Trends in Applied Intelligent Systems, 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part II

Cover......Page 1
Lecture Notes in Artificial Intelligence 6097......Page 2
Trends in Applied Intelligent Systems, Part II......Page 3
ISBN-13 9783642130243......Page 4
Preface......Page 6
Conference Organization......Page 8
Table of Contents – Part II......Page 14
Introduction......Page 22
Related Work......Page 23
Co-occurrence Approach......Page 24
Information-Theoretic Approach......Page 26
Experiments......Page 27
Conclusions and Future Works......Page 30
References......Page 31
Our Contribution......Page 33
Related Work......Page 34
Our Proposal......Page 35
Case Study......Page 36
Conclusions and Future Work......Page 37
References......Page 38
Introduction......Page 39
Innovation Meets Enterprise 2.0 and Semantic Technologies......Page 40
Innovation Process Stages and Idea Lifecycle......Page 42
The Indirect Interaction Layer......Page 44
Implementation Issues......Page 45
Conclusions and Future Work......Page 47
References......Page 48
Introduction......Page 49
Syllogistic Propositions......Page 50
Syllogistic Figures......Page 51
Set-Theoretical Analysis......Page 52
Algorithmic Decision......Page 53
Fuzzy Syllogistic System......Page 54
Recognising Fallacies and Fuzzy Syllogistic Reasoning......Page 55
References......Page 57
Appendix......Page 59
Introduction......Page 60
Semantic Grounding of Tags......Page 62
Identifying Individual Intelligence......Page 63
Evaluation of Multilingual Tag Matching......Page 64
Evaluation of Multilingual Resource Retrieval......Page 65
Concluding Remarks and Future Work......Page 66
References......Page 67
Introduction......Page 68
One Stage Classifiers......Page 69
Pair-Wise Classifiers......Page 70
Fusion Methods......Page 71
Data......Page 72
Results......Page 73
References......Page 76
Introduction......Page 78
Ensemble Classification......Page 80
Data......Page 81
Experimental Settings......Page 82
Results......Page 83
Conclusion......Page 85
References......Page 86
Introduction......Page 88
The Unsupervised Scenario......Page 89
Constructing an Ensemble of Near-Optimal Feature Subsets......Page 91
The Unsupervised Scenario......Page 92
Feature Selection......Page 93
Experiments......Page 94
Conclusion......Page 96
References......Page 97
Introduction......Page 98
Ensemble Approach for Streaming Unlabeled Data......Page 99
Suspicious Streaming Data......Page 100
Ensemble Method Using Suspicious Streaming Samples......Page 101
Experiments......Page 102
Results on Synthetic Streaming Data......Page 103
Results on Intrusion Detection Data......Page 104
Conclusions......Page 106
References......Page 107
Introduction......Page 108
Random Projections......Page 109
Experiments......Page 110
Kappa-Error Analysis......Page 113
Conclusions......Page 114
References......Page 115
Introduction......Page 117
Rotation Forest......Page 118
Experimental Setting......Page 119
Experimental Results......Page 120
Conclusions......Page 124
References......Page 125
Introduction......Page 127
Experimental Settings......Page 128
Results......Page 129
Conclusions......Page 134
References......Page 135
Introduction......Page 137
Possible Conflicts for On Line CBD and System Decomposition......Page 138
Time Series Classifiers for Fault Identification......Page 139
Attribute and Class Selection via Possible Conflicts......Page 140
A Case Study......Page 141
Experimental Evaluation......Page 143
References......Page 145
Introduction......Page 147
Related Work......Page 148
Entropy Measurement of Populations......Page 149
The Algorithm......Page 150
On-Demand Evaluation Strategy (ODES)......Page 151
Test Problems......Page 152
Experimental Results......Page 153
Conclusion......Page 155
References......Page 156
Introduction......Page 157
Problem Formulation......Page 158
The Proposed Approach......Page 159
Experimental Results......Page 161
References......Page 162
Introduction......Page 164
IP Assignment and Mapping Problems......Page 165
Task Graph and IP Repository Models......Page 166
Representation......Page 167
Assignment Evaluation......Page 168
Mapping Evaluation......Page 169
Results......Page 172
References......Page 173
Introduction......Page 174
WRALP Definition......Page 175
The Proposed Discrete Differential Evolution Algorithm......Page 176
Results......Page 179
References......Page 183
Introduction......Page 185
Multi-Processor System-on-Chip Platform......Page 186
Parallel Genetic Algorithm......Page 187
Topology Strategies......Page 188
Simulation Results......Page 190
Conclusions......Page 192
References......Page 193
Introduction......Page 194
Genetic Algorithms......Page 195
Design Patterns......Page 196
Evaluation Methods......Page 197
 The GATATest System......Page 198
Evaluation Study......Page 200
References......Page 203
Introduction......Page 204
Linguistic Variables......Page 205
Obtaining L-Fuzzy Contexts with Significant Relations......Page 206
Use of the Labels Associated with Objects and Attributes in L-Fuzzy Contexts with Anomalous Values......Page 209
Replacement of the Erroneous Values......Page 210
References......Page 212
Introduction......Page 214
Decision Trees......Page 215
Fuzzy Clustering......Page 216
SC.......Page 217
Data Gathering and Pre-processing......Page 218
FUDT Construction......Page 219
Experimental Results of FUDT......Page 220
Conclusions......Page 222
References......Page 223
Introduction......Page 224
Distributed Architecture for Fuzzy Rule-Based System Embedded in Wireless Sensor Network......Page 225
Fuzzy Rule-Based System Embedded in Sensor Modular Structure Description......Page 226
Results......Page 227
Conclusions......Page 231
References......Page 232
Introduction......Page 233
Online Self-organization of Fuzzy Controllers......Page 234
Stage One: Adaptation of the Rule Consequents......Page 235
Stage Two: Modification of the Topology......Page 236
Simulation Results......Page 238
References......Page 241
Mechanism of Output Constraint Handling......Page 243
Simulation Experiments......Page 246
References......Page 248
Introduction......Page 249
A Semantic Interpretability Index......Page 250
MFs Lateral Amplitude Rate Measure (γ)......Page 251
MOEA for Rule Selection and Tuning of FRBSs......Page 252
Crossover and Mutation......Page 253
Main Characteristics of TSSP2-SI......Page 254
Analysis of the Performance of the Combined Action of Both, Rule Selection and Tuning......Page 255
References......Page 258
Introduction......Page 260
Heteroskedasticity in Time Series Modeling......Page 261
Fuzzy Rule-Based Models for Time Series Analysis......Page 262
Test of Homoscedasticity of the Residuals of an FRBM......Page 263
Empirical Evaluation......Page 265
Conclusions and Final Remarks......Page 266
References......Page 267
Introduction......Page 268
Literature Review......Page 269
The School Bus Problem......Page 270
The School Bus Problem......Page 272
Results......Page 275
Conclusions and Future Works......Page 276
References......Page 277
Introduction......Page 278
PSO in Exploratory Data Analysis......Page 279
Principal Component Analysis......Page 280
Exploratory Projection Pursuit......Page 281
Principal Component Analysis with Q-Learning......Page 282
Topology Preserving Manifolds......Page 283
References......Page 286
Introduction......Page 288
Terminal Assignment Problem......Page 289
Bees Algorithm......Page 290
Results......Page 294
Conclusions......Page 296
References......Page 297
Introduction......Page 298
Problem Statement......Page 299
Solving Scheme......Page 302
Numerical Example......Page 303
Conclusion......Page 307
References......Page 308
Introduction......Page 309
The Lévy Probability Distribution......Page 310
Function Optimization Using ACORL......Page 312
Mathematical Test Functions......Page 313
Composite Laminates Design Optimization......Page 315
References......Page 317
Introduction......Page 319
Related Work......Page 320
Problem Description......Page 321
Algorithm Description......Page 322
Web Based Interface for the Teaching Assignment Problem Solver System (TAPS)......Page 324
Experimental Tests and Results......Page 326
References......Page 327
Introduction......Page 329
MEMS Gyroscope Model......Page 330
Particle Swam Optimization......Page 333
Control Swarm Approach......Page 336
References......Page 337
Introduction......Page 339
Condensed Graphs of Reactions......Page 340
ISIDA Fragment Descriptors......Page 341
Data......Page 342
Results......Page 343
Conclusion......Page 346
References......Page 347
Introduction......Page 348
Incident Trees......Page 350
Anti-unification of Trees......Page 351
Structure Dominance Tree Generalisation......Page 352
Empirical Results......Page 354
Conclusions and Further Work......Page 355
References......Page 356
Introduction......Page 358
The Learning Task......Page 360
Regression and Deterministic Alarms......Page 361
Regression with Broad Insensitive Zone: Nondeterministic Alarms......Page 362
Time Series Alarms......Page 363
Experimental Results......Page 364
Conclusion......Page 366
References......Page 367
Introduction......Page 368
Feature Selection......Page 369
Performance Level Differentiation via Clustering......Page 372
Predictive Analysis......Page 374
References......Page 376
Introduction......Page 378
Problem Characterization......Page 379
Data Transformation......Page 380
Proposed Model and Label Definition......Page 381
Variables Selection......Page 382
Logistic Regression and Model Ensemble......Page 383
Results and Interpretation......Page 384
Concluding Remarks......Page 385
References......Page 386
Introduction......Page 388
Nine Normative Principles to Preserve Privacy......Page 389
Multi-agent Trust Model......Page 390
Trust Model for Hippocratic Social Order......Page 391
Hippocatric Trust Relationship......Page 392
Scenario and Parameters......Page 393
Results......Page 395
Conclusions and Perspectives......Page 396
References......Page 397
Data Mining......Page 398
Trading Agents......Page 400
Negotiation......Page 404
Virtual Institutions......Page 405
References......Page 407
Introduction......Page 408
Rationale......Page 409
Agent Architecture......Page 410
's Reasoning......Page 411
Managing Dynamic Information Flows......Page 412
Updating the World Model with [info]......Page 413
Information Reliability......Page 414
Negotiation......Page 415
Negotiation Strategies......Page 416
References......Page 417
Introduction and Related Work......Page 418
Language and Interpretations......Page 419
Representing Beliefs and Desires......Page 420
Mental State......Page 421
Beliefs......Page 422
Desires......Page 423
Goals......Page 425
Conclusion......Page 426
References......Page 427
Introduction......Page 428
Agent-Based versus Population-Based Modelling......Page 429
The Agent-Based and Population-Based Simulation Model......Page 430
Simulation Results......Page 432
Mathematical Analysis......Page 434
References......Page 437
Introduction......Page 439
UML Activity Diagrams......Page 441
Formal Semantics.......Page 443
Mapping and Transforming Agent Hypergraph to ISPL......Page 444
Encoding Specifications in ISPL......Page 445
Experimental Results......Page 446
Conclusion and Future Work......Page 447
References......Page 448
Introduction......Page 449
Modelling Approach......Page 450
Case Study......Page 451
Cognitive Analysis......Page 452
Generating Predictors for Output States......Page 453
Representation Relations......Page 454
Behavioural Monitoring......Page 456
Discussion and Conclusions......Page 457
References......Page 458
Introduction......Page 459
Causal Memory in CTS Architecture......Page 461
Learning Extracted Rules......Page 464
Users’ Learning Situations......Page 465
CTS’ Performance after the Implementation of Causal Learning......Page 467
Conclusion......Page 468
References......Page 469
On-board Analysis......Page 471
Off-board Analysis......Page 472
System Architecture......Page 473
Face Tracking......Page 474
Preliminary Results......Page 475
Method Implementation......Page 476
Preliminary Results......Page 478
References......Page 479
Introduction......Page 481
Related Research on Telecare and Telemedicine......Page 482
Technologies......Page 483
System Overview......Page 484
eHealth Subsystem......Page 485
Multimedia Subsystem......Page 486
Experiments......Page 487
Conclusions and Future Work......Page 488
References......Page 489
Introduction......Page 491
Related Works......Page 492
System Design......Page 493
Obtaining and Filtering Data......Page 494
Classification of the User's Activity......Page 496
Classify Filter......Page 499
Server......Page 501
Experimental Results......Page 502
Conclusions......Page 503
References......Page 504
Introduction......Page 505
Statement of Problem......Page 506
Essential Components of Proposed Model......Page 507
Differential Evolution Technique: State of the Art......Page 508
Modeling the Query Log Components with $DE$......Page 510
Test Dataset and Validation of Results......Page 511
Conclusion......Page 513
References......Page 514
Introduction......Page 515
Background......Page 516
Model......Page 517
Ad Association (A2) Algorithm......Page 520
Experiments......Page 522
Conclusions......Page 523
References......Page 524
Introduction......Page 525
Preliminaries......Page 526
Mining Pattern Specification......Page 528
Transaction Extractor Foundations......Page 530
Evaluation......Page 532
Conclusions......Page 533
References......Page 534
Introduction......Page 535
Related Works......Page 537
Collaborative Community Discovery......Page 538
Concept Hierarchy Construction of Research Topics......Page 539
Evaluation Results......Page 542
Conclusion and Future Works......Page 543
References......Page 544
Arranging Events of News Articles......Page 546
Method for Arranging Events......Page 548
Event Detection......Page 549
Experiment of Event Arrangement System......Page 550
Experiment......Page 551
Discussion......Page 552
Conclusions......Page 554
References......Page 555
Aim of the Present Research......Page 556
Community Website......Page 557
Matchmaking and Brokering Architecture......Page 558
Brokering: Site Pool Layer......Page 559
User Load Reduction Hypothesis......Page 560
Placement......Page 561
Results......Page 562
References......Page 564
Introduction......Page 566
WordNet......Page 567
Semantic Similarity Measures and WordNet......Page 568
Approach......Page 570
Semantic Differences in WordNet Based Metrics......Page 571
Experimental Settings......Page 572
Results and Discussion......Page 573
References......Page 574
Introduction......Page 576
Related Works......Page 577
Document Representation......Page 578
Representation Comparison......Page 581
Experiments......Page 582
References......Page 584
Introduction......Page 586
Data Collection......Page 587
Design Methodology......Page 588
Implementation......Page 589
Incorporating Class Information......Page 591
Simulation Results......Page 592
Conclusion......Page 594
References......Page 595
Introduction......Page 596
Designing Tag Set......Page 597
Elementary Unit......Page 598
Data Analysis......Page 599
Discussion......Page 602
Related Works......Page 603
References......Page 604
Introduction......Page 606
Modeling of User's Utterance Timing......Page 607
Probability Defined by Using Barge-in Timing......Page 609
Probability Defined by Using ASR Results......Page 610
Implementation of Barge-in-able Dialogue System......Page 611
Experimental Results......Page 612
Extending Acceptable Utterances by LSM......Page 614
References......Page 615
Introduction......Page 616
A Brief Recall on the Bow Tie Diagrams Analysis......Page 617
A New Algorithm to Construct Bow Tie Diagrams......Page 618
New Approach to Learn Bow Ties Structure......Page 619
Learning Bow Ties Parameters......Page 620
Illustrative Example......Page 621
Learning Bow Tie Parameters......Page 622
Conclusion......Page 624
References......Page 625
Introduction......Page 626
Overview of the Preprocessing......Page 627
Feature Selection......Page 628
Periodogram and Autocorrelation......Page 629
Signal Energy......Page 630
Cross-Correlation and Wavelet Based Feature......Page 631
Classification Procedure and Results......Page 633
Concluding Remarks......Page 634
References......Page 635
Introduction......Page 636
Bregman Divergence......Page 637
Linking Metric MDS and Bregman Divergences......Page 638
The ExtendedSammon Mapping......Page 640
Criterion for Base Convex Function Selection......Page 642
Two Groups of Bregman Divergences......Page 643
References......Page 646
Independent Component Analysis......Page 648
Stone's Criterion......Page 649
Bregman Divergences......Page 650
The Exponential Family......Page 651
Bregmanising Stone's Method......Page 653
Image Identification......Page 655
References......Page 657
Introduction......Page 658
Activity Recognition......Page 659
Proposed Ranking Technique Based on Discrimination and Robustness......Page 660
Results......Page 661
Conclusions......Page 662
References......Page 663
Introduction......Page 664
Feature Selection......Page 665
Record Keeping Mechanisms......Page 666
Software Caching......Page 667
Experimental Setup......Page 668
Caching in the GA FSP Domain......Page 669
Experiments Results......Page 670
References......Page 672
Introduction......Page 674
Drug Activity Prediction Using Machine Learning......Page 675
Sensitivity Based Input Pruning......Page 676
Empirical Results......Page 677
Concluding Remarks and Further Work......Page 680
References......Page 681
Introduction......Page 683
Democratic Feature Selection Method......Page 685
Determining the Threshold of Votes......Page 686
Experimental Setup......Page 687
Experimental Results......Page 688
Summary of Results......Page 689
Conclusions and Future Work......Page 691
References......Page 692
Author Index......Page 694