Barolli / Zhang / Wang Advances in Internetworking, Data & Web Technologies

The 5th International Conference on Emerging Internetworking, Data & Web Technologies (EIDWT-2017)
1. Auflage 2018
ISBN: 978-3-319-59463-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 6, 806 Seiten, eBook

Reihe: Lecture Notes on Data Engineering and Communications Technologies

This book highlights the latest research findings, innovative research results, methods and development techniques, from both theoretical and practical perspectives, in the emerging areas of information networking, data and Web technologies. It gathers papers originally presented at the 5th International Conference on Emerging Internetworking, Data & Web Technologies (EIDWT-2017) held 10–11 June 2017 in Wuhan, China. The conference is dedicated to the dissemination of original contributions that are related to the theories, practices and concepts of emerging internetworking and data technologies – and most importantly, to how they can be applied in business and academia to achieve a collective intelligence approach.Information networking, data and Web technologies are currently undergoing a rapid evolution. As a result, they are now expected to manage increasing usage demand, provide support for a significant number of services, consistently deliver Quality of Service (QoS), and optimize network resources. Highlighting these aspects, the book discusses methods and practices that combine various internetworking and emerging data technologies to capture, integrate, analyze, mine, annotate, and visualize data, and make it available for various users and applications.

Zielgruppe

Research

Autoren/Hrsg.

Barolli, Leonard

Zhang, Mingwu

Wang, Xu An

Inhaltsverzeichnis

1;Welcome Message of EIDWT-2017 International Conference Organizers;6
1.1;EIDWT-2017 Steering Committee Chair;7
1.2;EIDWT-2017 General Co-chairs;7
1.3;EIDWT-2017 Program Committee Co-chairs;7
2;EIDWT-2017 International Conference Organizers;8
2.1;Honorary Co-chairs;8
2.2;General Co-chairs;8
2.3;Program Co-chairs;8
2.4;Workshops Co-chairs;8
2.5;International Advisory Committee;8
2.6;Publicity Co-chairs;9
2.7;International Liaison Co-chairs;9
2.8;Local Organizing Co-chairs;9
2.9;Web Administrators;9
2.10;Finance Chair;9
2.11;Steering Committee Chair;9
2.12;Track Area Co-chairs;10
2.13;1. Internetworking Issues and Challenges;10
2.14;2. Mobile and Wireless Networks;10
2.15;Chairs;10
2.16;3. Network Protocols, Modelling, Optimization and Performance Evaluation;10
2.17;Chairs;10
2.18;4. P2P and Grid Computing;10
2.19;Chairs;10
2.20;5. Distributed and Parallel Systems;10
2.21;Chairs;10
2.22;6. Ontologies and Metadata Representation;11
2.23;Chairs;11
2.24;7. Knowledge Discovery and Mining;11
2.25;Chairs;11
2.26;8. Databases and Data Warehouses;11
2.27;Chairs;11
2.28;9. Data Centers and IT Virtualization Technologies;11
2.29;Chairs;11
2.30;10. Web Science and Business Intelligence;11
2.31;Chairs;11
2.32;11. Data Analytics for Learning and Virtual Organisations;11
2.33;Chairs;11
2.34;12. Data Management and Information Retrieval;12
2.35;Chairs;12
2.36;13. Machine Learning on Large Data Sets & Massive Processing;12
2.37;Chairs;12
2.38;14. Data Modeling, Visualization and Representation Tools;12
2.39;Chairs;12
2.40;15. Nature Inspired Computing for Emerging Collective Intelligence;12
2.41;Chairs;12
2.42;16. Data Sensing, Integration and Querying Systems and Interfaces;12
2.43;Chairs;12
2.44;17. Data Security, Trust and Reputation;12
2.45;Chairs;12
2.46;18. eScience Data Sets, Repositories, Digital Infrastructures;13
2.47;Chairs;13
2.48;19. Energy-Aware and Green Computing in Data Centers;13
2.49;Chairs;13
2.50;20. Emerging Trends and Innovations in Inter-networking Data Technologies;13
2.51;Chairs;13
2.52;21. Bitcoin, Blockchain Techniques and Security;13
2.53;Chairs;13
2.54;Program Committee Members;13
2.55;EIDWT-2017 Reviewers;16
2.56;EIDWT-2017 Keynote Talks;17
2.57; A Security Management with Cyber Insurance—Event Study Approach with Social Network Sentimental Analysis for Cyber Risk Evaluation;18
2.58;Look Back! Earlier Versions will Reveal Weaknesses in Android Apps;19
2.59;Secret Sharing and Its Applications;20
2.60;New Short-Range Communication Technologies over Smartphones: Designs and Implementations;21
3;Contents;22
4;An Image Steganalysis Algorithm Based on Rotation Forest Transformation and Multiple Classifiers Ensemble;29
4.1;Abstract;29
4.2;1 Introduction;29
4.3;2 Basic Theory;30
4.3.1;2.1 Ensemble Classifiers;30
4.3.2;2.2 Rotation Forest;31
4.4;3 The Proposed Algorithm;32
4.4.1;3.1 Basic Classifier;33
4.4.2;3.2 Weight Assignment for Classifiers;34
4.4.3;3.3 Determination of dsub and L;35
4.5;4 Experiment Results;36
4.5.1;4.1 Experimental Environment;36
4.5.2;4.2 Comparison of Error Rate;36
4.5.3;4.3 Comparison of ROC Curve;37
4.5.4;4.4 Comparison of Training Time;38
4.6;5 Conclusion;39
4.7;Acknowledgments;39
4.8;References;39
5;Separable and Three-Dimensional Optical Reversible Data Hiding with Integral Imaging Cryptosystem;41
5.1;Abstract;41
5.2;1 Introduction;41
5.3;2 The Principle of the Proposed Scheme;42
5.4;3 Experimental Results and Discussions;45
5.5;4 Conclusion;47
5.6;References;48
6;Credit Risk Assessment of Peer-to-Peer Lending Borrower Utilizing BP Neural Network;50
6.1;Abstract;50
6.2;1 Introduction;50
6.3;2 BP Neutral Network;51
6.4;3 Application Flow Chart;52
6.5;4 Model Construction;53
6.5.1;4.1 Target Selection;53
6.5.2;4.2 Data Processing;54
6.6;5 Model Processing;56
6.6.1;5.1 Model Description;56
6.6.2;5.2 Model Simulation;56
6.7;6 Data Analysis;58
6.8;7 Conclusion;59
6.9;Acknowledgments;60
6.10;References;60
7;Implementation of a GA-based Simulation System for Placement of IoT Devices: Evaluation for a WSAN Scenario;62
7.1;1 Introduction;62
7.2;2 IoT and WSAN;63
7.2.1;2.1 Internet of Things (IoT);63
7.2.2;2.2 WSAN Architectures;64
7.2.3;2.3 Node Placement Problems and Their Applicability to WSANs;65
7.3;3 Overview of GA;66
7.4;4 Design and Implementation of IoT Device Placement Simulation System;66
7.5;5 Simulation Results;68
7.6;6 Conclusions;69
7.7;References;69
8;A Cryptographically Secure Scheme for Preserving Privacy in Association Rule Mining;71
8.1;Abstract;71
8.2;1 Introduction;71
8.3;2 Related Work;72
8.4;3 Preliminaries;72
8.5;4 Proposed Algorithm;73
8.6;5 Results;77
8.7;6 Conclusions and Future Work;80
8.8;References;80
9;A BGN Type Outsourcing the Decryption of CP-ABE Ciphertexts;82
9.1;Abstract;82
9.2;1 Introduction;82
9.3;2 Preliminares;83
9.3.1;2.1 Bilinear Map;83
9.3.2;2.2 Access Structures;83
9.3.3;2.3 Linear Secret Sharing Schemes;84
9.3.4;2.4 BGN Scheme;84
9.3.5;2.5 Outsourcing the Decryption of ABE Ciphertexts Model;85
9.4;3 Our Construction;86
9.5;4 Security;88
9.5.1;4.1 The Subgroup Decision Problem;88
9.5.2;4.2 Proof;89
9.6;5 Performance Analysis;89
9.7;6 Summary;90
9.8;References;90
10;Performance Evaluation of WMN-PSOHC and WMN-PSO Simulation Systems for Node Placement in Wireless Mesh Networks: A Comparison Study;92
10.1;1 Introduction;92
10.2;2 Node Placement Problem in WMNs;93
10.3;3 Proposed and Implemented Simulation System;94
10.3.1;3.1 PSO;94
10.3.2;3.2 HC Algorithm;96
10.3.3;3.3 Implemented Simulation Systems;97
10.4;4 Simulation Results;98
10.5;5 Conclusions;100
10.6;References;100
11;Effects of Number of Activities the Member Failures on Qualified Voting in P2P Mobile Collaborative Team: A Comparison Study for Two Fuzzy-Based Systems;103
11.1;1 Introduction;104
11.2;2 Scenarios of Collaborative Teamwork;105
11.2.1;2.1 Collaborative Teamwork and Virtual Campuses;105
11.2.2;2.2 Mobile Ad Hoc Networks (MANETs);106
11.3;3 Vote Weights;107
11.3.1;3.1 Votes with Embedded Weight;107
11.3.2;3.2 Voting Score;107
11.4;4 Application of Fuzzy Logic for Control;107
11.4.1;4.1 FC;108
11.4.2;4.2 Linguistic Variables;108
11.4.3;4.3 FC Rules;108
11.4.4;4.4 Control Knowledge Base;109
11.4.5;4.5 Defuzzification Methods;109
11.5;5 Proposed Fuzzy-Based Peer Voting Score System;109
11.6;6 Simulation Results;113
11.7;7 Conclusions and Future Work;115
11.8;References;115
12;A User Prediction and Identification System for Tor Networks Using ARIMA Model;117
12.1;1 Introduction;117
12.2;2 Deep Web and Tor Overview;119
12.2.1;2.1 Deep Web;119
12.2.2;2.2 Tor;120
12.3;3 ARIMA;120
12.4;4 The R Environment;121
12.5;5 Proposed Intrusion Detection Model for Tor Networks;122
12.6;6 Simulation Results;122
12.7;7 Conclusions;124
12.8;References;124
13;Implementation of an Actor Node for an Ambient Intelligence Testbed: Evaluation and Effects of Actor Node on Human Sleeping Condition;126
13.1;1 Introduction;126
13.2;2 Ambient Intelligence (AmI);127
13.3;3 The k-means Algorithm;128
13.4;4 Testbed Description;129
13.5;5 Experimental Results;131
13.6;6 Conclusions;132
13.7;References;132
14;Designing the Light Weight Rotation Boolean Permutation on Internet of Things;135
14.1;1 Introduction;135
14.2;2 Preliminaries;137
14.3;3 Rotation Linear Boolean Permutation;139
14.4;4 Rotation Nonlinear Boolean Permutation;139
14.4.1;4.1 The First Construction;139
14.4.2;4.2 The Second Construction;141
14.4.3;4.3 The Third Construction;143
14.5;5 Conclusions;146
14.6;References;146
15;The Construction Method of Clue Words Thesaurus in Chinese Patents Based on Iteration and Self-filtering;147
15.1;Abstract;147
15.2;1 Introduction;147
15.3;2 Related Work;148
15.4;3 Clue Words;148
15.5;4 Algorithm;149
15.5.1;4.1 Self-filtering;150
15.5.2;4.2 Locating Candidate Effect Statements;151
15.6;5 Experiments;152
15.6.1;5.1 Collection of Initial Clue Words;152
15.6.2;5.2 Iteration;152
15.7;6 Conclusion and Future Work;153
15.8;Acknowledgments;153
15.9;References;153
16;Numerical Simulation for the Nonlinear Elliptic Problem;154
16.1;1 Introduction;154
16.2;2 Notation, Weak Formulation and Approximation;155
16.3;3 Analysis of the Linearized Problem;157
16.4;4 Existence and Uniqueness;159
16.5;5 L2-Error Estimates;161
16.6;6 Numerical Examples;162
16.7;7 Conclusion;163
16.8;References;164
17;Encrypted Image-Based Reversible Data Hiding with Public Key Cryptography from Interpolation-Error Expansion;166
17.1;Abstract;166
17.2;1 Introduction;166
17.3;2 Preliminaries;168
17.3.1;2.1 Prediction Error Expansion;168
17.3.2;2.2 Paillier Encryption;168
17.4;3 The Proposed Algorithm;169
17.4.1;3.1 Preprocessing;169
17.4.2;3.2 Encryption and Embedding;171
17.4.3;3.3 Data Extraction and Image Recovery;172
17.5;4 Experimental Results;173
17.6;5 Conclusions;176
17.7;References;176
18;Reversible Image Data Hiding with Homomorphic Encryption and Contrast Enhancement;178
18.1;Abstract;178
18.2;1 Introduction;178
18.3;2 Image Encryption and Data Embedding;179
18.3.1;2.1 Preprocessing;180
18.3.2;2.2 Embedding in Plain Domain;180
18.3.3;2.3 Paillier-Based Image Encryption;180
18.3.4;2.4 Embedding in Encrypted Domain;181
18.4;3 Data Extraction and Image Recovery;181
18.4.1;3.1 Image Decryption;182
18.4.2;3.2 Data Extraction;182
18.4.3;3.3 Image Recovery;183
18.5;4 Experimental Results and Analysis;184
18.5.1;4.1 Feasibility;184
18.5.2;4.2 Visual Quality;184
18.5.3;4.3 Embedding Capacity;186
18.6;5 Conclusions;186
18.7;References;186
19;A Deep Network with Composite Residual Structure for Handwritten Character Recognition;188
19.1;Abstract;188
19.2;1 Introduction;188
19.3;2 Handwriting Recognition Framework Based on Deep Learning;189
19.4;3 Handwritten Character Recognition Algorithm Based on Composite Residual Structure;189
19.4.1;3.1 Data Preprocessing;190
19.4.2;3.2 Convolution Neural Network with Composite Residual Structure Kernel Structure;190
19.4.3;3.3 Composite Residual Structure Kernel Structure;191
19.5;4 Experimental Comparison;192
19.6;5 Conclusion;193
19.7;Acknowledgments;193
19.8;References;193
20;An Ensemble Hashing Framework for Fast Image Retrieval;195
20.1;Abstract;195
20.2;1 Introduction;195
20.3;2 Related Work;196
20.4;3 The Proposed Ensemble Hashing Framework;197
20.5;4 A Novel Weighted Bagging PCA-ITQ Method;198
20.5.1;4.1 PCA-ITQ;198
20.5.2;4.2 A Simple Weighted Method;199
20.5.3;4.3 Diverse Hash Tables Learning;200
20.6;5 Experiments;202
20.7;6 Conclusion;203
20.8;Acknowledgments;204
20.9;References;204
21;A Novel Construction and Design of Network Learning Platform in Cloud Computing Environment;206
21.1;Abstract;206
21.2;1 Introduction;206
21.3;2 Theoretical Foundation;207
21.3.1;2.1 Learning Theory;207
21.3.2;2.2 Object Analysis;207
21.3.3;2.3 Design Principles;208
21.4;3 Demand Analysis;208
21.4.1;3.1 Network Learning Environment in Cloud Computing;208
21.4.2;3.2 Application Analysis;208
21.4.3;3.3 Function Analysis;209
21.5;4 System Architecture;211
21.5.1;4.1 The Architecture of Our Proposed Platform;211
21.5.2;4.2 The Modular Construction of Our System in the Cloud;211
21.6;5 Conclusion;213
21.7;Acknowledgments;213
21.8;References;213
22;Automatic Kurdish Text Classification Using KDC 4007 Dataset;215
22.1;Abstract;215
22.2;1 Introduction;215
22.3;2 Literature Survey;216
22.4;3 Text Mining Functionalities;217
22.4.1;3.1 Naive Bayes Classifier;217
22.4.2;3.2 Decision Tree Classifier;218
22.4.3;3.3 Support Vector Machine Classifier;218
22.5;4 Methods and Materials;218
22.5.1;4.1 Kurdish Sorani Pre-processing Steps;219
22.5.2;4.2 Data Representation and Term Weighting;219
22.5.2.1;4.2.1 Boolean or Binary Weighting;220
22.5.2.2;4.2.2 Term Frequency (TF);220
22.5.2.3;4.2.3 Term Frequency Inverse Document Frequency (TF × IDF);220
22.6;5 Dataset, Experimentations, and Evaluation;220
22.6.1;5.1 Dataset;221
22.6.2;5.2 Experimentations;221
22.6.3;5.3 Evaluation;222
22.7;6 Results and Discussion;222
22.8;7 Conclusion;225
22.9;References;225
23;Outsourcing the Decryption of Ciphertexts for Predicate Encryption via Pallier Paradigm;227
23.1;Abstract;227
23.2;1 Introduction;227
23.3;2 Preliminares;228
23.3.1;2.1 Bilinear Map;228
23.3.2;2.2 Access Structures;228
23.3.3;2.3 Linear Secret Sharing Schemes;229
23.3.4;2.4 Paillier Scheme;229
23.3.5;2.5 Security Model for PE;230
23.3.6;2.6 Assumption;231
23.3.7;2.7 Paillier Type Outsourcing the Decryption of PE Ciphertexts Model;232
23.4;3 Our Construction;232
23.5;4 Security;235
23.6;5 Summary;237
23.7;References;238
24;A New Middleware Architecture for RFID Data Management;240
24.1;Abstract;240
24.2;1 Introduction;240
24.3;2 RFID Middleware Structure;242
24.4;3 Data Processing Module;244
24.4.1;3.1 Data Processing Module;244
24.4.2;3.2 Design of Redundancy Data Elimination;244
24.4.3;3.3 Design of Skipping Reading Data Process;246
24.4.4;3.4 Data Transferring Module;246
24.4.5;3.5 Other Modules’ Design;247
24.5;4 Conclusion;248
24.6;Acknowledgments;249
24.7;References;249
25;Multidimensional Zero-Correlation Linear Cryptanalysis on PRINCE;250
25.1;Abstract;250
25.2;1 Introduction;250
25.3;2 Backgrounds;251
25.3.1;2.1 The Encryption Process of PRINCE;251
25.3.2;2.2 Properties of the Matrix M;253
25.4;3 Multidimensional Zero-Correlation Linear Cryptanalysis;254
25.5;4 5-Round Zero Correlation Linear Approximations of PRINCE;255
25.6;5 9-Round Zero Correlation Linear Attack on PRINCE;257
25.7;6 Summary;259
25.8;Acknowledgments;259
25.9;References;259
26;Design and Implementation of Simulated DME/P Beaconing System Based on FPGA;261
26.1;Abstract;261
26.2;1 Introduction;261
26.3;2 Overall Scheme of Simulated DME/P Beaconing System;262
26.3.1;2.1 Host Computer;262
26.3.2;2.2 RF Front-End;263
26.3.3;2.3 Signal Processing Unit;263
26.3.3.1;2.3.1 RF Receiving;263
26.3.3.2;2.3.2 RF Transmission;263
26.3.3.3;2.3.3 FPGA Signal Processing [6];263
26.4;3 Implementation and Simulation Result of Baseband Signal Processing;266
26.4.1;3.1 Pulse Amplitude Test and Time Reference Sampling;266
26.4.2;3.2 Simulation on Fixed Distance and Fixed Rate;267
26.4.3;3.3 Simulation of Pulse Interference and Random Pulse Encoder;267
26.4.4;3.4 Noise Simulation;268
26.5;4 Conclusion;269
26.6;References;270
27;LDPC Codes Estimation Model of Decoding Parameter and Realization;271
27.1;Abstract;271
27.2;1 Introduction;271
27.3;2 Estimation Model;272
27.4;3 Precision Analysis of Model;274
27.5;4 Estimation Method;275
27.5.1;4.1 Moment Estimation and Maximum Likelihood Estimation;275
27.5.2;4.2 Bayesian Estimation;276
27.6;5 Performance Simulation and Analysis;277
27.7;6 Conclusion;279
27.8;References;279
28;A Novel Query Extension Method Based on LDA;281
28.1;Abstract;281
28.2;1 Introduction;281
28.3;2 Related Works;282
28.4;3 Our Method;283
28.4.1;3.1 Fit Document Set with LDA;283
28.4.2;3.2 Retrieve the First Relevant Documents for Input Query;286
28.4.3;3.3 Extend the Original Query;287
28.5;4 Experiments;287
28.6;Acknowledgments;288
28.7;References;288
29;Target Recognition Method Based on Multi-class SVM and Evidence Theory;290
29.1;Abstract;290
29.2;1 Introduction;290
29.3;2 SVM and D-S Theory;291
29.3.1;2.1 SVM Theory;291
29.3.2;2.2 Multi Classification SVM;291
29.3.3;2.3 D-S Evidence Theory;292
29.4;3 The Combination of MSVM and Evidence Theory;293
29.4.1;3.1 SVM Probability Output;293
29.4.2;3.2 MSVM Soft Output;293
29.4.3;3.3 Fast Murphy Combination Rule;295
29.4.4;3.4 Multi-sensor Target Recognition Structure Model;296
29.5;4 Simulation Experiments;297
29.6;5 Conclusion;298
29.7;Acknowledgement;299
29.8;References;299
30;Selective Ensemble Based on Probability PSO Algorithm;301
30.1;Abstract;301
30.2;1 Introduction;301
30.3;2 Particle Swarm Optimization Algorithm and Its Improvement;302
30.4;3 Selective Integration Based on Probabilistic PSO;303
30.4.1;3.1 Basic Ideas;303
30.4.2;3.2 Selection of Fitness Function;304
30.4.3;3.3 Algorithm Flow;304
30.5;4 Numerical Experiments and Analysis;306
30.6;5 Conclusion;307
30.7;Acknowledgment;307
30.8;References;307
31;The Research of QoS Monitoring-Based Cloud Service Selection;309
31.1;Abstract;309
31.2;1 Introduction;309
31.3;2 Related Works;310
31.4;3 QoS Factor for Cloud Services;311
31.5;4 The Architecture of Agent-Based Cloud Service Provider;312
31.6;5 Experiment and Results;314
31.7;6 Conclusion and Future Works;315
31.8;References;316
32;Developing Cloud-Based Tools for Water Resources Data Analysis Using R and Shiny;317
32.1;Abstract;317
32.2;1 Introduction;317
32.3;2 Related Works;318
32.4;3 Key Technologies;319
32.4.1;3.1 Cloud Computing and Apache CloudStack;319
32.4.2;3.2 Map-Reduce Model, Hadoop and Cascading;319
32.4.3;3.3 R and Shiny;320
32.5;4 The Architecture Proposed;320
32.6;5 Use Case;322
32.7;6 The Conclusion and Future Work;323
32.8;Acknowledgments;324
32.9;References;324
33;Perception Mining of Network Protocol’s Dormant Behavior;326
33.1;Abstract;326
33.2;1 Introduction;326
33.3;2 Related Work;328
33.4;3 Design of the Protocol Behavior Analysis System;330
33.5;4 Protocol Dormant Behavior Analysis;331
33.5.1;4.1 Experimental Scheme;331
33.5.2;4.2 Experimental Results;332
33.6;5 Conclusions;333
33.7;Acknowledgments;333
33.8;References;334
34;Video Stabilization Algorithm Based on Kalman Filter and Homography Transformation;336
34.1;Abstract;336
34.2;1 Introduction;336
34.3;2 Research Methods;337
34.3.1;2.1 SURF Feature Point Extraction;337
34.3.2;2.2 Nearest Neighbor Algorithm;338
34.3.3;2.3 Motion Compensation;339
34.4;3 Experimental Results;339
34.4.1;3.1 Video Rotation Compensation;340
34.4.2;3.2 Evaluation of Image Stabilization;340
34.5;4 Conclusion;341
34.6;Acknowledgments;341
34.7;References;341
35;Towards a Web-Based Teaching Tool to Measure and Represent the Emotional Climate of Virtual Classrooms;342
35.1;Abstract;342
35.2;1 Introduction;342
35.3;2 Background;344
35.3.1;2.1 Models for Emotion Recognition and Affective Feedback;345
35.3.2;2.2 Emotion-Aware Methods and Techniques for ELearning;346
35.3.2.1;2.2.1 Emotional Detection;346
35.3.2.2;2.2.2 Affective Feedback;346
35.4;3 Research Methodology;347
35.4.1;3.1 Conceptual Approach and Requirements;347
35.4.2;3.2 Development and Prototyping;348
35.4.2.1;3.2.1 Post Classification;348
35.4.2.2;3.2.2 Graphical Representation;350
35.5;4 Evaluation Results;352
35.6;5 Conclusions and Future Work;353
35.7;Acknowledgements;354
35.8;References;354
36;An Efficient and Secure Outsourcing Algorithm for Bilinear Pairing Computation;356
36.1;Abstract;356
36.2;1 Introduction;356
36.2.1;1.1 Related Works;357
36.2.2;1.2 Paper Organization;358
36.3;2 Preliminaries;359
36.3.1;2.1 Bilinear Pairing;359
36.3.2;2.2 Pre-computation Algorithm;359
36.3.3;2.3 Computational Indistinguishability;360
36.4;3 Security Model;360
36.5;4 The Efficient Secure Algorithm;363
36.5.1;4.1 Construction;363
36.5.2;4.2 Proofs;364
36.5.3;4.3 Comparisons;365
36.6;5 Conclusions;366
36.7;Acknowledgments;366
36.8;References;366
37;A New Broadcast Encryption Scheme for Multi Sets;368
37.1;Abstract;368
37.2;1 Introduction;368
37.3;2 Preliminaries;369
37.3.1;2.1 Multilinear Maps;369
37.3.2;2.2 The Diffie-Hellman Inversion Assumption;370
37.3.3;2.3 Broadcast Encryption Scheme for Multi Sets;370
37.3.4;2.4 Broadcast Encryption Scheme for Multi Sets Security Model;371
37.4;3 Our Construction;372
37.5;4 Program Analysis;373
37.5.1;4.1 Correctness;373
37.5.2;4.2 Security Proof;373
37.6;5 Performance Analysis;374
37.7;6 Summary;375
37.8;Acknowledgments;375
37.9;References;375
38;Key Encapsulation Mechanism from Multilinear Maps;377
38.1;Abstract;377
38.2;1 Introduction;377
38.3;2 Preliminaries;378
38.3.1;2.1 Multilinear Maps and Assumption;378
38.3.2;2.2 Identity-Based Key Encapsulation Mechanism;379
38.3.3;2.3 Identity-Based Key Encapsulation Mechanism Security Model;379
38.4;3 Our Construction;380
38.5;4 Program Analysis;381
38.5.1;4.1 Correctness;381
38.5.2;4.2 Security Proof;381
38.6;5 Performance Analysis;383
38.7;6 Summary;384
38.8;References;384
39;An Multi-hop Broadcast Protocol for VANETs;386
39.1;Abstract;386
39.2;1 Introduction;386
39.3;2 ELCMBP Enhanced Broadcast Protocol;386
39.3.1;2.1 Disadvantages of DV-CAST;387
39.3.2;2.2 ELCMBP Protocol Design;388
39.3.2.1;2.2.1 Broadcast Message Initiation;389
39.3.2.2;2.2.2 Broadcast Message Forwarding;389
39.4;3 Simulation Experiment and Result Analysis;391
39.5;4 Conclusion;393
39.6;References;393
40;DPHKMS: An Efficient Hybrid Clustering Preserving Differential Privacy in Spark;395
40.1;Abstract;395
40.2;1 Introduction;395
40.3;2 Preliminaries;397
40.3.1;2.1 k-means Clustering;397
40.3.2;2.2 Differential Privacy;397
40.4;3 DPHKS Algorithm;398
40.4.1;3.1 Attack Model;398
40.4.2;3.2 Design of DPHKS Algorithm;399
40.4.3;3.3 Privacy Analysis of DPHKS;401
40.5;4 Experiment and Analysis;401
40.5.1;4.1 Clustering Efficiency;402
40.5.2;4.2 Computation Time;402
40.5.3;4.3 Clustering Results Usability;403
40.6;5 Conclusion;404
40.7;Acknowledgments;404
40.8;References;404
41;Technique for Image Fusion Based on PCNN and Convolutional Neural Network;406
41.1;Abstract;406
41.2;1 Introduction;406
41.3;2 Convolution Neural Network;408
41.4;3 Improved Pulse Coupled Neural Network;409
41.4.1;3.1 IPCNN and Its Time Matrix;409
41.4.2;3.2 Parameters Determination of IPCNN;411
41.5;4 Experimental Results and Analysis;413
41.5.1;4.1 Methods Introduction and Parameters Setting;413
41.5.2;4.2 Subjective and Objective Evaluation on the Experimental Results;414
41.6;5 Conclusions;416
41.7;Acknowledgements;416
41.8;References;416
42;Fast Iterative Reconstruction Based on Condensed Hierarchy Tree;418
42.1;Abstract;418
42.2;1 Introduction;418
42.3;2 Related Research;418
42.4;3 FIRA Algorithm;419
42.4.1;3.1 Image Similarity Calculation;419
42.4.2;3.2 Hierarchical Tree Generation;423
42.5;4 Experimental Results;424
42.5.1;4.1 Performance;424
42.5.2;4.2 Accuracy;425
42.6;5 Summary;426
42.7;Acknowledgement;426
42.8;References;426
43;An Optimal Model of Web Cache Based on Improved K-Means Algorithm;428
43.1;Abstract;428
43.2;1 Introduction;428
43.3;2 The Performance of the Web Cache Replacement Model;429
43.4;3 The Existing Web Replacement Strategy;429
43.5;4 Web Cache Optimization Model Based on Improved K-Means Clustering Algorithm;430
43.5.1;4.1 Several Definitions;430
43.5.2;4.2 Establishment of HSF Model Based on Improved K-Means Algorithm;431
43.5.3;4.3 The K-Means Clustering Analysis Algorithm;431
43.5.4;4.4 The Improved K-Means Algorithm;432
43.6;5 Experiment;433
43.6.1;5.1 Matrix Transformation;433
43.6.2;5.2 K-Means Algorithm Clustering Effect;434
43.6.3;5.3 The Improved K-Means Algorithm Clustering Result;434
43.6.4;5.4 Comparison of RFS Model and HSF Model;436
43.7;6 Conclusion;438
43.8;References;438
44;Detecting Crowdsourcing Spammers in Community Question Answering Websites;440
44.1;1 Introduction;440
44.2;2 Background and Data Collection;441
44.2.1;2.1 Link Crowdsourcing Services to CQA Websites;441
44.2.2;2.2 Data Collection;442
44.3;3 Analysis of Non-semantic Features and Semantic Features;443
44.3.1;3.1 Non-Semantic Analysis;443
44.3.2;3.2 Semantic Analysis;444
44.4;4 Detect Crowdsourcing Spammers;445
44.4.1;4.1 Features;445
44.4.2;4.2 Feature Selection;447
44.5;5 Results and Evaluation;447
44.5.1;5.1 Settings and Metrics;447
44.5.2;5.2 Classification Results;448
44.5.3;5.3 Compare with Existing Methods;448
44.6;6 Related Work;449
44.7;7 Conclusions;450
44.8;References;450
45;A Spam Message Detection Model Based on Bayesian Classification;452
45.1;Abstract;452
45.2;1 Introduction;452
45.3;2 The Bayesian Spam Detection Model;454
45.3.1;2.1 Introduction to Bayesian Algorithm;454
45.3.2;2.2 The Bayesian Spam Detection Model;455
45.4;3 Spam Message Forensics;457
45.4.1;3.1 Background;457
45.4.2;3.2 Forensics Process;458
45.5;4 Experiments and Discussion;460
45.5.1;4.1 Experiment Objectives;460
45.5.2;4.2 Experimental Environment;460
45.5.3;4.3 Experimental Process;460
45.5.4;4.4 Result Analysis;461
45.6;5 Conclusion;461
45.7;References;462
46;Spam Mail Filtering Method Based on Suffix Tree;464
46.1;Abstract;464
46.2;1 Introduction;464
46.2.1;1.1 Black/White List Filtering;465
46.2.2;1.2 Naïve Bayes Classification;465
46.2.3;1.3 K-NN Algorithm;465
46.2.4;1.4 Suffix Tree Algorithm;465
46.3;2 Ukkonen’s Algorithm;467
46.4;3 Search and Matching Algorithm;469
46.4.1;3.1 Break the Limitation of Language;469
46.4.2;3.2 Search and Matching;469
46.5;4 Experiment;471
46.5.1;4.1 Spam Corpus Collection;471
46.5.2;4.2 Spam Mail Key-Words Database;471
46.5.3;4.3 Ham Text Dataset;472
46.5.4;4.4 Evaluation Indicator;472
46.5.5;4.5 Weight Choosing·;473
46.5.6;4.6 Complexity Analysis;474
46.6;5 Conclusion;475
46.7;References;475
47;MP3 Audio Watermarking Algorithm Based on Unipolar Quantization;476
47.1;Abstract;476
47.2;1 Introduction;476
47.3;2 MP3 Audio Watermarking Based on Discrete Wavelet Transform;477
47.3.1;2.1 Watermark Embedding;477
47.3.2;2.2 Watermark Extraction;481
47.4;3 Simulation;481
47.4.1;3.1 Auditory Transparency;481
47.4.2;3.2 Robustness;482
47.5;4 Conclusion;483
47.6;References;483
48;Multi-documents Summarization Based on the TextRank and Its Application in Argumentation System;485
48.1;Abstract;485
48.2;1 Introduction;485
48.3;2 Multi-document Summarization Method Based on TextRank Algorithm;486
48.3.1;2.1 Text Preprocessing;487
48.3.2;2.2 Text Preprocessing;487
48.3.2.1;2.2.1 Text Feature Weighting;487
48.3.2.2;2.2.2 Vector Space Model (VSM);488
48.3.2.3;2.2.3 Text Similarity Calculation;488
48.3.2.4;2.2.4 Expert Opinion in Text Clustering;489
48.3.3;2.3 Multiple Document Summary;489
48.3.3.1;2.3.1 Text Summary Algorithm;489
48.3.3.2;2.3.2 Similarity Calculation Between Sentences;490
48.4;3 Application Effect Analysis;491
48.5;4 Conclusion;493
48.6;Acknowledgements;493
48.7;References;493
49;An Unconstrained Face Detection Algorithm Based on Visual Saliency;495
49.1;Abstract;495
49.2;1 Introduction;495
49.3;2 Saliency Detection Algorithm Based on Log_Gabor_GBVS;496
49.4;3 Segmentation Method Based on Maximum Entropy Criterion;497
49.5;4 Face Detection Algorithm Based on Object Region Centroid;498
49.6;5 Experiment Results;500
49.7;6 Conclusions;501
49.8;Acknowledgement;501
49.9;References;501
50;Pavement Crack Detection Fused HOG and Watershed Algorithm of Range Image;503
50.1;Abstract;503
50.2;1 Introduction;503
50.3;2 3-D Measurement of Line Structured Light and Crack Feature of Pavement Range Image;505
50.4;3 Crack Edge Detection by HOG;507
50.5;4 Crack Detection by Direction Watershed Algorithm;510
50.6;5 Experiment and Analysis;511
50.7;6 Conclusion;514
50.8;Acknowledgments;515
50.9;References;515
51;Compressed Video Sensing with Multi-hypothesis Prediction;517
51.1;Abstract;517
51.2;1 Introduction;517
51.3;2 Compressed Sensing Overview;518
51.4;3 The Proposed CVS Scheme Based on MH;519
51.4.1;3.1 The Block Diagram of the Proposed CVS Scheme;519
51.4.2;3.2 Side Information Estimation Based on MH in Measurement Domain;520
51.4.3;3.3 Multi-hypothesis Prediction for Non-key Frame Reconstruction;521
51.5;4 Experimental Results;522
51.6;5 Conclusions;523
51.7;Acknowledgments;524
51.8;References;524
52;Security Analysis and Improvements of Three-Party Password-Based Authenticated Key Exchange Protocol;525
52.1;Abstract;525
52.2;1 Introduction;525
52.3;2 Review of Xu et al.’s Protocol;526
52.3.1;2.1 Notations;526
52.3.2;2.2 Protocol Description;527
52.4;3 Attacks on Xu et al.’s 3PAKE Protocol;529
52.5;4 Improved Scheme;531
52.6;5 Security Analysis and Performance Comparison;533
52.6.1;5.1 Security Analysis;533
52.6.2;5.2 Efficiency Analysis;534
52.7;6 Conclusion;535
52.8;Acknowledgments;535
52.9;References;535
53;A Combined Security Scheme for Network Coding;537
53.1;Abstract;537
53.2;1 Introduction;537
53.3;2 Network Model and Operation;538
53.3.1;2.1 Network Model;538
53.3.2;2.2 Adversary Model;539
53.4;3 Our Scheme;539
53.4.1;3.1 Basic Idea;539
53.4.2;3.2 Define Our Scheme;540
53.4.3;3.3 Construction Our Scheme;541
53.5;4 Security Analysis;542
53.6;5 Comparison with Existing Schemes;543
53.7;6 Conclusion;544
53.8;Acknowledgements;544
53.9;References;544
54;Gaussian Scale Patch Group Sparse Representation for Image Restoration;546
54.1;Abstract;546
54.2;1 Introduction;546
54.3;2 Sparse Representation Model;547
54.4;3 Algorithm Implementation;547
54.5;4 Simulation Analysis;548
54.6;5 Conclusion;550
54.7;Acknowledgments;551
54.8;References;551
55;An Efficient Identity-Based Homomorphic Signature Scheme for Network Coding;552
55.1;1 Introduction;552
55.2;2 Preliminaries;553
55.2.1;2.1 Linear Network Coding;553
55.2.2;2.2 Identity-Based Signature;554
55.2.3;2.3 Network Coding Signature Scheme;555
55.2.4;2.4 Bilinear Groups and Complexity Assumptions;555
55.3;3 Our Construction;556
55.4;4 Security Analysis;557
55.5;5 Conclusion;558
55.6;References;558
56;A Three-Dimensional Digital Watermarking Technique Based on Integral Image Cryptosystem and Discrete Fresnel Diffraction;560
56.1;Abstract;560
56.2;1 Introduction;560
56.3;2 The Principle of the System;561
56.4;3 Experimental Results and Discussions;564
56.5;4 Conclusion;566
56.6;References;566
57;Building Real-Time Travel Itineraries Using `off-the-shelf' Data from the Web;569
57.1;1 Introduction;569
57.2;2 Related Work;570
57.3;3 Methodology;572
57.3.1;3.1 System Design;572
57.3.2;3.2 Phase-1: Data Collection;573
57.3.3;3.3 Phase-2: Algorithms for Itinerary Construction;574
57.4;4 Results and Conclusion;576
57.4.1;4.1 Results;577
57.4.2;4.2 Conclusion;578
57.4.3;4.3 Limitations;579
57.5;References;579
58;Energy Efficient Integration of Renewable Energy Sources in Smart Grid;581
58.1;1 Introduction;581
58.2;2 Related Work;582
58.3;3 System Model;583
58.3.1;3.1 Appliances Classification;583
58.3.2;3.2 RE Integration Model;585
58.4;4 Simulations and Discussion;586
58.4.1;4.1 Electricity Cost Analysis for Different Scheduling Scheme;587
58.4.2;4.2 Trade-Off Analysis of Electricity Cost and User Comfort;588
58.4.3;4.3 PAR Performance Analysis;588
58.5;5 Conclusion;589
58.6;References;589
59;Cost and Comfort Based Optimization of Residential Load in Smart Grid;591
59.1;1 Introduction;591
59.2;2 Related Work;592
59.3;3 Problem Statement;594
59.4;4 System Model;595
59.4.1;4.1 Energy Management Controller;595
59.4.2;4.2 Residential Consumers;595
59.4.3;4.3 Communication Network;595
59.5;5 Simulations and Discussions;597
59.6;6 Conclusion;599
59.7;References;599
60;Efficient Utilization of HEM Controller Using Heuristic Optimization Techniques;601
60.1;1 Introduction;601
60.2;2 Related Work and Motivation;602
60.3;3 Proposed System Model;603
60.4;4 Results;605
60.4.1;4.1 Clothes Dryer;605
60.4.2;4.2 Dishwasher;607
60.4.3;4.3 Refrigerator;608
60.5;5 Conclusion;611
60.6;References;611
61;A Shadow Elimination Algorithm Based on HSV Spatial Feature and Texture Feature;613
61.1;Abstract;613
61.2;1 Introduction;613
61.3;2 Shadow Detection of Traditional HSV Color Space;614
61.3.1;2.1 Background Subtraction Method;614
61.3.2;2.2 Model of the HSV Color Space to Removing Shadow;614
61.4;3 The Proposed Shadow Elimination Method;615
61.4.1;3.1 OTSU;615
61.5;4 Experimental Results;616
61.6;5 Conclusions;618
61.7;Acknowledgments;618
61.8;References;618
62;A Provably Secure Certificateless User Authentication Protocol for Mobile Client-Server Environment;620
62.1;1 Introduction;620
62.2;2 Related Work;621
62.3;3 Preliminaries;622
62.3.1;3.1 Bilinear Pairings;622
62.3.2;3.2 Security Model;622
62.4;4 Proposed Protocol;623
62.4.1;4.1 Initialization Phase;624
62.4.2;4.2 User Authentication and Key Agreement Phase;625
62.4.3;4.3 Correctness of Our Protocol;626
62.5;5 Security analysis;626
62.5.1;5.1 Client-to-server Authentication;626
62.5.2;5.2 Key Agreement;627
62.5.3;5.3 Sever-to-client Authentication;627
62.6;6 Performance Analysis;628
62.7;7 Conclusion;629
62.8;References;629
63;Improved Online/Offline Attribute Based Encryption and More;631
63.1;1 Introduction;631
63.2;2 Review of HW's Online/Offline ABE Scheme;632
63.2.1;2.1 Our Improved Online/Offline ABE Scheme;633
63.3;3 Generalization;635
63.3.1;3.1 CF's Vector Commitments;635
63.3.2;3.2 Our Improved Algorithm;636
63.4;4 Conclusion;637
63.5;References;637
64;On the Security of a Cloud Data Storage Auditing Protocol IPAD;639
64.1;1 Introduction;639
64.2;2 Review of Zhang et al.'s IPAD Scheme;640
64.3;3 Our Attack;642
64.4;4 Conclusion;644
64.5;References;644
65;LF-LDA: A Topic Model for Multi-label Classification;646
65.1;Abstract;646
65.2;1 Introduction;646
65.3;2 LF-LDA;648
65.3.1;2.1 Review LDA and L-LDA;648
65.3.2;2.2 Our Proposed LF-LDA;651
65.4;3 Inference and Parameter Estimation;653
65.5;4 Experiments;654
65.6;5 Conclusion;655
65.7;Acknowledgments;655
65.8;References;655
66;Data Analysis for Infant Formula Nutrients;657
66.1;Abstract;657
66.2;1 Introduction;657
66.3;2 Nutrients Data Analysis;657
66.3.1;2.1 Observations;658
66.3.2;2.2 Design of Dataset;660
66.4;3 Experimental Results;660
66.5;4 Conclusion;663
66.6;Acknowledgments;663
66.7;References;664
67;A Classification Method Based on Improved BIA Model for Operation and Maintenance of Information System in Large Electric Power Enterprise;665
67.1;Abstract;665
67.2;1 Introduction;665
67.3;2 Status Analysis;665
67.4;3 The Improved Information System Classification Model;666
67.4.1;3.1 Objective and Principle of the Model;666
67.4.2;3.2 Content of the Model;667
67.4.3;3.3 Application of the Model;670
67.5;4 Method for System Classification Service;670
67.6;5 Conclusions;671
67.7;Acknowledgments;671
67.8;References;671
68;A Model Profile for Pattern-Based Definition and Verification of Composite Cloud Services;673
68.1;1 Introduction;673
68.2;2 MetaMORP(h)OSy Profile for Cloud Patterns;674
68.3;3 Model Transformation;678
68.4;4 A Case Study;680
68.5;5 Conclusions and Future Works;682
68.6;References;683
69;A Routing Based on Geographical Location Information for Wireless Ad Hoc Networks;685
69.1;Abstract;685
69.2;1 Introduction;685
69.3;2 Forwarding Algorithm of Backup Copy Based on Geographic Location;685
69.3.1;2.1 Forwarding Strategy Based on Geographic Location;686
69.3.2;2.2 Backup Copy Forwarding Strategy;689
69.3.3;2.3 Backup Copy Forwarding Algorithm Based on Geographic Location Information;690
69.4;3 Simulation Analysis;691
69.5;4 Conclusion;694
69.6;References;694
70;Cyber-Attack Risks Analysis Based on Attack-Defense Trees;695
70.1;Abstract;695
70.2;1 Introduction;695
70.3;2 Related Work;696
70.4;3 Modeling with ADTree;696
70.4.1;3.1 Attack-Defense Tree Model;696
70.4.2;3.2 Risk Analysis Framework with ADTree;697
70.5;4 Risk Analysis Framework;702
70.5.1;4.1 Framework Construction;702
70.5.2;4.2 Numerical Illustrations;703
70.6;5 Conclusion;705
70.7;Acknowledgments;705
70.8;References;705
71;Multi-focus Image Fusion Method Based on NSST and IICM;707
71.1;Abstract;707
71.2;1 Introduction;707
71.3;2 Improved Intersecting Cortical Model;708
71.3.1;2.1 Basic ICM;708
71.3.2;2.2 Improved ICM;709
71.4;3 Fusion Framework of Multi-focus Images;710
71.5;4 Experimental Results and Analysis;711
71.5.1;4.1 Methods Introduction and Parameters Setting;711
71.5.2;4.2 Comparative Experiments of Multi-focus Image Fusion;713
71.6;5 Conclusions;715
71.7;Acknowledgements;715
71.8;References;715
72;Pilot Contamination Elimination in Massive MIMO Systems;718
72.1;Abstract;718
72.2;1 Introduction;718
72.3;2 System Model;720
72.4;3 Intelligent Assignment Pilot Sequence Scheme Based on User Area Location Priority;722
72.5;4 Experimental Simulation and Result Analysis;725
72.6;5 Conclusion;728
72.7;References;728
73;Improved Leader-Follower Method in Formation Transformation Based on Greedy Algorithm;730
73.1;Abstract;730
73.2;1 Introduction;730
73.3;2 Greedy Algorithm Based on Leader-Follower Method;731
73.3.1;2.1 Formation Maintenance Based on Leader-Follower Algorithm;731
73.3.2;2.2 Greedy Algorithm;733
73.4;3 Modeling and Simulation;733
73.4.1;3.1 Formation Control with Greedy Algorithm Based on Conditional Formation Feedback;734
73.4.2;3.2 Collision Prediction;736
73.4.3;3.3 Collision Avoidance;736
73.5;4 Simulation Experiment and Result Analysis;737
73.6;5 Conclusion;738
73.7;Acknowledgments;739
73.8;References;739
74;A Kind of Improved Hidden Native Bayesian Classifier;740
74.1;Abstract;740
74.2;1 Introduction;740
74.3;2 The Naive Bayesian Classification;741
74.4;3 The Hidden Naive Bayesian Classifier;742
74.4.1;3.1 Brief Introduction of Hidden Naive Bayes Classifier;742
74.4.2;3.2 Evaluations of the Hidden Naive Bayesian Classifier;744
74.5;4 The Implicit Naive Bayes Classifier with Improved Mutual Information;744
74.5.1;4.1 The Basic Ideas;745
74.5.2;4.2 The Algorithm Flow;745
74.5.3;4.3 Test Procedures and Results;746
74.6;5 Summary;747
74.7;References;748
75;Study of a Disaster Relief Troop’s Transportation Problem Based on Minimum Cost Maximum Flow;749
75.1;Abstract;749
75.2;1 Introduction;749
75.3;2 Model of Rescue and Relief Troop Dispatching Network;750
75.4;3 The Troops Dispatching the Algorithm in Rescue and Relief Work is Based on a Path Priority Limit;752
75.5;4 Example Analysis of Troops Dispatching in Rescue and Relief Work;755
75.6;5 Conclusion;760
75.7;Acknowledgments;760
75.8;References;760
76;A Cascading Diffusion Prediction Model in Micro-blog Based on Multi-dimensional Features;762
76.1;Abstract;762
76.2;1 Introduction;762
76.2.1;1.1 Forwarding Mechanism;763
76.2.2;1.2 Model of Information Dissemination;763
76.3;2 Notations and Problem Statement;764
76.4;3 Model Framework;765
76.4.1;3.1 Features Extraction;765
76.4.1.1;3.1.1 Node Attributes ?U;765
76.4.1.2;3.1.2 Content Attributes ?C;766
76.4.1.3;3.1.3 Edge Features ?E;767
76.4.2;3.2 Model Construction;767
76.4.3;3.3 Solution Method;770
76.5;4 Experiment and Evaluation;770
76.5.1;4.1 The Experimental Data and Method;770
76.5.2;4.2 Experimentation Results;771
76.5.2.1;4.2.1 Analysis of Transmission Probability and Transmission Delay;771
76.5.2.2;4.2.2 Experimentation Results and Conclusion;772
76.6;References;773
77;Multi-target Detection of FMCW Radar Based on Width Filtering;775
77.1;Abstract;775
77.2;1 Introduction;775
77.3;2 Related Work;776
77.3.1;2.1 The Basic Theory of FMCW Radar;776
77.3.2;2.2 Distance and Speed Estimation;778
77.3.3;2.3 Range and Speed Resolution;778
77.3.4;2.4 The Influence of Radar Sampling Points on the Target Spectrum Width;779
77.4;3 The Method of Width Filter and Spectrum Association;779
77.5;4 Numerical Simulation;781
77.6;5 Conclusion;783
77.7;References;783
78;DroidMark: A Lightweight Android Text and Space Watermark Scheme Based on Semantics of XML and DEX;784
78.1;1 Introduction;784
78.2;2 Related Work;785
78.3;3 Proposed Scheme - DroidMark;786
78.3.1;3.1 Design Goals;786
78.3.2;3.2 Notations;787
78.3.3;3.3 Scheme Construction;787
78.4;4 Security Analysis and Performance Evaluation;789
78.4.1;4.1 Security Analysis;790
78.4.2;4.2 Performance Analysis;791
78.5;5 Conclusions;794
78.6;References;794
79;Research on CSER Rumor Spreading Model in Online Social Network;795
79.1;Abstract;795
79.2;1 Introduction;795
79.3;2 CSER Rumor Spreading Model;796
79.4;3 Analysis;798
79.5;4 Experimental Simulation;799
79.5.1;4.1 Experimental Environment;799
79.5.2;4.2 Homogeneous Network;799
79.5.3;4.3 Heterogeneous Network;801
79.6;5 Conclusions;802
79.7;Acknowledgments;802
79.8;References;802
80;Author Index;804