Knowledge-Based Intelligent Information and Engineering Systems: 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part II

Front matter......Page 1
Title......Page 2
Preface......Page 4
KES 2005 Conference Organization......Page 6
International Program Committee......Page 8
Invited Session Chairs Committee......Page 10
IIHMSPWorkshop Organization Committee......Page 12
IIHMSPWorkshop Technical Committee......Page 14
KES 2005 Reviewers......Page 15
KES 2005 Keynote Speakers......Page 21
Table of Contents, Part II......Page 22
Table of Contents, Part III......Page 37
Table of Contents, Part IV......Page 52
Table of Contents, Part I......Page 62
1 Introduction......Page 77
2.1 System Overview......Page 78
2.3 Automatic Content Organisation......Page 79
3 The POISE System......Page 80
4 Conclusions......Page 81
References......Page 83
Introduction......Page 84
2 Tools Used......Page 85
3 The Proposed Architecture......Page 86
4 Experimental Results......Page 87
5 Conclusions......Page 89
References......Page 90
1.1 The Ubiquity of Time Series Data......Page 92
2 The Ratanamahatana-Keogh Band (R-K Band)......Page 94
2.2 Learning Multiple R-K$_c$ Bands for Classification......Page 95
3.1 Query Refinement......Page 96
3.2 R-K Band Learning in Relevance Feedback......Page 97
References......Page 98
1 Introduction......Page 100
2 Generic Semantic Extraction......Page 102
3 Classified Semantic......Page 104
References......Page 106
1 Introduction......Page 107
2.2 Texture Browsing Descriptor......Page 108
3 Experiments......Page 109
4 Conclusion......Page 111
References......Page 112
1 Introduction......Page 113
2 Our Approach......Page 114
2.1 Pruning Power......Page 116
3 Experiment......Page 117
3.1 Comparison to AQ15 and C4.5......Page 118
References......Page 119
1 Introduction......Page 121
2 System Architecture......Page 122
3 Knowledge Encoding Phase......Page 123
4 Knowledge Integration Phase......Page 124
4.1 The Strength of a Rule......Page 125
5.1 Saturday Morning Problem......Page 126
5.2 Multiplexer Problem......Page 127
References......Page 130
1 Introduction......Page 131
2 Learning by Doing Cycle......Page 132
3 JV$^2$M: General Description......Page 133
4.1 Conceptual Hierarchy......Page 134
4.2 Execution Graphs......Page 135
4.3 Exercises......Page 136
5 Detailed Example......Page 137
References......Page 140
1 Introduction......Page 141
2 Description of the Model......Page 142
3 Long-Term Immune Memory......Page 143
4 Immune Tolerance and Timing of Antigen Dose......Page 145
5 Conclusions......Page 146
References......Page 147
1 Introduction......Page 148
3.1 Two Approaches......Page 149
3.2 Diagnosis and Repair Process......Page 150
4 Simulation Results......Page 151
5 Mathematical Model......Page 152
6 Conclusions and Further Work......Page 153
References......Page 154
1 Introduction......Page 155
2 Spatial Strategies in SPD......Page 156
3 A Model Incorporating SPD......Page 157
4 Computer Simulations with a Square Lattice......Page 158
References......Page 160
2 Self-repair by Copying: The Double Edged Sword......Page 162
3 Models of Probabilistic Cellular Automata......Page 163
4 Relation with Domany-Kinzel Model and Other PCA......Page 165
5 Simulation Results......Page 166
References......Page 167
1 Introduction......Page 169
2 Algorithm of the Worm Filter......Page 170
3 Analysis of Normal Behavior......Page 171
4 Implementation and Evaluation......Page 173
5 Weaknesses of the Worm Filter......Page 174
References......Page 175
1 Introduction......Page 176
2 Structure of Neuralnet......Page 177
3 Structure of Wavenet......Page 178
5 Stopping Conditions for Training......Page 180
6.2 Dynamic System Simulation......Page 181
References......Page 182
1.1 Neuronal Geometry and Neurone Networks......Page 184
2 Using L-Systems for Modelling of Trees......Page 185
2.1 Turtle Interpretation of an L-System......Page 186
3 Method and Design......Page 187
3.1 L-Systems Implementation in Neurone Editor......Page 188
4 Conclusions and Future Directions......Page 189
References......Page 190
1 Introduction......Page 192
3 Material and Methods......Page 193
3.2 Artificial Neural Network Ensemble......Page 194
4 Results......Page 196
5 Conclusion and Discussion......Page 197
References......Page 198
1 Introduction......Page 199
2 Multilayered Perceptron Network (MLP)......Page 200
4 Methodology and Data......Page 201
5 Results and Discussion......Page 203
6 Conclusion......Page 205
References......Page 206
1 Introduction......Page 207
2 Machine Learning Rules Induction......Page 208
3 Experiment......Page 209
3.1 Results and Discussion......Page 211
4 Conclusion and Future Work......Page 212
References......Page 213
1 Introduction......Page 214
2 The Modified Seed Based Region Growing Algorithm......Page 215
3 Methodology for Automatic Edge Detection......Page 216
5 Future Work......Page 218
6 Conclusion......Page 219
References......Page 220
1 Introduction......Page 221
2 Proposed Body ROI Determination......Page 222
3.1 Feature Extraction for SVM......Page 224
4.1 Preliminary for Experiments......Page 226
4.2 Performance Evaluation......Page 227
5 Conclusion......Page 228
References......Page 229
Introduction......Page 230
Functionality of the Diagnostic System......Page 231
Conclusions......Page 235
References......Page 236
1 Introduction......Page 237
2.1 Subjects......Page 238
2.2 Lifestyle of Subjects and Definition of Unhealthiness......Page 239
3.1 DataBase......Page 240
3.2 Association Rule Analysis......Page 241
3.3 Odds Ratio-adjusted for Confounding in Association Rule Analysis......Page 242
4.2 Abnormality Rate in Subjects Without the Risk in Lifestyle/Family Medical History......Page 243
4.3 Extracted Rules......Page 244
4.4 Comparison of Association Rule Analysis with Conventional Modeling (The Logistic Regression Analysis)......Page 246
References......Page 247
1 Introduction......Page 248
2 Equations of Motion......Page 249
3 Moving Mass Control Algorithm......Page 250
5 Conclusions......Page 252
Appendix......Page 254
1 Introduction......Page 255
2 Brightness Profile......Page 256
3.1 Evolution Strategies......Page 257
4 Implementation......Page 258
5 Results......Page 260
References......Page 261
1 Introduction......Page 262
2 KT-Clustering Algorithm......Page 263
3 Experiment Analysis......Page 265
4 Conclusions......Page 267
References......Page 268
1 Introduction......Page 269
2 Problem Formulation......Page 270
3 Neural Network Approach to Feedback Scheduling......Page 271
4 Performance Evaluation......Page 272
References......Page 274
1 Introduction......Page 276
2 Modeling the Elbow Manipulators for HILS......Page 277
3 Controller Design for RCP......Page 278
4 Simulations and Evaluation......Page 279
References......Page 282
1 Introduction......Page 283
2 Neural Network Based Intelligent Technology......Page 284
3 Simulation Results......Page 286
References......Page 288
1 Introduction......Page 290
2 Bayesian Inference Driven Behavior Network......Page 291
3 Experimental Results......Page 292
3.1 Behavior Network and Bayesian Network......Page 293
3.2 Comparison......Page 294
4 Conclusions......Page 295
References......Page 296
2 Mathematical Model for Loss Minimization......Page 298
3 Loss Minimization Vector Control Using GA-PSO......Page 299
4 Simulation and Discussion......Page 300
5 Conclusions......Page 301
References......Page 302
1 Introduction......Page 304
2.1 Role......Page 305
2.2 Context......Page 306
3 Emotion Knowledge Representation......Page 307
4 Case Study......Page 308
5 Conclusion......Page 310
References......Page 311
1 Introduction......Page 312
2 Overall Learning Approach......Page 313
3 Implicit User Preference Learning Algorithm......Page 315
4 Performance Evaluation......Page 317
References......Page 318
1 Introduction......Page 319
2 Selling Behavioural Model......Page 320
3 Affect Space Model......Page 321
4.1 Pre-processing......Page 322
5 Results......Page 323
6 Conclusion......Page 325
References......Page 326
I Introduction......Page 327
II Fundamental Intelligent Method......Page 328
III Intelligent Module for Portable Devices......Page 329
IV Experiments......Page 331
References......Page 333
1 Introduction......Page 334
3.2 Impression Words......Page 335
5 Creation of the Data Matrix......Page 336
8 Conclusions......Page 337
References......Page 338
1 Introduction......Page 339
4 Algorithm......Page 340
5 Experiments......Page 342
References......Page 345
1 Introduction......Page 346
2 Model of Context-Aware System......Page 347
3 Framework of Context-Aware Evolvable System......Page 350
4 Design Examples......Page 353
5 Conluding Remarks......Page 357
References......Page 358
1 Introduction......Page 360
2 Patient Adaptable Diagnosis Algorithm......Page 361
2.2 Feature Extraction Block......Page 362
3 Experimental Results......Page 363
References......Page 365
1 Introduction......Page 367
3 Sensor Fusion of Snake Robot......Page 368
4 The Snake Robot.s Inference Algorithm......Page 369
4.1 Inference System......Page 370
5.2 Implementing a Real-Time System of Snake Robot......Page 371
References......Page 372
1 Introduction......Page 374
2 The Proposed Face Detection Architecture......Page 375
3 Adaptive Skin Color Modeling Using Context Awareness......Page 377
4 Context-Based Bayesian Classifiers......Page 378
5 Experiment......Page 380
Reference......Page 383
1 Introduction......Page 384
2 Situational Awareness Under Varying Illumination......Page 385
3 Gabor Wavelets......Page 386
4 Gabor Kernel and Feature Space......Page 387
5.1 Operation Parameter Construct......Page 388
6 Experimental Results......Page 389
References......Page 393
1 Introduction......Page 395
2.1 Reconfigurable Hardware Module......Page 396
2.2 Evolvable Software Module......Page 398
3 Experimental Results......Page 399
References......Page 402
1 Introduction......Page 403
2 The Proposed Face Detection Scheme......Page 404
3.2 Feature from Texture......Page 405
4.1 Modeling of Face and Nonface......Page 406
4.2 Choosing Face-Like Nofaces......Page 407
6 Experiment......Page 408
References......Page 410
1 Introduction......Page 412
2 Motivation......Page 413
3.1 Fuzzy Prediction of Traffic from LTF Flows......Page 414
3.2 Predictive Preferential Dropping......Page 415
4 Simulation Results and Discussion......Page 416
5 Conclusion and Future Work......Page 417
References......Page 418
1 Introduction......Page 419
2 The Data Envelopment Analysis Model......Page 420
3 Fuzzy DEA Models......Page 421
References......Page 425
1 Introduction......Page 426
2 Problem Statement......Page 427
3 Fuzzy Feedback Scheduler......Page 428
4 Example......Page 430
References......Page 431
1 Introduction......Page 433
3 Fuzzy Logic Coupled Controller......Page 434
3.2 Discussion About FLAQM......Page 435
4 Applying FLAQM on a Simulated Network......Page 436
4.2 Simulation Results......Page 437
References......Page 439
1 Introduction......Page 440
2.1 Preprocessing of Iris Images......Page 441
2.2 Pattern Extraction Using the Gabor Wavelet......Page 442
3 Fuzzy-Based Linear Discriminant Classifier for Iris Recognition......Page 443
References......Page 445
1 Introduction......Page 447
2 Target Segmentation Based on Fuzzy Reasoning......Page 448
3 Experimental Results......Page 451
4 Conclusions......Page 452
References......Page 453
2 Fuzzy Logic Systems......Page 454
4.1 Choosing the Membership Functions for the Difference Method......Page 455
4.2 Forming Rule Base and Fuzzification......Page 457
4.3 Average Method for Designing Lowpass Filter......Page 459
5 Simulation Results......Page 460
References......Page 461
2 Problems Associate to Classical Query......Page 462
3.1 The Fuzzyfying Module......Page 464
3.2 The Fuzzy Querying Module......Page 465
References......Page 467
1 Introduction......Page 469
2 Related Work......Page 470
3 Modelling Knowledge......Page 471
4 Rules from Domain Knowledge......Page 472
5 Generated Knowledge......Page 473
5.1 Meta-rules......Page 474
6 Modelling from Initial and Generated Rules......Page 475
7 Uncertainty Management......Page 476
References......Page 477
1 Introduction......Page 479
2 The Need for Knowledge Management in Schools......Page 480
3 The Case Study......Page 481
4 Knowledge Engineering for Schools......Page 482
5 Conclusions and Further Work......Page 483
References......Page 484
1 Introduction......Page 486
3 Knowledge Based Hypermedia System......Page 487
4 Some Aspects of Consideration......Page 488
References......Page 490
1 Introduction......Page 492
3 The Controller......Page 493
3.1 Requirements on the Controller......Page 494
3.3 An Example of Using Semantic Tags......Page 495
References......Page 497
1 Introduction......Page 498
2 Intelligent Service Robots......Page 499
3 The Task Pattern Analysis Method, TAPAS......Page 500
4 Task Pattern Sets......Page 503
5 Concluding Discussion......Page 504
References......Page 505
2 Petri Nets with Time Tokens......Page 506
3.1 Reasoning......Page 508
4 Example......Page 509
References......Page 511
1 Introduction......Page 513
2 Strong Relevant Logics and Spatio-temporal Relevant Logics......Page 515
3 Reciprocal Logics......Page 518
References......Page 520
1 Introduction......Page 522
2 A Handoff Decision Making Process and Algorithm......Page 523
3.2 The New Call Blocking Probability of WLAN and CDMA System......Page 525
3.4 The Number of Handoffs and Grade of Service (GoS)......Page 526
5 Conclusion and Future Work......Page 527
References......Page 528
1 Introduction......Page 529
2 Our Mediated Certificateless Public Key Encryption......Page 530
4 A Mediated Certificateless Signature Scheme......Page 532
5 Mediated Hierarchical CL-PKE......Page 533
6 Application......Page 534
References......Page 535
1 Introduction......Page 536
3.2 Definition of Privacy......Page 537
5 Conclusions......Page 538
References......Page 539
1 Introduction......Page 540
2 Mathematical Model of Economic Dispatch for Cogenerating Systems......Page 541
3.2 Case Study #2......Page 543
4 Conclusions......Page 544
References......Page 545
1 Introduction......Page 546
2 Media Access of IEEE 802.15.4......Page 547
3 Media Access Mechanism Using Priority Time Period......Page 548
4 Performance Analysis......Page 550
References......Page 551
2 Pilot-Symbol Aided SFBC-OFDM Channel Estimation......Page 553
2.1 Pilot-Symbol Aided Channel Impulse Response Estimation......Page 554
3 Simulation Results......Page 556
4 Conclusions......Page 557
References......Page 558
1 Introduction......Page 559
2 The System Model......Page 560
3 Cryptographic Techniques for Distributed Protocols......Page 563
4 Conclusion......Page 564
References......Page 565
1 Introduction......Page 566
2 IEEE 802.11e EDCF......Page 567
3.1 AC Priority Analysis......Page 568
4 Numerical Results......Page 572
5 Conclusions......Page 574
References......Page 575
1 Introduction......Page 576
2 Defining Epistemic Satisfaction and Grounding of Modal Conjunctions......Page 577
3 Properties of Epistemic Satisfaction and Grounding of Modal Conjunctions......Page 580
4 Final Remarks......Page 581
References......Page 582
1 Introduction......Page 583
2 Personalized Web Advertising......Page 584
3 The Multi-agent Architecture......Page 585
4 Knowledge Maintenance......Page 587
References......Page 588
1 Introduction......Page 590
3 The Architecture of the Multiagent System......Page 591
4 Agent\'s Database and Communication......Page 593
5 Conclusions......Page 594
References......Page 595
2 Related Works......Page 596
3 Non-textual Factors of Document Ranking......Page 597
4 Ranking Calculation......Page 600
6 Conclusions......Page 601
References......Page 602
1 Introduction......Page 603
2 Motivating Value Propagation Example......Page 604
3 Value Propagation Operator......Page 606
4 Related Work......Page 607
References......Page 608
1 Introduction to Negotiation in Agent-Based Systems......Page 610
2 Recommendations in Information Systems......Page 611
3.2 Distance Definition Between Holiday\'s Descriptions and Customer\'s Profiles......Page 612
3.4 Application of Consensus Methods in the Holiday Travel Negotiation......Page 614
5 Conclusions......Page 615
References......Page 616
2 Basic Notions......Page 617
3 Net k-Distinguishability and Test Points......Page 618
4 The Fault Isolation Method......Page 619
5 Conclusions......Page 622
References......Page 623
1 Introduction......Page 624
3.1 The Evolution of the New Algorithm......Page 625
3.2 The Embedding Stage......Page 626
4 Experimental Results......Page 627
References......Page 628
1 Introduction......Page 630
2 The Proposed Method......Page 631
3 Experimental Results......Page 633
References......Page 635
1 Introduction......Page 636
2 Analysis of HVS by DWT Coefficients......Page 637
3.2 Fuzzy Inference Rules by HVS and SC......Page 638
4 Experimental Results......Page 639
References......Page 641
2.1 Audio Watermark Scheme......Page 643
2.3 VI(Voice Information) Watermark Scheme Using Intelligent HVS Scheme......Page 644
3.1 OFDM/QPSK System......Page 645
4 Simulation and Analysis......Page 646
References......Page 648
1 Introduction......Page 649
2.1 Global Invariant Feature......Page 650
2.3 Hu Moments......Page 651
4 Online Image Retrieval with Various Query Strategies......Page 652
6 Conclusions......Page 653
References......Page 654
1 Introduction......Page 656
2.1 Watermark Embedding in Motion Vector......Page 657
2.2 Watermark Extraction......Page 659
3 Simulation Results......Page 660
References......Page 661
2 Quantization Audio Watermarking Model......Page 663
3 The Effects of DA/AD Conversions on Audio Watermarking......Page 664
4 Performance Analysis of Quantization Audio Watermarking......Page 665
5 Experimental Results......Page 667
References......Page 668
1 Introduction......Page 669
2 Data Watermarking for Halftone Images......Page 670
3 Lossless Watermarking for Halftone Images......Page 671
4 Experimental Results......Page 673
References......Page 674
1 Introduction......Page 676
2 Current Mode Fuzzy-Tuning PI Control Scheme......Page 677
3 Simulation and Experimental Results......Page 680
References......Page 682
1 Introduction......Page 684
2 Sequencing Batch Reactor (SBR) and Fuzzy Logic Controller......Page 685
3.2 Applicability of  Fuzzy Logic Controller......Page 687
References......Page 689
Circuit Theory......Page 691
Fine Tuning......Page 692
VHDL Simulation......Page 694
Conclusion......Page 696
References......Page 697
Introduction......Page 698
Discretisation Method and Terrain Modeling......Page 699
Discussion of Results......Page 700
Conclusion......Page 701
References......Page 703
1 Introduction......Page 704
3 The Algorithm of GCDDP: Integration of a GA and CDDP......Page 705
4 Design Example......Page 706
5 Results and Discussion......Page 708
References......Page 709
1 Introduction......Page 711
2 Multi-criteria Decision Making Using the Choquet Integral......Page 712
3 Application of the Choquet Integral to Robot Selection Problem......Page 714
References......Page 716
1 Introduction......Page 718
2.1 Pure Fuzzy Logic Model......Page 719
3 Experimental Work......Page 720
3.2 Pure Fuzzy Inference Model......Page 721
4 Results and Discussion......Page 723
4.1 Model Validation......Page 724
5 Conclusions......Page 725
References......Page 726
1 Introduction......Page 727
2 Related Works......Page 728
3.1 Overview of Integrating Architecture......Page 729
3.2 Integrating Architecture Based on Agent......Page 730
4.3 User Information Integrating......Page 731
5 Conclusions......Page 732
References......Page 733
1 Introduction......Page 734
2 Pedagogical Approaches......Page 735
3 e-Learning Approaches......Page 736
4.1 Hannafin\'s Open Learning Environments (OLEs)......Page 737
4.3 Jonassen\'s Constructivist Learning Environments (CLEs)......Page 738
5 Conclusions......Page 739
References......Page 740
1 Introduction......Page 741
2 Approach......Page 742
3.2 Solution Network......Page 744
3.3 Estimation of Understanding Level......Page 745
4 Experiment......Page 746
5 Conclusion......Page 747
References......Page 748
1 Introduction......Page 749
2 Approach......Page 750
3.2 Rules for Drawing Diagrams......Page 752
4 Prototype System......Page 753
5 Conclusion and Future Work......Page 755
References......Page 756
2 Information Technologies for e-Learning......Page 757
3 MOLEAS: An Educational Software Framework......Page 759
References......Page 763
1 Introduction......Page 764
2.2 Definition and Characteristics of the Gifted Students in Computer......Page 765
3.2 Implementation of the Proposed System......Page 767
4 Conclusions and Further Work......Page 768
References......Page 769
1 Introduction......Page 770
2.1 Domain Analysis......Page 771
2.4 Systems Design Description......Page 772
3 System Architecture and Implementation......Page 774
4 Testing......Page 775
References......Page 776
1 Introduction......Page 777
2.2 Previous Work......Page 778
3.1 Performance of Multi vs. Solo Programmers......Page 779
3.5 Performance of International Students on Multi-programming......Page 780
4.2 Student Assessment......Page 781
5 Conclusions......Page 782
References......Page 783
1 Introduction......Page 784
2 EVALPSN......Page 785
3 Safety Verification......Page 786
References......Page 790
1 Introduction......Page 792
2 Paraconsistent Artificial Neural Network......Page 793
3 Architecture of the Paraconsistent Artificial Neural Network......Page 796
4 Discussion......Page 797
References......Page 798
1 Introduction......Page 800
2 The Non-alethic Multimodal Logics $\\Omega_n (1 \\le n < \\omega)$......Page 801
3 Semantic Analysis of $\\Omega_1$......Page 803
References......Page 806
1 Introduction......Page 808
3 Forward Reasoning Using Connectionist System......Page 809
3.1 Obtaining Coarse-Coded Distributed Representations from Localist Representations......Page 810
3.3 Variable Binding During Processing of the Complex Rule......Page 811
4 Testing......Page 812
References......Page 813
2 Automated Forward Deduction Based on Modal Logics......Page 815
3 Requirements and Functions for General-Purpose Forward Deduction Engine for Modal Logics......Page 816
4 Implementation and Experiments......Page 818
5 Possible Applications......Page 819
References......Page 820
1 Introduction......Page 822
2.3 Description of Requests and Preferences......Page 823
3.1 Multi-agent Framework......Page 824
3.2 System Architecture and Modules......Page 825
References......Page 827
1 Introduction......Page 829
2 Preliminaries......Page 830
3 Top-Down Query Answering......Page 832
References......Page 836
1 Introduction......Page 837
2 An Authorization Language ${\\mathcal AL}$......Page 839
3 Semantics of ${\\mathcal AL}$......Page 841
References......Page 843
1 Introduction......Page 844
2 Overview of the System......Page 845
4 The Dialogue Management System......Page 846
5 The Behavior Generator......Page 847
6 Experimental Results......Page 848
References......Page 850
1 Introduction......Page 851
2.2 Pre-process Data......Page 852
2.4 Hierarchical Neural Network System......Page 853
2.5 Building a Hierarchy Neural Network System by Combining Groups......Page 855
3 Conclusions......Page 856
References......Page 857
1 Introduction......Page 858
2.1 Encoding and Decoding of Fuzzy Rules by GAs......Page 859
3.3 Fuzzy Membership Functions......Page 860
3.5 Hierarchical Fuzzy Logic System......Page 861
References......Page 863
1 Introduction......Page 865
2 Structure of MEDADVIS......Page 866
3 MEDADVIS Project Development......Page 868
References......Page 870
1 Introduction......Page 872
2.1 Clustering Heuristic......Page 874
2.2 Decentralised Adaptive Clustering (DAC)......Page 875
2.3 Fixed-Order Centralised Adaptive Clustering (FCAC)......Page 876
3 Experimental Results......Page 878
References......Page 880
1 Introduction......Page 882
3 The Intelligent Deliberation and the Filtering Strategies......Page 883
3.1 The Option Selection in the Intelligent Deliberation......Page 884
4 Experiment Results......Page 886
5 Conclusion......Page 888
References......Page 889
1 Introduction......Page 890
2 The i* Framework......Page 891
3 Best Practice Patterns......Page 892
References......Page 896
1 Introduction......Page 897
2 Session Papers......Page 898
3 Summary......Page 899
References......Page 900
1 Introduction......Page 901
1.3 Building Blocks: MAS Theory......Page 902
2 New Directions for Agent Development......Page 903
2.2 Human Interaction......Page 904
References......Page 905
2 Overview of Technical Details......Page 907
2.1 Autonomous Action Selection......Page 908
2.2 Selecting Target Information Providers......Page 909
3 Application in UAV Target Tracking......Page 910
4 Conclusion......Page 912
References......Page 913
1 Introduction......Page 914
2 Cognitive Hybrid Reasoning Intelligent Agent System......Page 915
3 Example Learning Agent......Page 916
References......Page 918
1 Introduction......Page 920
2.2 Scenario 2......Page 921
2.5 Discussion......Page 922
3.2 Building a Model of Trust......Page 923
5 Implementing the Model of Trust: Extending JACK Teams......Page 924
6 Summary and Future Research......Page 925
References......Page 926
1 Introduction......Page 927
2 BDI: An Overview......Page 928
2.2 Logic of Rational Agents......Page 929
3.2 The Topology of Time......Page 930
4 BDI + Temporal Logic......Page 931
References......Page 932
1 Introduction......Page 934
2 The Delegation Model......Page 935
2.1 Delegation and Chain of Delegation......Page 936
2.2 Delegation of Responsibility......Page 937
2.4 Delegation of Obligation......Page 938
3 Related Work and Conclusions......Page 939
References......Page 940
1 Introduction......Page 942
3.1 WOMap Model......Page 943
3.2 Links Construction......Page 944
3.4 Interactivities......Page 945
3.5 Hierarchy Trees......Page 946
References......Page 947
1 Introduction......Page 949
2 Ontology View......Page 950
3.1 RDF Query......Page 951
3.2 The Query Semantics......Page 952
References......Page 955
1 Introduction and Motivation......Page 956
2.3 Cognitive Ontology Pattern......Page 957
3.1 Psychological Essentialism......Page 958
4 Example......Page 959
References......Page 961
1 Introduction......Page 963
3 Customized Search Results......Page 964
3.2 Document Profile (DP)......Page 965
3.4 Personalized Search Results......Page 967
4 Conclusions and Future Work......Page 968
References......Page 969
1 Introduction......Page 970
2 WWW.HR Directory and Search......Page 971
4 Implementation......Page 972
5 Results......Page 973
References......Page 975
1 Introduction......Page 977
2 STAR......Page 978
3 STAR-IT......Page 979
References......Page 983
1 Introduction......Page 984
2 Related Work......Page 985
3 Semantic Web Based Tour Guide Services......Page 986
4 Realizing Tour Information Services......Page 988
5 Conclusion......Page 989
References......Page 990
2 Web Services Description......Page 991
3.1 Signature Discovery......Page 992
3.2 Constraint Discovery......Page 993
3.3 Partial Discovery......Page 995
3.4 Dynamic Selection of Web Service Discovery Strategy......Page 996
References......Page 997
1 Introduction......Page 998
2 A Fuzzy Logic Approach to UDM......Page 999
3 Evaluation of the Model in a Road Safety Scenario......Page 1001
References......Page 1003
1 Introduction......Page 1005
2.2 Proposed Solution......Page 1006
3 Cluster Discovery......Page 1009
4 Related Works......Page 1010
References......Page 1011
1 Introduction......Page 1012
2 Kernel Method......Page 1013
3 Relation Kernel for Gene/Protein Interaction......Page 1014
4 Experimental Results......Page 1016
5 Conclusions......Page 1017
References......Page 1018
1 Introduction......Page 1019
2 An Order Perspective of Text Similarity......Page 1020
3 An Order-Semisensitive Similarity......Page 1021
5 Experiment Results and Evaluation......Page 1023
References......Page 1025
1.2 State of Art......Page 1026
2.2 Japanese Conditional Clauses......Page 1027
3.1 Our System......Page 1028
4 Retrieval Experiment......Page 1029
5 Applications Experiment......Page 1030
6 Conclusions and Future Work......Page 1031
References......Page 1032
1 Introduction......Page 1033
3 Continuous Forecasting Query Model and Definition......Page 1034
4 Adaptive Interpolating Algorithm......Page 1036
5 Experiment Evaluation......Page 1037
References......Page 1039
1 Introduction......Page 1040
2 Problem Definition......Page 1041
3 Working with Bounded Memory......Page 1042
4 Accessing Data in One-Sequential Pass......Page 1044
5 Algorithm......Page 1045
References......Page 1046
1 Introduction......Page 1048
3 Our Matching Approach......Page 1049
3.1 Linguistic Matching......Page 1050
3.2 Structural Matching......Page 1051
4 Experimental Evaluation......Page 1052
References......Page 1054
1 Introduction......Page 1055
2 Measure Metrics for Category Ordering and Mapping......Page 1056
3.2 Kruskal-Style Ordering Algorithm......Page 1057
4 Experiments......Page 1058
5 Conclusions......Page 1060
References......Page 1061
1 Introduction......Page 1062
2 A Motivating Example......Page 1063
3 Mutual Influence Algorithm......Page 1064
4.1 Negotiation Without Mutual Influence Algorithm in One-Buyer-Two-Seller Games......Page 1065
4.2 Negotiation with Mutual Influence Algorithm in One-Buyer-Two-Seller Games......Page 1066
4.3 Negotiation with Mutual Influence Algorithm in One-Buyer-n-Seller Games......Page 1067
References......Page 1068
2 Biometrics System......Page 1069
4 Vulnerability Evaluation of Matching Algorithm......Page 1070
4.3 Minutiae Variation Algorithm......Page 1071
5 The Integrity Verification of Fingerprint Data......Page 1072
7 Conclusion......Page 1074
References......Page 1075
1 Introduction......Page 1076
2 Syntax and Semantics of the CTL Kripke Model......Page 1077
3 Algorithms for CTL Model Update......Page 1078
4 An Example......Page 1081
References......Page 1082
1 Introduction......Page 1083
2.2 Mathematical Model of Order-Picking Problem......Page 1084
3 Methodology......Page 1085
3.2 Fuzzy Modeling Approach......Page 1086
4.2 The Performance of Tabu Search......Page 1088
References......Page 1089
1 Introduction......Page 1091
2.2 GMA on C-STRESS......Page 1092
3.1 Experimental Conditions......Page 1093
3.2 Comparison of Results......Page 1094
References......Page 1097
1.1 Related Work and Discussion......Page 1098
2 Preliminaries......Page 1099
3.1 IIDTED Protocol Description......Page 1100
4 Security Analysis......Page 1101
References......Page 1103
1 Introduction......Page 1105
2 Related Works......Page 1106
3 An Information Security Index System......Page 1107
3.2 Information Security Environment Level......Page 1108
3.3 Information Security Support Level......Page 1109
3.6 Comparison with the Existing Methods......Page 1110
References......Page 1111
1 Introduction......Page 1112
2.2 Agent Table......Page 1113
2.4 Procedure for the Policy-Request Processing......Page 1114
3 Policy Conflicts: Classification, Detection and Resolution......Page 1115
4 Conclusion and Future Works......Page 1117
References......Page 1118
1 Introduction......Page 1119
2 Adaptive Modulation Method for OFDM System Using Cluster......Page 1120
3 Simulation Results......Page 1123
References......Page 1125
1 Introduction......Page 1126
3 Demodulation Methods in CDMA2000 1X......Page 1127
4 Performance Analysis......Page 1129
References......Page 1131
1 Introduction......Page 1132
2.4 OVAL(Open Vulnerability Assessment Language)......Page 1133
3.2 Vulnerability Database......Page 1134
3.3 Enterprise Vulnerability Assessment Tool......Page 1135
3.4 Web-Manager System......Page 1136
References......Page 1137
1.1 Background of the Study......Page 1138
3.1 Security Requirements of System......Page 1139
4 Implementation......Page 1140
4.1 Client Implementation......Page 1141
4.2 Server Implementation......Page 1142
References......Page 1143
1 Introduction......Page 1145
2.2 Consideration of Strength of Mechanisms......Page 1146
2.5 Determination of Assurance Level......Page 1147
3 Dividing IT Systems......Page 1148
References......Page 1150
1 Introduction......Page 1152
2.2 Watermark Embedding......Page 1153
3.1 Watermark Estimation......Page 1154
4 Experimental Results......Page 1155
References......Page 1158
1 Audio CoFIP......Page 1159
2.2 Proposed Method......Page 1161
3.2 Auditory Secrecy......Page 1163
3.4 Watermark Detection......Page 1164
References......Page 1165
1 Introduction......Page 1166
2 Sign Language Encoding......Page 1168
3 Data Embedding and Extraction......Page 1171
5 Conclusion......Page 1173
References......Page 1174
1 Introduction......Page 1175
2 Characteristics of Commercial Cameras......Page 1176
3.1 Watermark Embedding......Page 1177
3.2 Watermark Detection......Page 1178
4 Experimental Results......Page 1179
References......Page 1182
1 Introduction......Page 1184
2.1 Integer Wavelet Transform......Page 1185
2.2 Zernike Moments and Watermark Generation......Page 1186
2.4 Tamper Detection......Page 1187
4 Experimental Results......Page 1188
References......Page 1189
1 Introduction......Page 1191
2 Proposed Watermarking System......Page 1192
3.3 Polarization......Page 1193
3.5 Watermark Recovery......Page 1194
5 Discussion......Page 1195
6 Conclusion......Page 1196
References......Page 1197
1 Introduction......Page 1198
3 Monaural Supported Method......Page 1199
4 Experimental Results......Page 1201
4.2 Experiment by Manural Method......Page 1202
5 Conlusions......Page 1203
References......Page 1204
2 The Concept of SVG......Page 1205
3 Geometric Distortion Detection and Control Method......Page 1206
4 Experimental Results......Page 1207
References......Page 1208
1 Introduction......Page 1210
2.1 Invertibility Attack......Page 1211
2.2 Non-invertible Algorithm and Protocol......Page 1212
3 A Basic Protocol Compatible with Secret Algorithms......Page 1213
4 Scenarios of Possible Attacks......Page 1215
4.2 Second Type of Possible Attack: Forged Detector Attack Based on Watermark Subset......Page 1216
5.2 Enhancement Against Watermark Subset Based Attack......Page 1217
6 Conclusion......Page 1219
References......Page 1220
1 Introduction......Page 1221
2 Overview of the Hybrid Watermarking System......Page 1222
5 Experimental Results Analysis......Page 1223
5.1 Analysis of Robust Part Results......Page 1224
5.2 Analysis of Fragile Part Results......Page 1225
6 Conclusions......Page 1226
References......Page 1227
1.1 Visual Secret Sharing......Page 1228
1.3 Algorithm of Encryption and Decryption......Page 1229
2 Shared Signals Heard as Intended Decoy Sound Signals......Page 1230
3.1 Analysis of the Time Domain......Page 1232
3.2 Analysis of the Frequency Domain......Page 1233
References......Page 1234
1 Introduction......Page 1235
2 Background......Page 1236
3.1 Association Rules of Image......Page 1237
3.2 Watermark Embedding......Page 1238
3.3 Watermark Detection......Page 1239
4 Experimental Results......Page 1240
References......Page 1242
1 Introduction......Page 1244
2.2 Lossless Compression......Page 1245
3.1 Embedding and Extracting Process......Page 1246
4 Experimental Results......Page 1247
5 Conclusion......Page 1249
References......Page 1250
1 Introduction......Page 1251
3 Image Experiments......Page 1252
References......Page 1257
1 Introduction......Page 1259
2.1 Normal-Block Embedding......Page 1260
2.2 Reduction of Clipping Errors......Page 1261
3 Proposed Tamper Detection Scheme......Page 1262
4 Experimental Results......Page 1263
References......Page 1264
1 Introduction......Page 1266
2 Multi-level Semantics Tree by Human Subject (MLST)......Page 1267
3.1 Image Semantic Description......Page 1268
4.1 Experiment Environment......Page 1269
4.2 Multi-level Semantic Classifier......Page 1270
5 Conclusions and Further Work......Page 1271
References......Page 1272
1 Introduction......Page 1273
3.1 Audio Features......Page 1274
4.1 Scene Slice Clustering......Page 1275
4.2 Dialog Scene Detection......Page 1276
4.3 Action Scene Detection......Page 1277
5 Experimental Results......Page 1278
References......Page 1279
1 Introduction......Page 1280
2.1 3D Wavelet Transform and Denoising......Page 1281
3.1 Objective of the Algorithm......Page 1282
4 Experimental Result......Page 1283
5 Conclusion......Page 1284
References......Page 1285
1 Introduction......Page 1286
2.1 Discriminant Functions of GPDNN......Page 1287
2.2 Architecture......Page 1289
3 Learning Rules for GPDNN......Page 1290
References......Page 1291
1 Introduction......Page 1293
2.1 Feature Extraction and Selection......Page 1294
2.2 Prominent Region Extraction Based on Fuzzy Granular Classifier......Page 1295
3 Shot Type Classification......Page 1296
4 Experimental Results......Page 1297
References......Page 1299
1 Introduction......Page 1300
2 Related Works......Page 1301
3 The Proposed Method......Page 1302
4.1 The Retrieval Accuracy Experiments......Page 1303
4.2 The Category Retrieval Precision......Page 1304
5 Conclusions......Page 1305
References......Page 1306
1 Introduction......Page 1307
2 Radon Transform and Its Properties......Page 1308
3 Edge Projection-Based Image Registration......Page 1309
4.2 Results Using Real Image Sequence......Page 1311
5 Conclusions and Future Work......Page 1312
References......Page 1313
1 Introduction......Page 1314
2.2 Analysis Units......Page 1316
2.3 Semantic Labels of Units......Page 1317
3.1 Mine the News Preference of a TV Station......Page 1318
3.3 The Mining on TV Commercial......Page 1319
References......Page 1320
1 Introduction......Page 1321
2.1 EEWM......Page 1322
2.4 GOM......Page 1323
3 Model Implementation......Page 1324
References......Page 1326
1 Introduction......Page 1328
2 Framework of the Proposed Agent......Page 1329
2.1 Grid Consistency Service Module......Page 1330
2.2 Grid Transaction Module......Page 1331
3.1 Implementation......Page 1332
References......Page 1333
1 Introduction......Page 1334
2.1 Connecting Authentication Module (CAM)......Page 1335
2.3 Trusting Module (TM)......Page 1336
3 Model Implementation......Page 1338
References......Page 1339
1 Introduction......Page 1341
2 Framework of the Proposed Agent......Page 1342
2.1 Web Template Generator Module......Page 1343
2.3 Remote User Interface Module......Page 1344
3 Examples Implementation......Page 1345
4 Conclusion......Page 1346
References......Page 1347
1 Introduction......Page 1348
2 System Configuration......Page 1349
4.1 Investigation by Questionnaire......Page 1350
4.2 Principal Component Analysis......Page 1351
6 Dance and Kansei Word......Page 1354
7.1 Experiment......Page 1356
7.2 Results and Consideration......Page 1357
References......Page 1358
1 Introduction......Page 1359
3 Concluding Remarks......Page 1361
References......Page 1363
1 Introduction......Page 1367
2 Kansei Engineering......Page 1368
2.1 Comfortable Space......Page 1369
3 Electrocardiogram (ECG)......Page 1370
4.2 Results......Page 1371
5 Conclusions......Page 1372
References......Page 1373
1 Introduction......Page 1374
2.1 Model on Controlling of Human Capital......Page 1375
3 Fuzzy Logic......Page 1376
5 Our Approach......Page 1377
6 A Numerical Example......Page 1378
7 Conclusions......Page 1379
References......Page 1380
1 Introduction......Page 1381
3 Development Process for Business Rule Engine and Builder......Page 1382
3.2 CBD Specification Development Phase......Page 1383
4.2 Business Static Model......Page 1384
4.4 Architecture Model......Page 1385
References......Page 1386
2 Analyzing Process of Defect Types......Page 1388
2.1 Definition of a Chip\'s Defect Types......Page 1389
2.3 Data Preprocess......Page 1390
4.1 Experiment Environment......Page 1391
References......Page 1392
1 Introduction......Page 1393
2.2 Requirements of Component Integration Technique......Page 1394
3.1 Binary Component Integration Technique......Page 1395
4.1 Introduction to BCA Based Component Integration System......Page 1397
5 Conclusion......Page 1398
References......Page 1399
2 Electrical Die Sorting (EDS)......Page 1401
3.1 Definition of a Chip.s Defect Types......Page 1402
5 Wafer Level Fail Pattern Classification......Page 1403
5.2 Experiment and Result......Page 1404
6 Conclusion......Page 1405
References......Page 1406
2.1 Why Is Earned Value Needed for IT Business?......Page 1407
3 PCM (Project Capability Model)......Page 1408
3.3 Project Capability Measurement Model......Page 1409
4.1 Qualitative Questionnaire......Page 1410
4.3 Most Suitable Improvement Route Selection for Vision Achievement......Page 1411
6 Conclusion and Hereafter Research......Page 1412
References......Page 1413
1 Introduction......Page 1414
2 Datasets......Page 1415
3 Initial Classification Process......Page 1416
4 Feature Subset Selection......Page 1417
5 Attribute Construction......Page 1419
6 Conclusions......Page 1420
References......Page 1422
1.1 Problem in Similarity Measures......Page 1423
2 Outline......Page 1424
3.1 Process Based on Two Bilingual Sentence Pairs......Page 1426
5.1 Experimental Procedure......Page 1427
5.2 Experiments and Discussion......Page 1428
References......Page 1429
Back matter......Page 1430