Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (Advances in Cognitive Informatics and Natural Intelligence (Acini) Book Series)

✍ Scribed by Yingxu Wang, Yingxu Wang

• Publisher: Information Science Reference
• Year: 2010
• Tongue: English
• Leaves: 605
• Series: Advances in Cognitive Informatics and Natural Intelligence Acini Book Series
• Edition: 1
• Category: Library

✦ Synopsis

Cognitive informatics is a multidisciplinary field that acts as the bridge between natural science and information science. Specifically, it investigates the potential applications of information processing and natural intelligence to science and engineering disciplines. This collection, entitled Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence provides emerging research topics in cognitive informatics research with a focus on such topics as reducing cognitive overload, real-time process algebra, and neural networks for iris recognition, emotion recognition in speech, and the classification of musical chords.

✦ Table of Contents

COVER PAGE......Page 1
TITLE PAGE......Page 2
ISBN 1605669024......Page 3
LIST OF REVIEWERS......Page 4
TABLE OF CONTENTS......Page 6
DETAILED TABLE OF CONTENTS......Page 10
PREFACE......Page 22
ABSTRACT......Page 29
INTRODUCTION......Page 30
RELATED WORK......Page 31
CONNECTED COMPONENT NETWORK......Page 33
USING CCN TO MODEL THE BRAIN......Page 34
LEARNING OF A CCNB......Page 36
CONSCIOUSNESS AND SUBCONSCIOUSNESS......Page 37
SIMILARITY AND DISAMBIGUATION......Page 38
FTHINKINGFL IN ACCNB......Page 39
MAKING INFERENCE IN A CCNB......Page 40
EXPERIMENTAL EVIDENCE......Page 41
CONCLUSION......Page 42
REFERENCES......Page 43
ENDNOTES......Page 45
INTRODUCTION......Page 46
A DEFINITION OF INTELLIGENCE AND A MODEL OF HUMAN INTELLIGENCE FORMATION......Page 48
THE TRANSFORMATION 1: FROM ONTOLOGICAL INFORMATION TO EPISTEMOLOGICAL INFORMATION......Page 50
THE TRANSFORMATION 2: FROM INFORMATION TO KNOWLEDGE......Page 52
TRANSFORMATION 3: FROM KNOWLEDGE TO INTELLIGENCE IN THE NARROW SENSE (INTELLIGENT STRATEGY)......Page 54
TRANSFORMATION 4: FROM STRATEGY TO ACTION......Page 55
THE MECHANISM OF INTELLIGENCE FORMATION (A SUMMARY)......Page 56
A UNIFYING THEORY OF AI......Page 57
THE MATHEMATICAL DIMENSION......Page 59
THE BIOLOGICAL DIMENSION......Page 60
REFERENCES......Page 61
ENDNOTE......Page 63
ABSTRACT......Page 64
META-LEARNING BASIC CONCEPT......Page 65
EXISTING META-LEARNING METHODS AND ALGORITHM SELECTION SYSTEMS DATA CHARACTERIZATION METHODS......Page 66
DECISION TREE BASED DATA CHARACTERIZATION......Page 67
EXISTING ALGORITHM SELECTION SYSTEMS......Page 68
METHOD......Page 69
THE DISTANCE BASED MULTICRITERIA EVALUATION MEASUREMENT......Page 70
FEATURE SELECTION AND ROUGH SET ASSISTED REDUCTION......Page 71
WEIGHT ADJUSTMENT BY USING MUTUAL INFORMATION......Page 72
EXPERIMENT EXPERIMENTAL DESCRIPTIONS......Page 73
EXPERIMENTAL PROCEDURE......Page 74
RESULTS AND DISCUSSION......Page 75
CONCLUSION AND FUTURE WORK......Page 76
ABSTRACT......Page 80
ANALYZING LEARNING METHODS IN A FUNCTIONAL ENVIRONMENT......Page 81
THE PROGRAMMING LANGUAGE......Page 82
BASIC ENVIRONMENT......Page 84
DEFINING COGNITIVE PROCESSES......Page 85
INTRODUCING OBSERVATIONS IN EDEN......Page 87
BASIC IDEAS OF OUR IMPLEMENTATION......Page 88
OBSERVING ENTITIES......Page 89
BEHAVIORAL OBSERVATIONS......Page 90
CONSTRUCTIVISM OBSERVATIONS......Page 91
CONCLUSION AND CURRENT WORK......Page 95
REFERENCES......Page 96
ENDNOTES......Page 98
ABSTRACT......Page 99
HUMAN LEARNING NATURE OF LEARNING......Page 100
CONCLUSION......Page 105
LEARNING OF NATURE......Page 106
HUMAN AND MACHINE ASSOCIATED LEARNING OF NATURE......Page 109
A LITTLE ALLEGORY OF LEARNING FROM EACH OTHER......Page 110
IMPLEMENTATION OF THE LAST VARIATION......Page 112
CONCLUSION......Page 114
WHAT CAN WE EXPECT, OR NOT, FROM MACHINE ASSISTED HUMAN LEARNING?......Page 115
REFERENCES......Page 116
ENDNOTES......Page 119
INTRODUCTION......Page 121
AXIOMATIC HUMAN TRAITS......Page 122
THE HIERARCHICAL MODEL OF BASIC HUMAN NEEDS......Page 123
CHARACTERISTICS OF HUMAN FACTORS......Page 126
PROPERTIES OF HUMAN FACTORS IN ENGINEERING......Page 127
PROPERTIES OF HUMAN FACTORS IN SOCIALIZATION......Page 128
SOCIAL ENVIRONMENTS FOR SOFTWARE ENGINEERING......Page 129
TAXONOMY OF HUMAN ERRORS......Page 130
THE BEHAVIORAL MODEL OF HUMAN ERRORS......Page 131
THE RANDOM FEATURE OF HUMAN ERRORS......Page 132
THE THEORETICAL FOUNDATION OF QUALITY ASSURANCE IN CREATIVE WORK......Page 133
CONCLUSION......Page 135
REFERENCES......Page 136
ABSTRACT......Page 138
MODELLING USER PREFERENCE......Page 139
A USER-CENTERED THREE-LAYERED FRAMEWORK......Page 141
LANGUAGES FOR RULE DESCRIPTION......Page 143
MEASURES OF RULES......Page 144
INTERPRETATIONS OF RULES......Page 146
MULTIPLE STRATEGIES IN THE TECHNIQUE LAYER......Page 147
COMPROMISING STRATEGIES......Page 148
MULTIPLE EXPLANATIONS IN THE APPLICATION LAYER......Page 149
CONCLUSION......Page 150
REFERENCES......Page 151
INTRODUCTION......Page 154
THE MATHEMATICAL MODEL OF ABSTRACT CONCEPTS......Page 156
RELATIONAL OPERATIONS OF CONCEPTS......Page 161
COMPOSITIONAL OPERATIONS OF CONCEPTS......Page 163
THE MATHEMATICAL MODEL OF KNOWLEDGE......Page 170
THE HIERARCHICAL MODEL OF CONCEPT NETWORKS......Page 171
CONCLUSION......Page 173
REFERENCES......Page 174
INTRODUCTION......Page 177
THE ABSTRACT SYSTEM THEORY......Page 178
THE MATHEMATICAL MODEL OF CLOSED SYSTEMS......Page 179
THE MATHEMATICAL MODEL OF OPEN SYSTEMS......Page 181
MAGNITUDE OF SYSTEMS......Page 183
RELATIONAL OPERATIONS ON SYSTEMS......Page 186
ALGEBRAIC RELATIONS OF OPENSYSTEMS......Page 187
RELATIONS BETWEEN OPEN AND CLOSED SYSTEMS......Page 189
SYSTEM TAILORING......Page 190
SYSTEM EXTENSION......Page 192
SYSTEM SUBSTITUTION......Page 193
SYSTEM COMPOSITION......Page 194
SYSTEM DIFFERENCE......Page 198
SYSTEM DECOMPOSITION......Page 200
SYSTEM AGGREGATION......Page 201
REFERENCES......Page 203
INTRODUCTION......Page 206
THE TYPE SYSTEM OF RTPA......Page 208
THE TYPE SYSTEM FOR DATA OBJECTS MODELING IN RTPA......Page 209
ADVANCED TYPES OF RTPA......Page 211
FORMAL TYPE RULES OF RTPA......Page 212
METAPROCESSES OF RTPA......Page 213
ALGEBRAIC PROCESS OPERATIONS IN RTPA......Page 215
THE UNIVERSAL MATHEMATICAL MODEL OF PROGRAMS BASED ON RTPA......Page 217
THE COGNITIVE PROCESS OF MEMORIZATION......Page 221
FORMAL DESCRIPTION OF THE MEMORIZATION PROCESS IN RTPA......Page 222
CONCLUSION......Page 223
REFERENCES......Page 224
INTRODUCTION......Page 228
VARIABLES AND VALUES......Page 229
ENVIRONMENTS......Page 230
TEMPORAL ORDERED DURATION SEQUENCES......Page 231
SEQUENCE OPERATIONS......Page 232
VARIABLES OF RTPA......Page 233
THE SEMANTIC DOMAINS OF RTPA......Page 234
VARIABLES AND VALUES IN RTPA......Page 235
THE DENOTATIONAL SEMANTIC FUNCTIONS OF RTPA META PROCESSES......Page 236
THE DENOTATIONAL SEMANTIC FUNCTIONS OF RTPA PROCESS RELATIONS......Page 239
CONCLUSION......Page 243
REFERENCES......Page 244
INTRODUCTION......Page 246
THE ABSTRACT SYNTAX OF RTPA......Page 247
THE META PROCESSES OF SOFTWARE BEHAVIORS IN RTPA......Page 248
THE TYPE SYSTEM OF RTPA......Page 249
THE REDUCTION MACHINE OF RTPA......Page 251
OPERATIONAL SEMANTICS OF RTPA META-PROCESSES......Page 253
OPERATIONAL SEMANTICS OF PROCESS RELATIONS......Page 257
REFERENCES......Page 264
INTRODUCTION......Page 267
THE LAYOUT OBJECT MODEL......Page 269
THE CONSTRAINT DIAGRAMS......Page 270
OTHER APPROACHES......Page 271
THE GENERIC MODEL OF CLASSES IN RTPA......Page 272
THE GENERIC MODEL OF PATTERNS IN RTPA......Page 274
CASE STUDIES ON FORMAL SPECIFICATIONS OF PATTERNS IN RTPA......Page 275
CONCLUSION......Page 278
REFERENCES......Page 280
INTRODUCTION......Page 282
THE SEMANTIC ENVIRONMENT AND SEMANTIC FUNCTION......Page 284
DEDUCTIVE SEMANTICS OF PROGRAMS AT DIFFERENT LEVELS OF COMPOSITIONS......Page 287
PROPERTIES OF SOFTWARE SEMANTICS......Page 290
THE EVALUATION PROCESS......Page 292
THE ADDRESSING PROCESS......Page 293
THE MEMORY RELEASE PROCESS......Page 294
THE INPUT PROCESS......Page 295
THE DURATION PROCESS......Page 296
THE DECREASE PROCESS......Page 297
THE SKIP PROCESS......Page 298
THE STOP PROCESS......Page 299
THE SEQUENTIAL PROCESS RELATION......Page 300
THE SWITCH PROCESS RELATION......Page 302
THE WHILE-LOOP PROCESS RELATION......Page 304
THE REPEAT-LOOP PROCESS RELATION......Page 305
THE FOR-LOOP PROCESS RELATION......Page 306
THE RECURSIVE PROCESS RELATION......Page 307
THE PARALLEL PROCESS RELATION......Page 308
THE INTERLEAVE PROCESS RELATION......Page 310
THE INTERRUPT PROCESS RELATION......Page 312
THE SYSTEM PROCESS......Page 313
THE TIME-DRIVEN DISPATCHING PROCESS RELATION......Page 314
THE INTERRUPT-DRIVEN DISPATCHING PROCESS RELATION......Page 315
CONCLUSION......Page 316
REFERENCES......Page 317
INTRODUCTION......Page 320
THE BASIC CONTROL STRUCTURES OF COMPUTING......Page 321
THE BIG-R NOTATION FOR DENOTING ITERATIONS AND RECURSIONS......Page 323
EXISTING SEMANTIC MODELS OF ITERATIONS......Page 325
PROPERTIES OF RECURSIONS......Page 328
THE MATHEMATICAL MODEL OF RECURSIONS......Page 330
COMPARATIVE ANALYSIS OF ITERATIONS AND RECURSIONS......Page 331
REFERENCES......Page 333
INTRODUCTION......Page 336
THE COGNITIVE PROCESS OF OBJECT IDENTIFICATION......Page 338
THE COGNITIVE PROCESS OF CONCEPT ESTABLISHMENT......Page 340
THE COGNITIVE PROCESS OF SEARCH......Page 342
THE COGNITIVE PROCESS OF CATEGORIZATION......Page 343
THE COGNITIVE PROCESS OF COMPARISON......Page 344
THE COGNITIVE PROCESS OF QUALIFICATION......Page 345
THE COGNITIVE PROCESS OF QUANTIFICATION......Page 347
THE COGNITIVE PROCESS OF SELECTION......Page 348
REFERENCES......Page 350
ABSTRACT......Page 353
INTRODUCTION......Page 354
GRANULAR STRUCTURES......Page 355
THE LOGIC LANGUAGE L......Page 356
SYNTAX AND SEMANTICS......Page 357
DIFFERENCES BETWEEN L AND OTHER DECISION LOGIC LANGUAGES......Page 358
INFORMATION TABLES......Page 360
THE LANGUAGE L IN FORMAL CONCEPT ANALYSIS......Page 366
ACKNOWLEDGMENT......Page 373
REFERENCES......Page 374
NOTIONS CENTRAL TO GRANULATION OF KNOWLEDGE......Page 378
ROUGH SET ANALYSIS OF KNOWLEDGE......Page 379
ROUGH MEREOLOGY AND ROUGH INCLUSIONS......Page 381
ROUGH INCLUSIONS IN INFORMATION SYSTEMS......Page 382
ROUGH INCLUSIONS FROM T-NORMS......Page 383
GRANULATION OF KNOWLEDGE......Page 384
GRANULES FROM ROUGH INCLUSIONS......Page 385
GRANULAR RM-LOGICS......Page 386
NETWORKS OF GRANULAR AGENTS......Page 387
REFERENCES......Page 392
INTRODUCTION......Page 396
INDEPENDENT COMPONENT ANALYSIS......Page 398
JADE......Page 399
MEASURES OF OUTLIER ROBUSTNESS......Page 400
DESIGN OF EXPERIMENTS......Page 401
CONTRAST FUNCTION SETUP......Page 402
RESULTS AND DISCUSSION......Page 403
OPTIMUM ANGLE OF ROTATION ERROR......Page 404
CONTRAST FUNCTION DIFFERENCE......Page 405
CONCLUSION......Page 406
REFERENCES......Page 407
INTRODUCTION......Page 409
BACKGROUND ON FRACTAL MEASURES SINGLE FRACTAL DIMENSION MEASURES......Page 410
MULTIFRACTAL DIMENSION MEASURES......Page 411
RÉNYI FRACTAL DIMENSION SPECTRUM......Page 412
MEASURING CLOSENESS OF PROBABILITY MODELS......Page 413
RELATIVE MULTIFRACTAL DIMENSION MEASURES DERIVATION......Page 414
CHOICE OF PROBABILITIES......Page 418
AVOIDANCE OF ZERO PROBABILITIES......Page 419
EXPERIMENTAL RESULTS......Page 420
CONCLUSION......Page 422
REFERENCES......Page 423
ABSTRACT......Page 426
PROBLEM DESCRIPTION......Page 427
VOLUMETRIC REPRESENTATION: SUPERQUADRIC-BASED GEON (SBG)......Page 428
SBG EXTRACTION......Page 429
FEATURES AND CORRESPONDING CONSTRAINTS......Page 430
CONSTRAINED TREE SEARCH......Page 433
PART SIMILARITY MEASURE -......Page 434
WHOLE AND PARTIAL SIMILARITY MEASURE - MSIM(W,L) AND MSIM (P,L)......Page 435
WHOLE MATCH......Page 436
WHOLE MATCH AND PARTIAL MATCH......Page 437
CONCLUSION AND FURTHER DIRECTIONS......Page 438
REFERENCES......Page 439
ABSTRACT......Page 441
INTRODUCTION......Page 442
AQUA AND THE AQUASENSOR......Page 444
OBTAINING LOCAL SURFACE MODELS......Page 445
VISUAL EGOMOTION ESTIMATION......Page 446
3-D LAZY SNAPPING......Page 447
DISCUSSION AND FUTURE WORK......Page 450
REFERENCES......Page 452
INTRODUCTION......Page 456
THE WEAKNESS OF BAYESIAN NETWORK......Page 458
CELLULAR BAYESIAN NETWORKS (CBNS) MOTIVATION......Page 459
MODEL DESCRIPTION......Page 460
LEARNING A CBNS......Page 461
INFERENCE ON A CBNS......Page 462
DATASET......Page 463
CONTRAST DETECTOR......Page 465
PERFORMANCE EVALUATION......Page 466
DISCUSSION......Page 467
ACKNOWLEDGMENT......Page 468
REFERENCES......Page 469
ENDNOTES......Page 472
ABSTRACT......Page 473
IRIS IMAGE PREPROCESSING......Page 474
IRIS OUTER BOUNDARY LOCATION......Page 476
IRIS ENHANCEMENT......Page 480
EXPERIMENTAL RESULTS AND DISCUSSION EXPERIMENTAL RESULTS......Page 483
DISCUSSION......Page 484
REFERENCE......Page 486
ABSTRACT......Page 489
INTRODUCTION GENERAL BACKGROUND......Page 490
PREVIOUS WORK......Page 491
EMOTIONAL SPEECH ACQUISITION......Page 493
SPECTRAL ANALYSIS AND FEATURE EXTRACTION......Page 494
FEATURE SELECTION......Page 497
SEQUENTIAL FORWARD SELECTION METHOD......Page 498
GENERAL REGRESSION NEURAL NETWORK......Page 499
CONSISTENCY BASED FEATURE SELECTION......Page 501
RECOGNIZING EMOTIONS......Page 502
NEURAL NETWORK BASED CLASSIFICATION......Page 503
RECOGNITION RESULTS......Page 505
DISCUSSION......Page 506
REFERENCES......Page 510
INTRODUCTION......Page 513
RELATED WORK......Page 514
CHORD CLASSIFICATION BY NEURAL NETWORKS......Page 518
STUDY 1: CHORDS DEFINED WITH PITCH CLASS REPRESENTATION METHOD TRAINING SET......Page 519
NETWORK TRAINING......Page 520
INTERPRETATION OF WEIGHTS FROM INPUT UNITS......Page 521
USING HIDDEN UNIT RESPONSES TO CLASSIFY CHORDS......Page 522
STUDY 2: CHORD CLASSIFICATION USING LOCAL REPRESENTATION......Page 523
METHOD TRAINING SET......Page 524
NETWORK TRAINING......Page 525
USING HIDDEN UNIT RESPONSES TO CLASSIFY CHORDS......Page 526
DISCUSSION......Page 530
REFERENCES......Page 531
ABSTRACT......Page 536
INTRODUCTION......Page 537
CLASSIFICATION OF NONCONVENTIONAL NEURAL UNITS CLASSIFICATION OF NEURAL UNITS BY NONLINEAR AGGREGATING FUNCTION......Page 539
CLASSIFICATION OF NEURAL UNITS BY NEURAL DYNAMICS......Page 540
CLASSIFICATION OF NEURAL UNITS BY IMPLEMENTATION OF TIME DELAYS......Page 542
SYNAPTIC AND NONSYNAPTIC NATURE OF NONLINEAR NEURAL AGGREGATION FUNCTION......Page 544
SUMMARY......Page 547
ACKNOWLEDGMENT......Page 549
REFERENCES......Page 550
ABSTRACT......Page 552
RESULTS......Page 553
SUMMARY AND DISCUSSION......Page 558
REFERENCES......Page 559
COMPILATION OF REFERENCES......Page 560
ABOUT THE CONTRIBUTORS......Page 594
C......Page 600
G......Page 601
L......Page 602
P......Page 603
T......Page 604
V......Page 605