Intelligent Information Processing VIII

Name: Intelligent Information Processing VIII | 9th IFIP TC 12 International Conference, IIP 2016, Melbourne, VIC, Australia, November 18-21, 2016, Proceedings
Brand: Springer
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9th IFIP TC 12 International Conference, IIP 2016, Melbourne, VIC, Australia, November 18-21, 2016, Proceedings

Zhongzhi Shi Sunil Vadera Gang Li(Editor)

Springer (Publisher)

Published on 9. November 2016

XVII, 278 pages

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978-3-319-48390-0 (ISBN)

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Content

Intro
Preface
Organization
Keynote and Invited Presentations (Abstracts)
Automated Reasoning and Cognitive Computing
An Elastic, On-demand, Data Supply Chain for Human Centred Information Dominance
Why Is My Entity Typical or Special? Approaches for Inlying and Outlying Aspects Mining
Advanced Reasoning Services for Description Logic Ontologies
Brain-Like Computing
Contents
Machine Learning
An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets: A Granulation Method to Construct Classifier
Abstract
1 Introduction
2 Preliminaries
2.1 Decision Systems and Relative Reducts
2.2 Decision Logic and Attribute-Value Blocks
3 Minimum Rule Sets
4 Methods of Acquiring Decision Rules
4.1 Rule Reduction
4.2 Minimum of Decision Rule Sets
4.3 An Algorithm for Acquiring Minimum Rule Sets
5 Experiment Analysis
6 Conclusion
Acknowledgements
References
Collective Interpretation and Potential Joint Information Maximization
1 Introduction
2 Theory and Computational Methods
2.1 Concept of Joint Information Maximization
2.2 Potential Joint Information Maximization
2.3 Computing Pseudo-Potential Joint Information Maximization
3 Results and Discussion
3.1 Experimental Outline
3.2 Mutual Information
3.3 Connection Weights
3.4 Generalization Performance
4 Conclusion
References
A Novel Locally Multiple Kernel k-means Based on Similarity
Abstract
1 Introduction
2 Related Work
2.1 Kernel K-Means
2.2 Multiple Kernel k-means
3 Locally Multiple Kernel k-means
3.1 Similarity Measure
3.2 Algorithm
4 Experiments
4.1 Artificial Data Clustering
4.2 Clustering Results
5 Conclusions
Acknowledgements
References
Direction-of-Arrival Estimation for CS-MIMO Radar Using Subspace Sparse Bayesian Learning
Abstract
1 Introduction
2 Signal Model
3 Environment Awareness
4 Simulation Results
5 Conclusions
References
Data Mining
Application of Manifold Learning to Machinery Fault Diagnosis
1 Introduction
2 The principle of SMCE
3 The Method of Feature Extraction Based on SMCE
4 Experiment
5 Conclusion
References
p-Spectral Clustering Based on Neighborhood Attribute Granulation
Abstract
1 Introduction
2 p-Spectral Clustering
3 Neighborhood Attribute Granulation
4 p-Spectral Clustering Based on Neighborhood Attribute Granulation
5 Experimental Analysis
6 Conclusions
Acknowledgements
References
Assembly Sequence Planning Based on Hybrid Artificial Bee Colony Algorithm
Abstract
1 Introduction
2 Optimization Model of Assembly Sequences
2.1 Geometric Feasibility
2.2 Assembly Stability
2.3 The Number of Parts Violating Local Assembly Precedence
2.4 Changes of Assembly Directions, Tools And connections
2.5 Fitness Function
3 Assembly Sequences of HABC
3.1 Nectar Source Code
3.2 Initial Nectar Source
3.3 Employed Bee Phase
3.4 Onlooker Bee Phase
3.5 Scout Bee Phase
3.6 A Local Search Algorithm
3.7 Computational Procedure of the HABC Algorithm
4 Experimental Results and Analysis
4.1 Simulation Example
4.2 The Comparison Between Algorithms
5 Conclusions and Future Work
Acknowledgments
References
A Novel Track Initiation Method Based on Prior Motion Information and Hough Transform
Abstract
1 Introduction
2 Review of Current Techniques
3 Our Approach
4 Simulation and Results
5 Conclusions
Acknowledgements
References
Deep Learning
A Hybrid Architecture Based on CNN for Image Semantic Annotation
Abstract
1 Introduction
2 CNN Visual Feature Extraction
2.1 Extracting Visual Features from Pre-trained CNN Model
2.2 Exacting Visual Feature from Fine-Tuned CNN Model
3 Ensembles of Classification Classifiers for Semantic Learning
4 Hybrid Framework for Image Annotation
5 Experiments and Results
5.1 Datasets and Evaluation Measures
5.2 Results for Image Annotation on Corel5 K
5.3 Result Analysis
6 Conclusion
Acknowledgement
References
Convolutional Neural Networks Optimized by Logistic Regression Model
Abstract
1 Introduction
2 Structure of Convolutional Neural Network
3 Classifier Model Based on Logistic Regression
3.1 Logistic Regression Model
3.2 Softmax Regression Model
3.3 Back Propagation
4 Experiment and Result Analysis
5 Conclusions
Acknowledgments
References
Event Detection with Convolutional Neural Networks for Forensic Investigation
Abstract
1 Introduction
2 Preliminary
2.1 Related Work
2.2 Challenges Posed by Textual Evidence
3 Model Description
3.1 Basic CNN
3.2 CSV-CNN
4 Experiments
4.1 Datasets
4.2 The Performance and Analysis
5 Conclusions
References
Boltzmann Machine and its Applications in Image Recognition
Abstract
1 Introduction
2 Restricted Boltzmann Machine and Semi-restricted Boltzmann Machine
2.1 Restricted Boltzmann Machine Models
2.2 Spike-and-Slab Restricted Boltzmann Machine
3 The Training Algorithms About RBM and Boltzmann Machine
3.1 Mean Field Method
3.2 Persistent Markov Chain
4 Deep Belief Networks and Deep Boltzmann Machine
4.1 Deep Belief Networks
4.2 Deep Boltzmann Machine
5 Weight Uncertainty Spike-and-Slab Restricted Boltzmann Machine
5.1 Weight Uncertainty Method
5.2 Weight Uncertainty Spike-and-Slab Deep Boltzmann Machine
6 Experimental Analysis
7 Conclusion
Acknowledgements
References
Social Computing
Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering
1 Introduction
2 Related Work
3 Problem Statement
4 Case Study - Edinburgh Pedestrian Flow
5 Summarizing Related Flows
5.1 Synthetic Cases
5.2 Case of Real Trajectory Data
6 Identifying Time Periods with Similar Flows
6.1 Comparing Flow Patterns
6.2 Identifying Similar Time Periods
7 Identifying Anomalous Flow Patterns
8 Conclusions and Future Work
References
Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features
Abstract
1 Introduction
2 Related Work
3 The Proposed Approach
3.1 Image Preprocessing and Object Detection
3.2 Tracking
3.3 Feature Extraction and Representation
3.4 Clustering Based Anomaly Detection
4 Results and Discussion
4.1 Object Detection Performance
4.2 Accuracy Analysis
5 Conclusion
Acknowledgements
References
An Improved Genetic-Based Link Clustering for Overlapping Community Detection
Abstract
1 Introduction
2 Related Work
2.1 Objective Function
2.2 Objective Function
2.3 Genetic Operation
3 An Improved Genetic-Based Link Clustering for Overlapping Community Detection
3.1 Community Similarity
3.2 Belonging Coefficients
4 Experiments
4.1 Experimental Data Sets
4.2 Evaluation Criteria
4.3 Experimental Results
5 Conclusion
References
Opinion Targets Identification Based on Kernel Sentences Extraction and Candidates Selection
Abstract
1 Introduction
2 Related Work
3 Kernel Sentence Extraction
4 Candidate Opinion Targets Identification and Selection
5 Experiments and Analysis
6 Conclusions and Future Work
References
Semantic Web and Text Processing
A Study of URI Spotting for Question Answering over Linked Data (QALD)
Abstract
1 Introduction
2 URI Spotting for QALD
2.1 Spotting
2.2 Candidate Extension
2.3 URI Selection
3 Conclusion and Future Work
Acknowledgements
References
Short Text Feature Extension Based on Improved Frequent Term Sets
Abstract
1 Introduction
2 Frequent Term Sets Extraction
3 Word Similarity Matrix Construction
3.1 Improved Term Weighing Scheme
3.2 Word Similarity Calculation
4 Short Text Feature Extension Based on Semantic Similarity Matrix
5 Experiments
5.1 Data Set
5.2 Experiment Results
6 Conclusion
Acknowledgement
References
Research on Domain Ontology Generation Based on Semantic Web
Abstract
1 Introduction
2 Semantic Web and Ontology
2.1 What Is Semantic Web
2.2 What Is Ontology
3 Extraction Domain Concepts
3.1 Corpus Preprocessing
3.2 Algorithm of Extracting Domain Concepts
4 Acquisition of Classification Relationship
5 Ontology Construction
6 Conclusion
References
Towards Discovering Covert Communication Through Email Spam
Abstract
1 Introduction
2 Email Spam Characteristics
2.1 Email Spam Characteristics
2.2 Covert Communication Methods
3 Methodology
3.1 TF-IDF
3.2 Latent Dirichlet Allocation
3.3 Text Visualization Techniques
3.4 Our Method
4 Experiments
4.1 Dataset Description
4.2 Extracting URL Links
4.3 Identifying Clues by LDA
4.4 Text Visualization
5 Conclusions
References
Image Understanding
Combining Statistical Information and Semantic Similarity for Short Text Feature Extension
Abstract
1 Introduction
2 Preliminary Knowledge
3 Feature Extension Algorithm and Weight Computing
4 Experiment Results and Analysis
5 Conclusion
Acknowledgments
References
Automatic Image Annotation Based on Semi-supervised Probabilistic CCA
1 Introduction
2 Framework
2.1 Probabilistic Canonical Correlation Analysis
2.2 Semi-supervised PCCA
2.3 Projections in SemiPCCA Model
3 Annotation on Unlabelled Image
4 Experiments
4.1 Feature Representation
4.2 Evaluation and Results
5 Conclusions
References
A Confidence Weighted Real-Time Depth Filter for 3D Reconstruction
1 Introduction
2 CWDF: Confidence Weighted Depth Filter
2.1 Confidence Estimation
2.2 Determination of the Filtered Region
2.3 Approximation of the CWDF
3 Experimental Results
3.1 Quality and Effectiveness Analysis
3.2 Quantitative Accuracy
4 Conclusion
References
Brain-Machine Collaboration
Noisy Control About Discrete Liner Consensus Protocol
Abstract
1 Introduction
2 Preliminaries
3 Noise Uncontrollability of Discrete Linear Consistency Protocol
4 Noise Suppression Discrete Linear Consensus Protocol(NS-DLCP)
5 Conclusion
Acknowledgments
References
Incomplete Multi-view Clustering
1 Introduction
2 Preliminary
2.1 Spectral Clustering
2.2 Kernel Alignment
3 Proposed Methods
3.1 Model Description
3.2 Model Training
4 Experiment
4.1 Comparison Methods
4.2 Datasets
4.3 Results
5 Conclusion
References
Brain-Machine Collaboration for Cyborg Intelligence
Abstract
1 Introduction
2 Cyborg Intelligence
3 Environment Awareness
4 Motivation Driven Collaboration
4.1 Needs Based Motivation
4.2 Curiosity Based Motivation
4.3 Motivation Execution
4.4 Collaboration
5 Conclusions
Acknowledgements
References
A Cyclic Cascaded CRFs Model for Opinion Targets Identification Based on Rules and Statistics
Abstract
1 Introduction
2 Related Work
3 Candidate Opinion Targets Identification Based on Rules
4 CCCRFs: A Cyclic Cascaded CRFs Model for Opinion Targets Identification
4.1 Cascaded CRFs Model
4.2 Cyclic Model
4.3 Feature Selection
5 Experiments and Analysis
5.1 Dataset
5.2 Parameter Selection
5.3 Comparing Results with Different Methods of Opinion Targets Identification
6 Conclusions and Future Work
References
Author Index

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Intelligent Information Processing VIII

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