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• Translator's Foreword: Why I Translate So Intelligently: Translation Metis in the Era of Google Translate Acknowledgments Preface Introduction
• 1. g: Intelligence and Genetic Fate
• 2. The "Blue Brain"
• 3. Like a Pollock Painting Conclusion Postscript to the English Translation. Artificial Intelligence: The Fourth Blow to Our Narcissism Notes Index.
• (source: Nielsen Book Data)

What is intelligence? The concept crosses and blurs the boundaries between natural and artificial, bridging the human brain and the cybernetic world of AI. In this book, the acclaimed philosopher Catherine Malabou ventures a new approach that emphasizes the intertwined, networked relationships among the biological, the technological, and the symbolic. Malabou traces the modern metamorphoses of intelligence, seeking to understand how neurobiological and neurotechnological advances have transformed our view. She considers three crucial developments: the notion of intelligence as an empirical, genetically based quality measurable by standardized tests; the shift to the epigenetic paradigm, with its emphasis on neural plasticity; and the dawn of artificial intelligence, with its potential to simulate, replicate, and ultimately surpass the workings of the brain. Malabou concludes that a dialogue between human and cybernetic intelligence offers the best if not the only means to build a democratic future. A strikingly original exploration of our changing notions of intelligence and the human and their far-reaching philosophical and political implications, Morphing Intelligence is an essential analysis of the porous border between symbolic and biological life at a time when once-clear distinctions between mind and machine have become uncertain.
(source: Nielsen Book Data)

• The Process of Industrial Bioethanol Production Explained by Self-Organised Maps / Miguel A. Sanz-Bobi, Pablo Ruiz and Julio Montes
• Towards a Further Understanding of the Robotic Darwinian PSO / Micael S. Couceiro, Fernando M. L. Martins, Filipe Clemente, Rui P. Rocha and Nuno M. F. Ferreira
• A Comparison Study Between Two Hyperspectral Clustering Methods: KFCM and PSO-FCM / Amin Alizadeh Naeini, Saeid Niazmardi, Shahin Rahmatollahi Namin, Farhad Samadzadegan and Saeid Homayouni
• Comparison of Classification Methods for Golf Putting Performance Analysis / J. Miguel A. Luz, Micael S. Couceiro, David Portugal, Rui P. Rocha and Hélder Araújo, et al.
• Switched Unfalsified Multicontroller Nonparametric Model Based Design / Fernando Coito, Luís Brito Palma and Fernando Costa
• Evolving Fuzzy Uncalibrated Visual Servoing for Mobile Robots / P. J. S. Gonçalves, P. J. F. Lopes, P. M. B. Torres and J. M. R. Sequeira

• Evaluating the Potential of Particle Swarm Optimization for Hyperspectral Image Clustering in Minimum Noise Fraction Feature Space / Shahin Rahmatollahi Namin, Amin Alizadeh Naeini and Farhad Samadzadegan
• On a Ball's Trajectory Model for Putting's Evaluation / Gonçalo Dias, Rui Mendes, Micael S. Couceiro, Carlos M. Figueiredo and J. Miguel A. Luz
• Efficient Discriminative Models for Proteomics with Simple and Optimized Features / Lionel Morgado, Carlos Pereira, Paula Veríssimo and António Dourado
• Meta-heuristics Self-Parameterization in a Multi-agent Scheduling System Using Case-Based Reasoning / Ivo Pereira, Ana Madureira and Paulo de Moura Oliveira
• Haptic-Based Robot Teleoperation: Interacting with Real Environments / Pedro Neto, Nélio Mourato and J. Norberto Pires
• Multi-agent Predictive Control with Application in Intelligent Infrastructures / J. M. Igreja, S. J. Costa, J. M. Lemos and F. M. Cadete
• Single-Objective Spreading Algorithm / E. J. Solteiro Pires, Luís Mendes, António M. Lopes, P. B. de Moura Oliveira and J. A. Tenreiro Machado

• Fault Tolerant Control Based on Adaptive LQG and Fuzzy Controllers / Carla Viveiros, Luis Brito Palma and José Manuel Igreja
• P2P Web Service Based System for Supporting Decision-Making in Cellular Manufacturing Scheduling / Maria Leonilde R. Varela, Rui Barbosa and Susana Costa
• Web-Based Decision Support System for Orders Planning / António Arrais-Castro, Maria Leonilde R. Varela and Sílvio Carmo-Silva
• Product Documentation Management Through REST-Based Web Service / Filipe Rocha, Maria Leonilde R. Varela and Sílvio Carmo-Silva
• Fuzzy Web Platform for Electrical Energy Losses Management / Gaspar Gonçalves Vieira, Maria Leonilde R. Varela and Rita A. Ribeiro
• Web System for Supporting Project Management / Cátia Filipa Veiga Alves, André Filipe Nogueira da Silva and Maria Leonilde R. Varela
• Generation Capacity Expansion Planning in Restructured Electricity Markets Using Genetic Algorithms / Adelino J. C. Pereira and João Tomé Saraiva

• Decision Making in Maintainability of High Risk Industrial Equipment / José Sobral and Luis Ferreira
• The Classification Platform Applied to Mammographic Images / P. J. S. Gonçalves
• On an Optimization Model for Approximate Nonnegative Matrix Factorization / Ana Maria de Almeida
• Random Walks in Electric Networks / D. M. L. D. Rasteiro
• Business Intelligence Tools / Jorge Bernardino and Marco Tereso
• Food Service Management Web Platform Based on XML Specification and Web Services / Pedro Sabioni, Vinícius Carneiro and Maria Leonilde R. Varela
• Differential Evolution on the Minimization of Thermal Residual Stresses in Functionally Graded Structures / T. A. N. Silva and M. A. R. Loja
• Magnetic Wheeled Climbing Robot: Design and Implementation / M. F. Silva, R. S. Barbosa and A. L. C. Oliveira
• Development of an AGV Controlled by Fuzzy Logic / Ramiro S. Barbosa, Manuel F. Silva and Dário J. Osório
• Affect Recognition / Raquel Faria and Ana Almeida
• Web 2.0: Tagging Usefulness / Joaquim Filipe P. Santos and Ana Almeida

• Multidimensional Scaling Analysis of Electricity Market Prices / Filipe Azevedo and J. Tenreiro Machado
• PCMAT Metadata Authoring Tool / Paulo Couto, Constantino Martins, Luiz Faria, Marta Fernandes and Eurico Carrapatoso
• Collaborative Broker for Distributed Energy Resources / João Carlos Ferreira, Alberto Rodrigues da Silva, Vítor Monteiro and João L. Afonso
• A Multidimensional Scaling Classification of Robotic Sensors / Miguel F. M. Lima and J. A. Tenreiro Machado
• Rough Set Theory: Data Mining Technique Applied to the Electrical Power System / C. I. Faustino Agreira, C. M. Machado Ferreira and F. P. Maciel Barbosa
• Tuning a Fractional Order Controller from a Heat Diffusion System Using a PSO Algorithm / Isabel S. Jesus and Ramiro S. Barbosa
• A Tool for Biomedical - Documents Classification Using Support Vector Machines / João Oliveira, Daniel Correia, Carlos Pereira, Paula Veríssimo and António Dourado

• Conflicts Management in Retail Systems with Self-Regulation / Bruno Magalhães and Ana Madureira
• Adaptive e-Learning Systems Foundational Issues of the ADAPT Project / Eduardo Pratas and Viriato M. Marques
• Recognizing Music Styles - An Approach Based on the Zipf-Mandelbrot Law / Viriato M. Marques and Cecília Reis
• A Platform for Peptidase Detection Based on Text Mining Techniques and Support Vector Machines / Daniel Correia, Carlos Pereira, Paula Veríssimo and António Dourado
• Optimal Configuration of Uniplanar-Unilateral External Fixators in Tibia Fractures / Luis Roseiro and Augusta Neto
• Identification of the Forces in the Suspension System of a Race Car Using Artificial Neural Networks / Luis Roseiro, Carlos Alcobia, Pedro Ferreira, Abderrahmane Baïri and Najib Laraqi, et al.
• Combinational Logic Circuits Design Tool for a Learning Management System / Cecília Reis and Viriato M. Marques
• Labeling Methods for the General Case of the Multi-objective Shortest Path Problem - A Computational Study / J. M. Paixão and J. L. Santos.

This book provides a general overview and original analysis of new developments and applications in several areas of Computational Intelligence and Information Systems. Computational Intelligence has become an important tool for engineers to develop and analyze novel techniques to solve problems in basic sciences such as physics, chemistry, biology, engineering, environment and social sciences. The material contained in this book addresses the foundations and applications of Artificial Intelligence and Decision Support Systems, Complex and Biological Inspired Systems, Simulation and Evolution of Real and Artificial Life Forms, Intelligent Models and Control Systems, Knowledge and Learning Technologies, Web Semantics and Ontologies, Intelligent Tutoring Systems, Intelligent Power Systems, Self-Organized and Distributed Systems, Intelligent Manufacturing Systems and Affective Computing. The contributions have all been written by international experts, who provide current views on the topics discussed and present recent, original insights from their own experience in these fields.
(source: Nielsen Book Data)

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

• Chap. 1 Rethinking the Human-Agent Relationship: Which Social Cues Do Interactive Agents Really Need to Have?.- Chap. 2 Believability Through Psychosocial Behaviour: Creating Bots That Are More Engaging and Entertaining.- Chap. 3 Actor Bots.- Chap. 4 Embodied Conversational Agent Avatars in Virtual Worlds.- Chap. 5 Human-Like Combat Behaviour via Multiobjective Neuroevolution.- Chap. 6 Believable Bot Navigation via Playback of Human Traces.- Chap. 7 A Machine Consciousness Approach to the Design of Human-Like Bots.- Chap. 8 ConsScale FPS: Cognitive Integration for Improved Believability in Computer Game Bots.- Chap. 9 Assessing Believability.- Chap. 10 Making Diplomacy Bots Individual.- Chap. 11 Towards Imitation of Human Driving Style in Car Racing Games.
• (source: Nielsen Book Data)

We share our modern world with bots - chatbots to converse with, roombots to clean our houses, spambots to fill our e-mail inboxes, and medibots to assist our surgeons. This book is about computer game bots, virtual companions who accompany us in virtual worlds or sharpen our fighting skills. These bots must be believable, that is human players should believe they are interacting with entities operating at a human level - bots are more fun if they behave like we do. This book shows how to create believable bots that play computer games, and it discusses the implications of making them appear human. The chapters in this book present the state of the art in research on and development of game bots, and they also look beyond the design aspects to address deep questions: Is a bot that plays like a person intelligent? Does it have emotions? Is it conscious? The topic is inherently interdisciplinary, and the work draws from research and practice in many fields, such as design, creativity, entertainment, and graphics; learning, psychology, and sociology; artificial intelligence, embodiment, agents, machine learning, robotics, human-computer interaction, and artificial life; cognition and neuroscience; and evolutionary computing. The contributing authors are among the leading researchers and developers in this field, and most of the examples and case studies involve analysis of commercial products. The book will be of value to graduate students and academic researchers in artificial intelligence, and to engineers charged with the design of entertaining games.
(source: Nielsen Book Data)

"This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"-- Provided by publisher.

• Chapter 1 Introduction
• Chapter 2 Analysis Process
• Chapter 3 How it Works
• Chapter 4 Goals
• Chapter 5 Visitor Analysis
• Chapter 6 Traffic Analysis
• Chapter 7 How People Use Content
• Chapter 8 Clickpath Analysis
• Chapter 9 Segmentation
• Chapter 10 Putting it Together
• Chapter 11 Testing
• Chapter 12 Measuring Behavior within Pages
• Chapter 13 AB Testing
• Chapter 14 Profiles
• Chapter 15 Culture
• Chapter 16 Conclusion.
• (source: Nielsen Book Data)

Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively.
(source: Nielsen Book Data)

"Academic, industry, and government experts provide a global survey of the financial services sector's transformation by AI, data science, and blockchain"-- Provided by publisher.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
• (source: Nielsen Book Data)

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
(source: Nielsen Book Data)

• Preface xv Acknowledgments xix About the Author xxi 1 Introduction to Reinforcement and Systemic Machine Learning 1 1
• .1. Introduction 1 1
• .2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 1
• .3. Traditional Learning Methods and History of Machine Learning 4 1
• .4. What Is Machine Learning? 7 1
• .5. Machine-Learning Problem 8 1
• .6. Learning Paradigms 9 1
• .7. Machine-Learning Techniques and Paradigms 12 1
• .8. What Is Reinforcement Learning? 14 1
• .9. Reinforcement Function and Environment Function 16 1
• .10. Need of Reinforcement Learning 17 1
• .11. Reinforcement Learning and Machine Intelligence 17 1
• .12. What Is Systemic Learning? 18 1
• .13. What Is Systemic Machine Learning? 18 1
• .14. Challenges in Systemic Machine Learning 19 1
• .15. Reinforcement Machine Learning and Systemic Machine Learning 19 1
• .16. Case Study Problem Detection in a Vehicle 20 1
• .17. Summary 20 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 2
• .1. Introduction 23 2
• .2. What Is Systemic Machine Learning? 27 2
• .3. Generalized Systemic Machine-Learning Framework 30 2
• .4. Multiperspective Decision Making and Multiperspective Learning 33 2
• .5. Dynamic and Interactive Decision Making 43 2
• .6. The Systemic Learning Framework 47 2
• .7. System Analysis 52 2
• .8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 2
• .9. Summary 55 3 Reinforcement Learning 57 3
• .1. Introduction 57 3
• .2. Learning Agents 60 3
• .3. Returns and Reward Calculations 62 3
• .4. Reinforcement Learning and Adaptive Control 63 3
• .5. Dynamic Systems 66 3
• .6. Reinforcement Learning and Control 68 3
• .7. Markov Property and Markov Decision Process 68 3
• .8. Value Functions 69 3.8
• .1. Action and Value 70 3
• .9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 3
• .10. Dynamic Programming 71 3
• .11. Adaptive Dynamic Programming 71 3
• .12. Example: Reinforcement Learning for Boxing Trainer 75 3
• .13. Summary 75 4 Systemic Machine Learning and Model 77 4
• .1. Introduction 77 4
• .2. A Framework for Systemic Learning 78 4
• .3. Capturing the Systemic View 86 4
• .4. Mathematical Representation of System Interactions 89 4
• .5. Impact Function 91 4
• .6. Decision-Impact Analysis 91 4
• .7. Summary 97 5 Inference and Information Integration 99 5
• .1. Introduction 99 5
• .2. Inference Mechanisms and Need 101 5
• .3. Integration of Context and Inference 107 5
• .4. Statistical Inference and Induction 111 5
• .5. Pure Likelihood Approach 112 5
• .6. Bayesian Paradigm and Inference 113 5
• .7. Time-Based Inference 114 5
• .8. Inference to Build a System View 114 5
• .9. Summary 118 6 Adaptive Learning 119 6
• .1. Introduction 119 6
• .2. Adaptive Learning and Adaptive Systems 119 6
• .3. What Is Adaptive Machine Learning? 123 6
• .4. Adaptation and Learning Method Selection Based on Scenario 124 6
• .5. Systemic Learning and Adaptive Learning 127 6
• .6. Competitive Learning and Adaptive Learning 140 6
• .7. Examples 146 6
• .8. Summary 149 7 Multiperspective and Whole-System Learning 151 7
• .1. Introduction 151 7
• .2. Multiperspective Context Building 152 7
• .3. Multiperspective Decision Making and Multiperspective Learning 154 7
• .4. Whole-System Learning and Multiperspective Approaches 164 7
• .5. Case Study Based on Multiperspective Approach 167 7
• .6. Limitations to a Multiperspective Approach 174 7
• .7. Summary 174 8 Incremental Learning and Knowledge Representation 177 8
• .1. Introduction 177 8
• .2. Why Incremental Learning? 178 8
• .3. Learning from What Is Already Learned... 180 8
• .4. Supervised Incremental Learning 191 8
• .5. Incremental Unsupervised Learning and Incremental Clustering 191 8
• .6. Semisupervised Incremental Learning 196 8
• .7. Incremental and Systemic Learning 199 8
• .8. Incremental Closeness Value and Learning Method 200 8
• .9. Learning and Decision-Making Model 205 8
• .10. Incremental Classification Techniques 206 8
• .11. Case Study: Incremental Document Classification 207 8
• .12. Summary 208 9 Knowledge Augmentation: A Machine Learning Perspective 209 9
• .1. Introduction 209 9
• .2. Brief History and Related Work 211 9
• .3. Knowledge Augmentation and Knowledge Elicitation 215 9
• .4. Life Cycle of Knowledge 217 9
• .5. Incremental Knowledge Representation 222 9
• .6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 9
• .7. Knowledge Augmentation: Techniques and Methods 224 9
• .8. Heuristic Learning 228 9
• .9. Systemic Machine Learning and Knowledge Augmentation 229 9
• .10. Knowledge Augmentation in Complex Learning Scenarios 232 9
• .11. Case Studies 232 9
• .12. Summary 235 10 Building a Learning System 237 10
• .1. Introduction 237 10
• .2. Systemic Learning System 237 10
• .3. Algorithm Selection 242 10
• .4. Knowledge Representation 244 10
• .5. Designing a Learning System 245 10
• .6. Making System to Behave Intelligently 246 10
• .7. Example-Based Learning 246 10
• .8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 10
• .9. Intelligent Agents--Deployment and Knowledge Acquisition and Reuse 250 10
• .10. Case-Based Learning: Human Emotion-Detection System 251 10
• .11. Holistic View in Complex Decision Problem 253 10
• .12. Knowledge Representation and Data Discovery 255 10
• .13. Components 258 10
• .14. Future of Learning Systems and Intelligent Systems 259 10
• .15. Summary 259 Appendix A: Statistical Learning Methods 261 Appendix B: Markov Processes 271 Index 281.
• (source: Nielsen Book Data)

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes effective use of these learning methodologies to increase machine intelligence and help us in building the advance machine learning applications. Illuminating case studies reflecting the authors' industrial experiences and pragmatic downloadable tutorials are available for researchers and professionals.
(source: Nielsen Book Data)

• Low-Level Image Processing Based on Interval-Valued Fuzzy Sets and Scale-Space Smoothing.- Generalized Net Representation of Dataflow Process Networks.- Wireless Sensor Positioning ACO Algorithm.- Time Accounting Artificial Neural Networks for Biochemical Process Models.- Periodic Time-varying Observer-based Learning Control of A/F Ratio in Multi-cylinder IC Engines.- Fuzzy T-S Model Based Design of Min-Max Control for Uncertain Nonlinear Systems.- Modeling Parallel Optimization of the Early Stopping Method of Multilayer Perceptron.- Intelligent Controls for Switched Fuzzy Systems: Synthesis via Non-standard Lyapunov Functions.- A New Architecture for an Adaptive Switching Controller Based on Hybrid Multiple T-S Models.- Optimization of Linear Objective Function under min-Probabilistic Sum Fuzzy Linear Equations Constraint.- Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model Parameters Genesis.- Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System.- Generalized Net Model of the Scapulohumeral Rhythm.- Method for Interpretation of Functions of Propositional Logic by Specific Binary Markov Processes.- Generalized Net Models of Academic Promotion and Doctoral Candidature.- Modeling Telehealth Services with Generalized Nets.- State-Space Fuzzy-Neural Predictive Control.- Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests From Two to Hundred Dimensions.-Intuitionistic Fuzzy Sets Generated by Archimedean Metrics and Ultrametrics.-Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty.
• (source: Nielsen Book Data)

This volume is a brief, yet comprehensive account of new development, tools, techniques and solutions in the broadly perceived "intelligent systems". New concepts and ideas concern the development of effective and efficient models which would make it possible to effectively and efficiently describe and solve processes in various areas of science and technology. Special emphasis is on the dealing with uncertainty and imprecision that permeates virtually all real world processes and phenomena, and has to properly be modeled by formal and algorithmic tools and techniques so that they be adequate and useful. The papers in this volume concern a wide array of possible techniques exemplified by, on the one hand, logic, probabilistic, fuzzy, intuitionistic fuzzy, neuro-fuzzy, etc. approaches. On the other hand, they represent the use of such systems modeling tools as generalized nets, optimization and control models, systems analytic models, etc. They concerns a variety of approaches, from pattern recognition, image analysis, education system modeling, biological and medical systems modeling, etc.
(source: Nielsen Book Data)

• Chapter 1: Requirements for Digital Preservation.-
• Chapter 2: Metadata in Long-term Digital Preservation.-
• Chapter 3: The CREDO Project.-
• Chapter 4: CREDO Repository Architecture.-
• Chapter 5: Information Processing in CREDO Long-term Archive.-
• Chapter 6: Metadata in CREDO Long-term Archive.-
• Chapter 7: Persistence Management in Long-term Digital Archive.-
• Chapter 8: Power Efficiency and Scheduling Access to the Archive.-
• Chapter 9: Information Management in Federated Digital Archives.
• (source: Nielsen Book Data)

This book addresses the process of maintaining digital objects through time to ensure continued access, an aspect that has become a crucial issue in recent years. It offers a concise yet comprehensive discussion of key concepts and requirements for long-term digital preservation, and presents a pioneering framework for digital repositories that enables the long-term archiving and metadata management for large volumes of digital resources based on a system that has already been completely designed and launched. In the framework, the reliability of information readouts is ensured by the repository with two-level data recording replication and monitoring mechanisms in the repository management system (RMS) and the file systems, and by the RMS's distributed nature. The advanced RMS allows operations on the archival storage to be scheduled, while also taking into account low energy consumption requirements. After presenting the framework in detail, the book assesses and demonstrates the approach's viability in terms of delivering accessibility, authenticity and usability. As such, the book offers a valuable resource for information technology (IT) researchers and practitioners, as well as archivists and librarians.
(source: Nielsen Book Data)

• Part I. Introduction;
• Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web;
• Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning;
• Chapter 3. Aims of the Thesis;
• Chapter 4. Structure of the Thesis; Part II. Description Logics;
• Chapter 5. Foundations of Description Logics;
• Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics.

• Chapter 7. Significant Examples of Description Logics;
• Chapter 8. OWL: the Web Ontology Language;
• Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics;
• Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System.

• 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming;
• Chapter 11. DISPONTE;
• Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs;
• Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams;
• Chapter 14. BUNDLE;
• Chapter 15. TRILL; 15.1 TRILL on SWISH;
• Chapter 16. TRILL P;
• Chapter 17. Complexity of Inference;
• Chapter 18. Related Inference Systems;
• Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability.

• 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs;
• Chapter 20. Learning;
• Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE;
• Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP;
• Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR;
• Chapter 24. Related Learning Systems;
• Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems.

• 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work;
• Chapter 26. Conclusion;
• Chapter 27. Future Work.

• Machine generated contents note: Survey on Applying Machine Learning Techniques for Behavioral Awareness / Wouter Joosen
• Modelling Spatial and Temporal Context to Support Activity Recognition / Stephen Marsland
• Affect Aware Ambient Intelligence: Current and Future Directions / Anne James
• Behavioral Biometrics and Ambient Intelligence: New Opportunities for Context-Aware Applications / Paulo Novais
• Energy and Environmental Long-Term Monitoring System for Inhabitants' Weil-Being / Filippo Palumbo
• Behavioural Patterns from Cellular Data Streams and Outdoor Lighting as Strong Allies for Smart Urban Ecosystems / Adam Sedziwy
• Learning Daily Routines in Smart Office Environments / Stefano Ferilli
• EKRUCAmI Architecture -- Applications in Healthcare Domain / Hyun Yoe
• Qualitative Image Descriptor QIDL+N to Obtain Logics and Narratives Applied to Ambient Intelligent Systems / Zoe Falomir.

Annotation We are moving towards a future where environments respond to human preferences and needs. In this world, smart devices equipped with intelligent features and the capability to sense, communicate with and support humans in daily activities will be unremarkable. We already expect our cars to warn us of hazards, track our location and provide timely route advice, and in future we will speak to simple machines and hold conversations with more complex systems, such as intelligent homes, which will help us to monitor conditions, track routine tasks, and program the heating, lighting, garden watering and entertainment centre. But questions have been raised in recent years as to how intelligent these so called smart systems or ambient intelligence environments really are. This book, State of the Art in AI Applied to Ambient Intelligence, part of the outcome of the Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI) which has now run for 10 consecutive editions, aims to provide a clear picture of what has been achieved after a decade of discussion. It is representative of the diversity of approaches and issues which are currently being considered, and also indicates those avenues which are the most promising for exploration in the next decade. The book provides all those working in the field with an up-to-date reference where they will find inspiration to create better systems for the society of tomorrow.

• Introduction and Related Work
• Optimal State Change Attempts
• Different Kinds of Effect Estimators
• A Comparison with Planning under Uncertainty
• Experimental Evaluation
• Conclusions.

This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

• Title Page
• Foreword
• Contents
• List of Contributors
• Part I. Software Engineering Meets Semantic Web: Concepts and Theories
• Motivation and Introduction
• Close Encounters of the Semantic Web and MDA Kinds
• Generating Model Transformations Using Ontology Engineering Space
• Part II. Realize Software Engineering by Semantic Web Technologies
• Towards a Consistent Feature Model using OWL
• Using Semantic Web Technologies for Management Application Integration
• Semantic Web Enabled Software Analysis
• Semantically Enabling Web Service Repositories

• ABC: A methodology for Semantic Web Application DevelopmentModel-driven Design Frameworks for Semantic Web Applications
• Part III. Design Ontologies for Software Engineering
• A Software Process Ontology and Its Application
• Enriching SE Ontologies with Bug Quality
• Learning Ontologies from Software Artifacts: Exploring and Combining Multiple Choices
• References

Over the last decade, ontology has become an important modeling component in software engineering. Semantic Web Enabled Software Engineering presents some critical findings on opening a new direction of the research of Software Engineering, by exploiting Semantic Web technologies. Most of these findings are from selected papers from the Semantic Web Enabled Software Engineering (SWESE) series of workshops starting from 2005. Edited by two leading researchers, this advanced text presents a unifying and contemporary perspective on the field. The book integrates in one volume a unified perspectiv.

• Data Mining and Machine Learning.- Planning and Combinatorial Optimization.- AI Applications.- Natural Language Processing.- Uncertainty and Preference Reasoning.- Agent Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems.
(source: Nielsen Book Data)

• Analysis and Control of a Novel 4-D Hyperchaotic System.- Analysis, Control and Synchronization of a Nine-Term Novel 3-D Chaotic System.- Backstepping Controller Design for the Global Chaos Synchronization of Sprott's Jerk Systems.- Multi-Scroll Chaotic Oscillator Based on a First-Order Delay Differential Equation.- Projective Synchronization Scheme Based on Fuzzy Controller for Uncertain Multivariable Chaotic Systems.- Deadbeat Control for for Multivariable Discrete Time Systems with Time Varying Delay.- Control of Smart Grid Residential Buildings with Demand Response.- Application of Some Modern Techniques in Load Frequency Control in Power Systems.- Investigating Metaheuristics Applications for Capacitated Location Allocation Problem in Logistics Networks.- Classification of Heart Disorders Based on Tunable-Q Wavelet Transform of Cardiac Sound Signals.- Reliability-Constrained Optimal Distribution System Reconfiguration.- Machine Learning aided Efficient Tools for Risk Evaluation and Operational Planning of Multiple Contingencies.- Goal Directed Synthesis of Serial Manipulators Based on Task Descriptions.- Intelligent Tracking Control System for Fast Image Scanning of Atomic Force Microscopes.- Fault Diagnosis Algorithms by Combining Structural Graphs and PCA Approaches for Chemical Processes.
• (source: Nielsen Book Data)

The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 15 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.
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• Consequence
• Logics
• Classical Belief Revision
• AGM Contraction in Non-Classical Logics
• AGM Revision in Logics Without Negation
• Base Revision in Logics Without Negation
• Algorithms for Belief Bases
• Conclusion.

Since the advent of the Semantic Web, interest in the dynamics of ontologies (ontology evolution) has grown significantly. Belief revision presents a good theoretical framework for dealing with this problem; however, classical belief revision is not well suited for logics such as Description Logics. Belief Revision in Non-Classical Logics presents a framework which can be applied to a wide class of logics that include - besides most Description Logics such as the ones behind OWL - Horn Logic and Intuitionistic logic, amongst others. The author also presents algorithms for the most important constructions in belief bases. Researchers and practitioners in theoretical computing will find this an invaluable resource.
(source: Nielsen Book Data)

• Page Rank vs. Katz: Is the centrality algorithm choice relevant to measure user influence in Twitter?.- Weighted means based filters for SAR Imagery.- On Combination of Wavelet Transformation and Stabilized KH Interpolation for Fuzzy Inferences Based on High Dimensional Sampled Functions.- Abductive Reasoning on Molecular Interaction Maps.- Efficient unfolding of fuzzy connectives for multi-adjoint logic programs.- On Generalizations of Concept Lattices.- Generating fuzzy attribute rules via Fuzzy Formal Concept Analysis.
• (source: Nielsen Book Data)

This book presents recent research in the field of interaction between computational intelligence and mathematics, ranging from theory to applications. Computational intelligence, or soft computing consists of various bio-inspired methods, especially fuzzy systems, artificial neural networks, evolutionary and memetic algorithms. These research areas were initiated by professionals in various applied fields, such as engineers, economists, and financial and medical experts. Although computational intelligence offered solutions (at least quasi-optimal solutions) for problems with high complexity, vague and undeterministic features, initially little attention was paid to the mathematical models and analysis of the methods successfully applied. A typical example is the extremely successful Mamdani-algorithm, and its modifications and extensions, applied since the mid-1970s, where the first analysis of the simplest cases, showing why this algorithm was so efficient and stable, was not given until the early 1990s. Since the mid-2000s, the authors have organized international conferences annually to focus on the mathematical methodological issues in connection with computational intelligence approaches. These conferences have attracted a large number of submissions with a wide scope of topics and quality. The editors selected several high-quality papers and approached the authors to submit an essentially extended and improved book chapter based on the lectures.This volume is the first contributed book on the subject.
(source: Nielsen Book Data)

• Preface.-
• Chapter 1: Introduction.- Part I: Generalized Related Set Based Approach.-
• Chapter 2: Information System for Relational Data.-
• Chapter 3: Properties of Granular-Relational Data Mining Framework.-
• Chapter 4: Association Discovery and Classification Rule Mining.-
• Chapter 5: Rough-Granular Computing.- Part II: Description Language Based Approach.-
• Chapter 6: Compound Information Systems.-
• Chapter 7: From Granular-Data Mining Framework to its Relational Version.-
• Chapter 8: Relation-Based Granules.-
• Chapter 9: Compound Approximation Spaces.- Conclusions.- References.- Index.
• (source: Nielsen Book Data)

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.
(source: Nielsen Book Data)

• Neurocognitive Robot Assistant for Robust Fall Detection .-Smart Robot Control via Novel Computational Intelligence Methods for Ambient Assisted Living
• Valorization of Assistive Technologies for Cognition: Lessons & Practices
• Safe and Automatic Addition of Fault Tolerance for Smart Homes Dedicated to People with Disabilities
• Smart Homes in the Era of Big Data
• An Investigation of The Use of Innovative Biology-based Computational Intelligence in Ubiquitous Robotics Systems: Data Mining Perspective
• Ambient Stupidity
• Security Implementations in Smart Sensor Networks
• Automatic Music Composition from a Self- Learning Algorithm.

This book demonstrates the success of Ambient Intelligence in providing possible solutions for the daily needs of humans. The book addresses implications of ambient intelligence in areas of domestic living, elderly care, robotics, communication, philosophy and others. The objective of this edited volume is to show that Ambient Intelligence is a boon to humanity with conceptual, philosophical, methodical and applicative understanding. The book also aims to schematically demonstrate developments in the direction of augmented sensors, embedded systems and behavioral intelligence towards Ambient Intelligent Networks or Smart Living Technology. It contains chapters in the field of Ambient Intelligent Networks, which received highly positive feedback during the review process. The book contains research work, with in-depth state of the art from augmented sensors, embedded technology and artificial intelligence along with cutting-edge research and development of technologies and applications of Ambient Intelligent Networks. This book is intended to introduce ideas, methods, technologies of the future development of humanity, Science and Technology.

• Intro; Contents; Contributors; Introduction; Part I Search and Optimization; A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems; 1 Introduction; 2 Problem Description; 2.1 Machines; 2.2 Jobs; 2.3 Optimization Criteria; 3 Overview of Existing Scheduling Approaches; 3.1 Standard Scheduling Algorithms; 3.2 Scheduling with Job Runtime Estimates; 3.3 User-to-User Fairness and Fair-Share; 3.4 Advanced Optimization Methods; 3.5 Summary; 4 Metaheuristic Scheduler; 4.1 Linear Schedule Compression; 4.2 Schedule Evaluation.

• 4.3 Metaheuristic for Schedule Optimization5 Experiments; 5.1 Simulation Setup; 5.2 Experimental Results; 5.3 Summary; 6 Conclusion and Future Work; References; Hybrid ACO and Tabu Search for Large Scale Information Retrieval; 1 Introduction; 2 Information Retrieval Background; 3 Lex as a Tool for Documents Indexing; 4 AC-IR Algorithm; 4.1 Solutions Encoding; 4.2 Pheromone Table and Probabilistic Decision Rules; 4.3 Updating the Pheromone; 4.4 Building and Improving a Solution; 5 ACS-IR Algorithm; 6 The Overall Algorithm; 7 Experimental Results; 7.1 Benchmarks; 7.2 Setting the Parameters.

• 7.3 Comparison of AS-IR, ACS-IR and CL-IR Algorithms8 Conclusion; References; Hosting Clients in Clustered and Virtualized Environment: A Combinatorial Optimization Approach; 1 Introduction; 1.1 Hardware Virtualization Technology; 1.2 Cluster Computing Technology; 1.3 Clients Hosting Problem; 2 Resource Allocation Problem; 3 Helpful Optimization Problems and Tools; 3.1 2-Dimensional Bin-Packing Problem; 3.2 The Max-Min Problem; 3.3 Data-Set and Solving Tool; 3.4 Branch-and-Bound Search; 4 Proposed Approach; 5 Integer Programming Models; 5.1 Minimizing the Number of Clusters.

• 5.2 Heavy Clients Distribution5.3 Balancing the Use of Resources; 6 Discussions; 7 Conclusion; References; Part II Machine Learning; On the Application of Artificial Intelligence Techniques to Create Network Intelligence; 1 Introduction; 1.1 AI for Internet of Things; 1.2 AI for Telecommunication Networks; 2 Graph Theory for Virus Epidemic Prediction; 2.1 State of the Art; 2.2 Architecture and Implementation; 3 Machine Learning for Smart Building Energy Management; 3.1 State of the Art; 3.2 Architecture and Implementation; 4 Intelligent Middleware for Cloud Robotics; 4.1 State of the Art.

• 4.2 Architecture and Implementation5 Multiple Neural Networks for Client Profiling on Telecommunication Networks; 5.1 State of the Art; 5.2 Architecture and Implementation; 6 Alarm Prediction on Telecommunication Networks; 6.1 State of the Art; 6.2 Architecture and Implementation; 7 Conclusions and Further Applications of AI in ICT; 7.1 Future Work on the Reported Solutions; 7.2 Further Applications of AI on ICT; References; A Statistical Framework for Mental Targets Search Using Mixture Models; 1 Introduction; 2 Related Work; 3 The Framework Structure; 4 Data Model; 5 Update Model.

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.

• Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion. Introduction to the subject of an agent in computer science.- Agent versus decomposition of an algorithm.- M-agent.- The agent system for balancing the distribution of resources.- The examples of applications of the agent systems.- Conclusion.
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This monograph presents the concept of agents and agent systems. It starts with a formal approach and then presents examples of practical applications. In order to form the principles of construction of autonomous agents, a model of the agent is introduced. Subsequent parts of the monograph include several examples of applications of the term agent. Descriptions of different examples of applications of agent systems in such fields as evolution systems, mobile robot systems, artificial intelligence systems are given. The book constitutes an outline of methodology of the design and realization of agent systems based on the M-agent architecture oriented on different areas of applications.
(source: Nielsen Book Data)

• Swarm Intelligence and Evolutionary Computation: Overview and Analysis.- Globally convergent hybridization of particle swarm optimization using line search-based derivative-free techniques Fireflies in the Fruits and Vegetables: Combining the Firefly Algorithm with Goal Programming for Setting Optimal Osmotic Dehydration Parameters of Produce.- Hybrid Metaheuristic Algorithms: Past, Present and Future.- Binary Flower Pollination Algorithm and Its Application to Feature Selection.- Bat Algorithm Application for the Single Row Facility Layout Problem.- Discrete Cuckoo Search Applied to Job Shop Scheduling Problem.- Cuckoo Search and Bat Algorithm Applied to Training Feed-Forward Neural Networks.- The Potential of the Firefly Algorithm for Damage Localization and Stiffness Identification.- Synthesizing Cross-Ambiguity Functions Using An Improved Bat Algorithm.- Sustainable Building Design: A Review on Recent Metaheuristic Methods.- Firefly Algorithm for Flow Shop Optimization.- Evaluation of Harmony Search and Differential Evolution Optimization Algorithms on Solving the Booster Station Optimization Problems in Water Distribution Networks.- Web Document Clustering by Using PSO-Based Cuckoo Search Clustering Algorithm.- Analysis of Trusses Using Particle Swarm Optimization.
• (source: Nielsen Book Data)

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.
(source: Nielsen Book Data)

• A Performance Analysis of DF Model in the Energy Harvesting Half-duplex and Full-Duplex Relay Networks
• A Model of Swarm Intelligence Based Optimization Framework Adjustable According to Problems
• Domain Model Definition for Domain-Specific RuleGeneration Using Variability Model
• A set-partitioning-based model to save cost on the import processes
• Combining Genetic Algorithm with Variable Neighborhood Search for MAX-SAT
• Enzyme classification on DUD-E database using Logistic Regression Ensemble (Lorens)
• Consolidation of Host-Based Mobility Management Protocols in Wireless Mesh Network
• Application of parallel computing technologies for numerical simulation of air transport in the human nasal cavity
• Genetic Algorithms-based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries
• Text Segmentation Methods: A Critical Review
• On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm Modification
• Enhanced Security of Internet Banking Authentication with Extended Honey Encryption (XHE) Scheme
• An Enhanced Possibilistic Programming Model with Fuzzy Random Confidence-Interval for Multi-Objective Problem
• A Crowdsourcing Approach for Volunteering System
• One Dimensional Vehicle Tracking Analysis in Vehicular Ad hoc Networks
• Parallel Coordinates Visualization Tool on the Air Pollution Data for Northern Malaysia
• Application of Artificial bee colony algorithm for model parameter identification
• A novel weighting scheme applied to improve the text document clustering techniques
• A Methodological Framework To Emulate The Visual Of Malaysian Shadow Play With Computer-Generated Imagery
• Performance Evaluation of Hot Mix Asphalt Concrete by Using Polymeric Waste Polyethylene.

This book presents the latest research of the field of optimization, modeling and algorithms, discussing the real-world application problems associated with new innovative methodologies. The requirements and demands of problem solving have been increasing exponentially and new computer science and engineering technologies have reduced the scope of data coverage worldwide. The recent advances in information communication technology (ICT) have contributed to reducing the gaps in the coverage of domains around the globe. The book is a valuable reference work for researchers in the fields of computer science and engineering with a particular focus on modeling, simulation and optimization as well as for postgraduates, managers, economists and decision makers.
(source: Nielsen Book Data)

• Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems.- Designing Societies of Robots.- On the Origins of Imperfection and Apparent Non-Rationality.- Lasso Granger Causal Models: Some Strategies and their Efficiency for Gene Expression Regulatory Networks.- Cooperative Feature Selection in Personalized Medicine.- Imperfect Decision Making and Risk Taking are affected by Personality.
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This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: * task allocation to maximize "the wisdom of the crowd"; * design of a society of "edutainment" robots who account for one anothers' emotional states; * recognizing and counteracting seemingly non-rational human decision making; * coping with extreme scale when learning causality in networks; * efficiently incorporating expert knowledge in personalized medicine; * the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other fields.
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• Cyber Physical Systems Security.-Risk Management for CPS Security.-Wireless Sensor Network Security for Cyber Physical Systems.-WSN Security mechanisms for CPS.- ICS/SCADA System Security for CPS.-Embedded Systems Security for Cyber Physical Systems.-Distributed Control Systems Security for CPS.-Standards for CPS.
• (source: Nielsen Book Data)

This book is a pioneering yet primary general reference resource on cyber physical systems and their security concerns. Providing a fundamental theoretical background, and a clear and comprehensive overview of security issues in the domain of cyber physical systems, it is useful for students in the fields of information technology, computer science, or computer engineering where this topic is a substantial emerging area of study. .
(source: Nielsen Book Data)

• Part I: Main Track.- Long distance in-links for ranking enhancement.- Concept Tracking and Adaptation for Drifting Data Streams under Extreme Verification Latency.- Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures.- Slot Co-allocation Optimization in Distributed Computing with Heterogeneous Resources.- About Designing an Observer Pattern-Based Architecture for a Multi-Objective Metaheuristic Optimization Framework.- Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform.- Finding Best Compiler Options for Critical Software Using Parallel Algorithms.- Drift Detection over Non-stationary Data Streams using Evolving Spiking Neural Networks.- Part II: Energy.- A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms.- Bio-inspired approximation to MPPT under real irradiation conditions.- Part III: Industry.- Decision Making in Industry 4.0 Scenarios supported by Imbalanced Data Classification.
• (source: Nielsen Book Data)

This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
(source: Nielsen Book Data)

• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
• (source: Nielsen Book Data)

A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• Chapter 1 Introduction.-
• Chapter 2 Some Theoretical Background.-
• Chapter 3 Spatial Grasp Model.-
• Chapter 4 SGL Detailed Specification.-
• Chapter 5 Main Spatial Mechanisms in SGL.-
• Chapter 6 SGL Networked Interpreter.-
• Chapter 7 Creation, Activation and Management of a Distributed World.-
• Chapter 8 Parallel and Distributed Network Operations.-
• Chapter 9 Solving Social Problems.-
• Chapter 10 Automated Command and Control.-
• Chapter 11 Collective Robotics.-
• Chapter 12 Conclusions.
• (source: Nielsen Book Data)

The book describes a novel ideology and supporting information technology for integral management of both civil and defence-orientated large, distributed dynamic systems. The approach is based on a high-level Spatial Grasp Language, SGL, expressing solutions in physical, virtual, executive and combined environments in the form of active self-evolving and self-propagating patterns spatially matching the systems to be created, modified and controlled. The communicating interpreters of SGL can be installed in key system points, which may be in large numbers (up to millions and billions) and represent equipped humans, robots, laptops, smartphones, smart sensors, etc. Operating under gestalt-inspired scenarios in SGL initially injected from any points, these systems can be effectively converted into goal-driven spatial machines (rather than computers as dealing with physical matter too) capable of responding to numerous challenges caused by growing world dynamics in the 21st century. Including numerous practical examples, the book is a valuable resource for system managers and programmers.
(source: Nielsen Book Data)

• Aerial Vehicles progress On Pico Air Vehicles.- Perception and Mapping.- Planning.- Systems and Integration.- Control.
• (source: Nielsen Book Data)

This volume presents a collection of papers presented at the 15th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 15th edition took place in Flagstaff, Arizona on December 9 to December 12, 2011. As for the previous symposia, ISRR 2011 followed up on the successful concept of a mixture of invited contributions and open submissions. Therefore approximately half of the 37 contributions were invited contributions from outstanding researchers selected by the IFRR officers and the program committee, and the other half were chosen among the open submissions after peer review. This selection process resulted in a truly excellent technical program which featured some of the very best of robotic research. The program was organized around oral presentation in a single-track format and included for the first time a small number of interactive presentations. The symposium contributions contained in this volume report on a variety of new robotics research results covering a broad spectrum including perception, manipulation, grasping, vehicles and design, navigation, control and integration, estimation and SLAM. .
(source: Nielsen Book Data)

• Introduction
• Foundation of Neuro-Physiology
• Neural Computing
• Mind Model
• Perception
• Visual Information Processing
• Audio Information Processing
• Language
• Learning
• Memory
• Thought
• Development of Intelligence
• Emotion
• Immune System
• Consciousness
• Symbolic Logic
• The Machine Proves
• Perspective.
• (source: Nielsen Book Data)

Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists from the above three disciplines work together to explore new concepts, new theories, and methodologies.This book will introduce the concept and methodology of intelligence science systematically. The whole book is divided into 18 chapters altogether. It can be regarded as a textbook in courses of intelligence science, cognitive science, cognitive informatics etc. for senior and graduate students. It has important reference value for researchers engaged in fields such as intelligence science, brain science, cognitive science, neural science, artificial intelligence, psychology and so on.
(source: Nielsen Book Data)

• Scott Consequence Relations
• Biconsequence Relations
• Four-Valued Logics
• Nonmonotonic Semantics
• Default Consequence Relations
• Argumentation Theory
• Production and Causal Inference
• Epistemic Consequence Relations
• Modal Nonmonotonic Logics.
• (source: Nielsen Book Data)

Many approaches in the field of nonmonotonic and "commonsense" reasoning are actually different representations of the same basic ideas and constructions. This book gives a logical formalization of the original, explanatory approach to nonmonotonic reasoning. It uses the basic formalism of biconsequence relations, as well as derived systems of default, autoepistemic and causal inference, to cover in a single framework such diverse systems as default logic, autoepistemic and modal nonmonotonic logics, input/output and causal logics, argumentation theory, and semantics of general logic programs with negation as failure. This approach provides a clear separation between logical (monotonic) and nonmonotonic aspects of nonmonotonic reasoning. The separation allows, in particular, to single out the logics underlying modern logic programming and restore thereby the connection between logic programming and logic.
(source: Nielsen Book Data)

• Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables.

• EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup.

• Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing.

• TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation.

• Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography.

• History of Artificial Intelligence.- Symbolic Artificial Intelligence.- Computational Intelligence.- Search Methods.- Evolutionary Computing.- Logic-Based Reasoning.- Structural Models of Knowledge Representation.- Syntactic Pattern Analysis.- Rule-Based Systems.- Pattern Recognition and Cluster Analysis.- Neural Networks.- Reasoning with Imperfect Knowledge.- Defining Vague Notions in Knowledge-Based Systems.- Cognitive Architectures.- Theories of Intelligence in Philosophy and Psychology.- Application Areas of AI Systems.- Prospects of Artificial Intelligence.
• (source: Nielsen Book Data)

In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader.
(source: Nielsen Book Data)

• Case-Studies in Mining User-Generated Reviews for Recommendation
• Mining Newsworthy Topics from Social Media
• Sentiment Analysis Using Supervised Learning with Domain-Adaptation and Sentence-Based Analysis
• Pattern-based Emotion Classification on Social Media
• Entity-based Opinion Mining from Text and Multimedia
• Predicting Emotion Labels for Chinese Microblog Texts.

This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis.

• Advances in Intelligent Systems (Lakhmi C Jain and Kazumi Nakamatsu)
• Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems (Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K Nagar)
• Some Studies on Data Mining (Dilip Kumar Pratihar)
• Rough Non-Deterministic Information Analysis for Uncertain Information (Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata)
• Metamathematical Limits to Computation (N C A da Costa and F A Doria)
• Hypothesis Refinement: Building Hypotheses in an Intelligent Agent System (Gauvain Bourgne, Nicolas Maudet and Suzanne Pinson)
• A Heuristic Algorithmic Procedure to Solve Allocation Problems with Fuzzy Evaluations (R Bartholo, C A N Cosenza, F A Doria and M R Doria)
• Non-Classical Logics and Intelligent Systems (Seiki Akama)
• A Paraconsistent Annotated Logic Program Before-After EVALPSN and Its Application (Kazumi Nakamatsu and Jair Minoro Abe)
• Inspecting and Preferring Abductive Models (Luis Moniz Pereira, Pierangelo Dell'Acqua, Alexandre Miguel Pinto and Goncalo Lopes)
• Supervised Neural Network Learning: From Vectors to Graphs (Monica Bianchini, Marco Maggini and Lorenzo Sarti)
• Paraconsistent Artificial Neural Networks and Applications (Jair Minoro Abe and Kazumi Nakamatsu)
• Paraconsistent Annotated Evidential Logic E and Applications in Automation and Robotics (Jair Minoro Abe and Kazumi Nakamatsu)
• Adaptive Intelligent Learning System for Online Learning Environments (Fatma Cemile Serce, Ferda Nur Alpaslan and Lakhmi C Jain)
• Automatic Test Program Generation: How Artificial Evolution May Outperform Experience (Danilo Ravotto, Ernesto Sanchez and Giovanni Squillero)
• Discovery of Communications Patterns by the Use of Intelligent Reasoning (J Fulcher, M Zhang, Q Bai and F Ren)
• Adaptive Approach to Quality Enhancement and Storage of Signatures and Fingerprint Images (Roumen Kountchev)
• Knowledge Representation for Electronic Circuits in Logic Programming (Takushi Tanaka)
• An Intelligent CBR Model for Predicting Changes in Tropical Cyclones Intensities (James N K Liu, Simon C K Shiu, Jane You and Leon S K Law)
• Analysis of Sequential Data in Tool Manufacturing of Volkswagen AG (Kemal Ince, Thomas Schneider and Frank Klawonn)
• Reasoning-Based Artificial Agents in Agent-Based Computational Economics (Shu-Heng Chen)
• Reasoning and Knowledge Acquisition from Medical Database Using Lattice SOM and Tree Structure SOM (Takumi Ichimura, Takashi Yamaguchi and Kenneth James Mackin)
• Approximate Processing in Medical Diagnosis by Means of Deductive Agents (G Fenza, D Furno, V Loia and S Senatore).
• (source: Nielsen Book Data)

This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.
(source: Nielsen Book Data)

• Part I
• Searching and Sorting
• Overview
• 1 Binary Search
• 2 Insertion Sort
• 3 Fast Sorting Algorithms
• 4 Parallel Sorting
• The Need for Speed
• 5 Topological Sorting
• How Should I Begin to Complete My To Do List?
• 6 Searching Texts
• But Fast! The Boyer-Moore-Horspool Algorithm
• 7 Depth-First Search (Ariadne & Co.)
• 8 Pledge's Algorithm
• How to Escape from a Dark Maze
• 9 Cycles in Graphs
• 10 PageRank
• What Is Really Relevant in the World-Wide Web?
• Part II
• Arithmetic and Encryption
• Overview
• 11 Multiplication of Long Integers
• Faster than Long Multiplication
• 12 The Euclidean Algorithm
• 13 The Sieve of Eratosthenes
• How Fast Can We Compute a Prime Number Table?
• 14 One-Way Functions
• Mind the Trap
• Escape Only for the Initiated
• 15 The One-Time Pad Algorithm
• The Simplest and Most Secure Way to Keep Secrets
• 16 Public-Key Cryptography
• 17 How to Share a Secret
• 18 Playing Poker by Email
• 19 Fingerprinting
• 20 Hashing
• 21 Codes
• Protecting Data Against Errors and Loss
• Part III
• Planning, Coordination and Simulation
• Overview
• 22 Broadcasting
• How Can I Quickly Disseminate Information?
• 23 Coverting Numbers into English Words
• 24 Majority
• Who Gets Elected Class Rep?
• 25 Random Numbers
• How Can We Create Randomness in Computers?
• 26 Winning Strategies for a Matchstick Game
• 27 Scheduling of Tournaments or Sports Leagues
• 28 Eulerian Circuits
• 29 High-Speed Circles
• 30 Gauß-Seidel Iterative Method for the Computation of Physical Problems
• 31 Dynamic Programming
• Evolutionary Distance
• Part IV
• Optimisation
• Overview
• 32 Shortest Paths
• 33 Minimum Spanning Trees
• Sometimes Greed Pays Off
• 34 Maximum Flows
• Towards the Stadium During Rush Hour
• 35 Marriage Broker
• 36 The Smallest Enclosing Circle
• A Contribution to Democracy from Switzerland?
• 37 Online Algorithms
• What Is It Worth to Know the Future?
• 38 Bin Packing
• How Do I Get My Stuff into the Boxes
• 39 The Knapsack Problem
• 40 The Travelling Salesman Problem
• 41 Simulated Annealing.

Algorithms specify the way computers process information and how they execute tasks. Many recent technological innovations and achievements rely on algorithmic ideas - they facilitate new applications in science, medicine, production, logistics, traffic, communication and entertainment. Efficient algorithms not only enable your personal computer to execute the newest generation of games with features unimaginable only a few years ago, they are also key to several recent scientific breakthroughs - for example, the sequencing of the human genome would not have been possible without the invention.

• From Interval (Set) and Probabilistic Granules to Set-and-Probabilistic Granules of Higher Order .- Artificial Intelligence Perspectives on Granular Computing
• Calculi of Approximation Spaces in Intelligent Systems .- Feature Discovery through Hierarchies of Rough Fuzzy Sets .- Comparative Study of Fuzzy Information Processing in Type-2 Fuzzy Systems .- Type-2 Fuzzy Similarity on Partial Truth and Intuitionistic Reasoning .- Decision-Making with Second Order Information Granules .- On the Usefulness of Fuzzy Rule Based Systems based on Hierarchical Linguistic Fuzzy Partitions .- Fuzzy Information Granulation with Multiple Levels of Granularity
• A Rough Set Approach to Building Association Rules and Its Applications .- Fuzzy Modeling with Grey Prediction for Designing Power System Stabilizers .- A Weighted Fuzzy Time Series Based Neural Network Approach to Option Price Forecasting .- A Rough Set Approach to Human Resource Development in IT Corporations .- Environmental Applications of Granular Computing and Intelligent Systems.

Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

• Foundations of Reinforcement Learning
• Abstraction and Knowledge Transfer in Reinforcement Learning
• Qualitative State Space Abstraction
• Generalization and Transfer Learning with Qualitative Spatial Abstraction
• RLPR
• An Aspectualizable State Space Representation
• Empirical Evaluation
• Summary and Outlook.

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book, the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form.

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

• Processing of Hyperspectral Medical Images: Applications in Dermatology Using Matlab (R).- Foreword.- Preface.- Acknowledgments.- List of selected symbols.- Chapte
• r1: Introduction.- Chapte
• r2: Image acquisition.- Chapte
• r3: Image pre-processing.- Chapte
• r4: Image processing.- Chapte
• r5: Classification.- Chapte
• r6: Sensitivity to parameter changes.- Chapte
• r7: Conclusions.- Appendix.- References.- Summary.
• (source: Nielsen Book Data)

This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.
(source: Nielsen Book Data)

• Locomotion.- Haptics.- Manipulation.- Perception.- Human-Robot Interaction.- Mapping and Localization.- Mechanisms.- Perception and Planning.- Sensor Networks.- Many-Robot Systems.- Interactive Presentations.- Plenary Talk.
• (source: Nielsen Book Data)

The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings which are organized in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 14th ISER held on June 15-18, 2014 in Marrakech and Essaouira, Morocco. This present fourteenth edition of Experimental Robotics edited by M. Ani Hsieh, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-ce ntered robotics.
(source: Nielsen Book Data)

• Computer Input by Human Eyes Only and it's Applications
• New Ideas for Brain Modelling 2
• Neural-like Growing Networks the Artificial Intelligence Basic Structure
• Detection of Privilege Abuse in RBACAdministered Database
• Learning-based Leaf Image Recognition Frameworks
• Massively Parallel Feature Selection based on Ensemble of Filters and Multiple Robust Consensus Functions for Cancer Gene Identification
• Relationship Discovery and Navigation in Big Graphs
• A Fuzzy System for Three-Factor, Non-textual Authentication
• Efficient Graph-Based Volumetric Segmentation
• A Hybrid Intelligent System in Cultural Intelligence
• Semantic-Based Recommender System with Humain Feeling Relevance Measure
• Alignment of Time Series for Subsequence-to-Subsequence Time Series Matching
• The Effects of Typing Demand on EmotionalStress, Mouse and Keystroke Behaviours
• Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines
• Quantum Behaved Genetic Algorithm: Constraints-Handling and GPU Computing
• A Genetic Algorithm Approach for Optimizing a Single-Finger Arabic Keyboard Layout
• Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network
• Modeling Energy Consumption in A Educational Building: Comparative Study between Linear Regression, Fuzzy Set Theory and Neural Networks
• Delivering Faster Results Through Parallelisation and GPU Acceleration
• Probabilistic Roadmaps and Hierarchical Genetic Algorithms for Optimal Motion Planning
• Using Mouse and Keyboard Dynamics to Detect Cognitive Stress during Mental Arithmetic
• Towards Using Games Theory to Detect New U2R Attacks
• Development and Evaluation of Virtual Reality Medical Training System for Anatomy Education
• Compression of ECG signal using Hybrid technique
• Performance Analysis of MATLAB Parallel Computing Approaches to Implement Genetic Algorithm for Image Compression.-

The book Intelligent Systems in Science and Information 2014 is ℗ℓthe carefully edited collection of 25 extended chapters from selected papers in the field of Computational Intelligence that, which received highly recommended feedback during the Science and Information Conference (SAI) 2014 review process. All chapters have gone through substantial extension and consolidation and were subject to another round of rigorous review and additional modification and represent the state of the art of the cutting-edge research and technologies in the related areas.

• Preface
• Semi-supervised Learning
• Local Tangent Space Laplacian Eigenmaps
• Multi-Step Model of Switching Reinforcement Learning to Mimic Infants` Motor Development
• Reverse Engineering Networks as Ordinary Differential Equations Systems
• The Relevance of Bayesian Experimental Design for Modeling in Systems Biology
• Topographic Maps for clustering & fast identification of bacteria using 16S housekeeping gene
• Cognitive Modelling of Language Acquisition with Complex Networks
• Process Mining & Visual Analytics: Breathing Life into Business Process Models.
• (source: Nielsen Book Data)

Computational intelligence aims to emulate aspects of biological systems for developing software and/or hardware that learns and adapts. Such systems primarily include neural networks, fuzzy logic systems and evolutionary computation each of them including multiple active and fast growing research directions. This book presents new and ground-breaking research from world-renowned experts focusing on various topics related to computational intelligence. What makes this book particularly interesting is the variety of subjects attained, which offer a global perspective on this central research field in the Knowledge Age.
(source: Nielsen Book Data)