzy Logic System;135
9.3.1.1;2.1.1 Inputs;136
9.3.1.2;2.1.2 Outputs;137
9.3.1.3;2.1.3 Members Functions;137
9.3.1.4;2.1.4 Rules;137
9.3.1.5;2.1.5 Data Evaluation;138
9.3.2;2.2 Clustering;140
9.3.3;2.3 Export NetLogo;140
9.3.3.1;2.3.1 Executing FIS in NetLogo;142
9.4;3 Use Cases;142
9.4.1;3.1 Use Case 1: Empirical Configuration FIS;143
9.4.2;3.2 Use Case 2: Data Mining Configuration FIS;145
9.5;4 Discussion and Applications;148
9.5.1;4.1 From Simplistic to Realistic Model;148
9.5.2;4.2 Fuzziness and Uncertainty in Agent Behavior;149
9.5.3;4.3 Opportunity Areas for FISs Applications;150
9.6;5 Conclusions and Future Work;150
9.7;Acknowledgements;151
9.8;References;151
10;8 An Approach to Fuzzy Inference System Based Fuzzy Cognitive Maps;154
10.1;Abstract;154
10.2;1 Introduction;154
10.3;2 Fuzzy Cognitive Maps;155
10.4;3 Type-1 Fuzzy Inference Systems;157
10.5;4 Proposal of Fuzzy Inference System Based Fuzzy Cognitive Maps;159
10.6;5 Experimental Results and Discussion;162
10.7;6 Conclusions;167
10.8;Acknowledgements;167
10.9;References;167
11;9 Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features;170
11.1;Abstract;170
11.2;1 Introduction;170
11.3;2 Background;172
11.3.1;2.1 Epileptic States;172
11.3.2;2.2 Related Work;172
11.4;3 Applied Methods;173
11.4.1;3.1 Feature Extraction and Selection;174
11.4.1.1;3.1.1 Time Domain;174
11.4.1.2;3.1.2 Frequency Domain;175
11.4.1.3;3.1.3 Time-Frequency Domain;176
11.4.1.4;3.1.4 ReliefF;177
11.4.2;3.2 Classification Methods;177
11.5;4 Experimental Setup and Results;178
11.5.1;4.1 Dataset and Problem Formulation;178
11.5.2;4.2 Experimental Procedure;179
11.6;5 Results and Discussion;180
11.7;6 Conclusions and Future Work;183
11.8;Acknowledgements;184
11.9;References;184
12;10 Big Data and Computational Intelligence: Background, Trends, Challenges, and Opportunities;186
12.1;Abstract;186
12.2;1 Introduction;186
12.3;2 Evolution of Data Analysis;187
12.4;3 Emergence of Big Data;188
12.5;4 Big Data Value Chain;190
12.6;5 Challenge of Big Data;191
12.7;6 Areas of Application of Big Data;192
12.8;7 Computational Intelligence;192
12.9;8 Conclusions;196
12.10;References;197
13;11 Design of a Low-Cost Test Plan for Low-Cost MEMS Accelerometers;200
13.1;Abstract;200
13.2;1 Introduction;200
13.3;2 MEMS Accelerometer;202
13.4;3 Accelerometer Model;203
13.5;4 Testing Stages;203
13.5.1;4.1 Data Logging;203
13.5.2;4.2 Preliminary Evaluation;204
13.5.3;4.3 Static Testing;205
13.5.3.1;4.3.1 Multi-position Test;205
13.5.3.2;4.3.2 Long Term Stability Test;210
13.5.3.3;4.3.3 Repeatability Test;210
13.5.4;4.4 Dynamic Testing;211
13.5.4.1;4.4.1 Free Fall Test;211
13.5.4.2;4.4.2 Centrifuge Test;212
13.6;5 Conclusions;213
13.7;Acknowledgements;213
13.8;References;213
14;12 Evaluation of Scheduling Algorithms for 5G Mobile Systems;216
14.1;Abstract;216
14.2;1 Introduction;216
14.2.1;1.1 Long Term Evolution (LTE);218
14.2.2;1.2 Scheduling Fundamentals;219
14.3;2 Model-Based Design Approach;220
14.4;3 System Development;221
14.4.1;3.1 The Radio Resource Management Model;222
14.4.2;3.2 Environment Model;224
14.5;4 Experimental Setup;224
14.5.1;4.1 Maximum Rate Scheduler;224
14.5.2;4.2 Round Robin Scheduler;225
14.5.3;4.3 Proportional Fair Scheduler;226
14.5.4;4.4 UE-Based Maximum Rate Scheduler;228
14.6;5 Results Analysis;228
14.7;6 Conclusion and Future Trends;233
14.8;References;233
15;13 User Location Forecasting Based on Collective Preferences;237
15.1;Abstract;237
15.2;1 Introduction;237
15.2.1;1.1 Collecting Location Data Issues;238
15.2.2;1.2 Collective Preferences Rule Our Lives;239
15.3;2 User Mobility;239
15.3.1;2.1 Markovian Chain Among POIs;241
15.4;3 Spatio-temporal Prediction Model;242
15.4.1;3.1 Modeling User Mobility as a Hidden Markov Model;242
15.4.2;3.2 Defining User Prediction Model;242
15.4.3;3.3 Identifying Points of Interest;243
15.4.4;3.4 User Mobility Similarity;244
15.4.5;3.5 Converting User Mobility into a Vector;244
15.4.6;3.6 Updating POIs;245
15.4.7;3.7 Predictability of the User Mobility;245
15.5;4 Collaborative Filtering;245
15.5.1;4.1 User-User Collaborative Filtering;246
15.5.2;4.2 Item-Item Collaborative Filtering;246
15.5.3;4.3 Stages of CF;247
15.5.3.1;4.3.1 Building a User Profile;247
15.5.3.2;4.3.2 Measuring User Similarity;247
15.5.3.3;4.3.3 Generating a Prediction;247
15.6;5 User Location and CF;248
15.6.1;5.1 Building User Profile;248
15.6.2;5.2 Measuring User Similarity;250
15.6.3;5.3 Avoiding Missing Points of Interest;250
15.7;6 Evaluation;251
15.7.1;6.1 Dataset;251
15.7.2;6.2 Training Prediction Models;251
15.7.3;6.3 Defining User Profile;252
15.7.4;6.4 Predictions;253
15.7.5;6.5 Effectiveness of the Prediction Model;253
15.8;7 Results;253
15.8.1;7.1 POIs;253
15.8.2;7.2 Matrix R Vectors ru;254
15.8.3;7.3 Vectors Similarity;254
15.8.4;7.4 Incorporating POIs;256
15.8.5;7.5 Prediction;256
15.9;References;258
16;14 Unimodular Sequences with Low Complementary Autocorrelation Properties;260
16.1;Abstract;260
16.2;1 Introduction;260
16.3;2 Fundamental Concepts;262
16.4;3 System Identification Using Cyclostationary Statistics;265
16.4.1;3.1 SL System Identification;265
16.4.1.1;3.1.1 Second Order Characterization;269
16.4.2;3.2 WL System Identification;271
16.5;4 Sequences with Low Aperiodic Complementary Autocorrelation;278
16.5.1;4.1 Design of Sequences for an Aperiodic Second Order Characterization;278
16.6;5 Conclusions;282
16.7;Acknowledgements;282
16.8;References;282mehr

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