E-Book, Englisch, Band 40, 677 Seiten
Ranka / Aluru / Buyya Contemporary Computing
1. Auflage 2009
ISBN: 978-3-642-03547-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
Second International Conference, IC3 2009, Noida, India, August 17-19, 2009. Proceedings
E-Book, Englisch, Band 40, 677 Seiten
Reihe: Communications in Computer and Information Science
ISBN: 978-3-642-03547-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book constitutes the refereed papers of the 2nd International Conference on Contemporary Computing, which was held in Noida (New Delhi), India, in August 2009. The 61 revised full papers presented were carefully reviewed and selected from 213 submissions and focus on topics that are of contemporary interest to computer and computational scientists and engineers. The papers are organized in topical sections on Algorithms, Applications, Bioinformatics, and Systems.
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;5
2;Organization;6
3;Table of Contents;11
4;Technical Session-1: Algorithm-1 (AL-1);11
4.1;A Hybrid Grouping Genetic Algorithm for Multiprocessor Scheduling;17
4.1.1;Introduction;17
4.1.2;The Hybrid Approach (HGGA-SSR);19
4.1.2.1;The Grouping Genetic Algorithm;19
4.1.2.2;The Heuristic;20
4.1.3;Computational Results;22
4.1.4;Conclusions;23
4.1.5;References;23
4.2;PDE Based Unsharp Masking, Crispening and High Boost Filtering of Digital Images;24
4.2.1;Introduction;24
4.2.2;Proposed PDE Based Model;26
4.2.3;Results;27
4.2.4;Conclusion;28
4.2.5;References;29
4.3;A New Position-Based Fast Radix-2 Algorithm for Computing the DHT;30
4.3.1;Introduction;30
4.3.2;Discrete Hartley Transform;31
4.3.3;Proposed Algorithm;34
4.3.3.1;Operational Complexity;38
4.3.4;Results;39
4.3.5;Conclusions;40
4.3.6;References;40
4.4;Study of Bit-Parallel Approximate Parameterized String Matching Algorithms;42
4.4.1;Introduction;42
4.4.2;Related Concepts;43
4.4.2.1;Parameterized String Matching Problem;43
4.4.2.2;Approximate String Matching Problem;44
4.4.2.3;Dynamic Programming Algorithms;44
4.4.2.4;Algorithms Based on Finite Automata;46
4.4.2.5;Myers’ Bit-Parallel Algorithm;47
4.4.2.6;Parameterized Bit-Parallel String Matching Algorithm;47
4.4.2.7;Approximate Parameterized String Matching (APSM) Problem;49
4.4.3;Proposed Algorithms;49
4.4.3.1;Algorithm for APSM Problem Based on Finite Automata;49
4.4.3.2;Algorithm for APSM Problem Based on Myers’ Algorithm;50
4.4.4;Conclusions and Future Work;52
4.4.5;References;52
4.5;Optimization of Finite Difference Method with Multiwavelet Bases;53
4.5.1;Introduction;53
4.5.2;Discretization of PDEs;54
4.5.3;Grid Generation Using Multiwavelets;56
4.5.4;Results;58
4.5.5;Conclusion;63
4.5.6;References;63
5;Technical Session-2: Algorithm-2 (AL-2);11
5.1;A Novel Genetic Algorithm Approach to Mobility Prediction in Wireless Networks;65
5.1.1;Introduction;65
5.1.2;Mobility Prediction Techniques;66
5.1.3;Proposed Approach;67
5.1.3.1;GA Approach to Predict the Next Movement of the User;67
5.1.3.2;GA Methodology;69
5.1.3.3;GA Based Mobility Prediction Algorithm;69
5.1.4;Simulation Results and Performance Analysis;70
5.1.4.1;Effect of GA Based Mobility Prediction on Call Dropping;70
5.1.4.2;Accuracy of Prediction;71
5.1.5;Conclusion;72
5.1.6;References;72
5.2;A Beaconless Minimum Interference Based Routing Protocol for Mobile Ad Hoc Networks;74
5.2.1;Introduction;74
5.2.2;Minimum Interference Based Routing Protocol;76
5.2.2.1;Route Request-Reply Cycle to Collect Location Update Vectors;76
5.2.2.1.1;Route Selection and Route Reply;76
5.2.2.1.2;Data Packet Transmission and Route Maintenance;78
5.2.3;Simulations;78
5.2.3.1;End-to-End Delay per Data Packet;80
5.2.3.2;Route Lifetime;80
5.2.3.3;Average Hop Count per Path;81
5.2.3.4;Packet Delivery Ratio;81
5.2.3.5;Energy Consumed per Node;82
5.2.3.6;Impact of Interference Range per Link;83
5.2.4;Conclusions;84
5.2.5;References;85
5.3;An Optimal, Distributed Deadlock Detection andResolution Algorithm for Generalized Model in Distributed Systems;86
5.3.1;Introduction;86
5.3.1.1;Previous Work on Distributed Deadlock Detection Algorithms;87
5.3.1.2;Paper Objectives;88
5.3.1.3;Differences between the Proposed Algorithm and Earlier Algorithm;88
5.3.2;Preliminaries;89
5.3.3;Distributed Deadlock Detection Algorithm;90
5.3.3.1;The Description;90
5.3.3.2;Formal Specification;91
5.3.3.3;Example Execution;93
5.3.3.4;Deadlock Resolution;95
5.3.4;Performance Analysis;95
5.3.5;Conclusion;96
5.3.6;References;96
5.4;Throughput Considerations of Fault-Tolerant Routing in Network-on-Chip;97
5.4.1;Introduction;97
5.4.2;Fault-Tolerant Routing Enhancement;99
5.4.2.1;$f$-cube3 Algorithm;100
5.4.2.2;$If$-cube2 Algorithm;101
5.4.3;Performance Evaluation and Simulation Results;102
5.4.3.1;Simulation Methodology;103
5.4.3.2;Comparison of $f$-cube3 and $if$-cube2;103
5.4.4;Conclusion;106
5.4.5;References;107
5.5;A New Approach towards Bibliographic Reference Identification, Parsing and Inline Citation Matching;109
5.5.1;Introduction;109
5.5.2;Previous Works;110
5.5.3;Terms and Notations;111
5.5.4;Our Work;111
5.5.4.1;Input Files;112
5.5.4.2;Reference Block Identification;112
5.5.4.3;Reference Parsing;114
5.5.4.4;Citation Parsing;115
5.5.5;Experiment and Results;116
5.5.6;Conclusion and Future Scope;118
5.5.7;References;118
6;Technical Session-3: Algorithm-3 (AL-3);12
6.1;Optimized Graph Search Using Multi-Level Graph Clustering;119
6.1.1;Introduction;119
6.1.2;Related Work;121
6.1.3;Algorithm;121
6.1.3.1;Graph Clustering;122
6.1.3.2;Searching;126
6.1.4;Results;127
6.1.5;Conclusion;128
6.1.6;References;129
6.2;An Integrated Framework for Relational and Hierarchical Mining of Frequent Closed Patterns;131
6.2.1;Introduction;131
6.2.2;Related Works;132
6.2.3;Contributions of This Paper;132
6.2.3.1;CPF Framework;133
6.2.3.2;User Data;133
6.2.3.3;Inter-related Generalized Self Organized Mapping (IGSOM);133
6.2.3.4;Relational Attributed Oriented Induction (RAOI);135
6.2.3.5;CC-MINER;136
6.2.3.6;Application;138
6.2.4;Experimental Results;138
6.2.4.1;Educational Data Set;138
6.2.4.2;Prostrate Cancer Data Set;140
6.2.5;Conclusion;141
6.2.6;References;141
6.3;A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization;143
6.3.1;Introduction;143
6.3.2;Basic DE;144
6.3.3;Modified Differential Evolution;145
6.3.4;Experimental Settings and Numerical Results;147
6.3.4.1;Numerical Results;148
6.3.5;Conclusions;150
6.3.6;References;150
6.3.7;Appendix;151
6.4;Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts;154
6.4.1;Introduction;154
6.4.2;Brief Overview of South Indian Scripts;156
6.4.3;Data Set and Preprocessing;156
6.4.4;Proposed Feature Extraction Methodology;157
6.4.5;Experimental Results and Comparative Study;159
6.4.5.1;Experimental Result on Bangla Numeral Database;161
6.4.6;Conclusion;162
6.4.7;References;163
6.5;Local Subspace Based Outlier Detection;165
6.5.1;Introduction;165
6.5.2;Related Work;167
6.5.2.1;LOF Algorithm;167
6.5.2.2;DSNOF Algorithm;168
6.5.2.3;Subspace Outlier Detection in Data with Mixture of Variances and Noise;168
6.5.2.4;Our Contributions;168
6.5.3;The Proposed Algorithm (LSOF);168
6.5.4;Complexity Analysis;171
6.5.5;Experimental Evaluation;171
6.5.6;Conclusion;172
6.5.7;References;173
6.6;New Robust Fuzzy C-Means Based Gaussian Function in Classifying Brain Tissue Regions;174
6.6.1;Introduction;174
6.6.2;Fuzzy Clustering;175
6.6.2.1;Fuzzy C-Mean Algorithm;175
6.6.3;Kernel Induced Fuzzy C-Means Based Gaussian Function [KFCGF];176
6.6.3.1;Obtaining Membership;177
6.6.3.2;Obtaining Cluster Prototype Updating;178
6.6.4;Results and Discussions;179
6.6.5;Conclusion;183
6.6.6;References;184
7;Technical Session-4: Algorithm-4 (AL-4);12
7.1;On the Connectivity, Lifetime and Hop Count of Routes Determined Using the City Section and ManhattanMobility Models for Vehicular Ad Hoc Networks;186
7.1.1;Introduction;186
7.1.2;Review of the Mobility Models;187
7.1.2.1;Random Waypoint Mobility Model;188
7.1.2.2;City Section Mobility Model;188
7.1.2.3;Manhattan Mobility Model;188
7.1.3;Algorithm to Determine Optimal Number of Path Transitions;189
7.1.3.1;Mobile Graph;189
7.1.3.2;Mobile Path;189
7.1.3.3;Stable Mobile Path and Minimum Hop Mobile Path;189
7.1.3.4;Algorithm Description;189
7.1.4;Simulations;190
7.1.4.1;Percentage Network Connectivity;191
7.1.4.2;Average Route Lifetime;192
7.1.4.3;Route Lifetime – Hop Count Tradeoff;195
7.1.5;Conclusions;196
7.1.6;References;197
7.2;On the Privacy Protection of Biometric Traits: Palmprint, Face, and Signature;198
7.2.1;Introduction;199
7.2.1.1;Risks in Biometric Systems;200
7.2.2;Hill Cipher;202
7.2.2.1;Use of Involutory Key Matrix;202
7.2.2.2;Image Encryption Using Hill Cipher;203
7.2.3;Proposed Advanced Hill Cipher Encryption Algorithm;203
7.2.3.1;The AdvHill Algorithm;204
7.2.4;Results and Discussions;204
7.2.4.1; Palmprint, Face and Signature Image Encryption;207
7.2.5;Conclusion;208
7.2.6;References;208
7.3;Indexing Iris Biometric Database Using Energy Histogram of DCT Subbands;210
7.3.1;Introduction;210
7.3.2;Preprocessing and Feature Extraction;211
7.3.3;Indexing Scheme;213
7.3.3.1;Key Generation;213
7.3.3.2;Database Creation and Searching;214
7.3.4;Experimental Results;216
7.3.5;Conclusion and Future Work;219
7.3.6;References;219
7.4;Secured Communication for Business Process Outsourcing Using Optimized Arithmetic Cryptography Protocol Based on Virtual Parties;221
7.4.1;Introduction;244
7.4.2;Receipt-Free Sealed-Bid Auction: Properties and Security Requirements;246
7.4.2.1;Properties of Receipt-Free Sealed-Bid Auction;246
7.4.2.2;Physical Requirements for Receipt-Free Sealed-Bid Auction;247
7.4.2.3;Entities of Receipt-Free Sealed-Bid Auction;247
7.4.3;Receipt-Free Sealed-Bid Auction Procedure;248
7.4.3.1;Bidding Phase;248
7.4.3.2;Opening Phase;251
7.4.4;Security Analysis;252
7.4.5;Efficiency;253
7.4.6;Conclusion;254
7.4.7;References;254
7.5;Timing Analysis of Passive UHF RFID - EPC C1G2 System in Dynamic Frame;232
7.5.1;Introduction;232
7.5.2;Reader Tag Communication;233
7.5.2.1;The Tag Population;236
7.5.2.2;Timing Analysis;239
7.5.3;Speed Estimation in Dynamic Frame;242
7.5.4;Conclusion;242
7.5.5;References;243
8;Technical Session-5: Application-1 (AP-1);13
8.1;Secure Receipt-Free Sealed-Bid Electronic Auction;244
8.1.1;Introduction;244
8.1.2;Receipt-Free Sealed-Bid Auction: Properties and Security Requirements;246
8.1.2.1;Properties of Receipt-Free Sealed-Bid Auction;246
8.1.2.2;Physical Requirements for Receipt-Free Sealed-Bid Auction;247
8.1.2.3;Entities of Receipt-Free Sealed-Bid Auction;247
8.1.3;Receipt-Free Sealed-Bid Auction Procedure;248
8.1.3.1;Bidding Phase;248
8.1.3.2;Opening Phase;251
8.1.4;Security Analysis;252
8.1.5;Efficiency;253
8.1.6;Conclusion;254
8.1.7;References;254
8.2;An Architecture for Handling Fuzzy Queries in Data Warehouses;256
8.2.1;Introduction;257
8.2.2;Related Works;257
8.2.3;Proposed Architecture;258
8.2.3.1;Crisp Records to Fuzzy Sets;259
8.2.3.2;Support and a-Cut Operations;261
8.2.3.3;Fuzzy Extension of Relational Views;262
8.2.4;Designing of ANN Topologies, Training and Prediction;263
8.2.5;Conclusion;265
8.2.6;References;265
8.3;Palmprint Based Verification System Using SURF Features;266
8.3.1;Introduction;266
8.3.2;Speeded Up Robust Features;267
8.3.2.1;Key-Point Detectors;267
8.3.2.2;Key-Point Descriptor;268
8.3.3;Proposed System;269
8.3.3.1;Image Acquisition;269
8.3.3.2;Pre-processing and Region of Interest Extraction;270
8.3.3.3;Palmprint Image Feature Extraction;271
8.3.4;Matching;271
8.3.4.1;Matching Method in SURF;271
8.3.4.2;Sub-image Matching;272
8.3.5;Experimental Results;273
8.3.5.1;IITK Database;273
8.3.5.2;PolyU Database;275
8.3.5.3;Speed;275
8.3.6;Conclusions;277
8.3.7;References;277
8.4;A Personnel Centric Knowledge Management System;279
8.4.1;Introduction;279
8.4.2;Related Work;280
8.4.3;The Model;281
8.4.3.1;Principle;281
8.4.3.2;Framework;281
8.4.3.3;Feedback;282
8.4.4;Database Creation;283
8.4.4.1;Algorithm;283
8.4.4.2;Example of the Algorithm;284
8.4.5;Database Modification and Maintenance;285
8.4.5.1;Algorithm;285
8.4.5.2;Description of Algorithm;285
8.4.6;Advantages of the Model;285
8.4.7;Relevance of Proposed Model to the Knowledge Pyramid Model in E-Government;286
8.4.8;Applications of the KMS Model in E-Governance and E-Government Services;286
8.4.9;Conclusion and Future Works;287
8.4.10;References;288
9;Technical Session-6: Application-2 (AP-2);13
9.1;A Protocol for Energy Efficient, Location Aware, Uniform and Grid Based Hierarchical Organization of Wireless Sensor Networks;289
9.1.1;Introduction;289
9.1.2;Related Work;290
9.1.3;System Model;291
9.1.4;Implementation;293
9.1.5;Performance Evaluations;296
9.1.6;Conclusion and Future Work;298
9.1.7;References;298
9.2;Swarm Intelligence Inspired Classifiers in Comparison with Fuzzy and Rough Classifiers: A Remote Sensing Approach;300
9.2.1;Introduction;300
9.2.2;Rough Set Theory;301
9.2.2.1;Dependancy of Attributes;302
9.2.3;Fuzzy Set Approach;303
9.2.4;cAnt Miner Algorithm;303
9.2.5;From ACO to Hybrid PSO/ACO Algorithm;304
9.2.6;Algorithm;305
9.2.7;Experimental Study;306
9.2.8;Results;307
9.2.9;Application;307
9.2.10;Conclusion and Future Work;308
9.2.11;References;308
9.3;CDIS: Circle Density Based Iris Segmentation;311
9.3.1;Introduction;311
9.3.2;Prior Work;312
9.3.3;Motivation and Contribution;313
9.3.3.1;Motivation;313
9.3.3.2;Contribution;313
9.3.4;The System;313
9.3.4.1;Introduction;313
9.3.4.2;Terms and Notations Used;314
9.3.4.3;System Flow Model;314
9.3.4.4;Image Pre-processing;315
9.3.4.5;Circle Density Based Iris Segmentation (CDIS);315
9.3.5;Experimental Results;320
9.3.6;Conclusion and Future Work;321
9.3.7;References;321
9.4;Text and Language-Independent Speaker Recognition Using Suprasegmental Features and Support Vector Machines;323
9.4.1;Introduction;323
9.4.2;Related Work;325
9.4.3;Suprasegmental Features in the LP Residual Signal;326
9.4.3.1;Computation of LP Residual from Speech Signal;326
9.4.3.2;Suprasegmental Features in the LP Residual Signal;326
9.4.4;The Hilbert Envelope of the LP Residual Signal;326
9.4.4.1;Computation of the Hilbert Envelope from Residual Signal;326
9.4.4.2;Presence of Suprasegmental Features in the Hilbert Envelope;327
9.4.5;Extraction of Suprasegmental Features Present in the Hilbert Envelope;328
9.4.6;Support Vector Machines for Speaker Recognition;330
9.4.7;Experimental Results;331
9.4.8;Conclusions and Future Work;331
9.4.9;References;332
9.5;Face Recognition Using Fisher Linear Discriminant Analysis and Support Vector Machine;334
9.5.1;Introduction;334
9.5.2;Proposed Method;335
9.5.3;Feature Extraction Using FLDA;335
9.5.4;Support Vector Machine (SVM);337
9.5.4.1;Basic Theory of SVM for Two Classes;337
9.5.4.2;SVM for Multiclass Recognition;338
9.5.5;Experimental Results;338
9.5.5.1;Randomly Partitioning the Database;339
9.5.5.2;N-Fold Cross Validation Test;340
9.5.6;Conclusion;341
9.5.7;References;341
10;Technical Session-7: Application-3 (AP-3);13
10.1;Threshold Signature Cryptography Scheme in Wireless Ad-Hoc Computing;343
10.1.1;Introduction;343
10.1.2;Tate Pairing;344
10.1.3;ID-Based (t, n) Threshold Signature Scheme from Tate Pairings;345
10.1.4;Security Analysis of Our Threshold Scheme;348
10.1.5;Performance Analysis;349
10.1.6;Conclusion;350
10.1.7;References;350
10.1.8;Appendix;351
10.2;Vehicular Traffic Control: A Ubiquitous Computing Approach;352
10.2.1;Introduction;352
10.2.1.1;Ubiquitous Database;353
10.2.1.2;Proposed Method;353
10.2.1.3;Organization of the Rest of the Paper;353
10.2.2;Existing Works;354
10.2.3;Proposed Vehicular Traffic Control System;354
10.2.3.1;URAs in the Network;355
10.2.3.2;Agents for Information Dissemination;356
10.2.3.3;Vehicular Traffic Monitoring and Control;357
10.2.3.4;Vehicle Tracing;357
10.2.3.5;Creation of Ubiquitous Database;358
10.2.4;Simulation;360
10.2.4.1;Simulation Environment;360
10.2.4.2;Simulation Procedure;360
10.2.5;Illustration of Ubiquitous Database in Traffic Monitoring;363
10.2.6;Conclusion;363
10.2.7;References;363
10.3;Application of Particle Swarm Optimization Algorithm for Better Nano-Devices;365
10.3.1;Introduction;365
10.3.2;Analytical Model;366
10.3.3;Particle Swarm Optimization;368
10.3.4;Results and Discussions;369
10.3.5;Conclusion;373
10.3.6;References;373
10.4;Measuring the Storage and Retrieval of Knowledge Units: An Empirical Study Using MES;374
10.4.1;Introduction;374
10.4.2;Knowledge Based Storage Models;375
10.4.2.1;The Object-Structured Model (OSM);375
10.4.2.2;Concept-Structured Model (CSM);376
10.4.3;Measuring the ORSR;377
10.4.3.1;Object Size (OS);377
10.4.3.2;Relational Coupling (RC);378
10.4.4;Empirical Investigation;378
10.4.4.1;Overview of MES;379
10.4.4.2;Retrieval of OSM Objects;380
10.4.4.3;Metric Results and Interpretation;381
10.4.5;Conclusion;383
10.4.6;References;383
10.5;Implementation of QoS Aware Q-Routing Algorithm for Network-on-Chip;386
10.5.1;Introduction;386
10.5.2;NoC Simulation Framework;388
10.5.3;Experimental Setup;388
10.5.4;Routing Algorithms;389
10.5.4.1;XY Routing;389
10.5.4.2;Proposed Adaptation of Q-Routing for QoS;389
10.5.5;Results;392
10.5.5.1;Result Analysis;392
10.5.6;Conclusion and Future Work;395
10.5.7;References;396
11;Technical Session-8: Application-4 (AP-4);14
11.1;Color Image Restoration Using Morphological Detectors and Adaptive Filter;397
11.1.1;Introduction;397
11.1.2;Proposed Method;398
11.1.2.1;Morphological Noise Detection;398
11.1.2.2;Stage-I of Noise Detection;398
11.1.2.3;Stage-II of Noise Detection;399
11.1.2.4;Noise Elimination;399
11.1.3;Experimental Results;400
11.1.4;Conclusions;404
11.1.5;References;404
11.2;Secure Multi-party Computation Protocol for Defense Applications in Military Operations Using Virtual Cryptography;405
11.2.1;Introduction;406
11.2.2;Problem Statement;407
11.2.2.1;Description;407
11.2.3;VPP (Virtual Party Protocol);408
11.2.3.1;Informal Description;408
11.2.3.2;Formal Description;410
11.2.4;Security Analysis;411
11.2.5;Conclusion;414
11.2.6;References;415
11.3;An Angle QIM Watermarking in STDM Framework Robust against Amplitude Scaling Distortions;416
11.3.1;Introduction;416
11.3.2;Preliminaries;418
11.3.2.1;Quantization Index Modulation (QIM);418
11.3.2.2;Spread Transform Dither Modulation STDM;419
11.3.3;Angle Quantization Index Modulation (AQIM);419
11.3.4;Result and Discussions;421
11.3.5;Conclusion;425
11.3.6;References;425
11.4;Parallelization Issues of Domain Specific Question Answering System on Cell B.E. Processors;427
11.4.1;Introduction;427
11.4.2;INDOC;428
11.4.3;Sony-Toshiba IBM Cell BE Architecture;430
11.4.4;Issues in Porting a Typical Question Answering System;431
11.4.5;Potential Solutions to the Issues;432
11.4.6;Design and Implementation of INDOC Indexing Algorithm on CELL BE;433
11.4.6.1;PPE;433
11.4.6.2;SPE;433
11.4.7;General Observations;435
11.4.8;Results and Conclusion;435
11.4.9;Future Work;435
11.4.10;References;436
11.5;Security Issues in Cross-Organizational Peer-to-Peer Applications and Some Solutions;438
11.5.1;Introduction;438
11.5.2;Security Issues in P2P Systems;440
11.5.3;Peer Enterprises: Enabling Cross-Organizational P2P Interactions;442
11.5.4;Security Solutions;443
11.5.4.1;Identity Management;443
11.5.4.2;Ensuring Security of Individual Peers;443
11.5.4.3;Ensuring Security of Organization’s Data;444
11.5.4.4;Dealing with Malicious Peers;446
11.5.5;Conclusions;447
11.5.6;References;448
11.6;Protocols for Secure Node-to-Cluster Head Communication in Clustered Wireless Sensor Networks;450
11.6.1;Introduction;450
11.6.2;Problem Statement;452
11.6.3;New Protocols for Dynamic Key Establishment;453
11.6.3.1;Hash Based Protocol;453
11.6.3.2;Polynomial-Based Protocol;454
11.6.4;Analysis of the Proposed Protocols;456
11.6.4.1;Security Analysis;457
11.6.4.2;Performance Analysis;457
11.6.5;Conclusion;458
11.6.6;References;459
12;Technical Session-9: Bioinformatics-1 (B-1);14
12.1;Significant Deregulated Pathways in Diabetes Type II Complications Identified through Expression Based Network Biology;461
12.1.1;Introduction;461
12.1.2;Materials and Methods;462
12.1.3;Results and Discussion;463
12.1.3.1;Linking Stress Inflammation, Insulin Resistance and CVD Complications via Significant Signaling Molecules Such as P85A;463
12.1.3.2;SRC as a Link between Type 2 Diabetes and Cardiovascular Phenotype via NF-kB and Beta Catenin Interaction;464
12.1.3.3;Statistical Validation;466
12.1.4;References;468
12.2;Study of Drug-Nucleic Acid Interactions: 9-amino- [N-2-(4-morpholinyl)ethyl]acridine-4-carboxamide;470
12.2.1;Introduction;470
12.2.2;Method of Calculation;471
12.2.3;Results and Discussion;471
12.2.4;Conclusion;475
12.2.5;References;476
12.3;IDChase: Mitigating Identifier Migration Trap in Biological Databases;477
12.3.1;Introduction;477
12.3.1.1;A Motivating Example;478
12.3.2;Chasing ID Migration;481
12.3.2.1;Detecting Database Inconsistency in Materialized Views;482
12.3.2.2;Autonomous Online Reconciliation of ID Inconsistencies;483
12.3.3;ID Migration Management Using IDChase;485
12.3.3.1;IDChase as a Mechanism for Change Management;485
12.3.4;Summary and Future Research;487
12.3.5;References;488
12.4;Multi-domain Protein Family Classification Using Isomorphic Inter-property Relationships;489
12.4.1;Introduction;489
12.4.1.1;Homology Using Domain Information;490
12.4.1.2;Sequence and Structural Information;491
12.4.1.3;Dataset;492
12.4.2;Methodology;493
12.4.2.1;Feature Extraction;493
12.4.2.2;Vector Quantization;494
12.4.2.3;Association Rule Mining;495
12.4.2.4;Classifier Training;496
12.4.3;Results and Discussions;497
12.4.3.1;Multi-domain Classification;497
12.4.3.2;Superfamily Classification;497
12.4.3.3;Classification between Single and Multi-domain Proteins;498
12.4.4;Conclusion;499
12.4.5;References;500
12.5;IITKGP-SESC: Speech Database for Emotion Analysis;501
12.5.1;Introduction;501
12.5.2;IITKGP-SESC (Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus);503
12.5.3;Prosodic Analysis;504
12.5.4;Subjective Evaluation;507
12.5.5;Summary and Conclusions;508
12.5.6;References;508
13;Technical Session-10: Bioinformatics-2 (B-2);15
13.1;Detection of Splice Sites Using Support Vector Machine;509
13.1.1;Introduction;509
13.1.2;Materials and Methods;511
13.1.3;Result and Discussion;514
13.1.4;Conclusion and Future Work;516
13.1.5;References;516
13.2;Gibbs Motif Sampler, Weight Matrix and Artificial Neural Network for the Prediction of MHC Class-II Binding Peptides;519
13.2.1;Introduction;519
13.2.2;Materials and Methods;520
13.2.2.1;Wang et al. 2008 Dataset;520
13.2.2.2;Algorithms Used for the Prediction of MHC Class-II Binding Peptides;520
13.2.2.3;Evaluation Parameters;521
13.2.3;Results and Discussion;521
13.2.4;Conclusions;523
13.2.5;References;524
13.3;Classification of Phylogenetic Profiles for Protein Function Prediction: An SVM Approach;526
13.3.1;Introduction;526
13.3.2;Brief Review;528
13.3.3;Methods;529
13.3.3.1;Support Vector Machine;529
13.3.4;Data;530
13.3.5;Results and Discussion;531
13.3.5.1;Conclusion;535
13.3.5.2;References;535
13.4;FCM for Gene Expression Bioinformatics Data;537
13.4.1;Introduction;537
13.4.2;Related Work;538
13.4.3;Methodology;538
13.4.3.1;Problem Definition;538
13.4.3.2;Clustering;538
13.4.3.3;K-Means Clustering Algorithm;539
13.4.3.4;Fuzzy C-Means Clustering Algorithm;539
13.4.4;Experimental Work;539
13.4.4.1;Datasets;539
13.4.4.2;Parameter Selection for FCM;540
13.4.4.3;Results and Discussion;541
13.4.5;Conclusion;546
13.4.6;References;546
13.5;Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection;549
13.5.1;Introduction;549
13.5.2;Related Works;551
13.5.3;Dataset;552
13.5.4;Experimental Analysis;552
13.5.4.1;Experimental Design;552
13.5.4.2;Measures for Performance Evaluation;553
13.5.5;Results and Discussion;554
13.5.6;Conclusions;557
13.5.7;References;557
14;Technical Session-11: System-1 (S-1);15
14.1;An Electronically Tunable SIMO Biquad Filter Using CCCCTA;560
14.1.1;Introduction;560
14.1.2;Current Controlled Current Conveyor Transconductance Amplifier (CCCCTA);561
14.1.3;Proposed Configuration;562
14.1.4;Non Ideal Analysis;565
14.1.5;Sensitivity Analysis;566
14.1.6;Simulation Results;566
14.1.7;Conclusion;569
14.1.8;References;570
14.2;An Architecture for Cross-Cloud System Management;572
14.2.1;Introduction;572
14.2.2;System Management of Compute Resources;574
14.2.3;An Architecture for Cross-Cloud System Management;575
14.2.4;An Implementation Using Amazon Elastic Compute Cloud (EC2);577
14.2.4.1;User Interface of Architecture for Cross-Cloud System Management;579
14.2.5;Empirical Results;580
14.2.6;Conclusion;582
14.2.7;References;582
14.3;Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models;584
14.3.1;Introduction;584
14.3.2;Preliminaries;585
14.3.3;ALHI Model and XInt Job Scheduling Algorithms;586
14.3.3.1;The Abstract Linear Heat Interference Model;587
14.3.3.2;The XInt Algorithm Family;587
14.3.4;Cooling Models;588
14.3.5;Energy Consumption under Various Cooling Models;590
14.3.5.1;Simulation Setup;590
14.3.5.2;Energy Savings of under Constant Cooling Model;591
14.3.5.3;Energy Consumption under the Step-Wise Linear Cooling Model;592
14.3.6;Discussion and Conclusions;595
14.3.7;References;595
14.4;Predicting Maintainability of Component-Based Systems by Using Fuzzy Logic;597
14.4.1;Introduction;597
14.4.2;Maintainability;598
14.4.2.1;Maintainability Challenges for CBS;598
14.4.2.2;Estimating Maintainability;599
14.4.3;Proposed Fuzzy-Based Approach for Estimating Maintainability of CBS;600
14.4.3.1;Fuzzy Logic;601
14.4.3.2;Implementation of Fuzzy Logic;602
14.4.3.3;Experimental Desig;602
14.4.3.4;Empirical Evaluation;603
14.4.3.5;Validation of the Proposed Model;605
14.4.4;Conclusions;605
14.4.5;References;606
14.5;Energy-Constrained Scheduling of DAGs on Multi-core Processors;608
14.5.1;Introduction;608
14.5.2;Related Work;609
14.5.3;Task and Energy Model;610
14.5.3.1;LBL Algorithm;610
14.5.3.2;DCP Algorithm;611
14.5.4;ISVA Techniques;612
14.5.5;Testing Methodology;614
14.5.5.1;Workload Generation;614
14.5.5.2;Variable Values Used in Testing;615
14.5.6;Experimental Results;615
14.5.7;Future Research and Conclusions;617
14.5.8;References;618
15;Technical Session-12: System-2 (S-2);16
15.1;Evaluation of Code and Data Spatial Complexity Measures;620
15.1.1;Introduction;620
15.1.2;Code Spatial Complexity Measures;621
15.1.3;Data Spatial Complexity Measures;622
15.1.4;Evaluation of Spatial Measures Using Weyuker Properties;622
15.1.5;Validation of Spatial Measures Using Briand’s Criteria;627
15.1.6;Related Work;628
15.1.7;Conclusion;629
15.1.8;References;629
15.2;Pitcherpot: Avoiding Honeypot Detection;631
15.2.1;Introduction;631
15.2.2;Honeypot Identification;632
15.2.2.1;Traffic Scans in the Network;632
15.2.2.2;Send Fake Packets to Honeypots;633
15.2.2.3;Connection to System Results in No Acknowledgement;633
15.2.2.4;Detection of Virtual Computers;633
15.2.2.5;Proxy Checking through a Third Source;635
15.2.2.6;Cannot Handle Multiple Attacks at the Same Time;635
15.2.3;Architecture Proposed for Pitcherpot Systems;635
15.2.3.1;Way a System Is Imitated into the Proposed Pitcherpot;636
15.2.4;Detail of Flow Graph;637
15.2.5;Important Findings;639
15.2.6;Conclusion and Future Works;640
15.2.7;References;640
15.3;Verification of Liveness Properties in Distributed Systems;641
15.3.1;Introduction;641
15.3.2;Safety and Liveness in the Event-B Models;642
15.3.3;Event-B Models of Distributed Transactions;643
15.3.3.1;Abstract Transaction States;643
15.3.3.2;Refined Transaction States;644
15.3.4;Non-divergence;645
15.3.5;Enabledness Preservation;648
15.3.6;Conclusions;651
15.3.7;References;652
15.4;InfoSec-MobCop – Framework for Theft Detection and Data Security on Mobile Computing Devices;653
15.4.1;Introduction;653
15.4.2;Previous Works;654
15.4.3;Motivation and Contribution;655
15.4.4;Terms and Notations;655
15.4.5;InfoSec-MobCop System;656
15.4.5.1;System Design;656
15.4.5.2;Validation Components;660
15.4.6;Experiment and Results;661
15.4.7;Conclusions;663
15.4.8;References;664
15.5;Multi-scale Modeling and Analysis of Nano-RFID Systems on HPC Setup;665
15.5.1;Introduction;665
15.5.2;Modeling of RFID MEMS Devices;667
15.5.3;Modeling of Abstraction Level RFID;668
15.5.4;Implementation of Multi-scale Modeling on MCCS, Designing the Framework for NANO RFID;669
15.5.5;Conclusion;673
15.5.6;References;674
16;Author Index;676
