Nguyen / Zgrzywa / Czyzewski Advances in Multimedia and Network Information System Technologies

1. Auflage 2010
ISBN: 978-3-642-14989-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 80, 318 Seiten

Reihe: Advances in Intelligent and Soft Computing

Growth of knowledge, unparalleled in the history of the human race, results in the rapid development of technology. The solutions that until quite recently remained in the domain of science-fiction now become a part of our everyday life. Information systems and their technologies enter all the spheres of human's existence. Their influence is multiplied by network connections and by multimedia presentations and communications. Our intention was to offer to the readers of this monograph a very broad review of the recent scientific problems in that area. Searching for their solutions had became a principal task of numerous scientific teams all over the world. Preparing this book we have asked for cooperation many European research teams. In effect the monograph is a collection of carefully selected and the most representative - in our opinion - investigations, solutions, and applications presented by different scientific groups from nine countries. Content of the book has been divided into five parts: 1. Multimedia information technology 2. Data processing in information systems 3. Information system applications 4. Web systems and network technologies 5. E-learning methodologies and platforms.

Autoren/Hrsg.

Nguyen, Ngoc-Thanh

Zgrzywa, Aleksander

Czyzewski, Andrzej

Inhaltsverzeichnis

1;Title Page;1
2;Preface;5
3;Contents;7
4;List of Contributors;16
5;Part I Multimedia Information Technology;21
5.1;Chapter 1 Interseum - From Physical to Virtual Showrooms;22
5.1.1;Introduction;22
5.1.2;The Showroom Concept;24
5.1.2.1;The Physical Showroom;24
5.1.2.2;Concept of the Virtual Showroom “Interseum”;27
5.1.3;Conclusions;31
5.1.4;References;31
5.2;Chapter 2 The Synchronization of the Images Based on Normalized Mean Square Error Algorithm;33
5.2.1;Introduction;33
5.2.2;The Principle of the Algorithm of Determination of the Rotation Axis and Angle Based on the NMSE;38
5.2.3;The Experimental Results;41
5.2.4;Conclusions;42
5.2.5;References;42
5.3;Chapter 3 Evaluation of the Separation Algorithm Performance Employing ANNs;44
5.3.1;Introduction;44
5.3.2;AMT System Engineered at the MSD;45
5.3.3;Experiment Lay-Out;48
5.3.4;Comparative Analysis of the ANN-Based Recognition Results, Subjective Tests and Energy-Based Separation Error Evaluation;51
5.3.5;Conclusions;53
5.3.6;References;53
5.4;Chapter 4 Localization of Sound Source Direction in Real Time;55
5.4.1;Introduction;55
5.4.2;A and B Formats;56
5.4.2.1;Analysis of “Soundfield” Microphone Operation Principle;58
5.4.3;Experimental Testing;60
5.4.4;Conclusions;63
5.4.5;References;63
5.5;Chapter 5 Dangerous Sound Event Recognition Using Support Vector Machine Classifiers;64
5.5.1;Introduction;64
5.5.2;Feature Extraction;65
5.5.2.1;Energy-Based Parameters;65
5.5.2.2;Transient-Sensitive Parameters;66
5.5.2.3;MPEG-7 Features;67
5.5.3;Building SVM Model;68
5.5.3.1;Principles of Support Vector Machine Classification;68
5.5.3.2;Parameters of the Model;70
5.5.4;Classification Results;70
5.5.5;Conclusions;71
5.5.6;References;71
5.6;Chapter 6 Noise Tolerant Community Detection Using a Mixed Graph Model;73
5.6.1;Introduction;73
5.6.2;Overview of Previous Methods;74
5.6.2.1;What to Model by a Graph?;74
5.6.2.2;How to Build up the Graph?;74
5.6.2.3;How to Define a Group?;74
5.6.3;The New Algorithm;76
5.6.3.1;The Model;76
5.6.3.2;Handling Noisy and Missing Data;77
5.6.3.3;Finding Cores of Clusters Using Complete Information Vectors;77
5.6.3.4;Clustering the Nodes Using a Bipartite Graph and Fuzzy Membership Functions;78
5.6.4;Test Results;80
5.6.5;Conclusions;81
5.6.6;References;82
5.7;Chapter 7 Fuzzy Rule-Based Dynamic Gesture Recognition Employing Camera and Multimedia Projector;83
5.7.1;Introduction;83
5.7.1.1;Study Objectives;84
5.7.2;System Overview;85
5.7.2.1;Methodology;85
5.7.2.2;Image Processing;86
5.7.2.3;Hand Motion Modeling;86
5.7.2.4;Fuzzy Rule-Based Gesture Recognition;88
5.7.3;Results;89
5.7.4;Conclusions;91
5.7.5;References;92
5.8;Chapter 8 Video Structure Analysis and Content-Based Indexing in the Automatic Video Indexer AVI;93
5.8.1;Introduction;93
5.8.2;Related Works;94
5.8.3;Text Structure vs. Video Structure;96
5.8.4;AVI – Automatic Video Indexer;97
5.8.5;Temporal Segmentation Process;98
5.8.6;Automatic Scene Detection;100
5.8.6.1;Shot Clustering;100
5.8.6.2;TV Sports News Categorization;101
5.8.6.3;Scene Repetitive Patterns;102
5.8.7;Final Conclusion and Further Studies;103
5.8.8;References;103
6;Part II Data Processing in Information Systems;105
6.1;Chapter 9 Acoustic Radar Employing Particle Velocity Sensors;106
6.1.1;Introduction;106
6.1.2;Acoustic Particle Velocity Sensors;107
6.1.3;The Algorithm of the Acoustic Radar;108
6.1.4;Practical Evaluation of the Acoustic Radar;109
6.1.5;Measurement Results;110
6.1.5.1;The Pure Tone Measurement Results;110
6.1.5.2;One-Third Octave Band Noise Measurement Results;113
6.1.5.3;The Impulsive Sounds Measurement Results;114
6.1.5.4;PTZ Camera Control;115
6.1.6;Conclusions;115
6.1.7;References;115
6.2;Chapter 10 Superresolution Algorithm to Video Surveillance System;117
6.2.1;Introduction;117
6.2.2;Multiframe Superresolution Algorithm;118
6.2.3;Superresolution Challenges in Surveillance System;119
6.2.4;Algorithm;121
6.2.5;Experiment;121
6.2.6;Conclusion;123
6.2.7;References;124
6.3;Chapter 11 Social Network Analysis in Corporate Management;125
6.3.1;Introduction;125
6.3.2;Social Network Approach to Corporate Assessment;126
6.3.2.1;Social Network Extraction;126
6.3.2.2;Comparison with Corporate Hierarchy;127
6.3.3;Static Analysis of Social Networks;128
6.3.3.1;Centralities;128
6.3.3.2;Social Groups;128
6.3.3.3;Lonely Entities;129
6.3.4;Dynamic Social Network Analysis;129
6.3.5;Social Concept Networks (SCN);129
6.3.6;Discussion;130
6.3.6.1;Profile of Relationships;130
6.3.6.2;Decision Making;130
6.3.7;Conclusions and Future Work;130
6.3.8;References;131
6.4;Chapter 12 AAM Toolkit: A System for Visual Object Appearance Modeling;133
6.4.1;Introduction;133
6.4.2;The Architecture of AAM Toolkit;134
6.4.2.1;Training Set Creator;134
6.4.2.2;Model Creator;136
6.4.2.3;Regression Matrix Creator;139
6.4.3;Experiments;140
6.4.4;Conclusions;141
6.4.5;References;141
6.5;Chapter 13 Service Discovery Approach Based on Rough Sets for SOA Systems;142
6.5.1;Introduction;142
6.5.2;Service Discovery Problem;143
6.5.2.1;Problem Statement;143
6.5.2.2;Service Discovery in SOA Systems;144
6.5.2.3;SLA Contract Negotiation and Translation Using Ontologies;144
6.5.2.4;Performance Index for Matchmaking;145
6.5.3;Rough Set-Based Approach;146
6.5.3.1;Rough Set Theory – Fundamental Definitions;146
6.5.3.2;Rough Set-Based Approach;148
6.5.4;Application;148
6.5.4.1;Ontology of Vehicle Services;148
6.5.4.2;Illustrative Example;150
6.5.5;Discussion and Future Works;151
6.5.6;References;152
6.6;Chapter 14 Towards Self-defending Mechanisms Using Data Mining in the EFIPSANS Framework;153
6.6.1;Introduction;153
6.6.2;Self-defending Functionality;154
6.6.3;Current Threats to Be Detected;155
6.6.4;Method of Detection;155
6.6.5;Experiments and Results;157
6.6.6;Conclusions and Future Work;160
6.6.7;References;160
7;Part III Information Systems Applications;162
7.1;Chapter 15 User Adaptivity Features of Secured Biomedical User Adaptive System;163
7.1.1;Introduction;163
7.1.2;The User Adaptivity of the System;164
7.1.2.1;Logged User Context Adapting;165
7.1.2.2;UI Adapting by Interaction;165
7.1.2.3;Smart Environment Adaptation;165
7.1.2.4;Hardware Adaptability;166
7.1.3;Architecture and Backgrounds of Biomedical Adaptive System;166
7.1.3.1;Server and Database Parts;166
7.1.3.2;Embedded and Desktop Part;167
7.1.3.3;Mobile Parts;168
7.1.4;Safety Features in Our System;168
7.1.5;User Interface Designing Adaptation;170
7.1.6;Visualization Adaptation;170
7.1.7;Conclusions;171
7.1.8;References;172
7.2;Chapter 16 Exploration of Continuous Sequential Patterns Using the CPGrowth Algorithm;173
7.2.1;Introduction;173
7.2.2;Continuous Sequential Patterns;174
7.2.3;The UCP-Tree Index and the CPGrowth Algorithm;175
7.2.4;Pseudocodes;177
7.2.5;Experiments;178
7.2.6;Conclusion;179
7.2.7;References;179
7.3;Chapter 17 Detecting New and Unknown Malwares Using Honeynet;181
7.3.1;Introduction;181
7.3.2;Honeypot’s Network;182
7.3.3;Malware;183
7.3.4;Multi-agents System in a Honeynet;184
7.3.5;Detection of Malicious Traffic;186
7.3.6;Conclusion;187
7.3.7;References;188
7.4;Chapter 18 Average Prior Distribution of All Possible Probability Density Distributions;189
7.4.1;Introduction;189
7.4.2;Safe a Priori Distribution of Probability Density in the Case of Complete Ignorance of the Real Distribution;190
7.4.3;The Average Distribution in the Case when the Real, Unknown Distribution Is Unimodal;194
7.4.4;Conclusions;198
7.4.5;References;198
7.5;Chapter 19 Interactive Visualization of a Product Search Space;199
7.5.1;Introduction;199
7.5.2;Related Work;200
7.5.3;Requirements;201
7.5.4;Methodology;202
7.5.5;Presentation of the System Use;205
7.5.5.1;Use Case I – Searching an Item;205
7.5.5.2;Use Case II – Navigating through a Vector Space;207
7.5.5.3;Use Case III – Comparing Data Entity Arrangement Algorithms;207
7.5.6;Conclusions and Future Work;207
7.5.7;References;209
8;Part IV Web Systems and Network Technologies;211
8.1;Chapter 20 Adaptive User Profile in Web IR System with Heuristic-Based Acquisition of Significant Terms;212
8.1.1;Introduction;212
8.1.2;Analysis of Web System Answer;213
8.1.2.1;Relevant Document’s Terms Weighting;214
8.1.2.2;Significant Terms Selection from Relevant Documents;214
8.1.3;User Profile;216
8.1.4;Modification of User Profile;217
8.1.5;Experiments;218
8.1.6;Conclusions and Future Work;220
8.1.7;References;220
8.2;Chapter 21 Vertical Search Strategy in Federated Environment;222
8.2.1;Introduction;222
8.2.1.1;Methodology and a Draft of the Research Overall Framework;223
8.2.1.2;Related Work;225
8.2.2;Semantic Mutual Resemblance of Bi-Texts;225
8.2.2.1;Multilingual Information Technologies;228
8.2.2.2;Multilingual Federated Environment;229
8.2.2.3;Access to the Deep Web;230
8.2.3;Query Models Efficiency in Translingual Retrieval;231
8.2.4;Vertical Search Strategy;232
8.2.5;Conclusion;233
8.2.6;References;234
8.3;Chapter 22 Music Information Retrieval on the Internet;235
8.3.1;Introduction;235
8.3.2;Differences and Similarities between Music Information Retrieval and Text Information Retrieval;236
8.3.3;The Method of Extracting Information from Audio Files;237
8.3.4;Internet Music Information Retrieval Systems Based on MIDI Files;238
8.3.5;Algorithms for Songs Identification Based on Spectral Analysis and Fingerprint Generation;241
8.3.6;The Method of Updating Missing Metadata in Audio Files;242
8.3.7;Algorithms for Positioning Search Results Music Files URLS;243
8.3.8;Proposals for Quality Measures of Contemporary Internet Music Information Retrieval Systems;245
8.3.8.1;The File Retrieval Module;246
8.3.8.2;The Music Search Engine Module;247
8.3.9;The Trends in Development of Internet Music Information Retrieval Systems;248
8.3.10;References;249
8.4;Chapter 23 Verifying Text Similarity Measures for Two Layered Retrieval;250
8.4.1;Introduction;250
8.4.2;Two Layered Retrieval;251
8.4.3;Statistical Full Text Search;252
8.4.4;Semantic Full Text Search;253
8.4.4.1;BestSim Measure;253
8.4.4.2;The SimSum Measure;254
8.4.4.3;ExtSim Measure;255
8.4.4.4;Synsets Based Measures;255
8.4.4.5;Semantic Groups Based Measures;256
8.4.5;Verification;256
8.4.6;Conclusions;259
8.4.7;References;259
8.5;Chapter 24 Verification of Open SourceWeb Frameworks for Java Platform;261
8.5.1;Introduction;261
8.5.2;Application Performance Study;262
8.5.3;JavaMetrics Study;263
8.5.4;Page Templates Study;266
8.5.5;Accessibility and Maintainability Study of Trinidad Components;267
8.5.6;Conclusions and Future Work;269
8.5.7;References;270
9;Part V E-Learning Platforms;271
9.1;Chapter 25 E-Learning Usability Testing Platform;272
9.1.1;Introduction;272
9.1.2;Web Usability Testing;273
9.1.3;Testing and Measuring Web Usability;274
9.1.4;User Activity Tracking Platform;275
9.1.5;Tests;277
9.1.6;Conclusions and Future Work;280
9.1.7;References;280
9.2;Chapter 26 E-Learning in Teaching the Object Oriented Programming;282
9.2.1;Introduction;282
9.2.2;Materials for Students;283
9.2.3;Materials for Teachers;286
9.2.4;System of the e-Learning Materials;287
9.2.5;Conclusion;288
9.2.6;References;288
9.3;Chapter 27 Analytical Framework for Mirroring and Reflection of User Activities in E-Learning Environment;290
9.3.1;Introduction;290
9.3.1.1;Related Work;291
9.3.2;Technical Description of the Analytical Framework;292
9.3.2.1;Data for Analyses as Log of Events;293
9.3.2.2;Experimental Integration with Selected Systems;294
9.3.3;Analytical Approaches;295
9.3.3.1;Time-Line Based Analyses;295
9.3.3.2;Evaluation within KP-Lab System;296
9.3.4;Conclusions;298
9.3.5;References;298
9.4;Chapter 28 The Paradigm of Screencasting in E-Learning;300
9.4.1;Introduction;300
9.4.2;Educational Aspects of Screencasting;301
9.4.3;Experiment;302
9.4.4;Results and Discussion;304
9.4.5;Conclusions;307
9.4.6;References;307
9.5;Chapter 29 An Opened Agent-Oriented System for Collaborative Learning;309
9.5.1;Introduction;309
9.5.2;MAETIC Method;310
9.5.2.1;Description of the Method;310
9.5.2.2;Contribution of Multi-agents System;310
9.5.2.3;Related Works;311
9.5.3;A Multi-agent Approach for Modeling a Collaborative Learning System;311
9.5.3.1;The System Modeling;312
9.5.3.2;The Analysis Phase;312
9.5.3.3;The Design Phase;313
9.5.3.4;The Relaization Phase;314
9.5.3.5;The COLYPAN System;314
9.5.4;The GroupWorking Way;315
9.5.5;Conclusion and Future Work;317
9.5.6;References;318
10;Author Index;319