Intelligence Science I Second IFIP TC 12 International Conference, ICIS 2017, Shanghai, China, October 25-28, 2017, Proceedings

This book constitutes the refereed proceedings of the Second International Conference on Intelligence Science, ICIS 2017, held in Shanghai, China, in October 2017.The 38 full papers and 9 short papers presented were carefully reviewed and selected from 82 submissions.

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Bibliographic Details
Published inIntelligence Science I Second IFIP TC 12 International Conference, ICIS 2017, Shanghai, China, October 25-28, 2017, Proceedings Vol. 510
Main Authors Shi, Zhongzhi, Goertzel, Ben, Feng, Jiali
Format eBook Conference Proceeding Book
LanguageEnglish
Published Cham Springer Nature 2017
Springer International Publishing AG
Springer International Publishing
Springer
Edition1
SeriesIFIP Advances in Information and Communication Technology
Subjects
Algorithm Analysis and Problem Complexity
Artificial Intelligence
Artificial intelligence-Congresses
Computer Communication Networks
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Information Storage and Retrieval
Special computer methods
Online AccessGet full text
ISBN9783319681214
3319681214
9783319681207
3319681206
ISSN1868-4238
1868-422X
DOI10.1007/978-3-319-68121-4

Cover

Table of Contents:
  • References -- Resting State fMRI Data Classification Method Based on K-means Algorithm Optimized by Rough Set -- Abstract -- 1 Introduction -- 2 Knowledge of Rough Sets -- 3 Experimental Methods -- 3.1 Data Acquisition and Data Preprocessing -- 3.2 Attribute Importance Calculation -- 3.3 Best Attribute Reduction -- 3.4 K-means Algorithm -- 4 Result Analyses -- 4.1 Reductions in fMRI Data for Different Eye States -- 4.2 Reductions in fMRI Data for Alzheimer's Disease and Healthy Controls -- 4.3 Data Atlas -- 4.4 Clustering Algorithm Based on Rough Set Optimization -- 5 Discuss -- References -- The Research of Attribute Granular Computing Model in Cognitive and Decision-Making -- Abstract -- 1 Introduction -- 2 Qualitative Mapping Method in Cognition -- 3 Attribute Granules in Cognitive and Decision-Making -- 4 Cognitive Decision-Making and Fuzzy Attribute Granule -- 5 Set Up Cognition Formal Model with Attribute Granule -- 6 Decision-Making in CSPT -- 7 Conclusion -- Acknowledgments -- References -- Power Control in D2D Network Based on Game Theory -- Abstract -- 1 Introduction -- 2 System Model -- 3 Non-cooperative Power Control Game Analysis -- 3.1 Existence -- 3.2 Uniqueness -- 3.3 Distributed Iterative Game Algorithm -- 4 Simulation Results -- 5 Conclusion -- Acknowledgement -- References -- HCI Based on Gesture Recognition in an Augmented Reality System for Diagnosis Planning and Training -- Abstract -- 1 Introduction -- 2 System Architecture -- 2.1 The Hardware Architecture of ARS-CADPT -- 2.2 The Software Framework of ARS-CADPT -- 3 Real-Time HCI -- 3.1 Gesture Definition -- 3.2 Static Gesture Recognition -- 3.3 Dynamic Gesture Spotting -- 3.4 Dynamic Gesture Recognition -- 4 Experimental Results -- 5 Conclusion and the Future Work -- Acknowledgement -- References
  • 3 The Attribute Topos Induced by Mechanism of Mutual Change Between Quality and Quantity -- 4 The Fixation Image Operator Induced by Orthogonal Expanded of Function -- 4.1 The Tensor Flow Induced by Restriction Morphism F and Image Thinking -- Acknowledgement -- References -- Go Mapping Theory and Factor Space Theory Part I: An Outline -- 1 Factor Space Theory (FST) -- 2 Formal Concept Analysis (FCA) -- 3 Gouguen's L-Fuzzy Sets and Barr's Embedding -- 4 The GO Mapping Theory (GMT) -- References -- Cognitive Computing -- A Case-Based Approach for Modelling the Risk of Driver Fatigue -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Case Representation -- 3.2 Case Retrieval and Reuse -- 4 Case Study: Based on the Traffic Crash Data in China -- 4.1 Evaluation Settings -- 4.2 Results and Discussion -- 4.3 Further Implications -- 5 Limitations and Future Work -- References -- Gazes Induce Similar Sequential Effects as Arrows in a Target Discrimination Task -- Abstract -- 1 Introduction -- 2 Experiment 1 -- 2.1 Participants -- 2.2 Apparatus and Stimuli -- 2.3 Design and Procedure -- 2.4 Results -- 3 Experiment 2 -- 3.1 Participants -- 3.2 Apparatus and Stimuli -- 3.3 Design and Procedure -- 3.4 Results -- 4 Discussion -- Acknowledgments -- References -- Discrete Cuckoo Search with Local Search for Max-cut Problem -- Abstract -- 1 Introduction -- 2 Hybrid Algorithm -- 2.1 Discrete Cuckoo Search Algorithm -- 2.2 Local Search Strategy -- 2.3 Proposed Hybrid Algorithm -- 3 Performance Evolution -- 4 Conclusion -- Acknowledgments -- References -- A New Cuckoo Search -- 1 Introduction -- 2 The New CS -- 2.1 The Model of New CS Algorithm -- 2.2 Pseudo Code of the NCS Algorithm -- 3 Numerical Simulation -- 3.1 Test Functions -- 3.2 Experimental Results and Comparison Used Against Test Function with Big Size -- 4 Conclusions and Perspectives
  • Intro -- Preface -- Organization -- Keynote and Invited Presentations -- Pattern Recognition by the Brain: Neural Circuit Mechanisms -- Interactive Granular Computing: Toward Computing Model for Turing Test -- Optimal Mass Transportation Theory Applied for Machine Learning -- Quantifying Your Brain and Identifying Brain Disease Roots -- Multi-objective Ensemble Learning and Its Applications -- On Intelligence: Symbiotic, Holonic, and Immunological Agents -- Extreme Learning Machines (ELM) - Filling the Gap between Machine Learning and Biological Learning -- How Can We Effectively Analyze Big Data in Terabytes or Even Petabytes? -- Cyborg Intelligent Systems -- Learning and Memory in Mind Model CAM -- Factor Space and Artificial Intelligence -- Contents -- Theory of Intelligence Science -- Ecological Methodology and Mechanism Approach -- Abstract -- 1 Introduction -- 2 Ecological Methodology vs. Reductionism Methodology -- 2.1 Definition 1. Methodology -- 2.2 Definition 2. Ecology and Ecosystem -- 3 The Model of Intelligence Process in Perspective of Ecological Methodology -- 4 Mechanism Approach vs. Other Approaches -- 5 Major Results Due to the Mechanism Approach -- 6 Conclusions -- References -- Collaborative Model in Brain-Computer Integration -- Abstract -- 1 Introduction -- 2 Conceptual Framework of Brain-Computer Integration -- 3 ABGP-CNN Based Environment Awareness -- 4 Motivation Driven Collaboration -- 4.1 Needs Based Motivation -- 4.2 Curiosity Based Motivation -- 4.3 Motivation Execution -- 4.4 Collaboration -- 5 Simulation Experiments -- 6 Conclusions -- Acknowledgements -- References -- Entanglement of Inner Product, Topos Induced by Opposition and Transformation of Contradiction, and Tensor Flow -- Abstract -- 1 Introduction -- 2 Entanglement Vectors Induced by Polarization Identity of Inner Product of Both Vectors z1 and z2
  • The Effect of Expression Geometry and Facial Identity on the Expression Aftereffect -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Subject -- 2.2 Stimuli and Apparatus -- 2.3 Procedure -- 3 Result -- 4 Discussion -- Acknowledgement -- References -- Big Data Analysis and Machine Learning -- A Dynamic Mining Algorithm for Multi-granularity User's Learning Preference Based on Ant Colony Optimization -- Abstract -- 1 Quotient Space Structure of Knowledge Points -- 2 Functions Definition of Multi-granularity Ant Colony Optimization -- 2.1 Ant Colony Optimization -- 2.2 Functions Definition of ACO in Multi-granularity Data -- 3 Dynamic Mining Algorithm for Multi-granularity Learning Preferences -- 4 Experiment and Result -- 4.1 Dynamic Change Process Experiment of User Learning Preference -- 4.2 Experiment Data Analysis of Practical Application System -- 5 Conclusion -- Acknowledgments -- References -- Driver Fatigue Detection Using Multitask Cascaded Convolutional Networks -- Abstract -- 1 Introduction -- 2 Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks -- 3 Extraction Area Eye -- 3.1 Face Normalization -- 3.2 Eye Area Extraction -- 4 Eye State Recognition -- 4.1 Convolutional Neural Network -- 4.2 Activation Functions -- 5 Fatigue Detection Based on PERCLOS -- 6 Experiment and Results -- 6.1 Train -- 6.2 Training Results -- 6.3 Fatigue Detection Based on PERCLOS -- 7 Conclusion -- Acknowledgments -- References -- A Fast Granular Method for Classifying Incomplete Inconsistent Data -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Incomplete Decision Systems (IDSs) and Attribute-Value Blocks -- 2.2 Incomplete Inconsistent Decision Systems (IIDSs) -- 3 A Granulation Model Based on IIDSs -- 4 A Granulation-Model-Based Method for Constructing Classifier
  • 3.2 Level 1: Entity Assessment
  • 4.1 An Attribute-Value Block Based Method of Acquiring Classification Rules -- 4.2 Rule Set Minimum -- 4.3 A Classification Algorithm for Constructing Rule-Based Classifier -- 5 Experimental Analysis -- 6 Conclusion -- References -- Sentiment Analysis of Movie Reviews Based on CNN-BLSTM -- Abstract -- 1 Introduction -- 2 Methods and Models -- 2.1 CNN -- 2.2 LSTM and BLSTM -- 3 CNN-BLSTM Model -- 3.1 Word Embedding -- 3.2 CNN-BLSTM Model -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Experiments -- 4.2.1 Experiment 1 -- 4.2.2 Experiment 2 -- 5 Conclusion and Future Work -- Acknowledgment -- References -- Playlist Recommendation Based on Reinforcement Learning -- 1 Introduction -- 2 Preliminary -- 3 Reinforcement Learning Based Playlist Recommendation Model -- 3.1 Problem Description -- 3.2 Recommendation Framework Based on Reinforcement Learning -- 3.3 Model Challenges -- 4 Model Learning -- 4.1 State Compression Based on Collaborative Filter -- 4.2 Learning Algorithm -- 4.3 Recommendation Strategy -- 5 Experiment -- 5.1 Dataset -- 5.2 Comparison Methods and Metrics -- 5.3 Effectiveness Experiments -- 5.4 Influence of User's Listening Frequency -- 5.5 Influence of the Window Size -- 5.6 Influence of Different Recommendation Strategies -- 6 Conclusion -- References -- Transfer Learning for Music Genre Classification -- 1 Introduction -- 2 Transfer Learning Process -- 2.1 Scattering Transform -- 2.2 Deep Recurrent Neural Network -- 3 Datasets and Experiment Setup -- 4 Experiment Results and Analysis -- 5 Conclusion -- References -- A Functional Model of AIS Data Fusion -- Abstract -- 1 Introduction -- 2 Functional Model of AIS Data Fusion -- 2.1 Level 0: Preprocessing -- 2.2 Level 1: Entity Assessment -- 2.3 Level 2: Relationship Assessment -- 2.4 Level 3: Impact Assessment -- 3 Our Existing Works -- 3.1 Level 0: Preprocessing