Algorithms and data structures Books

Book SynopsisThis comprehensive guide describes how evolutionary algorithms (EA) may be used to identify, model, and optimize day-to-day problems that arise for researchers in optimization and mobile networking.Table of ContentsPreface xiii PART I BASIC CONCEPTS AND LITERATURE REVIEW 1 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 1.1 Mobile Ad Hoc Networks 6 1.2 Vehicular Ad Hoc Networks 9 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 1.2.2 Communication Access for Land Mobiles (CALM) 12 1.2.3 C2C Network 13 1.3 Sensor Networks 14 1.3.1 IEEE 1451 17 1.3.2 IEEE 802.15.4 17 1.3.3 ZigBee 18 1.3.4 6LoWPAN 19 1.3.5 Bluetooth 19 1.3.6 Wireless Industrial Automation System 20 1.4 Conclusion 20 References 21 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 2.1 Optimization Basics 28 2.2 Evolutionary Algorithms 29 2.3 Basic Components of Evolutionary Algorithms 32 2.3.1 Representation 32 2.3.2 Fitness Function 32 2.3.3 Selection 32 2.3.4 Crossover 33 2.3.5 Mutation 34 2.3.6 Replacement 35 2.3.7 Elitism 35 2.3.8 Stopping Criteria 35 2.4 Panmictic Evolutionary Algorithms 36 2.4.1 Generational EA 36 2.4.2 Steady-State EA 36 2.5 Evolutionary Algorithms with Structured Populations 36 2.5.1 Cellular EAs 37 2.5.2 Cooperative Coevolutionary EAs 38 2.6 Multi-Objective Evolutionary Algorithms 39 2.6.1 Basic Concepts in Multi-Objective Optimization 40 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 2.7 Conclusion 44 References 45 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 3.1 Taxonomy of the Optimization Process 51 3.1.1 Online and Offline Techniques 51 3.1.2 Using Global or Local Knowledge 52 3.1.3 Centralized and Decentralized Systems 52 3.2 State of the Art 53 3.2.1 Topology Management 53 3.2.2 Broadcasting Algorithms 58 3.2.3 Routing Protocols 59 3.2.4 Clustering Approaches 63 3.2.5 Protocol Optimization 64 3.2.6 Modeling the Mobility of Nodes 65 3.2.7 Selfish Behaviors 66 3.2.8 Security Issues 67 3.2.9 Other Applications 67 3.3 Conclusion 68 References 69 4 MOBILE NETWORKS SIMULATION 79 4.1 Signal Propagation Modeling 80 4.1.1 Physical Phenomena 81 4.1.2 Signal Propagation Models 85 4.2 State of the Art of Network Simulators 89 4.2.1 Simulators 89 4.2.2 Analysis 92 4.3 Mobility Simulation 93 4.3.1 Mobility Models 93 4.3.2 State of the Art of Mobility Simulators 96 4.4 Conclusion 98 References 98 PART II PROBLEMS OPTIMIZATION 105 5 PROPOSED OPTIMIZATION FRAMEWORK 107 5.1 Architecture 108 5.2 Optimization Algorithms 110 5.2.1 Single-Objective Algorithms 110 5.2.2 Multi-Objective Algorithms 115 5.3 Simulators 121 5.3.1 Network Simulator: ns-3 121 5.3.2 Mobility Simulator: SUMO 123 5.3.3 Graph-Based Simulations 126 5.4 Experimental Setup 127 5.5 Conclusion 131 References 131 6 BROADCASTING PROTOCOL 135 6.1 The Problem 136 6.1.1 DFCN Protocol 136 6.1.2 Optimization Problem Definition 138 6.2 Experiments 140 6.2.1 Algorithm Configurations 140 6.2.2 Comparison of the Performance of the Algorithms 141 6.3 Analysis of Results 142 6.3.1 Building a Representative Subset of Best Solutions 143 6.3.2 Interpretation of the Results 145 6.3.3 Selected Improved DFCN Configurations 148 6.4 Conclusion 150 References 151 7 ENERGY MANAGEMENT 153 7.1 The Problem 154 7.1.1 AEDB Protocol 154 7.1.2 Optimization Problem Definition 156 7.2 Experiments 159 7.2.1 Algorithm Configurations 159 7.2.2 Comparison of the Performance of the Algorithms 160 7.3 Analysis of Results 161 7.4 Selecting Solutions from the Pareto Front 164 7.4.1 Performance of the Selected Solutions 167 7.5 Conclusion 170 References 171 8 NETWORK TOPOLOGY 173 8.1 The Problem 175 8.1.1 Injection Networks 175 8.1.2 Optimization Problem Definition 176 8.2 Heuristics 178 8.2.1 Centralized 178 8.2.2 Distributed 179 8.3 Experiments 180 8.3.1 Algorithm Configurations 180 8.3.2 Comparison of the Performance of the Algorithms 180 8.4 Analysis of Results 183 8.4.1 Analysis of the Objective Values 183 8.4.2 Comparison with Heuristics 185 8.5 Conclusion 187 References 188 9 REALISTIC VEHICULAR MOBILITY 191 9.1 The Problem 192 9.1.1 Vehicular Mobility Model 192 9.1.2 Optimization Problem Definition 196 9.2 Experiments 199 9.2.1 Algorithms Configuration 199 9.2.2 Comparison of the Performance of the Algorithms 200 9.3 Analysis of Results 202 9.3.1 Analysis of the Decision Variables 202 9.3.2 Analysis of the Objective Values 204 9.4 Conclusion 206 References 206 10 SUMMARY AND DISCUSSION 209 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 10.2 Performance of the Three Algorithmic Proposals 213 10.2.1 Broadcasting Protocol 213 10.2.2 Energy-Efficient Communications 214 10.2.3 Network Connectivity 214 10.2.4 Vehicular Mobility 215 10.3 Global Discussion on the Performance of the Algorithms 215 10.3.1 Single-Objective Case 216 10.3.2 Multi-Objective Case 217 10.4 Conclusion 218 References 218 INDEX 221

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Book SynopsisThe confluence of cloud computing, parallelism and advanced machine intelligence approaches has created a world in which the optimum knowledge system will usually be architected from the combination of two or more knowledge-generating systems. There is a need, then, to provide a reusable, broadly-applicable set of design patterns to empower the intelligent system architect to take advantage of this opportunity. This book explains how to design and build intelligent systems that are optimized for changing system requirements (adaptability), optimized for changing system input (robustness), and optimized for one or more other important system parameters (e.g., accuracy, efficiency, cost). It provides an overview of traditional parallel processing which is shown to consist primarily of task and component parallelism; before introducing meta-algorithmic parallelism which is based on combining two or more algorithms, classification engines or other systems. Key features:Table of Contents1 Introduction and Overview 1 1.1 Introduction 1 1.2 Why Is This Book Important? 2 1.3 Organization of the Book 3 1.4 Informatics 4 1.5 Ensemble Learning 6 1.6 Machine Learning/Intelligence 7 1.7 Artificial Intelligence 22 1.8 Data Mining/Knowledge Discovery 31 1.9 Classification 32 1.10 Recognition 38 1.11 System-Based Analysis 39 1.12 Summary 39 References 40 2 Parallel Forms of Parallelism 42 2.1 Introduction 42 2.2 Parallelism by Task 43 2.3 Parallelism by Component 52 2.4 Parallelism by Meta-algorithm 64 2.5 Summary 71 References 72 3 Domain Areas: Where Is This Relevant? 73 3.1 Introduction 73 3.2 Overview of the Domains 74 3.3 Primary Domains 75 3.4 Secondary Domains 86 3.5 Summary 101 References 102 4 Applications of Parallelism by Task 104 4.1 Introduction 104 4.2 Primary Domains 105 4.3 Summary 135 References 136 5 Application of Parallelism by Component 137 5.1 Introduction 137 5.2 Primary Domains 138 5.3 Summary 172 References 173 6 Introduction to Meta-algorithmics 175 6.1 Introduction 175 6.2 First-Order Meta-algorithmics 178 6.3 Second-Order Meta-algorithmics 195 6.4 Third-Order Meta-algorithmics 218 6.5 Summary 240 References 240 7 First-Order Meta-algorithmics and Their Applications 241 7.1 Introduction 241 7.2 First-Order Meta-algorithmics and the “Black Box” 241 7.3 Primary Domains 242 7.4 Secondary Domains 257 7.5 Summary 271 References 271 8 Second-Order Meta-algorithmics and Their Applications 272 8.1 Introduction 272 8.2 Second-Order Meta-algorithmics and Targeting the “Fringes” 273 8.3 Primary Domains 279 8.4 Secondary Domains 304 8.5 Summary 308 References 308 9 Third-Order Meta-algorithmics and Their Applications 310 9.1 Introduction 310 9.2 Third-Order Meta-algorithmic Patterns 311 9.3 Primary Domains 313 9.4 Secondary Domains 328 9.5 Summary 340 References 341 10 Building More Robust Systems 342 10.1 Introduction 342 10.2 Summarization 342 10.3 Cloud Systems 350 10.4 Mobile Systems 353 10.5 Scheduling 355 10.6 Classification 356 10.7 Summary 358 Reference 359 11 The Future 360 11.1 Recapitulation 360 11.2 The Pattern of all Patience 362 11.3 Beyond the Pale 365 11.4 Coming Soon 367 11.5 Summary 368 References 368 Index

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Book SynopsisEfficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert systemTable of ContentsPreface xv 1 Smart Health Care Development: Challenges and Solutions 1R. Sujatha, E.P. Ephzibah and S. Sree Dharinya 1.1 Introduction 2 1.2 ICT Explosion 3 1.2.1 RFID 4 1.2.2 IoT and Big Data 5 1.2.3 Wearable Sensors—Head to Toe 7 1.2.4 Cloud Computing 8 1.3 Intelligent Healthcare 10 1.4 Home Healthcare 11 1.5 Data Analytics 11 1.6 Technologies—Data Cognitive 13 1.6.1 Machine Learning 13 1.6.2 Image Processing 14 1.6.3 Deep Learning 14 1.7 Adoption Technologies 15 1.8 Conclusion 15 References 15 2 Working of Mobile Intelligent Agents on the Web—A Survey 21P.R. Joe Dhanith and B. Surendiran 2.1 Introduction 21 2.2 Mobile Crawler 23 2.3 Comparative Study of the Mobile Crawlers 47 2.4 Conclusion 47 References 47 3 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid 49T. Bharani Prakash and S. Nagakumararaj 3.1 Power Management Scheme 50 3.2 Internal Power Flow Management 50 3.2.1 PI Controller 51 3.2.2 State of Charge 53 3.3 Voltage Source Control 54 3.3.1 Phase-Locked Loop 55 3.3.2 Space Vector Pulse Width Modulation 56 3.3.3 Park Transformation (abc to dq0) 57 3.4 Simulation Diagram and Results 58 3.4.1 Simulation Diagram 58 3.4.2 Simulation Results 63 Conclusion 65 4 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models 67M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari 4.1 Introduction 68 4.2 Channel Equalization 72 4.2.1 Channel Models 73 4.2.1.1 Tapped Delay Line Model 74 4.2.1.2 Stanford University Interim (SUI) Channel Models 75 4.2.2 Artificial Neural Network 75 4.3 Functional Link Artificial Neural Network 76 4.4 Particle Swarm Optimization 76 4.5 Result and Discussion 77 4.5.1 Convergence Analysis 77 4.5.2 Comparison Between Different Parameters 79 4.5.3 Comparison Between Different Channel Models 80 4.6 Conclusion 81 References 82 5 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment 85P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey 5.1 Introduction 86 5.1.1 Hadoop Architecture 86 5.1.2 Hadoop and Big Data 88 5.1.3 Hadoop and Virtualization 88 5.1.4 What is OpenNebula? 89 5.2 Literature Survey 90 5.2.1 Performance Analysis of Hadoop 90 5.2.2 Evaluating Map Reduce on Virtual Machines 91 5.2.3 Virtualizing Hadoop Containers 94 5.2.4 Optimization of Hadoop Cluster Using Cloud Platform 95 5.2.5 Heterogeneous Clusters in Cloud Computing 96 5.2.6 Performance Analysis and Optimization in Hadoop 97 5.2.7 Virtual Technologies 97 5.2.8 Scheduling 98 5.2.9 Scheduling of Hadoop VMs 98 5.3 Discussion 99 5.4 Conclusion 100 References 101 6 Transmission Line Inspection Using Unmanned Aerial Vehicle 105A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev 6.1 Introduction 106 6.1.1 Unmanned Aerial Vehicle 106 6.1.2 Quadcopter 106 6.2 Literature Survey 107 6.3 System Architecture 108 6.4 ArduPilot 109 6.5 Arduino Mega 111 6.6 Brushless DC Motor 111 6.7 Battery 112 6.8 CMOS Camera 113 6.9 Electronic Speed Control 113 6.10 Power Module 115 6.11 Display Shield 116 6.12 Navigational LEDS 116 6.13 Role of Sensors in the Proposed System 118 6.13.1 Accelerometer and Gyroscope 118 6.13.2 Magnetometer 118 6.13.3 Barometric Pressure Sensor 119 6.13.4 Global Positioning System 119 6.14 Wireless Communication 120 6.15 Radio Controller 120 6.16 Telemetry Radio 121 6.17 Camera Transmitter 121 6.18 Results and Discussion 121 6.19 Conclusion 124 References 125 7 Smart City Infrastructure Management System Using IoT 127S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul 7.1 Introduction 128 7.2 Major Challenges in IoT-Based Technology 129 7.2.1 Peer to Peer Communication Security 129 7.2.2 Objective of Smart Infrastructure 130 7.3 Internet of Things (IoT) 131 7.3.1 Key Components of Components of IoT 131 7.3.1.1 Network Gateway 132 7.3.1.2 HTTP (HyperText Transfer Protocol) 132 7.3.1.3 LoRaWan (Long Range Wide Area Network) 133 7.3.1.4 Bluetooth 133 7.3.1.5 ZigBee 133 7.3.2 IoT Data Protocols 133 7.3.2.1 Message Queue Telemetry Transport (MQTT) 133 7.3.2.2 Constrained Application Protocol (CoAP) 134 7.3.2.3 Advanced Message Queuing Protocol (AMQP) 134 7.3.2.4 Data Analytics 134 7.4 Machine Learning-Based Smart Decision-Making Process 135 7.5 Cloud Computing 136 References 138 8 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices 139S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar 8.1 Introduction 139 8.1.1 Need of the Cryptography 140 8.2 Challenges on Lightweight Cryptography 141 8.3 Hashing Techniques on Lightweight Cryptography 142 8.4 Applications on Lighweight Cryptography 152 8.5 Conclusion 167 References 168 9 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map 171K. Rajathi and P. Sarasu 9.1 Introduction 172 9.2 Related Work 173 9.2.1 Semantic Segmentation for Images 173 9.3 Semantic Segmentation for LiDAR Point Cloud 173 9.4 Proposed Work 175 9.4.1 Data Acquisition 175 9.4.2 Our Approach 175 9.4.3 Pre-Learning Processing 179 9.5 Region of Interest (RoI) 180 9.6 Registration of Point Cloud 181 9.7 Semantic Segmentation 181 9.8 Self-Organized Map (SOM) 182 9.9 Experimental Result 183 9.10 Conclusion 186 References 187 10 Smart Load Balancing Algorithms in Cloud Computing—A Review 189K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan 10.1 Introduction 189 10.2 Research Challenges 192 10.2.1 Security & Routing 192 10.2.2 Storage/Replication 192 10.2.3 Spatial Spread of the Cloud Nodes 192 10.2.4 Fault Tolerance 193 10.2.5 Algorithm Complexity 193 10.3 Literature Survey 193 10.4 Survey Table 201 10.5 Discussion & Comparison 202 10.6 Conclusion 202 References 216 11 A Low-Cost Wearable Remote Healthcare Monitoring System 219Konguvel Elango and Kannan Muniandi 11.1 Introduction 219 11.1.1 Problem Statement 220 11.1.2 Objective of the Study 221 11.2 Related Works 222 11.2.1 Remote Healthcare Monitoring Systems 222 11.2.2 Pulse Rate Detection 224 11.2.3 Temperate Measurement 225 11.2.4 Fall Detection 225 11.3 Methodology 226 11.3.1 NodeMCU 226 11.3.2 Pulse Rate Detection System 227 11.3.3 Fall Detection System 230 11.3.4 Temperature Detection System 231 11.3.5 LCD Specification 234 11.3.6 ADC Specification 234 11.4 Results and Discussions 236 11.4.1 System Implementation 236 11.4.2 Fall Detection Results 236 11.4.3 ThingSpeak 236 11.5 Conclusion 239 11.6 Future Scope 240 References 241 12 IoT-Based Secure Smart Infrastructure Data Management 243R. Poorvadevi, M. Kowsigan, P. Balasubramanie and J. Rajeshkumar 12.1 Introduction 244 12.1.1 List of Security Threats Related to the Smart IoT Network 244 12.1.2 Major Application Areas of IoT 244 12.1.3 IoT Threats and Security Issues 245 12.1.4 Unpatched Vulnerabilities 245 12.1.5 Weak Authentication 245 12.1.6 Vulnerable API’s 245 12.2 Types of Threats to Users 245 12.3 Internet of Things Security Management 246 12.3.1 Managing IoT Devices 246 12.3.2 Role of External Devices in IoT Platform 247 12.3.3 Threats to Other Computer Networks 248 12.4 Significance of IoT Security 249 12.4.1 Aspects of Workplace Security 249 12.4.2 Important IoT Security Breaches and IoT Attacks 250 12.5 IoT Security Tools and Legislation 250 12.6 Protection of IoT Systems and Devices 251 12.6.1 IoT Issues and Security Challenges 251 12.6.2 Providing Secured Connections 252 12.7 Five Ways to Secure IoT Devices 253 12.8 Conclusion 255 References 255 13 A Study of Addiction Behavior for Smart Psychological Health Care System 257V. Sabapathi and K.P. Vijayakumar 13.1 Introduction 258 13.2 Basic Criteria of Addiction 258 13.3 Influencing Factors of Addiction Behavior 259 13.3.1 Peers Influence 259 13.3.2 Environment Influence 260 13.3.3 Media Influence 262 13.3.4 Family Group and Society 262 13.4 Types of Addiction and Their Effects 262 13.4.1 Gaming Addiction 263 13.4.2 Pornography Addiction 264 13.4.3 Smart Phone Addiction 265 13.4.4 Gambling Addiction 267 13.4.5 Food Addiction 267 13.4.6 Sexual Addiction 268 13.4.7 Cigarette and Alcohol Addiction 268 13.4.8 Status Expressive Addiction 269 13.4.9 Workaholic Addiction 269 13.5 Conclusion 269 References 270 14 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud 273Sukesh, B., Venkatesh, K. and Srinivas, L.N.B. 14.1 Introduction 274 14.2 Cluster Design with Raspberry Pi 276 14.2.1 Assembling Materials for Implementing Cluster 276 14.2.1.1 Raspberry Pi4 277 14.2.1.2 RPi 4 Model B Specifications 277 14.2.2 Setting Up Cluster 278 14.2.2.1 Installing Raspbian and Configuring Master Node 279 14.2.2.2 Installing MPICH and MPI4PY 279 14.2.2.3 Cloning the Slave Nodes 279 14.3 Parallel Computing and MPI on Raspberry Pi Cluster 279 14.4 Deployment of Private Cloud on Raspberry Pi Cluster 281 14.4.1 NextCloud Software 281 14.5 Implementation 281 14.5.1 NextCloud on RPi Cluster 281 14.5.2 Parallel Computing on RPi Cluster 282 14.6 Results and Discussions 286 14.7 Conclusion 287 References 287 15 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing 289Karthikeyan, K. and Madhavan, P. 15.1 Introduction 290 15.2 Energy Efficiency Offloading Data Transmission 290 15.2.1 Web-Based Offloading 291 15.3 Energy Harvesting 291 15.3.1 LODCO Algorithm 292 15.4 User-Level Online Offloading Framework (ULOOF) 293 15.5 Frequency Scaling 294 15.6 Computation Offloading and Resource Allocation 295 15.7 Communication Technology 296 15.8 Ultra-Dense Network 297 15.9 Conclusion 299 References 299 16 Blockchain-Based SDR Signature Scheme With Time-Stamp 303Swathi Singh, Divya Satish and Sree Rathna Lakshmi 16.1 Introduction 303 16.2 Literature Study 304 16.2.1 Signatures With Hashes 304 16.2.2 Signature Scheme With Server Support 305 16.2.3 Signatures Scheme Based on Interaction 305 16.3 Methodology 306 16.3.1 Preliminaries 306 16.3.1.1 Hash Trees 306 16.3.1.2 Chains of Hashes 306 16.3.2 Interactive Hash-Based Signature Scheme 307 16.3.3 Significant Properties of Hash-Based Signature Scheme 309 16.3.4 Proposed SDR Scheme Structure 310 16.3.4.1 One-Time Keys 310 16.3.4.2 Server Behavior Authentication 310 16.3.4.3 Pre-Authentication by Repository 311 16.4 SDR Signature Scheme 311 16.4.1 Pre-Requisites 311 16.4.2 Key Generation Algorithm 312 16.4.2.1 Server 313 16.4.3 Sign Algorithm 313 16.4.3.1 Signer 313 16.4.3.2 Server 313 16.4.3.3 Repository 314 16.4.4 Verification Algorithm 314 16.5 Supportive Theory 315 16.5.1 Signing Algorithm Supported by Server 315 16.5.2 Repository Deployment 316 16.5.3 SDR Signature Scheme Setup 316 16.5.4 Results and Observation 316 16.6 Conclusion 317 References 317 Index 321

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Book SynopsisALGORITHMS IN BIOINFORMATICS Explore a comprehensive and insightful treatment of the practical application of bioinformatic algorithms in a variety of fields Algorithms in Bioinformatics: Theory and Implementation delivers a fulsome treatment of some of the main algorithms used to explain biological functions and relationships. It introduces readers to the art of algorithms in a practical manner which is linked with biological theory and interpretation. The book covers many key areas of bioinformatics, including global and local sequence alignment, forced alignment, detection of motifs, Sequence logos, Markov chains or information entropy. Other novel approaches are also described, such as Self-Sequence alignment, Objective Digital Stains (ODSs) or Spectral Forecast and the Discrete Probability Detector (DPD) algorithm. The text incorporates graphical illustrations to highlight and emphasize the technical details oTable of ContentsPreface xv About the Companion Website xvii 1 The Tree of Life (I) 1 1.1 Introduction 1 1.2 Emergence of Life 1 1.2.1 Timeline Disagreements 3 1.3 Classifications and Mechanisms 4 1.4 Chromatin Structure 5 1.5 Molecular Mechanisms 9 1.5.1 Precursor Messenger RNA 9 1.5.2 Precursor Messenger RNA to Messenger RNA 10 1.5.3 Classes of Introns 10 1.5.4 Messenger RNA 10 1.5.5 mRNA to Proteins 11 1.5.6 Transfer RNA 12 1.5.7 Small RNA 12 1.5.8 The Transcriptome 13 1.5.9 Gene Networks and Information Processing 13 1.5.10 Eukaryotic vs. Prokaryotic Regulation 14 1.5.11 What Is Life? 14 1.6 Known Species 14 1.7 Approaches for Compartmentalization 15 1.7.1 Two Main Approaches for Organism Formation 16 1.7.2 Size and Metabolism 16 1.8 Sizes in Eukaryotes 16 1.8.1 Sizes in Unicellular Eukaryotes 17 1.8.2 Sizes in Multicellular Eukaryotes 17 1.9 Sizes in Prokaryotes 17 1.10 Virus Sizes 18 1.10.1 Viruses vs. the Spark of Metabolism 20 1.11 The Diffusion Coefficient 20 1.12 The Origins of Eukaryotic Cells 21 1.12.1 Endosymbiosis Theory 21 1.12.2 DNA and Organelles 22 1.12.3 Membrane-bound Organelles with DNA 23 1.12.4 Membrane-bound Organelles Without DNA 23 1.12.5 Control and Division of Organelles 24 1.12.6 The Horizontal Gene Transfer 24 1.12.7 On the Mechanisms of Horizontal Gene Transfer 25 1.13 Origins of Eukaryotic Multicellularity 26 1.13.1 Colonies Inside an Early Unicellular Common Ancestor 26 1.13.2 Colonies of Early Unicellular Common Ancestors 26 1.13.3 Colonies of Inseparable Early Unicellular Common Ancestors 1.13.4 Chimerism and Mosaicism 28 1.14 Conclusions 29 2 Tree of Life: Genomes (II) 31 2.1 Introduction 31 2.2 Rules of Engagement 31 2.3 Genome Sizes in the Tree of Life 32 2.3.1 Alternative Methods 33 2.3.2 The Weaving of Scales 33 2.3.3 Computations on the Average Genome Size 36 2.3.4 Observations on Data 38 2.4 Organellar Genomes 40 2.4.1 Chloroplasts 40 2.4.2 Apicoplasts 40 2.4.3 Chromatophores 42 2.4.4 Cyanelles 42 2.4.5 Kinetoplasts 42 2.4.6 Mitochondria 43 2.5 Plasmids 43 2.6 Virus Genomes 44 2.7 Viroids and Their Implications 46 2.8 Genes vs. Proteins in the Tree of Life 47 2.9 Conclusions 49 3 Sequence Alignment (I) 51 3.1 Introduction 51 3.2 Style and Visualization 51 3.3 Initialization of the Score Matrix 54 3.4 Calculation of Scores 57 3.4.1 Initialization of the Score Matrix for Global Alignment 57 3.4.2 Initialization of the Score Matrix for Local Alignment 62 3.4.3 Optimization of the Initialization Steps 65 3.4.4 Curiosities 66 3.5 Traceback 71 3.6 Global Alignment 75 3.7 Local Alignment 79 3.8 Alignment Layout 84 3.9 Local Sequence Alignment – The Final Version 87 3.10 Complementarity 91 3.11 Conclusions 97 4 Forced Alignment (II) 99 4.1 Introduction 99 4.2 Global and Local Sequence Alignment 100 4.2.1 Short Notes 100 4.2.2 Understanding the Technology 101 4.2.3 Main Objectives 102 4.3 Experiments and Discussions 102 4.3.1 Alignment Layout 106 4.3.2 Forced Alignment Regime 106 4.3.3 Alignment Scores and Significance 109 4.3.4 Optimal Alignments 110 4.3.5 The Main Significance Scores 110 4.3.6 The Information Content 110 4.3.7 The Match Percentage 112 4.3.8 Significance vs. Chance 113 4.3.9 The Importance of Randomness 113 4.3.10 Sequence Quality and the Score Matrix 114 4.3.11 The Significance Threshold 115 4.3.12 Optimal Alignments by Numbers 116 4.3.13 Chaos Theory on Sequence Alignment 116 4.3.14 Image-Encoding Possibilities 116 4.4 Advanced Features and Methods 117 4.4.1 Sequence Detector 117 4.4.2 Parameters 117 4.4.3 Heatmap 118 4.4.4 Text Visualization 123 4.4.5 Graphics for Manuscript Figures and Didactic Presentations 124 4.4.6 Dynamics 124 4.4.7 Independence 125 4.4.8 Limits 125 4.4.9 Local Storage 125 4.5 Conclusions 128 5 Self-Sequence Alignment (I) 129 5.1 Introduction 129 5.2 True Randomness 130 5.3 Information and Compression Algorithms 130 5.4 White Noise and Biological Sequences 131 5.5 The Mathematical Model 131 5.5.1 A Concrete Example 132 5.5.2 Model Dissection 133 5.5.3 Conditions for Maxima and Minima 136 5.6 Noise vs. Redundancy 137 5.7 Global and Local Information Content 137 5.8 Signal Sensitivity 138 5.9 Implementation 140 5.9.1 Global Self-Sequence Alignment 140 5.9.2 Local Self-Sequence Alignment 144 5.10 A Complete Scanner for Information Content 147 5.11 Conclusions 149 6 Frequencies and Percentages (II) 151 6.1 Introduction 151 6.2 Base Composition 152 6.3 Percentage of Nucleotide Combinations 152 6.4 Implementation 153 6.5 A Frequency Scanner 156 6.6 Examples of Known Significance 158 6.7 Observation vs. Expectation 160 6.8 A Frequency Scanner with a Threshold 161 6.9 Conclusions 163 7 Objective Digital Stains (III) 165 7.1 Introduction 165 7.2 Information and Frequency 166 7.3 The Objective Digital Stain 169 7.3.1 A 3D Representation Over a 2D Plane 173 7.3.2 ODSs Relative to the Background 177 7.4 Interpretation of ODSs 181 7.5 The Significance of the Areas in the ODS 183 7.6 Discussions 184 7.6.1 A Similarity Between Dissimilar Sequences 186 7.7 Conclusions 186 8 Detection of Motifs (I) 187 8.1 Introduction 187 8.2 DNA Motifs 187 8.2.1 DNA-binding Proteins vs. Motifs and Degeneracy 188 8.2.2 Concrete Examples of DNA Motifs 188 8.3 Major Functions of DNA Motifs 191 8.3.1 RNA Splicing and DNA Motifs 191 8.4 Conclusions 195 9 Representation of Motifs (II) 197 9.1 Introduction 197 9.2 The Training Data 197 9.3 A Visualization Function 198 9.4 The Alignment Matrix 200 9.5 Alphabet Detection 203 9.6 The Position-Specific Scoring Matrix (PSSM) Initialization 206 9.7 The Position Frequency Matrix (PFM) 207 9.8 The Position Probability Matrix (PPM) 208 9.8.1 A Kind of PPM Pseudo-Scanner 209 9.9 The Position Weight Matrix (PWM) 212 9.10 The Background Model 215 9.11 The Consensus Sequence 218 9.11.1 The Consensus – Not Necessarily Functional 219 9.12 Mutational Intolerance 221 9.13 From Motifs to PWMs 222 9.14 Pseudo-Counts and Negative Infinity 226 9.15 Conclusions 229 10 The Motif Scanner (III) 231 10.1 Introduction 231 10.2 Looking for Signals 232 10.3 A Functional Scanner 235 10.4 The Meaning of Scores 239 10.4.1 A Score Value Above Zero 239 10.4.2 A Score Value Below Zero 241 10.4.3 A Score Value of Zero 241 10.5 Conclusions 242 11 Understanding the Parameters (IV) 243 11.1 Introduction 243 11.2 Experimentation 243 11.2.1 A Scanner Implementation Based on Pseudo-Counts 244 11.2.2 A Scanner Implementation Based on Propagation of Zero Counts 246 11.3 Signal Discrimination 249 11.4 False-Positive Results 250 11.5 Sensitivity Adjustments 251 11.6 Beyond Bioinformatics 252 11.7 A Scanner That Uses a Known PWM 253 11.8 Signal Thresholds 256 11.8.1 Implementation and Filter Testing 258 11.9 Conclusions 262 12 Dynamic Backgrounds (V) 263 12.1 Introduction 263 12.2 Toward a Scanner with Two PFMs 263 12.2.1 The Implementation of Dynamic PWMs 264 12.2.2 Issues and Corrections for Dynamic PWMs 271 12.2.3 Solutions for Aberrant Positive Likelihood Values 274 12.3 A Scanner with Two PFMs 280 12.4 Information and Background Frequencies on Score Values 283 12.5 Dynamic Background vs. Null Model 285 12.6 Conclusions 285 13 Markov Chains: The Machine (I) 287 13.1 Introduction 287 13.2 Transition Matrices 287 13.3 Discrete Probability Detector 292 13.3.1 Alphabet Detection 292 13.3.2 Matrix Initialization 293 13.3.3 Frequency Detection 295 13.3.4 Calculation of Transition Probabilities 297 13.3.5 Particularities in Calculating the Transition Probabilities 306 13.4 Markov Chains Generators 307 13.4.1 The Experiment 308 13.4.2 The Implementation 312 13.4.3 Simulation of Transition Probabilities 315 13.4.4 The Markov machine 315 13.4.5 Result Verification 317 13.5 Conclusions 318 14 Markov Chains: Log Likelihood (II) 319 14.1 Introduction 319 14.2 The Log-Likelihood Matrix 319 14.2.1 A Log-Likelihood Matrix Based on the Null Model 320 14.2.2 A Log-Likelihood Matrix Based on Two Models 322 14.3 Interpretation and Use of the Log-Likelihood Matrix 326 14.4 Construction of a Markov Scanner 328 14.5 A Scanner That Uses a Known LLM 337 14.6 The Meaning of Scores 340 14.7 Beyond Bioinformatics 344 14.8 Conclusions 345 15 Spectral Forecast (I) 347 15.1 Introduction 347 15.2 The Spectral Forecast Model 347 15.3 The Spectral Forecast Equation 349 15.4 The Spectral Forecast Inner Workings 350 15.4.1 Each Part on a Single Matrix 351 15.4.2 Both Parts on a Single Matrix 352 15.4.3 Both Parts on Separate Matrices 353 15.4.4 Concrete Example 1 354 15.4.5 Concrete Example 2 357 15.4.6 Concrete Example 3 359 15.5 Implementations 360 15.5.1 Spectral Forecast for Signals 362 15.5.2 What Does the Value of d Mean? 364 15.5.3 Spectral Forecast for Matrices 368 15.6 The Spectral Forecast Model for Predictions 372 15.6.1 The Spectral Forecast Model for Signals 372 15.6.2 Experiments on the Similarity Index Values 381 15.6.3 The Spectral Forecast Model for Matrices 384 15.7 Conclusions 389 16 Entropy vs. Content (I) 391 16.1 Introduction 391 16.2 Information Entropy 391 16.3 Implementation 395 16.4 Information Content vs. Information Entropy 400 16.4.1 Implementation 403 16.4.2 Additional Considerations 409 16.5 Conclusions 409 17 Philosophical Transactions 411 17.1 Introduction 411 17.2 The Frame of Reference 411 17.2.1 The Fundamental Layer of Complexity 412 17.2.2 On the Complexity of Life 414 17.3 Random vs. Pseudo-random 415 17.4 Random Numbers and Noise 418 17.5 Determinism and Chaos 419 17.5.1 Chaos Without Noise 420 17.5.2 Chaos with Noise 427 17.5.3 Limits of Prediction 430 17.5.4 On the Wings of Chaos 431 17.6 Free Will and Determinism 431 17.6.1 The Greatest Disappointment 432 17.6.2 The Most Powerful Processor in Existence 433 17.6.3 Certainty vs. Interpretation 435 17.6.4 A Wisdom that Applies 436 17.7 Conclusions 439 Appendix A 441 A.1 Association of Numerical Values with Letters 441 A.2 Sorting Values on Columns 443 A.3 The Implementation of a Sequence Logo 446 A.4 Sequence Logos Based on Maximum Values 451 A.5 Using Logarithms to Build Sequence Logos 455 A.6 From a Motif Set to a Sequence Logo 459 References 467 Index 489

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Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data

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Book SynopsisUncovers the surprising ways math shapes our livesfrom whom we date to what we learn. How do dating sites match compatible partners? What do cell phones and sea coasts have in common? And why do computer scientists keep ant colonies? Jeff Suzuki answers these questions and more in Patently Mathematical, which explores the mathematics behind some of the key inventions that have changed our world. In recent years, patents based on mathematics have been issued by the thousandsfrom search engines and image recognition technology to educational software and LEGO designs. Suzuki delves into the details of cutting-edge devices, programs, and products to show how even the simplest mathematical principles can be turned into patentable ideas worth billions of dollars. Readers will discover whether secure credit cards are really secure how improved data compression made streaming video services like Netflix a hit the mathematics behind self-correcting golf balls why Google is such an effectiTrade ReviewPatently Mathematical by Jeff Suzuki is a chronicle of the various patents based on mathematical algorithm applications. Each of his twelve chapters itemizes a specific family of patents along with pertinent anecdotes and suitable-for-the-general-reader examples illustrating how the algorithms work . . . Suzuki's book is a kaleidoscopic guided tour of the patented mathematical innovations that by and large now distinctly characterize the twenty-first century with respect to past eras.—Andrew James Simoson, MathSciNetTable of ContentsAcknowledgments Introduction. My Billion-Dollar Blunder Chapter 1: The Informational Hokey PokeyChapter 2: The Trillion-Dollar EquationChapter 3: A Picture Is a Thousand WordsChapter 4: If You Like Piña ColadasChapter 5: The Education RevolutionChapter 6: Forget Your Password? Forget Your Password!Chapter 7: The Company We KeepChapter 8: The Best of All Possible WorldsChapter 9: The Complete SagaChapter 10: Complexity from SimplicityChapter 11: RSA . . .Chapter 12: . . . Is PasséEpilogueBibliographyIndex

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Book SynopsisGain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science classes of data science problems, data science techniques and their applications and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practiceprovides a common base of reTable of ContentsPart One: Fundamentals1. Introduction: The Data Science Process2. Data Science and your business 3. Monks vs. Cowboys: Data Science CulturesPart Two: Classes of Problems4. Classification 5. Regression6. Natural Language Processing 7. Clustering8. Anomaly Detection9. Recommendations10. Computer Vision11. Sequential Decision Making Part Three: Techniques & Technologies12. Overview13. Data Capture14. Data Preparation15. Data Visualization16. Machine Learning17. Inference18. Other tools and services19. Reference Architecture20. Monks vs. Cowboys: PraxisPart Four: Building Teams and Executing Projects21. The Skills Framework22. Building and structuring the team23. Data Science Projects Appendix FAQs

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Book SynopsisUnderstand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance-all designed to deliver data as a product within hybrid cloud landscapes.This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses artificial intelligence (AI) and machine learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.By the end of this book, you will understand the data fabric concept and architecture as it relates to themes such as automated unified dTable of ContentsPart I – Data Fabric FoundationChapter 1: Evolution of Data ArchitectureChapter 2: Terminology – Data Fabric and Data MeshChapter 3: Data Fabric and Data Mesh Use Case ScenariosChapter 4: Data Fabric and Data Mesh Business BenefitsPart II – Key Data Fabric Capabilities and ConceptsChapter 5: Key Data Fabric and Data Mesh CapabilitiesChapter 6: Relevant AI and ML ConceptsChapter 7: AI/ML for a Data Fabric and Data MeshChapter 8: AI for Entity ResolutionChapter 9: Data Fabric and Data Mesh for the AI LifecyclePart III – Deploying Data Fabric Solutions in ContextChapter 10: Data Fabric Architecture PatternsChapter 11: Role of Data Fabric within an Enterprise Architecture\Chapter 12: Data Fabric and Data Mesh in Hybrid Cloud LandscapeChapter 13: Intelligent Cataloging and Metadata ManagementChapter 14: Automated Data Fabric and Data Mesh AspectsChapter 15: Data Governance in the Context of Data Fabric and Data MeshPart IV – Current Offerings and Future AspectsChapter 16: Sample Vendor OfferingsChapter 17: Data Fabric and Data Mesh Research AreasChapter 18: In Summary and OnwardsAbbreviations.

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Book SynopsisThis book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts that in some cases have caused loss of life and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsiblTable of ContentsIntroductionPart I. Foundation1. Responsibility2. AI Principles3. DataPart II. Implementation4. Responsible AI Framework5. Fairness6. Safety7. Humans in the Loop8. Transparency9. Privacy and RobustnessPart III. Ethical Considerations10. Ethics of AI and MLReferences

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Book SynopsisWith this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior.

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Book SynopsisDonald Knuth's influence in computer science ranges from the invention of methods for translating and defining programming languages to the creation of the TeX and METAFONT systems for desktop publishing. His award-winning textbooks have become classics; his scientific papers are widely referenced and stand as milestones of development over a wide range of topics. The present volume, which is the fourth in a series of his collected works, is devoted to an important subfield of Computer Science that Knuth founded in the 1960s and still considers his main life's work. This field, to which he gave the name Analysis of Algorithms, deals with quantitative studies of computer techniques, leading to methods for understanding and predicting the efficiency of computer programs. More than 30 of the papers that helped to shape this field are reprinted and updated in the present collection, together with historical material that has not previously been published.Table of Contents1. An almost linear recurrence; 2. The problem of compatible representatives; 3. The analysis of algorithms; 4. Mathematical analysis of algorithms; 5. The average height of planted plane trees; 6. An experiment in optimal sorting; 7. Shellsort with three increments; 8. The dangers of computer science theory; 9. Optimum measurement points for program frequency counts; 10. Ordered Hash tables; 11. Recurrence relations based on minimization; 12. Estimating the efficiency of backtrack programs; 13. An analysis of alpha-beta pruning; 14. Linear probing and graphs; 15. Activity in an interleaved memory; 16. Notes on generalized Dedekind sums; 17. Analysis of the subtractive algorithm for greatest common divisors; 18. Complexity results for bandwidth minimization; 19. Analysis of a simple factorization algorithm; 20. The complexity of nonuniform random number generation; 21. A trivial algorithm whose analysis isn't; 22. Evaluation of Porter's constant; 23. The expectant linearity of a simple equivalence algorithm; 24. Deletions that preserve randomness; 25. The average time for carry propogation; 26. A terminological proposal; 27. An analysis of optimum caching; 28. Optimal prepaging and font caching; 29. The distribution of continued fraction approximations; 30. The toilet paper problem; 31. A recurrence related to trees; 32. Stable husbands; 33. Postscript about NP-hard problems; 34. Nested satisfiability; 35. Textbook examples of recursion; 36. An exact analysis of stable allocation; 37. Big omicron and big omega and big theta.

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Book SynopsisThis book is a French translation of seventeen papers by Donald E. Knuth on algorithms both in the field of analysis of algorithms and in the design of new algorithms. They cover fundamental concepts and techniques and numerous discrete problems such as sorting, searching, data compression, theorem-proving, and cryptography, as well as methods for controlling errors in numerical computations.

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Book SynopsisEigenvalue computations are ubiquitous in science and engineering. John Francis’s implicitly shifted QR algorithm has been the method of choice for small to medium sized eigenvalue problems since its invention in 1959. This book presents a new view of this classical algorithm. While Francis’s original procedure chases bulges, the new version chases core transformations, which allows the development of fast algorithms for eigenvalue problems with a variety of special structures. This also leads to a fast and backward stable algorithm for computing the roots of a polynomial by solving the companion matrix eigenvalue problem. The authors received a SIAM Outstanding Paper prize for this work.This book will be of interest to researchers in numerical linear algebra and their students.

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Book SynopsisHow do we understand the world around us? How do we solve problems? Often the answer to these questions follows a certain pattern, an algorithm if you wish. This is the case when our analytical left-brain side is at work. However, there are also elements in our behaviour where intelligence appears to follow a more elusive path, which cannot easily be characterised as a specific sequence of steps. Is Intelligence an Algorithm? offers an insight into intelligence as it functions in nature, like human or animal intelligence, but also sheds light on modern developments in the field of artificial intelligence, proposing further architectural solutions for the creation of a so-called global Webmind.

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Book Synopsis Geomatics is a field of science that has been intimately intertwined with our daily lives for almost 30 years, to the point where we often forget all the challenges it entails. Who does not have a navigation application on their phone or regularly engage with geolocated data? What is more, in the coming decades, the accumulation of geo-referenced data is expected to increase significantly. This book focuses on the notion of the imperfection of geographic data, an important topic in geomatics. It is essential to be able to define and represent the imperfections that are encountered in geographical data. Ignoring these imperfections can lead to many risks, for example in the use of maps which may be rendered inaccurate. It is, therefore, essential to know how to model and treat the different categories of imperfection. A better awareness of these imperfections will improve the analysis and the use of this type of data. Table of ContentsPart 1. Bases and Concepts 1. Imperfection and Geographic Information, François Pinet, Mireille Batton-Hubert and Eric Desjardin. 2. Imperfection of Geographic Information: Concepts and Terminologies, Rodolphe Devillers, Eric Desjardin and Cyril De Runz. 3. The Origins of Imperfection in Geographic Data, Jean-Michel Follin, Jean-François Girres, Ana-Maria Olteanu-Raimond and David Sheeren. 4. Integrity and Trust of Geographic Information, Clément Iphar, Benjamin Costé, Aldo Napoli, Cyril Ray and Rodolphe Devillers. Part 2. Representation 5. Formalisms and Representations of Imperfect Geographic Objects, Mireille Batton-Hubert and François Pinet. 6. Representing Diagrams of Imperfect Geographic Objects, François Pinet and Cyril De Runz. Part 3. Reasoning and Treatment 7. Algebraic Reasoning for Uncertain Data, Florence Le Ber. 8. Reasoning in Modal Logic for Uncertain Data, Elisabeth Gavignet and Nadine Cullot. 9. Reviewing the Qualifiers of Imperfection in Geographic Information, Giovanni Fusco and Andrea Tettamanzi. 10. The Features of Decision Aid and? Analysis Processes in Geography: How to Grasp Complexity, Uncertainty, and Risks?, Myriam Merad.

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Book SynopsisAlmost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.Table of Contents1. Some Definitions. 2. Difficulty of the Difficulty. 3. Landscape Typology. 4. LandGener. 5. Test Cases. 6. Difficulty vs Dimension. 7. Exploitation and Exploration vs Difficulty. 8. The Explo2 Algorithm. 9. Balance and Perceived Difficulty.

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Book SynopsisData structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.Table of ContentsPreface ix Acknowledgments xv Chapter 1. Introduction 1 1.1. History of algorithms 3 1.2. Definition, structure and properties of algorithms 4 1.2.1. Definition 4 1.2.2. Structure and properties 4 1.3. Development of an algorithm 5 1.4. Data structures and algorithms 6 1.5. Data structures -- definition and classification 7 1.5.1. Abstract data types 7 1.5.2. Classification 9 1.6. Algorithm design techniques 9 1.7. Organization of the book 11 Chapter 2. Analysis of Algorithms 13 2.1. Efficiency of algorithms 13 2.2. Apriori analysis 15 2.3. Asymptotic notations 17 2.4. Time complexity of an algorithm using the O notation 19 2.5. Polynomial time versus exponential time algorithms 20 2.6. Average, best and worst case complexities 21 2.7. Analyzing recursive programs 23 2.7.1. Recursive procedures 23 2.7.2. Apriori analysis of recursive functions 27 2.8. Illustrative problems 31 Chapter 3. Arrays 45 3.1. Introduction 45 3.2. Array operations 46 3.3. Number of elements in an array 46 3.3.1. One-dimensional array 46 3.3.2. Two-dimensional array 47 3.3.3. Multidimensional array 47 3.4. Representation of arrays in memory 48 3.4.1. One-dimensional array 49 3.4.2. Two-dimensional arrays 51 3.4.3. Three-dimensional arrays 52 3.4.4. N-dimensional array 53 3.5. Applications 54 3.5.1. Sparse matrix 54 3.5.2. Ordered lists 55 3.5.3. Strings 56 3.5.4. Bit array 58 3.6. Illustrative problems 60 Chapter 4. Stacks 71 4.1. Introduction 71 4.2. Stack operations 72 4.2.1. Stack implementation 73 4.2.2. Implementation of push and pop operations 74 4.3. Applications 76 4.3.1. Recursive programming 76 4.3.2. Evaluation of expressions 79 4.4. Illustrative problems 83 Chapter 5. Queues 101 5.1. Introduction 101 5.2. Operations on queues 102 5.2.1. Queue implementation 102 5.2.2. Implementation of insert and delete operations on a queue 103 5.2.3. Limitations of linear queues 105 5.3. Circular queues 106 5.3.1. Operations on a circular queue 106 5.3.2. Implementation of insertion and deletion operations in circular queue 109 5.4. Other types of queues 112 5.4.1. Priority queues 112 5.4.2. Deques 117 5.5. Applications 119 5.5.1. Application of a linear queue 119 5.5.2. Application of priority queues 120 5.6. Illustrative problems 125 Chapter 6. Linked Lists 143 6.1. Introduction 143 6.1.1. Drawbacks of sequential data structures 143 6.1.2. Merits of linked data structures 145 6.1.3. Linked lists -- structure and implementation 145 6.2. Singly linked lists 147 6.2.1. Representation of a singly linked list 147 6.2.2. Insertion and deletion in a singly linked list 149 6.3. Circularly linked lists 155 6.3.1. Representation 155 6.3.2. Advantages of circularly linked lists over singly linked lists 155 6.3.3. Disadvantages of circularly linked lists 156 6.3.4. Primitive operations on circularly linked lists 158 6.3.5. Other operations on circularly linked lists 159 6.4. Doubly linked lists 160 6.4.1. Representation of a doubly linked list 161 6.4.2. Advantages and disadvantages of a doubly linked list 162 6.4.3. Operations on doubly linked lists 163 6.5. Multiply linked lists 166 6.6. Unrolled linked lists 171 6.6.1. Retrieval of an element 172 6.6.2. Insert an element 172 6.6.3. Delete an element 173 6.7. Self-organizing lists 175 6.8. Applications 175 6.8.1. Addition of polynomials 176 6.8.2. Sparse matrix representation 178 6.9. Illustrative problems 182 Chapter 7. Linked Stacks and Linked Queues 201 7.1. Introduction 201 7.1.1. Linked stack 202 7.1.2. Linked queues 203 7.2. Operations on linked stacks and linked queues 203 7.2.1. Linked stack operations 203 7.2.2. Linked queue operations 204 7.2.3. Algorithms for Push/Pop operations on a linked stack 205 7.2.4. Algorithms for insert and delete operations in a linked queue 206 7.3. Dynamic memory management and linked stacks 209 7.4. Implementation of linked representations 214 7.5. Applications 216 7.5.1. Balancing symbols 216 7.5.2. Polynomial representation 218 7.6. Illustrative problems 222 References 241 Index 243 Summaries of other volumes 245

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Book SynopsisData structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.Table of ContentsPreface ix Acknowledgments xv Chapter 8 Trees and Binary Trees 1 8.1 Introduction 1 8.2 Trees: definition and basic terminologies 1 8.2.1 Definition of trees 1 8.2.2 Basic terminologies of trees 2 8.3 Representation of trees 3 8.4 Binary trees: basic terminologies and types 6 8.4.1 Basic terminologies 6 8.4.2 Types of binary trees 7 8.5 Representation of binary trees 8 8.5.1 Array representation of binary trees 8 8.5.2 Linked representation of binary trees 10 8.6 Binary tree traversals 11 8.6.1 Inorder traversal 12 8.6.2 Postorder traversal 16 8.6.3 Preorder traversal 19 8.7 Threaded binary trees 22 8.7.1 Linked representation of a threaded binary tree 24 8.7.2 Growing threaded binary trees 24 8.8 Applications 25 8.8.1 Expression trees 26 8.8.2 Traversals of an expression tree 27 8.8.3 Conversion of infix expression to postfix expression 27 8.8.4 Segment trees 31 8.9 Illustrative problems 42 Chapter 9 Graphs 61 9.1 Introduction 61 9.2 Definitions and basic terminologies 63 9.3 Representations of graphs 75 9.3.1 Sequential representation of graphs 76 9.3.2 Linked representation of graphs 80 9.4 Graph traversals 81 9.4.1 Breadth first traversal 81 9.4.2 Depth first traversal 83 9.5 Applications 87 9.5.1 Single source shortest path problem 87 9.5.2 Minimum cost spanning trees 90 9.6 Illustrative problems 97 Chapter 10 Binary Search Trees and AVL Trees 115 10.1 Introduction 115 10.2 Binary search trees: definition and operations 115 10.2.1 Definition 115 10.2.2 Representation of a binary search tree 116 10.2.3 Retrieval from a binary search tree 117 10.2.4 Why are binary search tree retrievals more efficient than sequential list retrievals? 118 10.2.5 Insertion into a binary search tree 120 10.2.6 Deletion from a binary search tree 122 10.2.7 Drawbacks of a binary search tree 125 10.2.8 Counting binary search trees 128 10.3 AVL trees: definition and operations 130 10.3.1 Definition 131 10.3.2 Retrieval from an AVL search tree 132 10.3.3 Insertion into an AVL search tree 133 10.3.4 Deletion from an AVL search tree 141 10.3.5 R category rotations associated with the delete operation 146 10.3.6 L category rotations associated with the delete operation 150 10.4 Applications 151 10.4.1 Representation of symbol tables in compiler design 151 10.5 Illustrative problems 154 Chapter 11 B Trees and Tries 175 11.1 Introduction 175 11.2 m-way search trees: definition and operations 176 11.2.1 Definition 176 11.2.2 Node structure and representation 176 11.2.3 Searching an m-way search tree 178 11.2.4 Inserting into an m-way search tree 178 11.2.5 Deleting from an m-way search tree 179 11.2.6 Drawbacks of m-way search trees 184 11.3 B trees: definition and operations 184 11.3.1 Definition 184 11.3.2 Searching a B tree of order m 186 11.3.3 Inserting into a B tree of order m 186 11.3.4 Deletion from a B tree of order m 190 11.3.5 Height of a B tree of order m 194 11.4 Tries: definition and operations 195 11.4.1 Definition and representation 195 11.4.2 Searching a trie 197 11.4.3 Insertion into a trie 197 11.4.4 Deletion from a trie 198 11.4.5 Some remarks on tries 200 11.5 Applications 200 11.5.1 File indexing 201 11.5.2 Spell checker 203 11.6 Illustrative problems 204 Chapter 12 Red-Black Trees and Splay Trees 215 12.1 Red-black trees 215 12.1.1 Introduction to red-black trees 215 12.1.2 Definition 216 12.1.3 Representation of a red-black tree 219 12.1.4 Searching a red-black tree 220 12.1.5 Inserting into a red-black tree 220 12.1.6 Deleting from a red-black tree 228 12.1.7 Time complexity of search, insert and delete operations on a red-black tree 236 12.2 Splay trees 236 12.2.1 Introduction to splay trees 236 12.2.2 Splay rotations 237 12.2.3 Some remarks on amortized analysis of splay trees 242 12.3 Applications 244 12.4 Illustrative problems 245 References 261 Index 263 Summaries of other volumes 265

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Book SynopsisData structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.Table of ContentsPreface xi Acknowledgments xvii Chapter 13 Hash Tables 1 13.1 Introduction 1 13.1.1 Dictionaries 1 13.2 Hash table structure 2 13.3 Hash functions 4 13.3.1 Building hash functions 4 13.4 Linear open addressing 5 13.4.1 Operations on linear open addressed hash tables 8 13.4.2 Performance analysis 10 13.4.3 Other collision resolution techniques with open addressing 11 13.5 Chaining 13 13.5.1 Operations on chained hash tables 15 13.5.2 Performance analysis 17 13.6 Applications 18 13.6.1 Representation of a keyword table in a compiler 18 13.6.2 Hash tables in the evaluation of a join operation on relational databases 19 13.6.3 Hash tables in a direct file organization 22 13.7 Illustrative problems 23 Chapter 14 File Organizations 33 14.1 Introduction 33 14.2 Files 34 14.3 Keys 36 14.4 Basic file operations 38 14.5 Heap or pile organization 38 14.5.1 Insert, delete and update operations 39 14.6 Sequential file organization 39 14.6.1 Insert, delete and update operations 39 14.6.2 Making use of overflow blocks 40 14.7 Indexed sequential file organization 41 14.7.1 Structure of the ISAM files 41 14.7.2 Insert, delete and update operations for a naïve ISAM file 42 14.7.3 Types of indexing 43 14.8 Direct file organization 48 14.9 Illustrative problems 50 Chapter 15 k-d Trees and Treaps 61 15.1 Introduction 61 15.2 k-d trees: structure and operations 61 15.2.1 Construction of a k-d tree 65 15.2.2 Insert operation on k-d trees 69 15.2.3 Find minimum operation on k-d trees 70 15.2.4 Delete operation on k-d trees 72 15.2.5 Complexity analysis and applications of k-d trees 74 15.3 Treaps: structure and operations 76 15.3.1 Treap structure 76 15.3.2 Operations on treaps 77 15.3.3 Complexity analysis and applications of treaps 82 15.4 Illustrative problems 83 Chapter 16 Searching 93 16.1 Introduction 93 16.2 Linear search 94 16.2.1 Ordered linear search 94 16.2.2 Unordered linear search 94 16.3 Transpose sequential search 96 16.4 Interpolation search 98 16.5 Binary search 100 16.5.1 Decision tree for binary search 101 16.6 Fibonacci search 104 16.6.1 Decision tree for Fibonacci search 105 16.7 Skip list search 108 16.7.1 Implementing skip lists 112 16.7.2 Insert operation in a skip list 113 16.7.3 Delete operation in a skip list 114 16.8 Other search techniques 116 16.8.1 Tree search 116 16.8.2 Graph search 116 16.8.3 Indexed sequential search 116 16.9 Illustrative problems 118 Chapter 17 Internal Sorting 131 17.1 Introduction 131 17.2 Bubble sort 132 17.2.1 Stability and performance analysis 134 17.3 Insertion sort 135 17.3.1 Stability and performance analysis 136 17.4 Selection sort 138 17.4.1 Stability and performance analysis 140 17.5 Merge sort 140 17.5.1 Two-way merging 141 17.5.2 k-way merging 143 17.5.3 Non-recursive merge sort procedure 144 17.5.4 Recursive merge sort procedure 145 17.6 Shell sort 147 17.6.1 Analysis of shell sort 153 17.7 Quick sort 153 17.7.1 Partitioning 153 17.7.2 Quick sort procedure 156 17.7.3 Stability and performance analysis 158 17.8 Heap sort 159 17.8.1 Heap 159 17.8.2 Construction of heap 160 17.8.3 Heap sort procedure 163 17.8.4 Stability and performance analysis 167 17.9 Radix sort 167 17.9.1 Radix sort method 167 17.9.2 Most significant digit first sort 171 17.9.3 Performance analysis 171 17.10 Counting sort 171 17.10.1 Performance analysis 175 17.11 Bucket sort 175 17.11.1 Performance analysis 178 17.12 Illustrative problems 179 Chapter 18 External Sorting 197 18.1 Introduction 197 18.1.1 The principle behind external sorting 197 18.2 External storage devices 198 18.2.1 Magnetic tapes 199 18.2.2 Magnetic disks 200 18.3 Sorting with tapes: balanced merge 202 18.3.1 Buffer handling 204 18.3.2 Balanced P-way merging on tapes 205 18.4 Sorting with disks: balanced merge 206 18.4.1 Balanced k-way merging on disks 207 18.4.2 Selection tree 208 18.5 Polyphase merge sort 212 18.6 Cascade merge sort 214 18.7 Illustrative problems 216 Chapter 19 Divide and Conquer 229 19.1 Introduction 229 19.2 Principle and abstraction 229 19.3 Finding maximum and minimum 231 19.3.1 Time complexity analysis 232 19.4 Merge sort 233 19.4.1 Time complexity analysis 233 19.5 Matrix multiplication 234 19.5.1 Divide and Conquer-based approach to “high school” method of matrix multiplication 234 19.5.2 Strassen’s matrix multiplication algorithm 236 19.6 Illustrative problems 239 Chapter 20 Greedy Method 245 20.1 Introduction 245 20.2 Abstraction 245 20.3 Knapsack problem 246 20.3.1 Greedy solution to the knapsack problem 247 20.4 Minimum cost spanning tree algorithms 249 20.4.1 Prim’s algorithm as a greedy method 250 20.4.2 Kruskal’s algorithm as a greedy method 250 20.5 Dijkstra’s algorithm 251 20.6 Illustrative problems 251 Chapter 21 Dynamic Programming 261 21.1 Introduction 261 21.2 0/1 knapsack problem 263 21.2.1 Dynamic programming-based solution 264 21.3 Traveling salesperson problem 266 21.3.1 Dynamic programming-based solution 267 21.3.2 Time complexity analysis and applications of traveling salesperson problem 269 21.4 All-pairs shortest path problem 269 21.4.1 Dynamic programming-based solution 270 21.4.2 Time complexity analysis 272 21.5 Optimal binary search trees 272 21.5.1 Dynamic programming-based solution 274 21.5.2 Construction of the optimal binary search tree 276 21.5.3 Time complexity analysis 279 21.6 Illustrative problems 280 Chapter 22 P and NP Class of Problems 287 22.1 Introduction 287 22.2 Deterministic and nondeterministic algorithms 289 22.3 Satisfiability problem 292 22.3.1 Conjunctive normal form and Disjunctive normal form 294 22.3.2 Definition of the satisfiability problem 294 22.3.3 Construction of CNF and DNF from a logical formula 295 22.3.4 Transformation of a CNF into a 3-CNF 296 22.3.5 Deterministic algorithm for the satisfiability problem 297 22.3.6 Nondeterministic algorithm for the satisfiability problem 297 22.4 NP-complete and NP-hard problems 297 22.4.1 Definitions 298 22.5 Examples of NP-hard and NP-complete problems 300 22.6 Cook’s theorem 302 22.7 The unsolved problem P = NP 303 22.8 Illustrative problems 304 References 311 Index 313 Summaries of other volumes 317

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Book SynopsisThe primary aim of this book is to present a coherent introduction to graph theory, suitable as a textbook for advanced undergraduate and beginning graduate students in mathematics and computer science. It provides a systematic treatment of the theory of graphs without sacrificing its intuitive and aesthetic appeal. Commonly used proof techniques are described and illustrated. The book also serves as an introduction to research in graph theory.Trade Reviewdeveloped by Paul Seymour and Neil Robertson and followers), which certainly now deserves a monographic treatment of its own. Summing up: Recommended. Lower-division undergraduate through professional collections. CHOICE This book is a follow-on to the authors' 1976 text, Graphs with Applications. What began as a revision has evolved into a modern, first-class, graduate-level textbook reflecting changes in the discipline over the past thirty years... This text hits the mark by appearing in Springer’s Graduate Texts in Mathematics series, as it is a very rigorous treatment, compactly presented, with an assumption of a very complete undergraduate preparation in all of the standard topics. While the book could ably serve as a reference for many of the most important topics in graph theory, it fulfills the promise of being an effective textbook. The plentiful exercises in each subsection are divided into two groups, with the second group deemed "more challenging". Any exercises necessary for a complete understanding of the text have also been marked as such. There is plenty here to keep a graduate student busy, and any student would learn much in tackling a selection of the exercises... Not only is the content of this book exceptional, so too is its production. The high quality of its manufacture, the crisp and detailed illustrations, and the uncluttered design complement the attention to the typography and layout. Even in simple black and white with line art, it is a beautiful book. SIAM Book Reviews "A text which is designed to be usable both for a basic graph theory course … but also to be usable as an introduction to research in graph theory, by including more advanced topics in each chapter. There are a large number of exercises in the book … . The text contains drawings of many standard interesting graphs, which are listed at the end." (David B. Penman, Zentralblatt MATH, Vol. 1134 (12), 2008) MathSciNet Reviews "The present volume is intended to serve as a text for "advanced undergraduate and beginning graduate students in mathematics and computer science" (p. viii). It is well suited for this purpose. The writing is fully accessible to the stated groups of students, and indeed is not merely readable but is engaging… Even a complete listing of the chapters does not fully convey the breadth of this book… For researchers in graph theory, this book offers features which parallel the first Bondy and Murty book: it provides well-chosen terminology and notation, a multitude of especially interesting graphs, and a substantial unsolved problems section…One-hundred unsolved problems are listed in Appendix A, a treasure trove of problems worthy of study… (In short) this rewrite of a classic in graph theory stands a good chance of becoming a classic itself." "The present volume is intended to serve as a text for ‘advanced undergraduate and beginning graduate students in mathematics and computer science’ … . The writing is fully accessible to the stated groups of students, and indeed is not merely readable but is engaging. The book has many exercise sets, each containing problems … ." (Arthur M. Hobbs, Mathematical Reviews, Issue 2009 C) "A couple of fantastic features: Proof techniques: I love these nutshelled essences highlighted in bordered frames. They look like pictures on the wall and grab the view of the reader. Exercises: Their style, depth and logic remind me of Lovász’ classical exercise book. Also the fact that the name of the author is bracketed after the exercise…Figures: Extremely precise and high-tech…The book contains very recent results and ideas. It is clearly an up-to-date collection of fundamental results of graph theory…All-in-all, it is a marvelous book." (János Barát, Acta Scientiarum Mathematicarum, Vol. 75, 2009)Table of ContentsGraphs.- Subgraphs.- Connected Graphs.- Trees.- Nonseparable Graphs.- Tree-Search Algorithms.- Flows in Networks.- Complexity of Algorithms.- Connectivity.- Planar Graphs.- The Four-Colour Problem.- Stable Sets and Cliques.- The Probabilistic Method.- Vertex Colourings.- Colourings of Maps.- Matchings.- Edge Colourings.- Hamilton Cycles.- Coverings and Packings in Directed Graphs.- Electrical Networks.- Integer Flows and Coverings.

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Book SynopsisThe aim of the second edition of this book is to provide a comprehensive survey of the different algorithms and data structures useful for triangulation and meshing construction. In addition, several aspects are given full coverage, such as mesh modification tools, mesh evaluation criteria, mesh optimization, adaptive mesh construction and parallel meshing techniques. This new edition has been comprehensively updated and also includes a new chapter on mobile or deformable meshes.Table of ContentsChapter 1. General definitions. Chapter 2. Basic structures and algorithms. Chapter 3. A comprehensive survey of mesh generation methods. Chapter 4. Algebraic, PDE and multibloc methods. Chapter 5. Quadtree-octree-based methods. Chapter 6. Advancing front technique for mesh generation. Chapter 7. Delaunay-based mesh generation methods. Chapter 8. Other types of mesh generation methods. Chapter 9. Delaunay admissibility, media axis, mid-surface and other applications. Chapter 10. Quadratic forms and metrics. Chapter 11. Differential geometry. Chapter 12. Curve modeling. Chapter 13. Surface modeling. Chapter 14. Surface meshing and re-meshing. Chapter 15. Meshing implicit curves and surfaces. Chapter 16. Mesh modifications. Chapter 17. Mesh optimization. Chapter 18. Surface mesh optimization. Chapter 19. A touch of finite elements. Chapter 20. Mesh adaptation and h-methods. Chapter 21. Mesh adaptation and p or hp-methods. Chapter 22. Mobile or deformable meshes. Chapter 23. Parallel computing and meshing issues.

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Book SynopsisEvolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.Table of ContentsChapter 1 The Science of Biogeography 1 1.1 Introduction 1 1.2 Island biogeography 3 1.3 Influence factors for biogeography 6 Chapter 2 Biogeography and Biological Optimization 11 2.1 A mathematical model of biogeography 11 2.2 Biogeography as an optimization process 16 2.3 Biological optimization 19 2.3.1 Genetic algorithms 19 2.3.2 Evolution strategies 20 2.3.3 Particle swarm optimization 21 2.3.4 Artificial bee colony algorithm 22 2.4 Conclusion 23 Chapter 3 A Basic BBO Algorithm 25 3.1 BBO definitions and algorithm 25 3.1.1 Migration 26 3.1.2 Mutation 27 3.1.3 BBO implementation 27 3.2 Differences between BBO and other optimization algorithms 35 3.2.1 BBO and genetic algorithms 35 3.2.2 BBO and other algorithms 36 3.3 Simulations 37 3.4 Conclusion 44 Chapter 4 BBO Extensions 45 4.1 Migration curves 45 4.2 Blended migration 49 4.3 Other approaches to BBO 51 4.4 Applications 56 4.5 Conclusion 59 Chapter 5 BBO as a Markov Process 61 5.1 Markov definitions and notations 61 5.2 Markov model of BBO 72 5.3 BBO convergence 79 5.4 Markov models of BBO extensions 90 5.5 Conclusions 99 Chapter 6 Dynamic System Models of BBO 103 6.1 Basic notation 103 6.2 Dynamic system models of BBO 105 6.3 Applications to benchmark problems 119 6.4 Conclusions 122 Chapter 7 Statistical Mechanics Approximations of BBO 123 7.1 Preliminary foundation 123 7.2 Statistical mechanics model of BBO 128 7.2.1 Migration 128 7.2.2 Mutation 134 7.3 Further discussion 141 7.3.1 Finite population effects 141 7.3.2 Separable fitness functions 142 7.4 Conclusions 143 Chapter 8 BBO for Combinatorial Optimization 145 8.1 Traveling salesman problem 147 8.2 BBO for the TSP 148 8.2.1 Population initialization 148 8.2.2 Migration in the TSP 150 8.2.3 Mutation in the TSP 157 8.2.4 Implementation framework 159 8.3 Graph coloring 163 8.4 Knapsack problem 165 8.5 Conclusion 167 Chapter 9 Constrained BBO 169 9.1 Constrained optimization 170 9.2 Constraint-handling methods 172 9.2.1 Static penalty methods 172 9.2.2 Superiority of feasible points 173 9.2.3 The eclectic evolutionary algorithm 174 9.2.4 Dynamic penalty methods 174 9.2.5 Adaptive penalty methods 176 9.2.6 The niched-penalty approach 177 9.2.7 Stochastic ranking 178 9.2.8 ε-level comparisons 178 9.3 BBO for constrained optimization 179 9.4 Conclusion 185 Chapter 10 BBO in Noisy Environments 187 10.1 Noisy fitness functions 188 10.2 Influence of noise on BBO 190 10.3 BBO with re-sampling 193 10.4 The Kalman BBO 196 10.5 Experimental results 199 10.6 Conclusion 201 Chapter 11 Multi-objective BBO 203 11.1 Multi-objective optimization problems 204 11.2 Multi-objective BBO 211 11.2.1 Vector evaluated BBO 211 11.2.2 Non-dominated sorting BBO 213 11.2.3 Niched Pareto BBO 216 11.2.4 Strength Pareto BBO 218 11.3 Real-world applications 223 11.3.1 Warehouse scheduling model 223 11.3.2 Optimization of warehouse scheduling 229 11.4 Conclusion 231 Chapter 12 Hybrid BBO Algorithms 233 12.1 Opposition-based BBO 234 12.1.1 Opposition definitions and concepts 234 12.1.2 Oppositional BBO 236 12.1.3 Experimental results 238 12.2 BBO with local search 240 12.2.1 Local search methods 240 12.2.2 Simulation results 245 12.3 BBO with other EAs 247 12.3.1 Iteration-level hybridization 247 12.3.2 Algorithm-level hybridization 250 12.3.3 Experimental results 254 12.4 Conclusion 256 Appendices 259 Appendix A Unconstrained Benchmark Functions 261 Appendix B Constrained Benchmark Functions 265 Appendix C Multi-objective Benchmark Functions 289 Bibliography 309 Index 325

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Book SynopsisThe Fourier transform is one of the most fundamental tools for computing the frequency representation of signals. It plays a central role in signal processing, communications, audio and video compression, medical imaging, genomics, astronomy, as well as many other areas. Because of its widespread use, fast algorithms for computing the Fourier transform can benefit a large number of applications. The fastest algorithm for computing the Fourier transform is the Fast Fourier Transform (FFT), which runs in near-linear time making it an indispensable tool for many applications. However, today, the runtime of the FFT algorithm is no longer fast enough especially for big data problems where each dataset can be few terabytes. Hence, faster algorithms that run in sublinear time, i.e., do not even sample all the data points, have become necessary.This book addresses the above problem by developing the Sparse Fourier Transform algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits.This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award.Table of Contents Preface 1. Introduction PART I: THEORY OF THE SPARSE FOURIER TRANSFORM 2. Preliminaries 3. Simple and Practical Algorithm 4. Optimizing Runtime Complexity 5. Optimizing Sample Complexity 6. Numerical Evaluation PART II: APPLICATIONS OF THE SPARSE FOURIER TRANSFORM 7. GHz-Wide Spectrum Sensing and Decoding 8. Faster GPS Synchronization 9. Light Field Reconstruction Using Continuous Fourier Sparsity 10. Fast In-Vivo MRS Acquisition with Artifact Suppression 11. Fast Multi-Dimensional NMR Acquisition and Processing 12. Conclusion

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Book SynopsisThe Fourier transform is one of the most fundamental tools for computing the frequency representation of signals. It plays a central role in signal processing, communications, audio and video compression, medical imaging, genomics, astronomy, as well as many other areas. Because of its widespread use, fast algorithms for computing the Fourier transform can benefit a large number of applications. The fastest algorithm for computing the Fourier transform is the Fast Fourier Transform (FFT), which runs in near-linear time making it an indispensable tool for many applications. However, today, the runtime of the FFT algorithm is no longer fast enough especially for big data problems where each dataset can be few terabytes. Hence, faster algorithms that run in sublinear time, i.e., do not even sample all the data points, have become necessary.This book addresses the above problem by developing the Sparse Fourier Transform algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits.This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award.Table of Contents Preface 1. Introduction PART I: THEORY OF THE SPARSE FOURIER TRANSFORM 2. Preliminaries 3. Simple and Practical Algorithm 4. Optimizing Runtime Complexity 5. Optimizing Sample Complexity 6. Numerical Evaluation PART II: APPLICATIONS OF THE SPARSE FOURIER TRANSFORM 7. GHz-Wide Spectrum Sensing and Decoding 8. Faster GPS Synchronization 9. Light Field Reconstruction Using Continuous Fourier Sparsity 10. Fast In-Vivo MRS Acquisition with Artifact Suppression 11. Fast Multi-Dimensional NMR Acquisition and Processing 12. Conclusion

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Book SynopsisParallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many different settings. This thesis addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. The thesis provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice. The results developed in this thesis serve to ease the transition into the multicore era.The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The second part of this thesis introduces Ligra, the first high-level shared memory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory. This part of the thesis also introduces Ligra , which extends Ligra with graph compression techniques to reduce space usage and improve parallel performance at the same time, and is also the first graph processing system to support in-memory graph compression.The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. For example, the thesis develops the first linear-work and polylogarithmic-depth algorithms for suffix tree construction and graph connectivity that are also practical, as well as a work-efficient, polylogarithmic-depth, and cache-efficient shared-memory algorithm for triangle computations that achieves a 2–5x speedup over the best existing algorithms on 40 cores.This is a revised version of the thesis that won the 2015 ACM Doctoral Dissertation Award.Table of Contents Introduction Preliminaries and Notation Programming Techniques for Deterministic Parallelism Internally Deterministic Parallelism: Techniques and Algorithms Deterministic Parallelism in Sequential Iterative Algorithms A Deterministic Phase-Concurrent Parallel Hash Table Priority Updates: A Contention-Reducing Primitive for Deterministic Programming Large-Scale Shared-Memory Graph Analytics Ligra: A Lightweight Graph Processing Framework for Shared Memory Ligra : Adding Compression to Ligra Parallel Graph Algorithms Linear-Work Parallel Graph Connectivity Parallel and Cache-Oblivious Triangle Computations Parallel String Algorithms Parallel Cartesian Tree and Suffix Tree Construction Parallel Computation of Longest Common Prefixes Parallel Lempel-Ziv Factorization Parallel Wavelet Tree Construction Conclusion and Future Work Bibliography

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Book SynopsisParallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many different settings. This thesis addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. The thesis provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice. The results developed in this thesis serve to ease the transition into the multicore era.The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The second part of this thesis introduces Ligra, the first high-level shared memory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory. This part of the thesis also introduces Ligra , which extends Ligra with graph compression techniques to reduce space usage and improve parallel performance at the same time, and is also the first graph processing system to support in-memory graph compression.The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. For example, the thesis develops the first linear-work and polylogarithmic-depth algorithms for suffix tree construction and graph connectivity that are also practical, as well as a work-efficient, polylogarithmic-depth, and cache-efficient shared-memory algorithm for triangle computations that achieves a 2–5x speedup over the best existing algorithms on 40 cores.This is a revised version of the thesis that won the 2015 ACM Doctoral Dissertation Award.Table of Contents Introduction Preliminaries and Notation Programming Techniques for Deterministic Parallelism Internally Deterministic Parallelism: Techniques and Algorithms Deterministic Parallelism in Sequential Iterative Algorithms A Deterministic Phase-Concurrent Parallel Hash Table Priority Updates: A Contention-Reducing Primitive for Deterministic Programming Large-Scale Shared-Memory Graph Analytics Ligra: A Lightweight Graph Processing Framework for Shared Memory Ligra : Adding Compression to Ligra Parallel Graph Algorithms Linear-Work Parallel Graph Connectivity Parallel and Cache-Oblivious Triangle Computations Parallel String Algorithms Parallel Cartesian Tree and Suffix Tree Construction Parallel Computation of Longest Common Prefixes Parallel Lempel-Ziv Factorization Parallel Wavelet Tree Construction Conclusion and Future Work Bibliography

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Book SynopsisThis practically-oriented textbook presents an accessible introduction to discrete mathematics through a substantial collection of classroom-tested exercises. Each chapter opens with concise coverage of the theory underlying the topic, reviewing the basic concepts and establishing the terminology, as well as providing the key formulae and instructions on their use. This is then followed by a detailed account of the most common problems in the area, before the reader is invited to practice solving such problems for themselves through a varied series of questions and assignments.Topics and features: provides an extensive set of exercises and examples of varying levels of complexity, suitable for both laboratory practical training and self-study; offers detailed solutions to many problems, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus.This hands-on study guide is designed to address the core needs of undergraduate students training in computer science, informatics, and electronic engineering, emphasizing the skills required to develop and implement an algorithm in a specific programming language.Table of ContentsFundamentals of Mathematical Logic Set Theory Relations and Functions Combinatorics Graphs Boolean Algebra Complex Numbers Recurrence Relations Concept of an Algorithm, Correctness of Algorithms Turing Machine Asymptotic Analysis Basic Algorithms Parallel Algorithms

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Book SynopsisThis must-read textbook presents an essential introduction to Kolmogorov complexity (KC), a central theory and powerful tool in information science that deals with the quantity of information in individual objects. The text covers both the fundamental concepts and the most important practical applications, supported by a wealth of didactic features.This thoroughly revised and enhanced fourth edition includes new and updated material on, amongst other topics, the Miller-Yu theorem, the Gács-Kučera theorem, the Day-Gács theorem, increasing randomness, short lists computable from an input string containing the incomputable Kolmogorov complexity of the input, the Lovász local lemma, sorting, the algorithmic full Slepian-Wolf theorem for individual strings, multiset normalized information distance and normalized web distance, and conditional universal distribution.Topics and features: describes the mathematical theory of KC, including the theories of algorithmic complexity and algorithmic probability; presents a general theory of inductive reasoning and its applications, and reviews the utility of the incompressibility method; covers the practical application of KC in great detail, including the normalized information distance (the similarity metric) and information diameter of multisets in phylogeny, language trees, music, heterogeneous files, and clustering; discusses the many applications of resource-bounded KC, and examines different physical theories from a KC point of view; includes numerous examples that elaborate the theory, and a range of exercises of varying difficulty (with solutions); offers explanatory asides on technical issues, and extensive historical sections; suggests structures for several one-semester courses in the preface.As the definitive textbook on Kolmogorov complexity, this comprehensive and self-contained work is an invaluable resource for advanced undergraduate students, graduate students, and researchers in all fields of science.Table of ContentsPreliminaries Algorithmic Complexity Algorithmic Prefix Complexity Algorithmic Probability Inductive Reasoning The Incompressibility Method Resource-Bounded Complexity Physics, Information, and Computation

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Book SynopsisModern cryptosystems, used in numerous applications that require secrecy or privacy - electronic mail, financial transactions, medical-record keeping, government affairs, social media etc. - are based on sophisticated mathematics and algorithms that in implementation involve much computer arithmetic. And for speed it is necessary that the arithmetic be realized at the hardware (chip) level. This book is an introduction to the implementation of cryptosystems at that level.The aforementioned arithmetic is mostly the arithmetic of finite fields, and the book is essentially one on the arithmetic of prime fields and binary fields in the context of cryptography. The book has three main parts. The first part is on generic algorithms and hardware architectures for the basic arithmetic operations: addition, subtraction, multiplication, and division. The second part is on the arithmetic of prime fields. And the third part is on the arithmetic of binary fields. The mathematical fundamentals necessary for the latter two parts are included, as are descriptions of various types of cryptosystems, to provide appropriate context.This book is intended for advanced-level students in Computer Science, Computer Engineering, and Electrical and Electronic Engineering. Practitioners too will find it useful, as will those with a general interest in "hard" applications of mathematics.Table of Contents1 Basic Computer Arithmetic.- 2 Mathematical Fundamentals I: Number Theory.- 3 Modular-Arithmetic Cryptosystems.- 4 Modular Reduction.- 5 Modular Addition and Multiplication.- 6 Modular Exponentiation, Inversion, and Division.- 7 Mathematical Fundamentals II: Abstract Algebra.- 8 Elliptic-Curve Basics.- 9 Elliptic-Curve Cryptosystems.- 10 Polynomial-basis arithmetic.- 11 Normal-basis arithmetic.- A Mathematical Proofs.- Index.

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Book SynopsisIs everything Information? This is a tantalizing question which emerges in modern physics, life sciences, astronomy and in today’s information and technology-driven society. In Powers of Two expert authors undertake a unique expedition - in words and images - throughout the world (and scales) of information. The story resembles, in a way, the classic Powers of Ten journeys through space: from us to the macro and the micro worlds . However, by following Powers of Two through the world of information, a completely different and timely paradigm unfolds. Every power of two, 1, 2, 4, 8…. tells us a different story: starting from the creation of the very first bit at the Big Bang and the evolution of life, through 50 years of computational science, and finally into deep space, describing the information in black holes and even in the entire universe and beyond…. All this to address one question: Is our universe made of information? In this book, we experience the Information Universe in nature and in our society and how information lies at the very foundation of our understanding of the Universe.From the Foreword by Robbert Dijkgraaf: This book is in many ways a vastly extended version of Shannon’s one-page blueprint. It carries us all the way to the total information content of the Universe. And it bears testimony of how widespread the use of data has become in all aspects of life. Information is the connective tissue of the modern sciences. […] Undoubtedly, future generations will look back at this time, so much enthralled by Big Data and quantum computers, as beholden to the information metaphor. But that is exactly the value of this book. With its crisp descriptions and evocative illustrations, it brings the reader into the here and now, at the very frontier of scientific research, including the excitement and promise of all the outstanding questions and future discoveries.Message for the e-reader of the book Powers of Two The book has been designed to be read in two-page spreads in full screen mode. For optimal reader experience in a downloaded .pdf file we strongly recommend you use the following settings in Adobe Acrobat Reader: - Taskbar: View > Page Display > two page view - Taskbar: View > Page Display > Show Cover Page in Two Page View - Taskbar: ^ Preferences > Full Screen > deselect " Fill screen with one page at a time" - Taskbar: View > Full screen mode or ctrl L (cmd L on a Mac) ***** Note: for reading the previews on Spinger link (and on-line reading in a browser), the full screen two-page view only works with these browsers: Firefox - Taskbar: on top of the text, at the uppermost right you will see then >> (which is a drop-down menu) >> even double pages - Fullscreen: F11 or Control+Cmd+F with Mac Edge - Taskbar middle: Two-page view and select show cover page separatelyTrade Review“The book … a very unusual collection of some facts about the relationship between the immaterial world represented by bits and the real physical world described by fundamental physical equations. This book continues the very categorical point of view of J. A. Wheeler … . The book presents short articles on various areas of modern science … in which it is shown that in these areas in some mysterious way there is a connection with the theory of information.” (Vladimir Dzhunushaliev, zbMATH 1479.83004, 2022)Table of ContentsForeword by Robbert DijkgraafChapter 0: IntroductionJoy-riding the Universe – by the authorWorking as an astronomer, data scientist and professor of astro-informatics for nearly fifty years, Edwin Valentijn has witnessed and first-hand engineered the dawn of the era of Big Data in science and society. Throughout his career, he became increasingly aware of the role of information in our world: in computers, in our society, and even in nature and in the Universe itself.The Information UniverseFollowing the increasing powers of two, the story paints a journey through the whole world of information, both in society and in nature. Each step opens a door into a new world: from the first bits with the Big Bang and the dawn of life, going through fifty years of human technology, all the way up to the information content of the whole Universe.What is Information? - Item pageThe basics of information are introduced.Chapter 1: The beginningSpace-time foam – Ti (0 bit: 20 =1)The very first power of two: 20, corresponds to the value one. This identifies the single, eternal, indistinguishable state: the primordial sea from which our Universe emerged – sometimes called the Space-time foam. I call this Ti, the reverse of It. This is one of the miraculous new notions in the story of the Powers of Two.Multiverse: Anthropic principle (Item page)From Ti, the primordial space-time foam, countless universes arise with widely different characteristics: the Multiverse. The Anthropic Principle is a philosophical consideration which states that we, people, will find ourselves in a universe that is suitable for intelligent life to emerge. Therefore, this Principle demonstrates that conditions in our Universe are not “fine-tuned” to the existence of human life and a “creator” doesn’t exist.Big bang (1 bit: 21 =2 states)At the Big Bang the first bit is created. From the indistinguishable unity of the primordial foam Ti, “the zeros were separated from the 1’s”: the first bit corresponds to two possible states. This bit is the first step on our journey to capture the ever-increasing complexity of our expanding Universe in terms of information, through the increasing powers of two.What is a bit? (Item page)The bit is at the core of the concept of information. A bit is any system that can have two states. Humans assign meanings to these states, which are illustrated with the concept of the traffic light: red or green, stop or go. The combination of multiple bits creates an exponentially increasing number of possible states, and hence meanings.Multicellular life (2 bit: 22 =4 states) / (4 bit: 24 =16 states)?Life started with exchanging information between cells. This is fundamental for the evolution of any kind of life. It took at least two billion years for uni-cellular to evolve into multi-cellular organisms around 600 million years ago, and to start the exchange of information between their different cells. By exchanging information, cells collaborate and act as a unified whole: life.The game of life (Item page)The characteristic features of life (or any complex system in the Universe) can be created from information. A simple computer game is all you need to demonstrate this concept. A famous example is Conway's Game of Life, which is full of visuals of living, growing, moving and dying objects. This game was already made on the computers of the early 70's with just a few lines of code.Chapter 2: People's Information UniverseASCII (7 bit: 27 =128 states)There is currently no physical theory how the digital world connects to the human consciousness. In the world of Information Technology (IT) all information exchange is based on agreements between people. For instance, ASCII, a simple list relating each letter of the alphabet to a 7-bit string, connects the digital world to the human consciousness. Machu Picchu (8 bit: 28 =256, 1 byte)The Intiwatana stone, a giant rock carved by the Inca's of ancient Machu Picchu in Peru, can be considered as a first 8-bit hard disk. Why so? As the sunrays lit the different surfaces of this huge rock throughout the year, it triggered the Inca's activities: sowing, harvesting, celebrating and praying.This ancient stone dissolves both the boundaries between heaven and earth, and those between the digital and natural Information Universe. In fact, the stone represents an ultimate picture of the cross-over between the in vivo and the in vitro Information Universe - a main theme of the book. In vitro being the man made technology to handle information and in vivo being the information built in nature, in this case the orbit and the light rays of the sun.First computers (16 bit: 216 =65.536, 2 byte)When computers emerged in the 1970's, astronomers first adopted them to steer their telescopes. Back then, a maximal effort to understand the mathematics of the problem was needed to squeeze the solution into the small computer memory. Nowadays, with large amounts of computing power and machine learning at their disposal, scientists and computer programmers often do the reverse.Star Peace vs. Star Wars (Item page)King Juan Carlos adored the harmony of galaxies as a source of inspiration for people on earth, in those days when Ronald Reagan was promoting his Star-wars programme. With this adoration in mind, in 1985, he gave an inspiring speech at the Royal inauguration of the international astronomical observatory on La Palma, Canary Islands. The inauguration was attended by, for those days, an unprecedented large crowd of European royals and government officials despite the great threat of terrorist attacks by the ETA. (the next and later spreads on facts vs fakes elucidate the relevance of this spread in the story line).Pre-internet Facts and Fakes (Item page)“Edwin Valentijn saved the life of the Dutch Queen Beatrix by catching her just before falling off a cliff at the inauguration on La Palma”, according to the headlines in Dutch newspapers. Fake news-stories are at all times alike and can only be dispelled by tracing links of information to their source, links or associations being a fundamental property of the Information Universe. Later, I discuss the less innocent case of overdrawing attention to terrorist attacks in the past decade.Hard disk (24 bit: 224 =1.6*107, 2 Mb)Only sixty years ago, a 5 MB hard disk weighed over five tons, and had to be loaded onto an aeroplane by using a truck. Now, we carry a thousand times more information in our trouser pocket. This demonstrates the amazing advance of information technology over the past decades. (Picture: first IBM hard disk loaded onto a plane).The telephone (Item page) As a precursor of the Internet, the telephone offered many of the same advantages and dangers, and was heavily discussed at its introduction. Whether telephone or the Internet, it all revolves around communication or copying of information. The telephone, as example of it, is one of the major discoveries of the 20th century. DNA (32 bit: 232 = 4*109, 500 Mb) – Guest author: Charley Lineweaver The information in the DNA creates life. All base pairs of the human DNA can be stored on a 500 Mb drive. How is this information communicated? How does a cell know it has to build part of a liver and not an eye, while they all have the same DNA? Apoptosis and the role of information exchange.Where does biological Information come from? (Item page) – Guest author: Charley Lineweaver Charley Lineweaver, expert on evolutionary biology, exoplanetology and astrobiology, will expand on the role of information in the evolution of life.Lifelines (Item page) – Guest author: Morris SwertzWhat is the role of nature versus that of nurture? A key question in modern health research. In Lifesciences, this question is addressed now using Big Data, like the astronomers who acquire huge data volumes to address the same question on the nature of galaxies. In Lifelines, a cohort of 165.000 people is studied over a period of 30 years using hospital data, blood samples and DNA scans.DVD (33 bit: 233 =9*109, 1 Gb)It’ s amazing how fast the digital image revolution went since 1989.30 years ago, Philips lab approached me since they had made a big discovery: it was possible to store many digital images on a CD. They were chasing me for digital images. While NASA had less than a thousand, I had 32.000 galaxy images obtained by scanning photographic plates from the European Southern Observatory – the first large digital image collection.Human Brain (36 bit: 236 =7*1010, 9 Gb) – Guest author: Katrin Amunts- JulichIn the large EU human brain project, the activities of the human brain are simulated in computers. This is a very difficult mission since the transistors in computers consume 100.000 billion times more energy than the synapsis of neurons. Our brains consist of 1011 neurons, corresponding to 9 Gb of data.Thinking of Karlheinz Meier, coordinator of the Human Brain Project in Heidelberg, Katrin Amunts will author two spreads on the role of information in the human brain.Neuromorphic computing – Guest author: Katrin AmuntsCurrently, it takes a hundred years of a supercomputer’s time to compete with the learning power of only a single day of the human brain. “Neuromorphic computing” researchers design electronic systems inspired by the human brain, in order to make computers many times faster and more energy efficient.CT scan (38 bit: 238 =3*1011, 34 Gb) – Guest author: Anders YnnermanNow it is possible to look inside animal and human bodies on touchscreens. Forensic investigations on, for instance, corpses of victims can be done with touch-screen tables. You can look inside, rotate, scroll and zoom animal and human bodies using tens of gigabytes of CT scan data. Prof. Anders Ynnerman explains how he does it.Terabytes (45 bit: 245 =4.4*1012, 1 Tb) - The largest (astronomical) datasetsDark energy and dark matter: two mysterious constituents of our Universe. How do astronomers get and handle the data from the VLT Survey Telescope on a high mountain top in Chile to shed lights on these ‘still too dark’ topics. This Telescope surveys the sky every hour at night generating Terabytes of astronomical data.Gravity as a lens (Item page) – Guest author: Margot BrouwerWhen light rays are bent by the gravity of a heavy object, this object acts as a lens. This effect can be used to map dark matter, which is invisible but constitutes 80% of the matter in our visible Universe. In 1915, Albert Einstein posed that gravity is equivalent to the curvature of the fabric of space and time itself, leading to the lensing effect.Weak gravitational lensing surveys – Guest author: Margot BrouwerTerabytes of astronomical data are reduced to a few numbers, describing how dark matter behaves and what is its true nature. https://www.youtube.com/watch?v=ZCyYGWqCmFw&t=23sEntering the Petabyte regime (53 bit: 253 =1*1015, 1 Pb)How do we technically acquire and deal with Petabytes of data?Dark Matter maps (Item page)A first dark matter map projected on the night sky. An ultimate encounter between the digital world of modern astronomical observations, and nature: the mysterious dark matter mapped on top of the everyday “night” stellar sky. A visualization that condenses Terabytes of astronomical data to a simple map.Metadata for Peta-data (62 bit: 262 =6*1017, 600 Pb)With pointers, one can connect everything in the Information Universe. Pointers are often inserted in Metadata (data about data) - an ultimate tool for dealing with Big Data. It is possible to create unique pointers to hundreds of Petabytes of data, using a string of less than 64 bits. This is what makes pointers so powerful and indispensable in current and future stages of the big data era; not only for astronomical research, but also for companies like Google, Amazon and Facebook.Downloading the Universe (Item page)The universe can be seen as a spreadsheet, certainly in the way we map it on our computers (in vitro), but also in nature (in vivo). Perceiving the Universe as a spreadsheet links bit to It.Meta data (Item page)A visualisation of the enormous complexity of data models which trace all pointers between data items. (picture: thrilling still from a full dome animation of a data model)Future (astronomical) datasets (item page)While current telescopes collect astronomical datasets of Terabytes, future telescopes such as the LSST and the Euclid satellite, instead, will collect Petabytes. These enormous amounts of data need a whole new approach to data management. For the Euclid satellite my “Universe as a spreadsheet” approach has been adopted.The Euclid satellite (Item page) – Guest author: Margot BrouwerEuclid is ESA’s new space mission to map the Dark Universe. At a distance of 1.5 million kilometres from Earth, this telescope will observe billions of galaxies. Its goal: to shed light on the nature of Dark Matter and Dark Energy, which make up 95% of our Universe. Dr. Margot Brouwer, Dutch scientific communication officer for Euclid, will explain more.The Information Universe (Item page)The resemblance of the overall structure of the real observed Universe (in vivo) with the simulated universe (in vitro), based on the concurrent cosmological model, gave a lot of credit to the latter. When we zoom out the Universe, we see billions of galaxies forming a web-like structure. Amazingly, astronomers can now compute and simulate these structures with very large supercomputers.The lost boy (Item page)Information is timeless, and knows no boundaries. It crosses over the in vivo and the in vitro Information Universe. This concept is well illustrated through daily life stories involving time. At the age of five, a boy loses sight of his older brother on a train in India, and eventually gets lost on the streets of Mumbai. Twenty years later, after being adopted by a family in Australia, he is able to find his natural mother (in vivo) through only searching on Google maps (in vitro).Qbits (50 qbit: 250 =1.1*1015 qbit, 1 Pbit) – Guest author: Lieven VandersypenUsing fundamental particles (quanta, such as electrons) to perform calculations and build computers, is one of the most exciting cross-overs between the in vivo and the in vitro Information Universe. Prof. Lieven Vandersypen, who leads a Quantum Computing group at TU Delft in the Netherlands, will explain how this technology will change the way we compute.Quantum entanglement (Item page) – Guest author: Lieven VandersypenThe states of two particles can be intimately linked (entangled), no matter how far they are separated. What Einstein famously dismissed as “spooky action at a distance”, can now be established on demand at TU Delft in the Netherlands. Prof. Vandersypen will explain how his research group, for the first time ever, both create and apply this entanglement in laboratory.Entanglement (item page) - EVThe Square Kilometre Array (64 bit: 264 =1.3*1018, 1 Eb) – Guest author: TBAThe Square Kilometre Telescope will collect data at the rate of the global internet traffic of 2013, in its endeavour to answer fundamental questions about the origin and evolution of the Universe, and its search for extra-terrestrial life.Cryptography (128 bit: 2128 =3.4*1038) – Guest author Tanja LangeEncrypted messages should not be decoded by adversaries, be they criminals or hostile countries. Cryptography enables secure communications and is one of the few applications which require 128-bit numbers. A guest author will explain more.Chapter 3: Deep spaceThe Desert (128-256 bit) Theoretical physics is not progressing much in the last decennia – some call it a crisis. Likely, an observational breakthrough is out of reach: the highest man-made information density on earth is produced by the high energy accelerators at CERN. But these accelerators have to be 1013 -1015 more powerful to reach the fundamental unit of information, which is probably at the same level of the Planck length. Unfortunately, there is no way to reach this unit of information with these instruments. This enormous gap in reaching all the domains in the Information Universe is illustrated in a figure and in a very sobering, but instructive table in the Appendix.Black holes (128-256 bit?) – Guest author: Manus VisserCan information disappear into a black hole? The Information paradox. Stephen Hawking wondered it and started a field in which space and time are described in terms of information. Dr. Manus Visser, expert on gravity and space-time, will explain more.Observing a Black Hole: Event Horizon Telescope – Guest author: Heino FalckeThe first image of a black hole. Prof. Heino Falcke, chair of the Event Horizon Telescope Science Council, will explain how information from a world-wide network of telescopes was combined using atomic clocks, to create the first ever image of a black hole. (Picture: first image of a black hole)Cogwheels: a deeper level – Guest author: Gerard 't HooftNobel laureate ‘t Hooft explains his views on cogwheels, carrying the fundamental information in the Universe.Gravitational waves – Guest author: Chris van den BroeckLinks: The Universe as a spreadsheetLinks, joins, references, URLs, blockchain, associations and even entanglement in physics are all different words for the same building block, forming the connections in the Information Universe.Cosmic Microwave Background – Guest author: Margot BrouwerParticles of light created in the hot and dense state of the Universe after the Big Bang are still flying through the Universe today. Together, these 1077 photons contain the largest amount of information known in the Universe. This information can still be accessed through telescopes, and brings us invaluable information about the dawn of our Universe.Emergent Gravity – Guest author: Erik VerlindeProf. Erik Verlinde, professor of theoretical physics at the University of Amsterdam, won the Spinoza prize for his new theory explaining gravity. In his theory, all matter, space and time consist of information and are all connected by entanglement. If this theory is correct, the information content of the entire Universe is 2399. This is the highest power described in this book, and actually, in physics.Chapter 4: It from BitOne big information processing machine – Guest author: Gerard 't Hooft (TBC)t Hooftt Hooft: : ““there is something happening at a different level of nature”there is something happening at a different level of nature”..On the origin of physical information. – Guest author: Stefano GottardiThe ear In the ear information is copied a dozen times!The eye – on the visual perception of data- climate change. Links to - facts and fakes- the system of ScienceThe System of ScienceHow does this system work? Discussing Hegel’s system of science, logic, technology, Nature, life, physics, consciousness.Artificial IntelligenceThe machine learning and the data-base oriented communities are still living on different planets. I discuss and revisit Tegmark’s recent book Life 3.0 by comparing 3 crosscuts through the Information Universe: i) the classical computer centric view ii) the data centric view iii) the artificial intelligence view.Information densityThe average information density of the universe can be compared to that of written text.Black Body radiation On the information aspects of the third big physical breakthrough of the 20th century (next to General relativity and quantum mechanics).EntropyDiscussing Shannon’s work and identifying that “Information only exists in relation to its environment”. Examples will be given.Cosmic information, cosmogenesis and dark energy by PadmanabhanCosmic information connects the cosmological constant to cosmogenesisIt from BitIs the Universe one big information processing machine?ConsciousnessVery little is known about the consciousness and I refrain from addressing the consciousness per se. A relevant list of about 5 facts we do know are listed. Any view on the relation between the consciousness and the Information Universe should at least deal with this list.Somnium – Musician Jacco Gardner performing at DOTLiveplanetarium at Eurosonic 2019 show case music festival- Inspired by Kepler’s Somnium – directed by EV The Information UniverseAn overview.Facts and fakesHow is all this related to the current facts and fakes issues on the Internet? How do you make sure that what you are reading is accurate and comes from a reliable source?The link between Open Science, FAIR and reliability of data.

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Book SynopsisSecurity Policies and Access Control.- Public key encryption, digital signatures.- Number theory, primality tests, discrete log, factorisation.- Public-key cryptography, hardware, physical attacks.- Implementation aspects of cryptographic algorithms.- Hardware attacks.- Multi-party computation, voting schemes, digital signature schemes.- Web security.- DBMS and Application Security.- Biometrics.- Software Security.- Network Security.- Formal Methods and Assurance.- Sensor and Ad Hoc Networks.- DOS.- Privacy-preserving data mining.- Private information retrieval.- Privacy metrics and data protection.- Wireless Security.- Broadcast channel, secret sharing, threshold schemes, subliminal channels.- Risk management and organizational security and privacy.- Usable/user-centric privacy.- Less-constrained biometrics.- Access and Query Privacy.- Cryptocurrencies.- Encryption-Based Access Control Based on Public Key Cryptography.- Cyber-physical systems and infrastructure: security andprivacy.- Location privacy and privacy in locations-based applications.- Privacy in emerging scenarios.- Privacy and security in social networks.- Economics of security and privacy.- Key management.- Elliptic curve cryptography.- Sequences, Boolean functions, stream ciphers.- Secure multiparty computations.- Human Aspects in Security and Privacy.- Trustworthy Computing, Physical/Hardware Security.- AI approaches for security and privacy.- Privacy and anonymity in communication networks.- Privacy laws and directives.

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Book SynopsisThis textbook offers an algorithmic introduction to the field of computer algebra. A leading expert in the field, the author guides readers through numerous hands-on tutorials designed to build practical skills and algorithmic thinking. This implementation-oriented approach equips readers with versatile tools that can be used to enhance studies in mathematical theory, applications, or teaching. Presented using Mathematica code, the book is fully supported by downloadable sessions in Mathematica, Maple, and Maxima. Opening with an introduction to computer algebra systems and the basics of programming mathematical algorithms, the book goes on to explore integer arithmetic. A chapter on modular arithmetic completes the number-theoretic foundations, which are then applied to coding theory and cryptography. From here, the focus shifts to polynomial arithmetic and algebraic numbers, with modern algorithms allowing the efficient factorization of polynomials. The final chapters offer extensions into more advanced topics: simplification and normal forms, power series, summation formulas, and integration. Computer Algebra is an indispensable resource for mathematics and computer science students new to the field. Numerous examples illustrate algorithms and their implementation throughout, with online support materials to encourage hands-on exploration. Prerequisites are minimal, with only a knowledge of calculus and linear algebra assumed. In addition to classroom use, the elementary approach and detailed index make this book an ideal reference for algorithms in computer algebra.Trade Review“Strong interplay between the abstract exposition, which includes the relevant theorems as well as their proofs, and the practical utilization of those concepts in Mathematica is certainly a remarkable feature of this textbook. … Overall, the book is very well written and the approach to provide examples as actual Mathematica sessions is commendable.” (Andreas Maletti, zbMATH 1484.68004, 2022)Table of Contents

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Book SynopsisThis book provides empirical evidence and statistical analyses to uncover answers to some of the most debated questions in the NBA. The sports world lives and breathes off of debates on who deserves an MVP award, and which athletes should be considered all-stars. This book provides some statistics-backed perspectives to some of these debates that are specific to the NBA. Was LeBron snubbed of an MVP in the 2010-2011 season? Why has the G.O.A.T. debate turned into LeBron vs. Jordan….Did Kobe get overlooked? How come Klay Thompson didn’t get All-NBA honors in the 2018-2019 season? This book explores these questions and many more with empirical evidence. This book is invaluable for any undergraduate or masters level course in sport analytics, sports marketing, or sports management. It will also be incredibly useful for scouts, recruiters, and general managers in the NBA who would like to use analytics in their work.Table of ContentsIntroduction.- 1. Da Real MVP.- 2. A Tribe of Goats.- 3. The Myth of the Superteam.- 4. Hey Now, You're an All-Star...But Are You All-NBA?- 5. Small Ball in a Big Man's Game.- 6.Is the Clutch Gene Real.- 7. Offense Wins Games, But Does Defense Win Championships? - 8. Strategic Implications of the Findings in This Book.- 9. Debates the Future Work Should Consider.

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Book SynopsisThis book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science. Trade Review“Readers are taken on a journey where they will discover step-by-step methodologies for data-driven research. Judiciously, each key concept of data science is concisely defined, and examples and the when, why, and how to use them are provided. … I fully recommend it.” (Thierry Edoh, Computing Reviews, February 7, 2023)Table of ContentsPreface.- Chapter 1. Introduction.- Chapter 2. Inductivism.- Chapter 3. Phenomenological Science.- Chapter 4. Variational Induction.- Chapter 5. Causation As Difference Making.- Chapter 6. Evidence.- Chapter 7. Concept Formation.- Chapter 8. Analogy.- Chapter 9. Causal Probability.- Chapter 10. Conclusion.- Index.

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Book SynopsisIn this monograph, the authors develop a methodology that allows one to construct and substantiate optimal and suboptimal algorithms to solve problems in computational and applied mathematics. Throughout the book, the authors explore well-known and proposed algorithms with a view toward analyzing their quality and the range of their efficiency. The concept of the approach taken is based on several theories (of computations, of optimal algorithms, of interpolation, interlination, and interflatation of functions, to name several). Theoretical principles and practical aspects of testing the quality of algorithms and applied software, are a major component of the exposition. The computer technology in construction of T-efficient algorithms for computing ε-solutions to problems of computational and applied mathematics, is also explored. The readership for this monograph is aimed at scientists, postgraduate students, advanced students, and specialists dealing with issues of developing algorithmic and software support for the solution of problems of computational and applied mathematics.Table of Contents-Preface.- Introduction.- List of symbols and abbreviations.- 1. Elements of the computing theory.- 2. Theories of computational complexity.- 3. Interlination of functions.- 4. Interflatation of functions.- 5. Cubature formulae using interlanation functions.- 6. Testing the quality of algorithm programs.- 7. Computer technologies of solving problems of computational and applied mathematics with fixed values of quality characteristics.- Bilbiography.- Index.- About the Authors.

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Book SynopsisThis book is devoted to intelligent models and algorithms as the core components of cyber-physical systems. The complexity of cyber-physical systems developing and deploying requires new approaches to its modelling and design. Presents results in the field of modelling technologies that leverage the exploitation of artificial intelligence, including artificial general intelligence (AGI) and weak artificial intelligence. Provides scientific, practical, and methodological approaches based on bio-inspired methods, fuzzy models and algorithms, predictive modelling, computer vision and image processing. The target audience of the book are practitioners, enterprises representatives, scientists, PhD and Master students who perform scientific research or applications of intelligent models and algorithms in cyber-physical systems for various domains.Table of ContentsBio-inspired modelling.- Fuzzy models and algorithms.- Predictive modelling.- Computer Vision and Image Processing.

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Book SynopsisThis book presents recent advances in computational optimization. The book includes important real problems like modeling of physical processes, parameter settings for controlling different processes, transportation problems, machine scheduling, air pollution modeling, solving multiple integrals and systems of differential and integral equations which describe real processes, solving engineering and financial problems.It shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming Monte Carlo method and others. This research demonstrates how some real-world problems arising in engineering, economics and other domains can be formulated as optimization problems.Table of ContentsLearning to Optimize.- Optimal seating assignment in the COVID-19 era via Quantum Computing.- Hybrid Ant Colony Optimization Algorithms – Behaviour Investigation Based on Intuitionistic Fuzzy Logic.- Scheduling algorithms for single machine problem with release and delivery times.- Key Performance Indicators to Improve e-Mail Service Quality through ITIL Framework.- Contemporary Bioprocesses Control Algorithms for Educational Purposes.- Monitoring a Fleet of Autonomous Vehicles through A* like Algorithms and Reinforcement Learning.- Rather "good in, good out" than "garbage in, garbage out": A comparison of various discrete subsampling algorithms using COVID-19 data without a response variable.

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Book SynopsisThis book constitutes the refereed proceedings of the 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, held in Halifax, NS, Canada, in June 2022. The 39 full papers presented together with 7 short papers were selected from 113 submissions. The papers are grouped in topical sections on knowledge-based system; machine learning; medical image processing; predictive modeling; natural language processing.Table of ContentsKnowledge-Based Systems Explainable Decision Support Using Task Network Models in Notation.- Computerizing Lipid Management Clinical Guidelines as Interactive Task Networks.- Towards an AI planning-based pipeline for the management of multimorbid patients.- A Knowledge Graph Completion Method Applied to Literature-Based Discovery for Predicting Missing Links Targeting Cancer Drug Repurposing.- Ontological Representation of Causal Relations for a Deep Understanding of Associations between Variables in Epidemiology.- Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-term Behavior Change.- Machine Learning Assessing Knee Biomechanical Osteoarthritis Severity and Biomechanical Changes After Total Knee Arthroplasty Using Self-Organizing Maps.- NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Extracting Surrogate Decision Trees from Black-box Models to Explain the Temporal Importance of Clinical Features in Predicting Kidney Graft Survival.- Recurrence and Self-Attention vs the Transformer for Time-Series Classification: A Comparative Study.- Integrating Graph Convolutional Neural Networks and Long Short-Term Memory for Efficient Diagnosis of Autism.- Hierarchical Deep Multi-task learning for Classification of Patient Diagnoses.- TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network.- Predicting Next Kidney Offer for Transplant Candidate Declining Current One.- Wrist Ultrasound Segmentation by Deep Learning.- Early Detection and Classification of Patient-Ventilator Asynchrony using Machine Learning.- On graph construction for classification of clinical trials protocols using Graph Neural Networks.- Medical Image Processing Malignant Mesothelioma Subtyping of Tissue Images via Sampling Driven Multiple Instance Prediction.- Calibrating Histopathology Image Classifiers using Label Smoothing.- InvUNET: Involuted UNET for Breast Tumor Segmentation from Ultrasound.- MRI reconstruction with LassoNet and compressed sensing.- Predictive Modeling A 3-window-based framework for the discovery of predictive functional dependencies from clinical data.- When can I expect the mHealth user to return? Prediction meets time series with gaps.- A novel survival analysis approach to predict the need for intubation in intensive care units.- Awareness of being tested and its effect on reading behaviour.- Natural Language Processing Generating extremely short summaries from the scientific literature to support decisions in primary healthcare: a human evaluation study.- A Russian Medical Language Understanding Benchmark.- Biomedical Semantic Textual Similarity: Evaluation of Sentence Representations Enhanced With Principal Component Reduction and Word Frequency Weighting.

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Book SynopsisThis book constitutes the refereed proceedings of the 15th International Conference on Graph Transformation, ICGT 2022, which took place Nantes, France in July 2022.The 10 full papers and 1 tool paper presented in this book were carefully reviewed and selected from 19 submissions. The conference focuses on describing new unpublished contributions in the theory and applications of graph transformation as well as tool presentation papers that demonstrate main new features and functionalities of graph-based tools.Table of ContentsTheoretical Advances.- Application Domains.- Tool Presentation.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on the Theory and Application of Diagrams, Diagrams 2022, held in Rome, Italy, in September 2022. The 11 full papers and 19 short papers presented together with 5 posters were carefully reviewed and selected from 58 submissions. 8 chapters are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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Book SynopsisThis book constitutes the proceedings of the 16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022, which was held online during August 13-14, 2022. The conference was originally planned to take place in Guangzhou, China, but changed to a virtual event due to the COVID-19 pandemic.The 41 regular papers included in this book were carefully reviewed and selected from 59 submissions. Table of ContentsAn improvement of the bound on the odd chromatic number of 1-planar graphs.- AoI Minimizing of Wireless Rechargeable Sensor Network based on Trajectory Optimization of Laser-Charged UAV.- Monotone k-Submodular Knapsack Maximization: An Analysis of the Greedy+Singleton Algorithm.- The constrained parallel-machine scheduling problem with divisible processing times and penalties.- Energy-constrained Geometric Covering Problem.- Fast searching on $k$-combinable graphs.- Three Algorithms for Converting Control Flow Statements from Python to XD-M.- Class Ramsey numbers involving induced graphs.- An Approximation Algorithm for the Clustered Path Travelling Salesman Problem.- Hyperspectral Image Reconstruction for SD-CASSI systems based on Residual Attention Network.- Improved Approximation Algorithm for the Asymmetric Prize-Collecting TSP.- Injective edge coloring of power graphs and necklaces.- Guarantees for Maximization of $k$-Submodular Functions with a Knapsack and a Matroid Constraint.- Incremental SDN Deployment to Achieve Load Balance in ISP Networks.- Approximation scheme for single-machine rescheduling with job delay and rejection.- Defense of Scapegoating Attack in Network Tomography.- A Binary Search Double Greedy Algorithm for Non-monotone DR-submodular Maximization.- Streaming Adaptive Submodular Maximization.- Constrained Stochastic Submodular Maximization with State-Dependent Costs.- Online early work maximization problem on two hierarchical machines with buffer or rearrangements.- Polynomial time algorithm for k-vertex-edge dominating problem in interval graphs.- Adaptive Competition-based Diversified-profit Maximization with Online Seed Allocation.- Collaborative Service Caching in Mobile Edge Nodes.- A Decentralized Auction Framework with Privacy Protection in Mobile Crowdsourcing.- On-line single machine scheduling with release dates and submodular rejection penalties.- Obnoxious Facility Location Games with Candidate Locations.- Profit Maximization for Multiple Products in Community-based Social Networks.- MCM: A Robust Map Matching Method by Tracking Multiple Road Candidates.- Security on Ethereum: Ponzi Scheme Detection in Smart Contract.- Cyclically orderable generalized Petersen graphs.- The r-dynamic chromatic number of planar graphs without special short cycles.- Distance Labeling of the Halved Folded $n$-Cube.- Signed network embedding based on muti-attention mechanism.- Balanced Graph Partitioning based on Mixed 0-1 Linear Programming and Iteration Vertex Relocation Algorithm.- Partial inverse min-max spanning tree problem under the weighted bottleneck Hamming distance.- Mixed Metric Dimension of Some Plane Graphs.- The Optimal Dynamic Rationing Policy in the Stock-Rationing Queue.- Pilot Pattern Design with Branch and Bound in PSA-OFDM System.- Bicriteria Algorithms for Maximizing the Difference Between Submodular Function and Linear Function under Noise.- On the Transversal Number of k-Uniform Connected Hypergraphs.- Total coloring of planar graphs without some adjacent cycles.

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Book SynopsisThis book constitutes the refereed proceedings of the 20th International Conference on Business Process Management, BPM 2022, which took place in Münster, Germany, in September 2022. The 22 papers included in this book were carefully reviewed and selected from 98 submissions. They were organized in topical sections as follows: task mining; design methods; process mining; process mining practice; analytics; and systems. The book also includes one keynote talk in full-paper length and 5 tutorial papers. Table of ContentsKeynote.- Advancing Business Process Science via the Co-Evolution of Substantive and Methodological Knowledge.- Tutorials.- BPM in Digital Transformation: New Tools and Productivity Challenges.- Multi-Dimensional Process Analysis.- Theory and Practice - What, With What and How Is Business Process Management Taught at German Universities.-How to Leverage Process Mining in Organizations - Towards Process Mining Capabilities.- Mastering Robotic Process Automation with Process Mining.- Task Mining.- A Reference Data Model for Process-Related User Interaction Logs.- Analysing Variable Human Actions for Robotic Process Automation.- The SWORD is Mightier than the Interview: A Framework for Semi-automatic WORkaround Detection.- Design Methods.- Back to the Roots – Investigating the Theoretical Foundations of Business Process Maturity Models.- Applying Process Mining in Small and Medium sized IT Enterprises – Challenges and Guidelines.- A Process Mining Success Factors Model.- Process Mining.- No Time to Dice: Learning Execution Contexts from Event Logs for Resource-Oriented Process Mining.- A Purpose-Guided Log Generation Framework.- Conformance Checking with Uncertainty via SMT.- Process Mining Practice.- The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study From the Health Sector.- Analyzing How Process Mining Reports Answer Time Performance Questions.- Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing.- Process Mining Practices: Evidence from Interviews.- Analytics.- Measuring Inconsistency in Declarative Process Specifications.- Understanding and Decomposing Control-Flow Loops in Business Process Models.- Reasoning on Labelled Petri Nets and their Dynamics in a Stochastic Setting.- Incentive Alignment through Secure Computations.- Business Process Simulation with Differentiated Resources: Does it Make a Difference.- Uncovering Object-centric Data in Classical Event Logs for the Automated Transformation from XES to OCEL.- Systems.- Why Companies Use RPA: A Critical Reflection of Goals.- A trustworthy decentralized change propagation mechanism for declarative choreographies.- Architecture of decentralized Process Management Systems.

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Book SynopsisThis book gives a detailed information of various real-life applications from various fields using nature inspired optimization techniques. These techniques are proven to be efficient and robust in many difficult problems in literature. The authors provide detailed information about real-life problems and how various nature inspired optimizations are applied to solve these problems. The authors discuss techniques such as Biogeography Based Optimization, Glow Swarm Optimization, Elephant herd Optimization Algorithm, Cuckoo Search Algorithm, Ant Colony Optimization, and Grey Wolf Optimization etc. These algorithms are applied to a wide range of problems from the field of engineering, finance, medicinal etc. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in nature inspired optimization, inspiring women and men, girls and boys to enter and apply themselves to the field.Table of Contents1) AN OVERVIEW OF SWARM INTELLIGENCE BASED ALGORITHMS2) Particle Swarm Optimization and its Applications in the Manufacturing Industry3) Role of Machine Learning in Bioprocess Engineering: Current Perspectives and Future Directions 4) Advanced Selection Operation for Differential Evolution Algorithm 5) Profit Optimization of Two-Unit Briquetting System using grey wolf Optimization algorithm 6) Solving Portfolio optimization using Sine-cosine Algorithm embedded mutation operations7) Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer Technique8) SINGLE IMAGE REFLECTION REMOVAL USING DEEP LEARNING 9) Social media analysis: A tool for popularity prediction using machine learning classifiers

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Book SynopsisThis book constitutes the proceedings of the 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022, held in L'Aquila, Italy in July 2022.The 37 full papers in this book were reviewed and selected from 67 submissions. Another 10 demonstrations papers were selected from 11 submissions are presented here as short papers. The papers deal with the application and validation of agent-based models, methods, and technologies in a number of key applications areas, including: advanced models and learning, agent-based programming, decision-making, education and social interactions, formal and theoretic models, health and safety, mobility and the city, swarms and task allocation.

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Book SynopsisThis book constitutes extended, revised, and selected papers from the JSAI annual conference, JSAI 2022, and the 14th International Symposium on Artificial Intelligence, JSAI-isAI 2022, held in Kyoto, Japan, in June 2022. The 18 full papers were carefully selected from 67 submissions and presented during the two events: 16th International Workshop on Juris-informatics, JURISIN 2022, and JSAI 2022 Intenational Session. This papers present discussion on fundamental and practical issues in Juris-informatics among researchers from various backgrounds such as law, social science, information and intelligent technology, logic, and philosophy, including the conventional AI and Law area. Table of ContentsJURISIN 2022.- Differential-aware Transformer for Partially Amended Sentence Translation.- On Complexity and Generality of Contrary Prioritized Defeasible Theory.- Mapping Similar Provisions between Japanese and Foreign Laws.- COLIEE 2022 Summary: Methods for Legal Document Retrieval and Entailment.- JNLP team: Deep Learning Approaches for Tackling Long and Ambiguous Legal Documents in COLIEE 2022.- Semantic-based Classification of Relevant Case Law.- nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models.- HUKB at the COLIEE 2022 Statute Law Task.- Using Textbook Knowledge for Statute Retrieval and Entailment Classification.- Legal Textual Entailment using Ensemble of Rule-based and BERT-based method with Data Augmentation by Related Article Generation.- Less is Better: Constructing Legal Question Answering System by Weighing Longest Common Subsequence of Disjunctive Union Text.- JSAI 2022 International Session.- Proposal for Turning Point Detection Method using Financial Text and Transformer.- Product Portfolio Optimization for LTV Maximization.- An Examination of Eating Experiences in Relation to Psychological States, Loneliness, and Depression Using BERT.- Objective Detection of High-risk Tackle in Rugby by Combination of Pose Estimation and Machine Learning.- Incremental informational value of floorplans for rent price prediction - Applications of modern computer vision techniques in real-estate.- Transaction Prediction by Using Graph Neural Network and Textual Industry Information.- Overfitting Problem in the Approximate Bayesian Computation Method Based on Maxima Weighted Isolation Kernel.

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Book SynopsisThis book constitutes the joint refereed proceedings of the 22nd International Conference on Internet of Things, Smart Spaces, and Next Generation Networks and Systems, NEW2AN 2022, held in Tashkent, Uzbekistan, in December 2022.The 58 regular papers presented in this volume were carefully reviewed and selected from 282 submissions. The papers of NEW2AN address various aspects of next-generation data networks, while special attention is given to advanced wireless networking and applications. In particular, the authors have demonstrated novel and innovative approaches to performance and efficiency analysis of 5G and beyond systems, employed game-theoretical formulations, advanced queuing theory, and machine learning. It is also worth mentioning the rich coverage of the Internet of Things, optics, signal processing, as well as digital economy and business aspects.Table of ContentsTangential shear stress in an oscillatory flow of a viscoelastic fluid in a flat channel.- Comparison of Finite Difference Schemes of Different Orders of Accuracy for the Burgers Wave Equation Problem.- Numerical solution of the combustion process using the computer package ANSYS fluent.- Simulation Modeling of Reliability of Packet Switching Unit.- Analytical Model for Assessing the Reliability of the Functioning of the Adaptive Switching Node.- Artificial intelligence software architecture in the field of cardiology and application in the cardio vessel project using CJM and customer development methods.- Using discretization and numerical methods of problem 1d-3d-1d model for blood vessel walls with Navier-Stokes.- Numerical simulation of a flow in a two-dimensional channel on the basis of a two-liquid turbulence model.- Application fuzzy neural network methods to detect cryptoattacks on financial information systems based on blockchain technology.- Method authentication of objects information communication systems.- TEDCTSSA: Trust Enabled Data Collection Technique based Sparrow Search Algorithm for WSN-based Applications.- ISTOA: An improved Sooty Tern Optimization Algorithm for multilevel threshold image segmentation.- Implementing digital transformation in the logistics system of Uzbekistan.- Numerical modeling of vertical axis wind turbines using ANSYS FLUENT software.- "i’ll wait 4 ur answr!” A Study on Modern Style of Cyber-Writing and User Reactions.- Improvement of information support in intelligent information energy systems.- The Assessment of the Effectiveness of the Development of Digital Technologies in Commercial Banks in Uzbekistan.- The Study of the Impact of the Digital Economy on the Growth of E-Government Services in Uzbekistan.- Deep learning algorithm for classifying dilated cardiomyopathy and hypertrophy cardiomyopathy in transport workers.- eCommerce benchmarking: theoretical background, variety of types, and application of competitive-integration benchmarking.- Cryptocurrencies as the money of the future.- A Data Security Technique Combining Asymmetric Cryptography and Compressive Sensing for IoT Enabled Wireless Sensor Networks.- Energy Efficient and Secure Scheme based Compressive Sensing method for Internet of Vehicles.- Impact of digital technologies on women’s employment.- The impact of digitalisation on the safe development of individuals in society.- Econometric Evaluation of the Efficiency of the Management of theEnterprise through the Supply of Raw Materials in Oil Enterprises in the Conditions of the Digital Economy.- The impact of digital infrastructure, foreign direct investment and trade openness on economic growth: In the case of Uzbekistan.- The impact of the financial ratios on the financial performance. A caseof Chevron Corporation (CVX).- The impact of the digitalisation of payment systems on the profitabilityof commercial banks.- The main aspects and benefits of digital transformation of business entities.- The influence of the capital structure of state enterprises on the profitability of the enterprise.- Exploring the development of China’s digital trade in the context of the domestic and international double cycle.- A systematic mapping study of using the cutting-edge technologies in marketing: the state of the art of four key new-age technologies.- Social Media Marketing for Educational Purposes: Goals, Objectivesand Content of the Training Course.- Digital Marketing and Smart Technology Marketing Systems as thefuture of metaverse.- The impact of the digital economy on the development of higher education.- What is the state-of-the-art contribution of the higher education system to the digital economy: a systematic mapping study on changes and challenges.- Innovating primary education of promoting students’ languagecompetencies through mobile assisted language learning approach: Selection framework of innovative digital technologies.- Econometric assessment of the dynamics of development of the export authority of small business and private business subjects in the conditions of the digital economy.- The significance of the Internet of Things for ensuring the smooth operation of network functions in fintech.- Impact of E-government on Poverty Rate: a Cross- Country Empirical Assessment.- An empirical investigation of the relationship between e-government development and multidimensional poverty.- On Digital Twin Software and Cyber Threats.- On local services based on non-standard Wi-Fi Direct usage model.- Compatibility analysis between 5G NR and ultra-wideband devices in the 6425-7125 MHz frequency band.- 6 GHz band sharing study for FWA base stations and GEO satellite receivers.- Federated Learning Strategies Over Wireless Channels.- Data Routing in UAV Networks with Multiple Data Sources using Steiner Tree.- Reduced complexity distributed arithmetic architecture for FIR filters.- Blockchain-driven Hybrid Model for IoT Authentication.- An Heuristic Approach for Mapping of Service Function Chains in Softwarized 5G Networks.- Multi-threshold hysteresis-Based Congestion Control for UAV-based Detection Sensor Network.- Analysis of the capacity gain of Probability Shaping QAM.- LoRa Mesh Network for Image Transmission: An Experimental Study.- Blockchain Technology – Innovation for Better Collaboration and Increased Efficieny. The U.S. Logistics and Trucking Industry Case.- Econometric Study of the Impact of the Digital Economy on the Gross Product in Anti-monopoly Conditions.- Predictive models for effective management of e- commerce in New Uzbekistan.- The role of IT on transportation, logistics and the economic growth among Central Asian countries.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Algorithms and Complexity, CIAC 2023, which took place in Larnaca, Cyprus, during June 13–16, 2023. The 25 full papers included in this book were carefully reviewed and selected from 49 submissions. They cover all important areas of research on algorithms and complexity such as algorithm design and analysis; sequential, parallel and distributed algorithms; data structures; computational and structural complexity; lower bounds and limitations of algorithms; randomized and approximation algorithms; parameterized algorithms and parameterized complexity classes; smoothed analysis of algorithms; alternatives to the worst-case analysis of algorithms (e.g., algorithms with predictions), on-line computation and competitive analysis, streaming algorithms, quantum algorithms and complexity, algorithms in algebra, geometry, number theory and combinatorics, computational geometry, algorithmic game theory and mechanism design, algorithmic economics (including auctions and contests), computational learning theory, computational biology and bioinformatics, algorithmic issues in communication networks, algorithms for discrete optimization (including convex optimization) and algorithm engineering.Table of ContentsUnifying Gathering Strategies for Swarms of Mobile Robots.- The Complexity of Secure RAMs.- Selected Combinatorial Problems Through the Prism of Random Intersection Graphs Models.- The power of the Binary Value Principle.- Independent Set under a Change Constraint from an Initial Solution.- Asynchronous Fully-Decentralized SGD in the Cluster-Based Model.- Non-Crossing Shortest Paths Lengths in Planar Graphs in Linear Time.- How Vulnerable is an Undirected Planar Graph with respect to Max Flow.- Maximum Flows in Parametric Graph Templates.- Dynamic Coloring on Restricted Graph Classes.- Enumeration of Minimal Tropical Connected Sets.- Dynamic Flows with Time-Dependent Capacities.- On One-Sided Testing Affine Subspaces.- Stable Scheduling in Transactional Memory.- Parameterizing Path Partitions.- Maintaining Triconnected Components under Node Expansion.- Approximating Power Node-Deletion Problems.- Phase transition in count approximation by Count-Min sketch with conservative updates.- Minimum-link ´ $C$-Oriented Paths Visiting a Sequence of Regions in the Plane.- Grouped Domination Parameterized by Vertex Cover, Twin Cover, and Beyond.- Broadcasting in Split Graphs.- Partitioning Subclasses of Chordal Graphs with Few Deletions.- Complete Decomposition of Symmetric Tensors in Linear Time and Polylogarithmic Precision.- Improved Deterministic Leader Election in Diameter-Two Networks.- Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix Eigenvalues.- On the Parameterized Complexity of the Structure of Lineal Topologies (Depth-First Spanning Trees) of Finite Graphs: The Number of Leaves.- Efficiently Enumerating All Spanning Trees of a Plane 3-Tree.- Communication-Efficient Distributed Graph Clustering and Sparsification under Duplication Models.

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Book SynopsisThis open access book constitutes the proceedings of the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, which was held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023, during April 22-27, 2023, in Paris, France.The 56 full papers and 6 short tool demonstration papers presented in this volume were carefully reviewed and selected from 169 submissions. The proceedings also contain 1 invited talk in full paper length, 13 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, flexibility, and efficiency of tools and algorithms for building computer-controlled systems. Table of ContentsInvited Talk.-A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems.- Model Checking.- Bounded Model Checking for Asynchronous Hyperproperties.- Model Checking Linear Dynamical Systems under Floating-point Rounding.- Efficient Loop Conditions for Bounded Model Checking Hyperproperties.- Reconciling Preemption Bounding with DPOR.- Optimal Stateless Model Checking for Causal Consistency.- Symbolic Model Checking for TLA+ Made Faster.- AutoHyper: Explicit-State Model Checking for HyperLTL.- Machine Learning/Neural Networks.- Feature Necessity & Relevancy in ML Classifier Explanations.- Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks.- OccRob: Effcient SMT-Based Occlusion Robustness Verification of Deep Neural Networks.- Neural Network-Guided Synthesis of Recursive List Functions.- Automata.- Modular Mix-and-Match Complementation of Buechi automata.- Validating Streaming JSON Documents With Learned VPAs.- Antichains Algorithms for the Inclusion Problem Between ω -VPL.- Stack-Aware Hyperproperties.- Proofs.- Propositional Proof Skeletons.- Unsatisfiability Proofs for Distributed Clause-Sharing SAT Solvers.- Carcara: An effcient proof checker and elaborator for SMT proofs in the Alethe format.- Constraint Solving/Blockchain.- The Packing Chromatic Number of the Infinite Square Grid is 15.- Active Learning for SAT Solver Benchmarking.- ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving.- Inferring Needless Write Memory Accesses on Ethereum Bytecode.- Markov Chains/Stochastic Control.- A Practitioner’s Guide to MDP Model Checking Algorithms.- Correct Approximation of Stationary Distributions.- Robust Almost-Sure Reachability in Multi-Environment MDPs.- Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning.- Verification.- A Formal CHERI-C Semantics for Verification.- Automated Verification for Real-Time Systems via Implicit Clocks and an Extended Antimirov Algorithm.- Parameterized Verification under TSO with Data Types.- Verifying Learning-Based Robotic Navigation Systems: A Case Study.- Make flows small again: revisiting the flow framework.- ALASCA: Reasoning in Quantified Linear Arithmetic.- A Matrix-Based Approach to Parity Games.- A GPU Tree Database for Many-Core Explicit State Space Exploration.

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