Advances in Internetworking, Data & Web Technologies

The 5th International Conference on Emerging Internetworking, Data & Web Technologies (EIDWT-2017)
 
 
Springer (Verlag)
  • erschienen am 25. Mai 2017
  • |
  • XXIX, 779 Seiten
 
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-319-59463-7 (ISBN)
 
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.
weitere Ausgaben werden ermittelt
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]

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