Advances in Internetworking, Data & Web Technologies

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