Abawajy / Choo / Islam International Conference on Applications and Techniques in Cyber Security and Intelligence

Applications and Techniques in Cyber Security and Intelligence
1. Auflage 2018
ISBN: 978-3-319-67071-3
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 580, 534 Seiten

Reihe: Advances in Intelligent Systems and Computing

This book presents the outcomes of the 2017 International Conference on Applications and Techniques in Cyber Security and Intelligence, which focused on all aspects of techniques and applications in cyber and electronic security and intelligence research. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings, and novel techniques, methods and applications on all aspects of cyber and electronic security and intelligence.

Autoren/Hrsg.

Abawajy, Jemal

Choo, Kim-Kwang Raymond

Islam, Rafiqul

Inhaltsverzeichnis

1;Foreword;6
2;Organization;8
2.1;Committee;8
2.2;General Chairs;8
2.3;Program Chairs;8
2.4;Publication Chairs;8
2.5;Publicity Chairs;8
2.6;Local Organizing Chairs;9
2.7;Program Committee Members;9
2.8;External Reviewers;10
3;Contents;11
4;The Fast Lane Detection of Road Using RANSAC Algorithm;16
4.1;Abstract;16
4.2;1 Introduction;16
4.3;2 Related Work;17
4.4;3 Candidate Lane Detection;17
4.5;4 Line and Curve Fitting;19
4.6;5 Experiment Results;20
4.7;6 Conclusions;21
4.8;Acknowledgment;21
4.9;References;21
5;Face Detection and Description Based on Video Structural Description Technologies;23
5.1;Abstract;23
5.2;1 Introduction;23
5.3;2 Related Work;24
5.4;3 Face Detection Methods;24
5.5;4 Identity Verification System;26
5.6;5 Conclusions;27
5.7;Acknowledgment;27
5.8;References;27
6;Cloud Based Image Retrieval Scheme Using Feature Vector;28
6.1;Abstract;28
6.2;1 Introduction;28
6.3;2 Preliminaries;29
6.4;3 Retrieval Model;29
6.5;4 Conclusions;30
6.6;Acknowledgment;30
6.7;References;31
7;Research on Security Outsourcing Privacy in Cloud Environments;32
7.1;Abstract;32
7.2;1 Introduction;32
7.3;2 Related Work;33
7.4;3 Cloud Data Encryption;34
7.5;4 Conclusions;34
7.6;Acknowledgment;34
7.7;References;35
8;MapReduce-Based Approach to Find Accompany Vehicle in Traffic Data;36
8.1;Abstract;36
8.2;1 Introduction;36
8.3;2 Related Work;37
8.3.1;2.1 MapReduce and Hadoop;37
8.3.2;2.2 Accompany Vehicle Discovery;38
8.4;3 Preliminaries and Problem Statement;38
8.5;4 MapReduce-Based Approach;39
8.5.1;4.1 Computing the Possible Accompany Vehicle at MapReduce1st;39
8.5.2;4.2 Computing the Total Numbers of Accompany Vehicle at MapReduce2st;40
8.6;5 Conclusions;41
8.7;Acknowledgment;41
8.8;References;42
9;Research on the Architecture of Road Traffic Accident Analysis Platform Based on Big Data;43
9.1;Abstract;43
9.2;1 Introduction;43
9.3;2 Road Traffic Accidents Platform Architecture Based on the Technology of Big Data;44
9.4;3 Key Techniques of Accidents Platform;44
9.4.1;3.1 Data Flow and Processing;44
9.4.2;3.2 Platform Security;46
9.4.3;3.3 Accident Big Data Analysis;48
9.5;4 Conclusion;48
9.6;Acknowledgment;48
9.7;References;49
10;Operating the Public Information Platform for Logistics with Internet Thinking;50
10.1;Abstract;50
10.2;1 Significance of Development of the Public Information Platform for Logistics;51
10.2.1;1.1 Improving Efficiency of Operation and Quality of Service for the Logistics Companies;51
10.2.2;1.2 Improving the Level of the Government’s Management on the Logistics Industry;52
10.2.3;1.3 Promoting Information Standardization in the Road Logistics Industry;52
10.3;2 Analysis of Problems in Development of the Public Information Platform for Logistics;52
10.4;3 Operating the Public Information Platform for Logistics with Internet Thinking;53
10.4.1;3.1 Profit Mode of the Public Information Platform for Logistics - Specialized Operation and Diversi ...;54
10.4.2;3.2 Design of User Experience on the Public Information Platform for Logistics;54
10.4.2.1;3.2.1 The Platform Name Should Be Simple and Easy to Remember;55
10.4.2.2;3.2.2 The Operation Procedure Needs to Simplified as Much as Possible;55
10.4.2.3;3.2.3 The Platform Interface Elements Can Be Recognized;55
10.5;4 Summary;55
10.6;References;56
11;Deep Neural Network with Limited Numerical Precision;57
11.1;Abstract;57
11.2;1 Introduction;57
11.3;2 Related Works;58
11.4;3 Limited Precision Arithmetic;58
11.5;4 Rounding Mode;59
11.6;5 Training Deep Network;60
11.7;6 Performance Evaluation;61
11.7.1;6.1 Fixed-Point Result;61
11.7.2;6.2 Dynamic Fixed-Point Result;62
11.8;7 Conclusion and Future Work;63
11.9;Acknowledgements;63
12;Optimization Technology of CNN Based on DSP;66
12.1;Abstract;66
12.2;1 Introduction;66
12.3;2 Convolution Neural Network;68
12.4;3 Optimization of CNN on C66x DSP;69
12.4.1;3.1 Convolution Layer;70
12.4.2;3.2 Non-linear layer;71
12.4.3;3.3 Maxpool Layer;71
12.4.4;3.4 Fully Connected Layer;72
12.4.5;3.5 Softmax Layer;72
12.5;4 Assessment and Results;73
12.6;5 Conclusion;73
12.7;Acknowledgements;74
12.8;References;74
13;Fuzzy Keyword Search Based on Comparable Encryption;75
13.1;Abstract;75
13.2;1 Introduction;75
13.2.1;1.1 Organization;76
13.3;2 Related Work;76
13.4;3 Fuzzy Keyword Search Scheme;77
13.5;4 Security Analysis;79
13.6;5 Conclusion;80
13.7;Acknowledgement;80
13.8;References;80
14;Risk Evaluation of Financial Websites Based on Structure Mining;82
14.1;Abstract;82
14.2;1 Introduction;82
14.3;2 Structure Mining Method;83
14.4;3 Evaluation and Experiments;85
14.5;4 Conclusion;86
14.6;Acknowledgement;87
14.7;References;87
15;Word Vector Computation Based on Implicit Expression;88
15.1;Abstract;88
15.2;1 Introduction;88
15.3;2 Implicit Expression of Text;89
15.4;3 Word Vector Learning Based on Implicit Expression;89
15.5;4 Experiment;91
15.6;5 Conclusion;93
15.7;Acknowledgement;93
15.8;References;93
16;Security Homomorphic Encryption Scheme Over the MSB in Cloud;95
16.1;Abstract;95
16.2;1 Introduction;95
16.3;2 Related Work;96
16.4;3 Preliminaries;96
16.5;4 Our Proposal;97
16.5.1;4.1 Analysis of MSB in Remote Sensing;97
16.5.2;4.2 Supporting of Computation in Ciphertext Remote Sensing;97
16.6;5 Comparison;100
16.7;6 Security Analysis;102
16.8;7 Conclusion;103
16.9;Acknowledgments;103
16.10;References;103
17;Research on Performance Optimization of Several Frequently-Used Genetic Algorithm Selection Operators;105
17.1;Abstract;105
17.2;1 Introduction;105
17.3;2 Proportional Selection;106
17.4;3 Save the Best Individual Strategy;107
17.5;4 Deterministic Sampling Selection;109
17.6;5 Application;111
17.7;References;112
18;A Novel Representation of Academic Field Knowledge;113
18.1;Abstract;113
18.2;1 Introduction;113
18.3;2 Related Work;114
18.4;3 Representation of Academic Knowledge;115
18.4.1;3.1 Acquiring Concepts and Relationship Between Concepts;115
18.4.2;3.2 Evaluating Domain Energy of Concept;116
18.4.3;3.3 Hierarchical Division of Concepts;117
18.5;4 Experimental Analysis;119
18.6;5 Conclusions;121
18.7;Acknowledgements;121
18.8;References;121
19;Textual Keyword Optimization Using Priori Knowledge;123
19.1;Abstract;123
19.2;1 Introduction;123
19.3;2 Priori Knowledge for Keyword Extraction;124
19.4;3 Optimizing the Textual Keyword Extraction Algorithm;125
19.4.1;3.1 Semantic Distance Between Keywords;125
19.4.2;3.2 Keyword Quality Evaluation Method;125
19.4.3;3.3 Keyword Extraction Optimization;126
19.5;4 Experiments;126
19.5.1;4.1 Dataset;126
19.5.2;4.2 Experiment on Keyword Quality;127
19.5.3;4.3 Experiment on Priori Knowledge Guided Keyword Extraction;128
19.6;5 Conclusions;129
19.7;Acknowledgments;129
19.8;References;129
20;A Speed Estimation Method of Vehicles Based on Road Monitoring Video-Images;131
20.1;Abstract;131
20.2;1 Introduction;131
20.3;2 Preliminaries;132
20.4;3 Camera Calibration Based on the License Plate;132
20.5;4 Vehicle Speed Estimation Algorithm;133
20.6;5 Experiments;134
20.7;6 Conclusion;135
20.8;Acknowledgement;136
20.9;References;136
21;Document Security Identification Based on Multi-classifier;137
21.1;Abstract;137
21.2;1 Introduction;137
21.3;2 System Architecture;138
21.4;3 Experience and Result;139
21.4.1;3.1 Dataset Preprocess;139
21.4.2;3.2 Feature Extraction;140
21.4.3;3.3 Security Feature Selection and Dimensionality Reduction;140
21.4.4;3.4 Model Combination and Evaluation Result;141
21.5;4 Related Work and Conclusion;141
21.6;References;142
22;Collaborative Filtering-Based Matching and Recommendation of Suppliers in Prefabricated Component Su ...;143
22.1;Abstract;143
22.2;1 Introduction;143
22.2.1;1.1 Research Background;143
22.2.2;1.2 Literature Review;144
22.3;2 Supplier Selection in B2B Collaborative Platform of Prefabricated Components;145
22.3.1;2.1 B2B Collaboration Platform;145
22.3.2;2.2 Supplier Selection Problem;145
22.4;3 Collaborative Filtering-Based Supplier Personalized Recommendation;146
22.4.1;3.1 Experimental Design of Collaborative Filtering-Based Supplier Personalized Recommendation;146
22.4.2;3.2 The Sources of Experimental Data;147
22.4.3;3.3 Experimental Process;148
22.4.4;3.4 The Realization of the Experiment;150
22.4.5;3.5 Analysis of the Results of the Experiment;152
22.5;4 Summary;153
22.6;Acknowledgement;153
22.7;References;153
23;A Robust Facial Descriptor for Face Recognition;155
23.1;Abstract;155
23.2;1 Introduction;155
23.3;2 Proposed Robust Facial Descriptor;156
23.3.1;2.1 Illumination Normalization;156
23.3.2;2.2 Proposed Robust Facial Descriptor;157
23.3.3;2.3 Characteristics of RFD;158
23.4;3 Experiments Results;159
23.5;4 Conclusions;159
23.6;Acknowledgement;160
23.7;References;160
24;Multiple-Step Model Training for Face Recognition;161
24.1;Abstract;161
24.2;1 Introduction;161
24.3;2 Method;162
24.3.1;2.1 Network Architecture;163
24.3.2;2.2 Data Preparation;163
24.3.3;2.3 Implementation Details;165
24.4;3 Experiments;166
24.5;4 Conclusion;167
24.6;Acknowledgements;167
24.7;References;167
25;Public Security Big Data Processing Support Technology;169
25.1;Abstract;169
25.2;1 Large Data Acquisition and Preprocessing;169
25.3;2 Big Data Storage and Management;171
25.4;3 Large Data Computing Model and System;171
25.5;4 Big Data Analysis and Mining;173
25.6;5 Large Data Visualization;174
25.7;6 Big Data Security;175
25.8;References;175
26;A Survey on Risks of Big Data Privacy;176
26.1;Abstract;176
26.2;1 Introduction;176
26.3;2 Characteristics and Categories of Big Data Privacy;177
26.4;3 Big Data Privacy Risks;178
26.4.1;3.1 Risks of Data Collection;178
26.4.2;3.2 Risks of Data Integration and Fusion;179
26.4.3;3.3 Risks of Data Analysis;179
26.5;4 Goals and Solutions;180
26.6;5 Conclusion and Future Work;181
26.7;Acknowledgements;181
26.8;References;181
27;A Vehicle Model Data Classification Algorithm Based on Hierarchy Clustering;183
27.1;Abstract;183
27.2;1 Introduction;183
27.3;2 System Framework;184
27.4;3 Module Analysis;186
27.4.1;3.1 Feature Extraction and Distance Sorting Module;186
27.4.2;3.2 Test Result Statistics Module;187
27.4.3;3.3 Vehicle Model Merging Module;188
27.5;4 Experiment and Analysis;189
27.6;5 Conclusion;190
27.7;Acknowledgement;190
27.8;References;190
28;Research on Collaborative Innovation Between Smart Companies Based on the Industry 4.0 Standard;192
28.1;Abstract;192
28.2;1 Introduction;192
28.3;2 Status Quo of Research on Smart Companies at Home and Abroad as Well as Development Trend;193
28.3.1;2.1 Status Quo of Research Abroad;193
28.3.2;2.2 Status Quo of Research at Home;193
28.4;3 Architecture of the Collaborative Innovation System for Smart Companies;195
28.4.1;3.1 Characteristics of a Smart Company;195
28.4.2;3.2 Four Plans of Collaborative Innovation Between Smart Companies;196
28.5;4 Analysis of Application Cases of the Collaborative Innovation System for Smart Companies;197
28.6;5 Summary;198
28.7;References;199
29;The Development Trend Prediction of the Internet of Things Industry in China;200
29.1;Abstract;200
29.2;1 The Basic Principles of the Grey Forecasting Model;200
29.3;2 Establishing a Grey Forecasting Model;203
29.4;3 The Inspection of the Model;205
29.5;4 Conclusion and Suggestions;206
29.6;References;207
30;Publicly Verifiable Secret Sharing Scheme in Hierarchical Settings Using CLSC over IBC;209
30.1;1 Introduction;209
30.1.1;1.1 Motivation of Our Work;210
30.1.2;1.2 Problem Definition;210
30.2;2 Related Works;210
30.2.1;2.1 Review of Literature Related to Certificateless Signcryption;210
30.2.2;2.2 Review of Secret Sharing Schemes;211
30.2.3;2.3 Review of Hierarchical Secret Sharing Schemes;212
30.3;3 Preliminaries;212
30.3.1;3.1 Bilinear Pairing (BLP) Map;213
30.3.2;3.2 Review of an Efficient Certificateless Signcryption Scheme;213
30.4;4 The Proposed Scheme;214
30.4.1;4.1 Setup Phase;215
30.4.2;4.2 Share Generation;215
30.4.3;4.3 Shares Distribution;216
30.4.4;4.4 Decryption and Validation of the Shares;216
30.4.5;4.5 Dealer Deletes Secret (Shares) and Non Escrow;216
30.4.6;4.6 Regeneration of the Shares;216
30.4.7;4.7 Transmission and Receipt of Lower Level Shares;217
30.4.8;4.8 Reconstruction of the Secret;217
30.5;5 Analysis of Security and Performance of Our Scheme;218
30.5.1;5.1 Security of Our IBC-CLSC Based Hierarchical PVSS;218
30.5.2;5.2 Comparative Study of Efficiency;218
30.6;6 Conclusion;219
30.7;7 Future Work;219
30.8;References;219
31;A New Multidimensional and Fault-Tolerable Data Aggregation Scheme for Privacy-Preserving Smart Grid Communications;221
31.1;1 Introduction;221
31.2;2 Related Work;222
31.3;3 Preliminaries;223
31.3.1;3.1 System Structure;223
31.3.2;3.2 Security Model;224
31.3.3;3.3 Basic Mathematical Knowledge;224
31.4;4 Proposed Scheme;225
31.4.1;4.1 System Initialization;225
31.4.2;4.2 Key Generation;225
31.4.3;4.3 User Report Generation;226
31.4.4;4.4 Multidimensional Report Aggregation;227
31.4.5;4.5 Secure Report Reading;228
31.4.6;4.6 Fault Tolerance;229
31.5;5 Security Analysis;230
31.6;6 Computation Overhead and Communication Cost;231
31.6.1;6.1 Computation Overhead;231
31.6.2;6.2 Communication Cost;231
31.7;7 Conclusion;233
31.8;References;233
32;Discovering Trends for the Development of Novel Authentication Applications for Dementia Patients;235
32.1;Abstract;235
32.2;1 Introduction;235
32.3;2 Overview of the Authentication Methods;238
32.3.1;2.1 Test-Based Passwords;238
32.3.2;2.2 Test-Based Passwords;239
32.3.3;2.3 Biometric-Based Passwords;239
32.4;3 Text-Based Authentication;239
32.4.1;3.1 Advantages;239
32.4.2;3.2 Vulnerabilities;240
32.5;4 Graphical User Authentication;241
32.5.1;4.1 Categories of Graphical User Authentication;242
32.5.2;4.2 Advantages of the Graphical User Authentication;245
32.5.3;4.3 Vulnerabilities of the Graphical User Authentication;245
32.6;5 Usability of Different Graphical User Authentication Methods;247
32.7;6 Concluding Remarks and Future Work;248
32.8;References;249
33;A Novel Swarm Intelligence Based Sequence Generator;253
33.1;Abstract;253
33.2;1 Introduction;253
33.3;2 Literature Review;255
33.4;3 Description of SISEQ;256
33.5;4 Evaluation of SISEQ;259
33.6;5 Conclusion;260
33.7;References;260
34;A Novel Swarm Intelligence Based Strategy to Generate Optimum Test Data in T-Way Testing;262
34.1;Abstract;262
34.2;1 Introduction;262
34.3;2 mSITG Design;264
34.4;3 Evaluation;267
34.5;References;268
35;Alignment-Free Fingerprint Template Protection Technique Based on Minutiae Neighbourhood Information;271
35.1;Abstract;271
35.2;1 Introduction;271
35.3;2 Related Work;273
35.4;3 Proposed Method;274
35.5;4 Experimental Result and Analysis;277
35.5.1;4.1 Evaluation Criteria and Performance;277
35.5.2;4.2 Security Analysis;278
35.5.3;4.3 Analysis on Time Requirement and Complexity;279
35.6;5 Conclusion;280
35.7;References;280
36;Malware Analysis and Detection Using Data Mining and Machine Learning Classification;281
36.1;Abstract;281
36.2;1 Introduction;281
36.3;2 Proposed System Architecture;282
36.3.1;2.1 Pre-processing;283
36.3.2;2.2 Feature Extraction;283
36.3.2.1;2.2.1 N-Gram Features;283
36.3.2.2;2.2.2 Windows API Calls;283
36.3.3;2.3 Feature Selection/Refinement;284
36.3.4;2.4 Malware Classification and Detection;284
36.4;3 Experimental Results and Discussion;285
36.4.1;3.1 Data Set;285
36.4.2;3.2 Experimental Evaluation;285
36.5;4 Conclusion;288
36.6;References;289
37;Abnormal Event Detection Based on in Vehicle Monitoring System;290
37.1;Abstract;290
37.2;1 Introduction;290
37.3;2 System Description;291
37.3.1;2.1 Traffic Line Extraction and Recognition;291
37.3.2;2.2 Object Detection and Tracking;292
37.3.3;2.3 Traffic Markings Detection;293
37.3.4;2.4 Abnormal Events Discovery;293
37.4;3 Conclusion;294
37.5;Acknowledgement;294
37.6;References;295
38;A Novel Algorithm to Protect Code Injection Attacks;296
38.1;Abstract;296
38.2;1 Introduction;296
38.2.1;1.1 Problem Statement;297
38.2.2;1.2 XSS Attacks Methods;297
38.2.3;1.3 XSS Encoding Techniques;297
38.2.4;1.4 SQL Injection Methods;300
38.3;2 Literature Review;301
38.4;3 The Proposed Model;302
38.5;4 Experimental Setup;302
38.5.1;4.1 Proposed Algorithm;303
38.5.2;4.2 Algorithm Evaluation;305
38.6;5 Comparison with Other Approaches;306
38.7;6 Conclusion;306
38.8;6 Conclusion;306
38.9;References;307
39;Attacking Crypto-1 Cipher Based on Parallel Computing Using GPU;308
39.1;Abstract;308
39.2;1 Introduction;308
39.3;2 Attack to Ctypto-1 Cipher;309
39.3.1;2.1 Crypto-1 Cipher;310
39.3.2;2.2 MFKeys;312
39.4;3 Parallel Algorithm on GPU;313
39.4.1;3.1 Independent Half-State Processing;313
39.4.2;3.2 Static Number of Operated Objects;314
39.4.3;3.3 Pseudo Code;314
39.5;4 Experiments;316
39.6;5 Conclusion;317
39.7;Acknowledgement;317
39.8;References;317
40;A Conceptual Framework of Personally Controlled Electronic Health Record (PCEHR) System to Enhance S ...;319
40.1;Abstract;319
40.2;1 Introduction;319
40.3;2 Related Works;321
40.3.1;2.1 Privacy by Access Control;321
40.3.2;2.2 Privacy by Cryptographic Approach;321
40.4;3 The Overall Proposed Model;322
40.4.1;3.1 Architecture of the Proposed Model;323
40.4.2;3.2 The Proposed Authentication Method;325
40.4.3;3.3 The Proposed Access Control Model;326
40.5;4 Conclusion and Future Work;327
40.6;References;328
41;Frequency Switch, Secret Sharing and Recursive Use of Hash Functions Secure (Low Cost) Ad Hoc Networks;330
41.1;1 Introduction;330
41.1.1;1.1 Sensor Architecture;331
41.1.2;1.2 Network Topology and Communication;331
41.1.3;1.3 Applications and Tasks: Security Requirements;331
41.2;2 Related Works with Motivation of Our Proposal;331
41.2.1;2.1 Works on Adaptation of SKC to Secure WSN: Limitations;332
41.2.2;2.2 Frequency Regulation in WSN Security;333
41.3;3 Threat Model;333
41.3.1;3.1 Resiliency Metric fail(s);334
41.4;4 Basic Concepts and Definitions;334
41.4.1;4.1 Review of Combinatorial Design;334
41.4.2;4.2 Generic Lightweight Resiliency Improvement Techniques;335
41.4.3;4.3 Necessary Cryptographic Function/Concepts (not Cryptosystem);336
41.5;5 Frequency Predistribution: Nodes with Volatile Memory;337
41.6;6 Frequency Regulation Secures Conversations;337
41.7;7 Analysis and Comparative Performance of Our System;338
41.8;8 Conclusion;341
41.9;9 Future Research Directions;341
41.10;References;341
42;An Enhanced Anonymous Identification Scheme for Smart Grids;344
42.1;Abstract;344
42.2;1 Introduction;344
42.3;2 Related Work;345
42.4;3 System Model and Security Requirements;346
42.4.1;3.1 System Model;346
42.4.2;3.2 Security Requirements;347
42.5;4 Revisiting Sui et al.’s TAI Scheme;347
42.6;5 Proposed EAI Scheme;348
42.6.1;5.1 Setup;348
42.6.2;5.2 Joining;348
42.6.3;5.3 Report Generation;348
42.6.4;5.4 Report Reading;349
42.6.5;5.5 Instruction Generation;349
42.6.6;5.6 Identification;349
42.7;6 Security Analysis;350
42.8;7 Conclusion;351
42.9;Acknowledgment;351
42.10;References;352
43;Shellshock Vulnerability Exploitation and Mitigation: A Demonstration;353
43.1;Abstract;353
43.2;1 Introduction;353
43.3;2 Understanding the Vulnerability;354
43.4;3 Real-World Consequences;357
43.5;4 Exploit Demonstration;358
43.5.1;4.1 Setting up the Victim’s Machine;359
43.5.2;4.2 Information Gathering;359
43.5.3;4.3 Payload-Bind Shell;360
43.5.4;4.4 Payload-Reverse Shell;361
43.6;5 Potential Countermeasures;362
43.7;6 Concluding Remarks;362
43.8;References;363
44;Research on Web Table Positioning Technology Based on Table Structure and Heuristic Rules;366
44.1;Abstract;366
44.2;1 Introduction;366
44.3;2 Web Table Positioning;368
44.3.1;2.1 Two Problems Need to be Solved;368
44.3.2;2.2 Tree;371
44.4;3 Experimental Results and Analysis;373
44.5;4 Conclusion;374
44.6;Acknowledgements;374
44.7;References;374
45;Research on Data Security of Public Security Big Data Platform;376
45.1;Abstract;376
45.2;1 Introduction;376
45.3;2 Data Security Issues;377
45.3.1;2.1 Data Aggregation;377
45.3.2;2.2 Data Management;377
45.3.3;2.3 Data Service;378
45.4;3 Data Security Analysis and Prototyping;378
45.4.1;3.1 Data Storage Security;378
45.4.2;3.2 Data Management Security;379
45.4.3;3.3 Data Service Security;380
45.4.4;3.4 Disaster Recovery and Emergency Response;382
45.5;4 Conclusion;383
45.6;Acknowledgement;384
45.7;References;384
46;Deployment and Management of Tenant Network in Cloud Computing Platform of Openstack;385
46.1;Abstract;385
46.2;1 Introduction;385
46.3;2 The Architecture of Openstack;386
46.4;3 Openstack Virtual Network Infrastructure;388
46.5;4 Our Experimental Deployment and Management of Openstack Neutron Network;389
46.6;5 Conclusions and Future Work;393
46.7;Acknowledgements;393
46.8;References;394
47;The Extraction Method for Best Match of Food Nutrition;395
47.1;Abstract;395
47.2;1 Introduction;395
47.3;2 The Word Segmentation of Food Collocation;396
47.3.1;2.1 The Preparation Before Chinese Word Segmentation;396
47.3.2;2.2 The Word Segmentation of Forward Maximum Matching;397
47.4;3 The Analysis of Food Collocation;398
47.4.1;3.1 The Extraction of Food Collocation;398
47.4.2;3.2 The Classification of Food Collocation;398
47.5;4 Experiments;399
47.6;5 Conclusions;401
47.7;Acknowledgements;401
47.8;References;401
48;Extraction Method of Micro-Blog New Login Word Based on Improved Position-Word Probability;403
48.1;Abstract;403
48.2;1 Introduction;403
48.3;2 Basic Work;404
48.3.1;2.1 N-increment Algorithm;404
48.3.2;2.2 Improved Position-Word Probability;404
48.4;3 Extraction Method Based on Improved PWP;405
48.4.1;3.1 Micro-Blog Text Pre-treatment;405
48.4.2;3.2 New Login Word Set Pruning;405
48.4.3;3.3 Algorithm Procedure;406
48.5;4 Experiments;407
48.6;5 Conclusions;408
48.7;Acknowledgement;408
48.8;References;408
49;Building the Knowledge Flow of Micro-Blog Topic;409
49.1;Abstract;409
49.2;1 Introduction;409
49.3;2 Basic Concepts;410
49.4;3 The Knowledge Flow of Micro-Blog Topic;412
49.5;4 Experiments;413
49.6;5 Conclusions;414
49.7;Acknowledgements;414
49.8;References;415
50;A Parallel Algorithm of Mining Frequent Pattern on Uncertain Data Streams;416
50.1;Abstract;416
50.2;1 Introduction;416
50.3;2 Problem Definitions;418
50.4;3 The Algorithm MFPUS-MR;418
50.4.1;3.1 Generation of the Candidate Itemsets;419
50.4.2;3.2 Processing of the Candidate Itemsets;419
50.5;4 Experiment Results;420
50.5.1;4.1 Comparison Between the Operation Time Restricted by Different Minimum Expected Threshold Values;420
50.5.2;4.2 Speedup Comparison;421
50.6;5 Conclusion;422
50.7;References;422
51;Research on Rolling Planning of Distribution Network Based on Big Data Analysis;424
51.1;Abstract;424
51.2;1 Introduction;424
51.3;2 Analysis of the Rolling Planning of the Distribution Network;425
51.3.1;2.1 Geographical Overview;425
51.3.2;2.2 Social and Economic Overview;425
51.3.3;2.3 Planning Year and Analysis of Basis for Planning;426
51.3.4;2.4 Analysis on Demand for the Rolling Planning of Distribution Network;426
51.4;3 Analysis of the Current State of the Distribution Network;426
51.4.1;3.1 Current State of the Supply Area;426
51.4.2;3.2 Current State of High-Voltage Distribution Network;427
51.4.3;3.3 Current State of Medium-Voltage Distribution Network;427
51.4.4;3.4 Analysis of the Current State;428
51.5;4 Load Forecasting;428
51.5.1;4.1 Load Characteristic;428
51.5.2;4.2 Load Prediction;428
51.6;5 Rolling Planning of the Distribution Network;430
51.6.1;5.1 Objectives of Distribution Network Rolling Planning;430
51.6.2;5.2 Prospects of the 110 kV Distribution Network;431
51.7;6 Conclusions;432
51.8;References;432
52;Effect Analysis and Strategy Optimization of Endurance Training for Female College Students Based on ...;433
52.1;Abstract;433
52.2;1 Questions Raised;433
52.2.1;1.1 The Purpose Topics;433
52.2.2;1.2 Domestic and International Studies Are Reviewed;434
52.3;2 The Overall Design of the Study;434
52.3.1;2.1 Object of Study;434
52.3.2;2.2 Research Methods;435
52.3.2.1;2.2.1 Literature Method;435
52.3.2.2;2.2.2 Experimental Study;435
52.4;3 Results and Analysis;436
52.4.1;3.1 Comparative Analysis of Concentration and Relaxation of the Control Group and the Experimental G ...;436
52.4.2;3.2 Concentration and Relaxation Models of Control Group Established by Data Analysis Tool IBM® SPSS ...;438
52.4.3;3.3 Comparative Analysis of Exercise Effects of Experimental Group 1 and 2;438
52.5;4 Conclusion and Discussion;439
52.5.1;4.1 Conclusion;439
52.5.2;4.2 Discussion;440
52.6;References;440
53;Clustering XML Documents Using Frequent Edge-Sets;441
53.1;Abstract;441
53.2;1 Introduction;441
53.3;2 Clustering Algorithm;442
53.3.1;2.1 Constructing Disjoint Clusters;442
53.3.2;2.2 Merging Clusters;443
53.4;3 Experiments;444
53.4.1;3.1 Clustering Performance;445
53.4.2;3.2 Clustering Scalability;446
53.4.3;3.3 Cluster Description;447
53.5;4 Conclusion;448
53.6;References;448
54;Analytical Application of Hadoop-Based Collaborative Filtering Recommended Algorithm in Tea Sales System;449
54.1;Abstract;449
54.2;1 Introduction;449
54.2.1;1.1 A Subsection Sample;449
54.3;2 System Function Demand Analysis;450
54.3.1;2.1 Overall Demand Analysis of System;450
54.3.2;2.2 User Management Requirement Analysis;451
54.3.3;2.3 Product Management Requirement Analysis;451
54.3.4;2.4 Tea Gift Customization Demand Analysis;451
54.3.5;2.5 Activity Management Requirement Analysis;451
54.3.6;2.6 Order Management Requirement Analysis;451
54.4;3 System Function Analysis and Design;452
54.4.1;3.1 User Management Function Design;452
54.4.2;3.2 Product Management Function Design;452
54.4.3;3.3 Tea Gift Customization Function Design;452
54.4.4;3.4 Activity Management Function Design;452
54.4.5;3.5 Order Management Function Design;452
54.4.6;3.6 Database Design;453
54.5;4 Data Analysis;454
54.5.1;4.1 Data Analysis Tool – Hadoop;454
54.5.2;4.2 Algorithm Analysis;455
54.6;5 Conclusion;456
54.7;References;456
55;Semi-supervised Sparsity Preserving Projection for Face Recognition;457
55.1;Abstract;457
55.2;1 Introduction;457
55.3;2 Related Works;458
55.3.1;2.1 Sparse Representation;458
55.3.2;2.2 Sparsity Preserving Projection;458
55.4;3 Semi-supervised Sparsity Preserving Projection;459
55.5;4 Experiment;461
55.6;5 Conclusion;463
55.7;Acknowledgement;463
55.8;References;464
56;Animated Analysis of Comovement of Forex Pairs;465
56.1;Abstract;465
56.2;1 Introduction;465
56.3;2 Data Preparation and Preprocessing;466
56.3.1;2.1 Data Preparation;466
56.3.2;2.2 Data Cleansing;468
56.3.3;2.3 Correlation Matrix;468
56.4;3 Visualization of Currency Pair Comovement;470
56.5;4 Conclusion;471
56.6;Acknowledgement;472
56.7;References;472
57;The Study of WSN Node Localization Method Based on Back Propagation Neural Network;473
57.1;Abstract;473
57.2;1 Introduction;473
57.3;2 Computing Method;474
57.4;3 BPNN-Based WSN Node Localization;475
57.4.1;3.1 BPNN;475
57.4.2;3.2 Back Propagation-Based Node Localization;476
57.5;4 Simulation Experiment;478
57.6;5 Conclusions;480
57.7;References;480
58;Research on the Application of Big Data in China’s Commodity Exchange Market;482
58.1;Abstract;482
58.2;1 The Present Situation and Main Problems of Big Data Application in China’s Commodity Exchange Market;482
58.2.1;1.1 The Present Situation of Big Data Application in China’s Commodity Exchange Market;482
58.2.2;1.2 The Main Problems of Big Data Application in China’s Commodity Exchange Market;483
58.3;2 The Necessity and Feasibility of Speeding Up the Application of Big Data in China’s Commodity Exch ...;484
58.3.1;2.1 The Necessity of Speeding Up the Application of Big Bata in China’s Commodity Exchange Market;484
58.3.2;2.2 The Feasibility of Speeding Up the Application of Big Bata in China’s Commodity Exchange Market;485
58.4;3 The Framework and Development Path of Big Bata Application System in China’s Commodity Exchange Market;486
58.4.1;3.1 The Framework of Big Bata Application System in China’s Commodity Exchange Market;486
58.4.2;3.2 The Choice of the Development Path of Big Bata Application in China’s Commodity Exchange Market;488
58.5;4 Summary and Expectation;488
58.6;References;489
59;Research and Implementation of Multi-objects Centroid Localization System Based on FPGA&DSP;490
59.1;Abstract;490
59.2;1 Introduction;490
59.3;2 Design of the System;491
59.4;3 The Algorithm;492
59.4.1;3.1 Histogram Statistics Algorithm;492
59.4.2;3.2 Adaptive Threshold Algorithm;493
59.4.3;3.3 Connectivity Domain Tagging Algorithm and Improvement;494
59.4.4;3.4 Centroid Algorithm;494
59.5;4 Conclusion;496
59.6;References;496
60;Smart City Security Based on the Biological Self-defense Mechanism;498
60.1;Abstract;498
60.2;1 Introduction;498
60.3;2 Background;499
60.4;3 BSDS Model;501
60.5;4 Technical Principles;503
60.6;5 Implement Solution;505
60.7;6 Conclusion;507
60.8;Acknowledgements;507
60.9;References;507
61;Induced Generalized Intuitionistic Fuzzy Aggregation Distance Operators and Their Application to Decision Making;508
61.1;Abstract;508
61.2;1 Introduction;508
61.3;2 Preliminaries;509
61.4;3 Induced Intuitionistic Fuzzy Generalized Aggregation Distance Operators;510
61.5;4 Illustrative Example;512
61.6;5 Conclusions;514
61.7;Acknowledgements;514
61.8;References;514
62;New 2-Tuple Linguistic Aggregation Distance Operator and Its Application to Information Systems Security Assessment;516
62.1;Abstract;516
62.2;1 Introduction;516
62.3;2 Preliminaries;517
62.4;3 The 2LOWAWAD Operator;518
62.5;4 Decision Making with the 2LOWAWAD Operator;520
62.6;5 Conclusions;522
62.7;Acknowledgement;522
62.8;References;522
63;Research and Analysis on the Search Algorithm Based on Artificial Intelligence About Chess Game;524
63.1;Abstract;524
63.2;1 Introduction;524
63.3;2 Application Directions of Search Algorithm Based on Artificial Intelligence;525
63.4;3 Design of Search Algorithm Based on Artificial Intelligence;526
63.4.1;3.1 Demand Analysis;526
63.4.2;3.2 Modules Design;527
63.4.3;3.3 Process Design;527
63.5;4 Implementation of Search Algorithm Based on Artificial Intelligence;528
63.5.1;4.1 Algorithm Principle;528
63.5.2;4.2 Functions in Search Algorithm;529
63.6;5 Conclusions;530
63.7;References;531
64;Author Index;533