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• An Ontological Perspective of Web Services.- Hints from A Molecular Analogy.- Web Service Mining Framework.- Application: Biological Pathway Discovery.- Related Work.- Conclusions.
• (source: Nielsen Book Data)

The new computing environment enabled by advances in service oriented arc- tectures, mashups, and cloud computing will consist of service spaces comprising data, applications, infrastructure resources distributed over the Web. This envir- ment embraces a holistic paradigm in which users, services, and resources establish on-demand interactions, possibly in real-time, to realise useful experiences. Such interactions obtain relevant services that are targeted to the time and place of the user requesting the service and to the device used to access it. The bene?t of such environment originates from the added value generated by the possible interactions in a large scale rather than by the capabilities of its individual components se- rately. This offers tremendous automation opportunities in a variety of application domains including execution of forecasting, of?ce tasks, travel support, intelligent information gathering and analysis, environment monitoring, healthcare, e-business, community based systems, e-science and e-government. A key feature of this environment is the ability to dynamically compose services to realise user tasks. While recent advances in service discovery, composition and Semantic Web technologies contribute necessary ?rst steps to facilitate this task, the bene?ts of composition are still limited to take advantages of large-scale ubiq- tous environments. The main stream composition techniques and technologies rely on human understanding and manual programming to compose and aggregate s- vices. Recent advances improve composition by leveraging search technologies and ?ow-based composition languages as in mashups and process-centric service c- position.
(source: Nielsen Book Data)

• Preface.- Introduction.- Loss functions and their risks.- Surrogate loss functions.- Kernels and reproducing kernel Hilbert spaces.- Infinite samples versions of support vector machines.- Basic statistical analysis of SVMs.- Advanced statistical analysis of SVMs.- Support vector machines for classification.- Support vector machines for regression.- Robustness.- Computational aspects.- Data mining.- Appendix.- Notation and symbols.- Abbreviations.- Author index.- Subject index.- References.
• (source: Nielsen Book Data)

Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen, the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs, whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbook were found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists, sometimesprobablyonlytopeoplefrom one community but not the others.
(source: Nielsen Book Data)

• Introduction Rational Agents Strategic Games Coordination Partial Observability Mechanism Design Learning.
• (source: Nielsen Book Data)

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
(source: Nielsen Book Data)

• Frequent and Closed Sequence Patterns.- Classification, Clustering, Features and Distances of Sequence Data.- Sequence Motifs: Identifying and Characterizing Sequence Families.- Mining Partial Orders from Sequences.- Distinguishing Sequence Patterns.- Related Topics.
• (source: Nielsen Book Data)

Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering. Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.
(source: Nielsen Book Data)
Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.
(source: Nielsen Book Data)

• Methodologies Based on Particle Swarm Intelligence.- Swarm Intelligence: Foundations, Perspectives and Applications.- Waves of Swarm Particles (WoSP).- Grammatical Swarm: A Variable-Length Particle Swarm Algorithm.- SWARMs of Self-Organizing Polymorphic Agents.- Experiences Using Particle Swarm Intelligence.- Swarm Intelligence - Searchers, Cleaners and Hunters.- Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method.- Particle Swarm for Fuzzy Models Identification.- A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models.
• (source: Nielsen Book Data)

Systems designers have learned that many agents co-operating within the system can solve very complex problems with a minimal design effort. In general, multi-agent systems that use swarm intelligence are said to be swarm intelligent systems. Today, these are mostly used as search engines and optimization tools. This volume reviews innovative methodologies of swarm intelligence, outlines the foundations of engineering swarm intelligent systems and applications, and relates experiences using the particle swarm optimisation.
(source: Nielsen Book Data)

• Cortical images, self-organising neural networks and object classification / Nikolay Petkov
• Parallel implementation of a neural network ensemble on the connection machine / Daijin Kim, Minsoo Suk
• Boolean neural networks trained with simulated annealing / Jarkko Niittylahti
• On the computational complexity of analyzing the Hopfield-Clique network / Arun Jagota
• A harmony-maximisation network implementation of a compound labeling scheme for scene analysis / Tatiana Tambouratzis
• Optimal image boundary via Hopfield net and tunneling / William Cheung, Roland Chin, Tong Lee
• Shape matching based on invariants / Stan Z. Li.

• Intro
• Preface
• Invited Speakers
• DSS, Classifications, Trends and Enabling Modern Information and Communication Technologies
• Distorted Statistics based on Choquet Calculus
• Overview of QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
• Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers
• Seeing Is Believing
• Contents
• Short CVs of Guest Editors
• I Soft Computing and Conventional Techniques in Power Engineering Methods and Applications in Electrical Engineering I

• Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods
• Abstract
• 1 Introduction
• 2 Developed Algorithms
• 2.1 Artificial Neural Networks (ANN)
• 2.2 Multiple Linear Regression (MLR)
• 2.3 Curve Fitting (CF)
• 2.4 Decision Trees (DT)
• 3 Numerical Results. Discussions
• 3.1 Forecast Performed for 2017 Year
• 3.2 Forecast Performed for 2018 Year
• 3.3 Comparative Analysis for Several Forecasts
• 4 Conclusion
• Acknowledgment
• References
• LoRaWAN Based Airport Runway Lights Monitoring System
• Abstract
• 1 Introduction
• 2 LoRa Technology

• 3 Case Study and Results
• 4 Conclusions
• Acknowledgment
• References
• The Enhanced Crow Search Algorithm for Fuel-Cost Function Parameters Assessment of the Cogeneration Units from Thermal Power Plants
• Abstract
• 1 Introduction
• 2 Estimating the Fuel-Cost Function Parameters
• 3 The Crow Search Algorithm (CS)
• 4 The Enhanced CS Algorithm (ECS)
• 5 Case Studies and Results
• 6 Conclusions
• References
• Modelling a Photovoltaic Power Station
• Abstract
• 1 Introduction
• 2 Simulation of the Photovoltaic Panels Using MatLAB Software

• 2.1 Simulink: Modelling of a PV Solar Cell
• 2.2 The Intensity of the Induced Electrical Current by the Sun
• 2.3 Dependence on the Temperature
• 3 The Simulation of a PV Power Station
• 4 Conclusions
• References
• Identification of Fault Locations on Tree Structure Systems
• 1 Introduction
• 2 Description of the Troubleshooting System Currently Used by the Power Suppliers
• 3 Mathematical Background of the Proposed Method
• 4 Fault Probability Data Bank
• 5 SGFI Algorithm
• 5.1 Localization of the Fault Zone (Procedure 1)

• 5.2 Fault Position Localization in the Fault Zone (Procedure 2)
• 5.3 Result of the Proposed Algorithm
• 5.4 Simulation Analysis of the SGFI Algorithm
• 6 Conclusions
• References
• Probability Distribution Functions for Short-Term Wind Power Forecasting
• 1 Introduction
• 2 Wind Probability Distribution Functions
• 2.1 Weibull Distribution
• 2.2 Lindley Distribution for Random Variables
• 2.3 Joint Probability Distribution Function for Wind Forecasting
• 3 Short-Term Wind Power Forecasting Using SVR and ANN
• 3.1 Wind Power Forecasting Using Support Vector Regressors
• 3.2 Wind Power Forecasting Using ANN.

This book presents the proceedings of the 8th International Workshop on Soft Computing Applications, SOFA 2018, held on 13-15 September 2018 in Arad, Romania. The workshop was organized by Aurel Vlaicu University of Arad, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics - Arad Section, General Association of Engineers in Romania - Arad Section and BTM Resources Arad. The papers included in these proceedings, published post-conference, cover the research including Knowledge-Based Technologies for Web Applications, Cloud Computing, Security Algorithms and Computer Networks, Business Process Management, Computational Intelligence in Education and Modelling and Applications in Textiles and many other areas related to the Soft Computing. The book is directed to professors, researchers, and graduate students in area of soft computing techniques and applications.
(source: Nielsen Book Data)

• Frontmatter
• CONTENTS
• PREFACE
• WHAT IS THIS BOOK ABOUT, ACCORDING TO AI?
• INTRODUCTION: TECHNOLOGY MOVES FASTER THAN JOURNALISTIC STANDARDS
• 1. THE PROBLEM: A JOURNALISTIC MODEL IN TRANSITION
• 2. ENABLERS: THE AI TECHNOLOGIES DRIVING JOURNALISTIC CHANGE
• 3. WORKFLOW: A SCALABLE PROCESS FOR NEWSROOM TRANSFORMATION
• CONCLUSION
• ACKNOWLEDGMENTS
• NOTES
• BIBLIOGRAPHY
• INDEX

Will the use of artificial intelligence (AI), algorithms, and smart machines be the end of journalism as we know it-or its savior? In Newsmakers, Francesco Marconi, who has led the development of the Associated Press and Wall Street Journal's use of AI in journalism, offers a new perspective on the potential of these technologies. He explains how reporters, editors, and newsrooms of all sizes can take advantage of the possibilities they provide to develop new ways of telling stories and connecting with readers. Marconi analyzes the challenges and opportunities of AI through case studies ranging from financial publications using algorithms to write earnings reports to investigative reporters analyzing large data sets to outlets determining the distribution of news on social media. Newsmakers contends that AI can augment-not automate-the industry, allowing journalists to break more news more quickly while simultaneously freeing up their time for deeper analysis. Marshaling insights drawn from firsthand experience, Marconi maps a media landscape transformed by artificial intelligence for the better. In addition to considering the benefits of these new technologies, Marconi stresses the continuing need for editorial and institutional oversight. Newsmakers outlines the important questions that journalists and media organizations should consider when integrating AI and algorithms into their workflow. For journalism students as well as seasoned media professionals, Marconi's insights provide much-needed clarity and a practical roadmap for how AI can best serve journalism.
(source: Nielsen Book Data)

• Mathematical Analysis of Nature-Inspired Algorithms.- A Review of No Free Lunch Theorems, and their Implications for Metaheuristic Optimisation.- Global Convergence Analysis of Cuckoo Search Using Markov Theory.- On Effeciently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm.- Variants of the Flower Pollination Algorithm: A Review.
• (source: Nielsen Book Data)

This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.
(source: Nielsen Book Data)

• Analyzing the Effects of Alternative Decisions in a Multiagent System with Stigmergy-Based Interactions
• Modelling of Emotional Contagion in Soccer Fans
• An approach for Web Service selection and dynamic composition based on Linked Open Data
• Linguistic rules for ontology population from customer request
• Evolutionary Harmony Search Algorithm for sport scheduling problem
• Performance Analysis of Different Learning Algorithms of Feed Forward Neural Network Regarding Fetal Abnormality Detection
• DWIaaS: Data Warehouse Infrastructure as a Service for Big Data Analytics
• Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining
• Hiring Expert Consultants in E-Healthcare: An Analytics-Based Two Sided Matching Approach
• A fuzzy logic-based anticipation car-following model
• Fault-Tolerance in XJAF Agent Middleware.

These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as the semantic web, social networks, and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies, such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems. This thirtieth issue is a regular issue with 12 selected papers.

• Preface.- Acknowledgements.- 1 From Natural to Synthetic Swarms.- 2 Glowworm Swarm Optimization: Algorithm Development.- 3 Theoretical Foundations.- 4 Multimodal Function Optimization.- 5 Experiments using Physical Simulations and Real Robots.- 6 Applications to Ubiquitous Computing Environments.- 7 Pursuit of Multiple Mobile Signal Sources.- 8 GSO Applications and Extensions.
• (source: Nielsen Book Data)

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.
(source: Nielsen Book Data)

• Part I Physical Security and Surveillance.- Part II: Cyber Security and Intrusion Detection Systems.- Part III: Biometric Security and Authentication Systems.- Part IV: Situational Awareness and Threat Assessment.- Part V: Strategic/Mission Planning and Resource Management.
• (source: Nielsen Book Data)

This volume is an initiative undertaken by the IEEE Computational Intelligence Society's Task Force on Security, Surveillance and Defense to consolidate and disseminate the role of CI techniques in the design, development and deployment of security and defense solutions. Applications range from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks. Defense scientists, industry experts, academicians and practitioners alike will all benefit from the wide spectrum of successful applications compiled in this volume. Senior undergraduate or graduate students may also discover uncharted territory for their own research endeavors.
(source: Nielsen Book Data)

• Toward a Restriction-Centered Theory of Truth and Meaning
• A Note on Logical Connectives with Weak Duality
• Residual Implications from Discrete Uninorms. A Characterization
• A Classiffication Theorem for Continuous Quasi-Uninorms
• Globally Monotone Extended Aggregation Functions
• A Survey of Contributions To Fuzzy Logic and its Applications to Artificial Intelligence at the IIIA
• Gradual Structures of Oppositions
• Generation of Interval-Valued Fuzzy Negations from Trillas' Theorem. The Case of Interval Type-2 Fuzzy Sets
• On Conjectures in T-Norm Based Fuzzy Logics
• Many-Valued Preorders I: The Basis of Many-Valued Mathematics
• Many-Valued Preorders II: The Symmetry Axiom and Probabilistic Geometry
• A Metasemantic Interpretation of Mamdani Systems
• Experimental Modeling for a Natural Landing of Fuzzy Sets in New Domains
• On Linguistic Variables and Sparse Representations
• Activating Generalized Fuzzy Implications from Galois Connections
• Randomness and Fuzziness: Combined Better than Unified
• Interpretability, a Silver Lining to a Fuzzy Cloud
• Prototype based Fuzzy Clustering Algorithms in High-Dimensional Feature Spaces
• Computing with Words for Decision Making versus Linguistic Decision Making: A Reflection on Both Scenarios
• Classification Validity Index
• On Fuzzy Modus Ponens to Assess Fuzzy Association Rules.-Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era. Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era. Generation of Interval-Valued Fuzzy Negations from Trillas' Theorem. The Case of Interval Type-2 Fuzzy Sets
• On Conjectures in T-Norm Based Fuzzy Logics
• Many-Valued Preorders I: The Basis of Many-Valued Mathematics
• Many-Valued Preorders II: The Symmetry Axiom and Probabilistic Geometry
• A Metasemantic Interpretation of Mamdani Systems
• Experimental Modeling for a Natural Landing of Fuzzy Sets in New Domains
• On Linguistic Variables and Sparse Representations
• Activating Generalized Fuzzy Implications from Galois Connections
• Randomness and Fuzziness: Combined Better than Unified
• Interpretability, a Silver Lining to a Fuzzy Cloud
• Prototype based Fuzzy Clustering Algorithms in High-Dimensional Feature Spaces
• Computing with Words for Decision Making versus Linguistic Decision Making: A Reflection on Both Scenarios
• Classification Validity Index
• On Fuzzy Modus Ponens to Assess Fuzzy Association Rules.-Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era. On Fuzzy Modus Ponens to Assess Fuzzy Association Rules.-Applying Random Linear Oracles with Fuzzy Classifier Ensembles on WiFi Indoor Localization Problem
• Fuzzy Waves. Interference and Holography
• Fuzziness in CAR: Fuzzy Procedures for Robots and Cars
• Fuzzy Deformable Prototypes and Forest Fires Prediction and Prevention
• Hardware Implementation of Fuzzy Logic in Early Days of "Fuzzy" Era.

This book presents a comprehensive collection of the latest and most significant research advances and applications in the field of fuzzy logic. It covers fuzzy structures, rules, operations and mathematical formalisms, as well as important applications of fuzzy logic in a number of fields, like decision-making, environmental prediction and prevention, communication, controls and many others. Dedicated to Enric Trillas in recognition of his pioneering research in the field, the book also includes a foreword by Lotfi A. Zadeh and an outlook on the future of fuzzy logic.

• Performance Impact of New Interface for Non-Volatile Memory Storage
• Optimized Test Case Generation Based on Operational Profiles with Fault-Proneness Information
• Design Pattern for Self-adaptive RTE Systems monitoring
• Changing the Software Engineering Education: A Report from Current Situation in Mexico
• Analysis of Mouth Shape Deformation Rate for Generation of Japanese Utterance Images Automatically
• Predicting Fault-prone Modules by Word Occurrence in Identifiers
• The Influence of Alias and References Escape on Java Program Analysis
• Preliminary Evaluation of a Software Security Learning Environment
• Expression Strength for the Emotional Scene Detection from Lifelog Videos
• A Procedure for the Development of Mobile Applications Software
• The Relationships between Conversation Skill and Feeling of Load on Youth in Communicate with Elderly Persons using Video Image and Photographs
• Automatic Matching on Fracture Surface of Quarried Stone Using the Features of Plug-and-Feather Holes
• A Route Recommender System based on the User?s Visit Duration at Sightseeing Locations
• Predicting Access to Healthcare Using Data Mining Techniques
• Object Tracking Method Using PTAMM and Estimated Foreground Regions
• Application of Detecting Blinks for Communication Support Tool.

This edited book presents scientific results of the 12th International Conference on Software Engineering, Artificial Intelligence Research, Management and Applications (SERA 2014) held on August 31? September 4, 2014 in Kitakyushu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. This publication captures 17 of the conference?s most promising papers.

• pt. 1. Ethical and philosophical issues of agent-based AmI systems
• pt. 2. Methods for development of agent-based AmI systems
• pt. 3. Towards more intelligent and adaptive agent-based AmI systems
• pt. 4. Applications of agent-based AmI systems.

The concept of an intelligent agent - a computational system capable of performing certain tasks autonomously - derived from the growing potential of digital computers in the mid 20th century and had been widely adopted by the early 1990s. Partly in parallel with this concept, the perspective of ambient intelligence (AmI) emerged in the late 1990s. Agent technology and AmI have many similarities, and the main purpose of this book is to provide an overview of the state-of-the-art of the scientific area that integrates these two. The book addresses a wide variety of topics related to agents and AmI, including theoretical, practical, design, implementation, ethical and philosophical issues. The 12 chapters are arranged in four sections. The first consists of three chapters discussing ethical and philosophical issues; the second part explores various approaches that can be used to develop agent-based AmI Systems; the third part contains three chapters that share the goal to endow AmI systems with useful properties like intelligence and adaptivity and the last section presents concrete applications of agent-based AmI systems. This book provides an insight into recent achievements and future challenges at the intersection of agent technology and ambient intelligence and will assist the development of more intelligent, flexible, effective and user-friendly systems as well as posing critical questions about the future of the role of agents within the AmI perspective.

• Part A: Particle Swarm Optimization. From Theory to Practice in Particle Swarm Optimization / Maurice Clerc
• What Makes Particle Swarm Optimization a Very Interesting and Powerful Algorithm? / J.L. Fernández-Martínez, E. García-Gonzalo
• Developing Niching Algorithms in Particle Swarm Optimization / Xiaodong Li
• Test Function Generators for Assessing the Performance of PSO Algorithms in Multimodal Optimization / Julio Barrera, Carlos A. Coello Coello
• Linkage Sensitive Particle Swarm Optimization / Deepak Devicharan, Chilukuri K. Mohan
• Parallel Particle Swarm Optimization Algorithm Based on Graphic Processing Units / Ying Tan, You Zhou
• Velocity Adaptation in Particle Swarm Optimization / Sabine Helwig, Frank Neumann, Rolf Wanka
• Integral-Controlled Particle Swarm Optimization / Zhihua Cui, Xingjuan Cai, Ying Tan, Jianchao Zeng
• Particle Swarm Optimization for Markerless Full Body Motion Capture / Zheng Zhang, Hock Soon Seah, Chee Kwang Quah
• An Adaptive Multi-Objective Particle Swarm Optimization Algorithm with Constraint Handling / Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, Sankar Kumar Pal
• Multiobjective Particle Swarm Optimization for Optimal Power Flow Problem / M.A. Abido
• A Multi-objective Resource Assignment Problem in Product Driven Supply Chain Using Quantum Inspired Particle Swarm Algorithm / Sri Krishna Kumar, S.G. Ponnambalam, M.K. Tiwari
• Part B: Bee Colony Optimization. Honeybee Optimisation -- An Overview and a New Bee Inspired Optimisation Scheme / Konrad Diwold, Madeleine Beekman, Martin Middendorf
• Parallel Approaches for the Artificial Bee Colony Algorithm / Rafael Stubs Parpinelli, César Manuel Vargas Benitez, Heitor Silvério Lopes
• Bumble Bees Mating Optimization Algorithm for the Vehicle Routing Problem / Yannis Marinakis, Magdalene Marinaki
• Part C: Ant Colony Optimization. Ant Colony Optimization: Principle, Convergence and Application / Haibin Duan
• Optimization of Fuzzy Logic Controllers for Robotic Autonomous Systems with PSO and ACO / Oscar Castillo, Patricia Melin, Fevrier Valdez, Ricardo Martínez-Marroquín
• Part D: Other Swarm Techniques. A New Framework for Optimization Based-On Hybrid Swarm Intelligence / Pei-Wei Tsai, Jeng-Shyang Pan, Peng Shi, Bin-Yih Liao
• Glowworm Swarm Optimization for Multimodal Search Spaces / K.N. Krishnanand, D. Ghose
• Direct and Inverse Modeling of Plants Using Cat Swarm Optimization / Ganapati Panda, Pyari Mohan Pradhan, Babita Majhi
• Reliability-Redundancy Optimization Using a Chaotic Differential Harmony Search Algorithm / Leandro dos Santos Coelho, Diego L. de A. Bernert, Viviana Cocco Mariani
• Gene Regulatory Network Identification from Gene Expression Time Series Data Using Swarm Intelligence / Debasish Datta, Amit Konar, Swagatam Das, B.K. Panigrahi.

Recent work on the behavior of swarming creatures such as bees posits an innate collective intelligence that gives rise to myriad computational problem-solving techniques. This volume is both an introduction to the topic and a survey of leading-edge research.

• Introduction
• Convex Probabilistic Wavelet like Approximation
• Bidimensional Constrained Wavelet like Approximation
• Multidimensional Probabilistic Scale Approximation
• Multidimensional probabilistic approximation in wavelet like structure.- About L-Positive Approximations: About Shape Preserving Weighted Uniform Approximation
• Jackson-Type Nonpositive Approximations for Definite Integrals.- Discrete Best L1 Approximation using the Gauges Way
• Quantitative Uniform Convergence of Smooth Picard Singular Integral Operators
• Global Smoothness and Simultaneous Approximation by Smooth Picard Singular Operators
• Convergence Results.- Approximation with Rates by Fractional Smooth Picard
• Singular Operators
• Multivariate Generalized Picard Singular Integral Operators
• Approximation by q-Gauss-Weierstrass Singular Integral Operators.- Quantitative Approximation by Univariate Shift-Invariant
• Integral Operators.

Currently the methods of Soft Computing are successfully used for risk analysis in: budgeting, e-commerce development, portfolio selection, Black-Scholes option pricing models, corporate acquisition systems, evaluating investments in advanced manufacturing technology, interactive fuzzy interval reasoning for smart web shopping, fuzzy scheduling and logistic. An essential feature of economic and financial problems it that there are always at least two criteria to be taken into account: profit maximization and risk minimization. Therefore, the economic and financial problems are multiple criteria.

• Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- A Population Based Study of Evolutionary Dynamics in Genetic Programming.- An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs.- Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study.- Genetic Programming with Historically Assessed Hardness.- Crossover and Sampling Biases on Nearly Uniform Landscapes.- Analysis of the Effects of Elitism on Bloat in Linear and Tree-based Genetic Programming.- Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results.- Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in Common Human Diseases.- Exploiting Trustable Models via Pareto GP for Targeted Data Collection.- Evolving Effective Incremental SAT Solvers with GP.- Constrained Genetic Programming To Minimize Overfitting in Stock Selection.- Co-Evolving Trading Strategies to Analyze Bounded Rationality.- Profiling Symbolic Regression-Classification.- Accelerating Genetic Programming through Graphics Processing Units.- Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework.- Index.
• (source: Nielsen Book Data)

Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. These contributions address several significant interdependent themes which emerged from this year's workshop, including: (1) Making efficient and effective use of test data. (2) Sustaining the long-term evolvability of our GP systems. (3) Exploiting discovered subsolutions for reuse. (4) Increasing the role of a Domain Expert.
(source: Nielsen Book Data)

• I Languages.- Executing Logical Agent Specifications.- IndiGolog: A High-Level Programming Language for Embedded Reasoning Agents.- Brahms An Agent-Oriented Language for Work Practice Simulation and Multi-Agent Systems Development.- ProgrammingRationalAgents in GOAL.- Merging Agents and Services - the JIAC Agent Platform.- Towards Pervasive Intelligence: Reflections on the Evolution of the Agent Factory Framework.- II Tools.- Debugging and Testing of Multi-Agent Systems using Design Artefacts.- Environment Programming in CArtAgO.- A Survey of Agent-oriented Development Tools.- III Applications.- A Multi-Agent Environment for Negotiation.
• (source: Nielsen Book Data)

Multi-Agent Systems are a promising technology to develop the next generation open distributed complex software systems. The main focus of the research community has been on the development of concepts (concerning both mental and social attitudes), architectures, techniques, and general approaches to the analysis and specification of multi-agent systems. This contribution has been fragmented, without any clear way of "putting it all together", rendering it inaccessible to students and young researchers, non-experts, and practitioners. Successful multi-agent systems development is guaranteed only if we can bridge the gap from analysis and design to effective implementation. Multi-Agent Programming: Languages, Tools and Applications presents a number of mature and influential multi-agent programming languages, platforms, development tools and methodologies, and realistic applications, summarizing the state of the art in an accessible manner for professionals and computer science students at all levels.
(source: Nielsen Book Data)

• Preface Acknowledgment PART I. Theoretical Foundation
• Chapter 1. The Goal of Artificial Intelligence 1.1 To define intelligence 1.2 Various schools in AI research 1.3 AI as a whole
• Chapter 2. A New Approach Toward AI 2.1 To define AI 2.2 Intelligent reasoning systems 2.3 Major design issues of NARS PART II. Non-Axiomatic Reasoning System
• Chapter 3. The Core Logic 3.1 NAL-0: binary inheritance 3.2 The language of NAL-1 3.3 The inference rules of NAL-1
• Chapter 4. First-Order Inference 4.1 Compound terms 4.2 NAL-2: sets and variants of inheritance 4.3 NAL-3: intersections and differences 4.4 NAL-4: products, images, and ordinary relations
• Chapter 5. Higher-Order Inference 5.1 NAL-5: statements as terms 5.2 NAL-6: statements with variables 5.3 NAL-7: temporal statements 5.4 NAL-8: procedural statements
• Chapter 6. Inference Control 6.1 Task management 6.2 Memory structure 6.3 Inference processes 6.4 Budget assessment . PART III. Comparison and Discussion
• Chapter 7. Semantics 7.1 Experience vs. model 7.2 Extension and intension 7.3 Meaning of term 7.4 Truth of statement
• Chapter 8. Uncertainty 8.1 The non-numerical approaches 8.2 The fuzzy approach 8.3 The Bayesian approach 8.4 Other probabilistic approaches 8.5 Unified representation of uncertainty
• Chapter 9. Inference Rules 9.1 Deduction 9.2 Induction 9.3 Abduction 9.4 Implication
• Chapter 10. NAL as a Logic 10.1 NAL as a term logic 10.2 NAL vs. predicate logic 10.3 Logic and AI
• Chapter 11. Categorization and Learning 11.1 Concept and categorization 11.2 Learning in NARS
• Chapter 12. Control and Computation 12.1 NARS and theoretical computer science 12.2 Various assumptions about resources 12.3 Dynamic natures of NARS PART IV. Conclusions
• Chapter 13. Current Results 13.1 Theoretical foundation 13.2 Formal model 13.3 Computer implementation
• Chapter 14. NARS in the Future 14.1 Next steps of the project 14.2 What NARS is not 14.3 General implications Bibliography Index.
• (source: Nielsen Book Data)

This book is the most comprehensive description of the decades-long Non-Axiomatic Reasoning System (NARS) project, including its philosophical foundation, methodological consideration, conceptual design details, implications in the related fields, and its similarities and differences to many related works in cognitive science. While most current works in Artificial Intelligence (AI) focus on individual aspects of intelligence and cognition, NARS is designed and developed to attack the AI problem as a whole.
(source: Nielsen Book Data)

• Cover
• Contents
• Preface
• 1 Introduction
• 1.1 Background
• 2 Mobile Agent System
• 2.1 Components of a Mobile Agent System
• 2.2 Characteristics and Advantages of a Mobile Agent System
• 3 Attacks and Countermeasures of Software System Security
• 3.1 General Security Objectives
• 3.2 Types of Attacks
• 3.2.1 Attacks against availability
• 3.2.2 Attacks against confidentiality
• 3.2.3 Attacks against integrity
• 3.2.4 Attacks against miscellaneous security objectives
• 3.3 Countermeasures of Attacks
• 3.3.1 Authentication
• 3.3.2 Access control
• 3.3.3 Audit and intrusion detection
• 3.3.4 Cryptography
• 3.3.5 Firewall
• 3.3.6 Anti-virus software
• 4 Security Issues in a Mobile Agent System
• 4.1 Security Issues in a Mobile Agent System
• 4.1.1 Possible attacks to a mobile agent system
• 4.1.2 Security requirements for a secure mobile agent system &

A mobile agent system could be attacked by malicious agents, platforms and third parties. Mobile agents simply offer greater opportunities for abuse and misuse, which broadens the scale of threats significantly. In addition, since mobile agents have some unique characteristics such as their mobility, security problems have become more complicated in these systems. These security problems have become a bottleneck in the development and maintenance of mobile agent systems, especially in security sensitive applications such as electronic commerce. This book introduces the concept and structure of mobile agent systems and discusses various attacks and countermeasures. The emphasis is on the formal modeling and analysis of secure mobile agent systems and their applications.

• Mobile Agent System
• Attack and Countermeasure
• Security Issues in a Mobile Agent System
• Formal Models
• A Formal Framework of a Generic Secure Mobile Agent System
• Translating the EEOS Model to Colored Petri Net Model
• Simulation and Analysis of the Extended Elementary Object System Model of a Secure Mobile Agent System
• A Case Study in Electronic Commerce
• A Case Study in E-Auction System.
• (source: Nielsen Book Data)

A mobile agent system could be attacked by malicious agents, platforms and third parties. Mobile agents simply offer greater opportunities for abuse and misuse, which broadens the scale of threats significantly. In addition, since mobile agents have some unique characteristics such as their mobility, security problems have become more complicated in these systems. These security problems have become a bottleneck in the development and maintenance of mobile agent systems, especially in security sensitive applications such as electronic commerce. This book introduces the concept and structure of mobile agent systems and discusses various attacks and countermeasures. The emphasis is on the formal modeling and analysis of secure mobile agent systems and their applications.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Acknowledgments; Contents; I. Data Collection; Toward Active Mining from On-line Scientific Text Abstracts Using Pre-existing Sources; Data Mining on the WAVEs
• Word-of-mouth-Assisting Virtual Environments; Immune Network-based Clustering for WWW Information Gathering/Visualization; Interactive Web Page Retrieval with Relational Learning-based Filtering Rules; Monitoring Partial Update of Web Pages by Interactive Relational Learning; Context-based Classification of Technical Terms Using Support Vector Machines.

Focusing on data mining, this work is a joint effort from researchers in Japan, and includes a report on the forefront of data collection, user-centred mining and user interaction/reaction. It offers an overview of modern solutions with real-world applications, sharing hard-learned experiences.

• Introduction.- Logic and Inferencing.- First Order Predicate Logic.- Rule Based Reasoning.- Introduction to Prolog Language.- Knowledge Representation and Reasoning.- Networks Based Representation.- State Space Search.- Heuristic Search.- Constraint Satisfaction Problems.- Search and Game Playing.- Reasoning in Uncertain Environments.- Planning.- Natural Language Processing.- Machine Learning.- Machine Vision.- Bibliography.
• (source: Nielsen Book Data)

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
(source: Nielsen Book Data)

• A Combined Approach for Ontology Enrichment from Textual and Open Data.- C-SPARQL Extension for Sampling RDF Graphs Streams.- Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.
• (source: Nielsen Book Data)

This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of "Knowledge Discovery and Management" (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC'2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.
(source: Nielsen Book Data)

• Fuzzy Numbers.- Dual Numbers.- Fuzzy Dual Numbers.- Fuzzy Dual Linear Matrix Inequalities.- Fuzzy Estimation of Distribution Matrices.- Fuzzy Dual Entropy and Applications.- Fuzzy Dual Programming.
• (source: Nielsen Book Data)

This is the first book focusing exclusively on fuzzy dual numbers. In addition to offering a concise guide to their properties, operations and applications, it discusses some of their advantages with regard to classical fuzzy numbers, and describes the most important operations together with a set of interesting applications in e.g. optimization, decision-making and system design. The book provides students, researchers and professionals the necessary theoretical background to apply this particular subset of fuzzy numbers to decision-making problems involving uncertainty. Further, it shows how to solve selected engineering and management problems and includes detailed numerical examples.
(source: Nielsen Book Data)

• Introduction.- Pairwise Comparison Matrices.- Fuzzy Set Theory.- Fuzzy Pairwise Comparison Matrices.- Incomplete Large-dimensional Pairwise Comparison Matrices.- Discussion and Future Research. .
• (source: Nielsen Book Data)

This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.
(source: Nielsen Book Data)

• Intro; Title Page; Contents; Introduction; Motivation; Problem Statement; Challenges and Overview of the State-of-the-Art; Challenges for Efficient SPARQL Query Processing; Challenges for Effective SPARQL Query Processing; Hypotheses and Research Questions; Contributions; Outline; Foundations of Linked Data Management; Linked Data; The Resource Description Framework (RDF); Querying RDF Data: The SPARQL Query Language; Querying RDF Data on the Web; URI Dereferencing and Link Traversal; SPARQL Endpoints; Linked Data Fragments; Foundations of Query Processing; Query Optimization

• Adaptive Query ProcessingAdaptive Query Processing over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Adaptive Link Traversal Approaches; Adaptive Query Processing Against SPARQL Endpoints; Query Processing Approaches Against TPF Servers; The nLDE Approach; nLDE Query Optimizer; Estimation of Query Plan Cardinalities; Placing Physical Operators; Building Query Tree Plans; nLDE Optimizer: Algorithm Description; Complexity of the nLDE Query Optimizer; nLDE Adaptive Routing Query Engine; Adaptive Operators; Eddies

• Network of Linked Data Eddies (nLDE)Termination of nLDE; Correctness of nLDE; Routing Policies; Routing Policy from Eddies to Adaptive Operators; Routing Policy from Adaptive Operators to Eddies; Experimental Study; Experimental Settings; Efficiency of the nLDE Optimizer; Effectiveness of the nLDE Optimizer; Impact of the nLDE Routing-based Adaptivity on Execution Time in Perfect Networks; Effectiveness of the nLDE Routing-based Adaptivity Under the Presence of Network Delays; Summary and Future Work; Foundations of Crowdsourcing; Overview; Types of Crowdsourcing; Microtasks; Contests

• Crowdsourcing WorkflowsHybrid Crowdsourcing Workflows; Human-based Workflow: Find-Fix-Verify; Crowdsourcing Query Answer Completeness over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Hybrid Query Processing for Relational Data; Crowd-based Linked Data Management Applications; Web Data Quality Assessment; The HARE Approach; Problem Definition; Proposed Solution; RDF Completeness Model; Representation of the Crowd Knowledge; Crowd Contradiction; Crowd Unknownness; HARE Microtask Manager; User Interface Generator

• Microtask ExecutorHARE Query Optimizer; Complexity of the HARE Query Optimizer; HARE Query Engine; A SPARQL Fuzzy Set Semantics; HARE BGP Executor; Complexity of HARE Query Evaluation; Experimental Study; Experimental Settings; HARE Crowdsourcing Capabilities; Size of Query Answer; Quality of Crowd Answers; Crowd Response Time; Summary and Future Work; Crowdsourcing Linked Data Quality Issues; Introduction; Research Questions; Contributions; Structure of the Chapter; Preliminaries: Linked Data Quality Issues; Related Work; Crowdsourcing in Linked Data Management; Web Data Quality Assessment

• Sequential Predictive Scheduling in Partitioned Data Domains.- Wildfire Optimizations in Modeling and Calibrations for Bulgarian Test Cases.- Process Control with the Variability Constraints.- Comparison of Different ACO Start Strategies Based on InterCriteria Analysis.- Evolutionary Approach for Tuning of Longwall Scraper Conveyor Model.- Solving Graph Partitioning Problems with Parallel Metaheuristics.- Comparison of Selected Fuzzy PSO Algorithms.
• (source: Nielsen Book Data)

This book presents new optimization approaches and methods and their application in real-world and industrial problems, and demonstrates how many of the problems arising in engineering, economics and other domains can be formulated as optimization problems. Constituting a comprehensive collection of extended contributions from the 9th International Workshop on Computational Optimization (WCO) held in Gdansk, Poland, September 11-14, 2016, the book discusses important applications such as job scheduling, wildfire modeling, parameter settings for controlling different processes, capital budgeting, data mining, finding the location of sensors in a given network, identifying the conformation of molecules, algorithm correctness, decision support system, and computer memory management. Further, it shows how to develop algorithms for these based on new intelligent methods like evolutionary computations, ant colony optimization and constraint programming. The book is a valuable resource for researchers and practitioners alike.
(source: Nielsen Book Data)

• A Review of Soft Computing Techniques in Maritime Logistics and its Related Fields.- Intelligent Data Analysis, Soft Computing and Imperfect Data.- Soft Computing Methods in Transport and Logistics.- Applications of Soft Computing in Intelligent Transportation Systems.- Fuzzy Cognitive Maps based Models for Pattern Classification: Advances and Challenges.- A proposal of on-line detection of new faults and automatic learning in fault diagnosis.- Fuzzy Portfolio Selection Models for dealing with Investor's Preferences.- Gradual numbers and fuzzy solutions to fuzzy optimization problems.- A modal account of preference in a fuzzy setting.- The single parameter family of Gini Bonferroni welfare functions and the binomial decomposition, transfer sensitivity and positional transfer sensitivity.
• (source: Nielsen Book Data)

This book offers a timely snapshot of current soft-computing research and solutions to decision-making and optimization problems, which are ubiquitous in the current social and technological context, addressing fields including logistics, transportation and data analysis. Written by leading international experts from the United States, Brazil and Cuba, as well as the United Kingdom, France, Finland and Spain, it discusses theoretical developments in and practical applications of soft computing in fields where these methods are crucial to obtaining better models, including: intelligent transportation systems, maritime logistics, portfolio selection, decision- making, fuzzy cognitive maps, and fault detection. The book is dedicated to Professor Jose L. Verdegay, a pioneer who has been actively pursuing research in fuzzy sets theory and soft computing since 1982, in honor of his 65th birthday.
(source: Nielsen Book Data)

• Sequence Classification based on Active Learning.- Using MongoDB Databases For Training and Combining Intrusion Detection Datasets.- Service Oriented Network Management with Knowledge-Based Network Management System in Fluctuating Environment.- A Topic Structuration Method on Time Series for a Meeting from Text Data.- Joint Embedding of Hierarchical Structure and Context for Entity Disambiguation.- Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs.- Should Duration and Team Size be Used for Effort Estimation?.- A Lifelog Data Portfolio for Privacy Protection based on Dynamic Data Attributes in a Lifelog Service.- Estimation of Emotional Scene from Lifelog Videos in Consideration of Intensity of Various Facial Expressions.- Developing a Framework to Support Designing of Active Learning Class.- Where Do Drivers Look When Driving in a Foreign Country?.- On Password Strength: A Survey and Analysis.- Probabilistic Model-Based Multistep Crossover Considering Dependency between Nodes in Tree Optimization.- Digital Watermark Design for Two-dimensional Codes Displayed on Smart Phone Screen Using Multi-Objective Optimization and Optical Simulation.
• (source: Nielsen Book Data)

This book gathers 14 of the most promising papers presented at the 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2017), which was held on June 26-28, 2017 in Kanazawa, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The book presents research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered along the way, as well as the solutions adopted to solve them.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Intelligence computer data analysis techniques
• 3. The fundamentals of Cognitive Informatics
• 4. Cognitive information systems
• 5. The fundamentals of management sciences
• 6. Intelligent cognitive information systems in management application
• 7. Summary.
• (source: Nielsen Book Data)

Cognitive Information Systems in Management Sciences summarizes the body of work in this area, taking an analytical approach to interpreting the data, while also providing an approach that can be used for practical implementation in the fields of computing, economics, and engineering. Using numerous illustrative examples, and following both theoretical and practical results, Dr. Lidia Ogiela discusses the concepts and principles of cognitive information systems, the relationship between intelligent computer data analysis, and how to utilize computational intelligent approaches to enhance information retrieval. Real world implantation use cases round out the book, with valuable scenarios covering management science, computer science, and engineering. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS.
(source: Nielsen Book Data)

Annotation The Symposium series refers to all apects of theory, algorithm design, applications and emerging techniques related to computational intelligence.

• Part I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization
• On making skyline queries resistant to outliers
• Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
• Exact and Approximate Minimal Pattern Mining
• Part II: Comparison of proximity measures for a topological discrimination
• Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit
• A novel approach to feature selection based on quality estimation metrics
• Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining
• Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation
• Multi-layer ontologies for integrated 3D shape segmentation and annotation
• Ontology Alignment Using Web Linked Ontologies as Background Knowledge
• LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks
• Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks.

This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.

• Introduction
• Basic Notions
• Classical Sentential Calculus and Lukasiewicz Sentential Calculus
• MV -Algebras: Generalities
• Local MV -algebras
• Perfect MV -algebras
• The Variety Generated by Perfect MV -algebras
• Representations of Perfect MV -algebras
• The Logic of Perfect Algebras
• The Logic of Quasi True
• Perfect Pavelka Logic.

This book presents the first algebraic treatment of quasi-truth fuzzy logic and covers the algebraic foundations of many-valued logic. It offers a comprehensive account of basic techniques and reports on important results showing the pivotal role played by perfect many-valued algebras (MV-algebras). It is well known that the first-order predicate Łukasiewicz logic is not complete with respect to the canonical set of truth values. However, it is complete with respect to all linearly ordered MV -algebras. As there are no simple linearly ordered MV-algebras in this case, infinitesimal elements of an MV-algebra are allowed to be truth values. The book presents perfect algebras as an interesting subclass of local MV-algebras and provides readers with the necessary knowledge and tools for formalizing the fuzzy concept of quasi true and quasi false. All basic concepts are introduced in detail to promote a better understanding of the more complex ones. It is an advanced and inspiring reference-guide for graduate students and researchers in the field of non-classical many-valued logics.

• Intelligent Agents: Integrating Multiple Components Through a Symbolic Structure / Razvan Dinu, Tiberiu Stratulat
• An Architecture for Multi-Dimensional Temporal Abstraction Supporting Decision Making in Oil-Well Drilling / Odd Erik Gundersen, Frode Sørmo
• A New Impulse Noise Filtering Algorithm Based on a Neuro-Fuzzy Network / Yueyang Li, Haichi Luo, Jun Sun
• A Fuzzy System for Educational Tasks for Children with Reading and Writing Disabilities / Adalberto Bosco C. Pereira
• Optimizing the Performance of a Refrigeration System Using an Invasive Weed Optimization Algorithm / Roozbeh Razavi-Far, Vasile Palade, Jun Sun
• A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization / Daniel Leal Souza, Otávio Noura Teixeira
• Hybrid Approach of Genetic Programming and Quantum-Behaved Particle Swarm Optimization for Modeling and Optimization of Fermentation Processes / Jun Sun, Vasile Palade, Zhenyu Wang
• Hybrid Client Specific Discriminant Analysis and its Application to Face Verification / Xiao-Qi Sun, Xiao-Jun Wu, Jun Sun.

The combination of different intelligent methods is a very active research area in Artificial Intelligence (AI). The aim is to create integrated or hybrid methods that benefit from each of their components. The 3rd Workshop on ""Combinations of Intelligent Methods and Applications"" (CIMA 2012) was intended to become a forum for exchanging experience and ideas among researchers and practitioners who are dealing with combining intelligent methods either based on first principles or in the context of specific applications. CIMA 2012 was held in conjunction with the 22nd European Conference on A.

• 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)

• Title Page; Preface; Conference Organization; Contents; Invited Talks; Some Real-World Applications of Soft Artificial Intelligence: Scientogram Mining, Assembly Line Balancing, and Forensic Identification; Challenges of Automation and Safety in Field Robotics; KDD, DM and Machine Learning; Data Mining and Query Answer Techniques Applied to a Bio-Nutritional Trials Focused Expert System; The Use of the Traffic Lights Panel as a Goodness-of-Clustering Indicator: An Application to Financial Assets

• Using Gabriel Graphs in Borderline-SMOTE to Deal with Severe Two-Class Imbalance Problems on Neural NetworksActive Learning of Actions Based on Support Vector Machines; Towards the Formalization of Re-Identification for Some Data Masking Methods; Natural Language Processing and Recommenders; Towards Object Descriptions in Natural Language from Qualitative Models; Semantically-Enhanced Recommenders; Computer Vision; Supervised Texture Classification Using Optimization Techniques; Modelling Facial Expressions Dynamics with Gaussian Process Regression; Survey on 2D and 3D Human Pose Recovery

• A Study of Registration Techniques for 6DoF SLAMRobotics; Object Detection Methods for Robot Grasping: Experimental Assessment and Tuning; The Role of i-Walker in Post-Stroke Training; Using a RGB-D Camera for 6DoF SLAM; Learning Topological SLAM Using Visual Information; AI for Optimization Problems; Exploring Genetic Algorithms and Simulated Annealing for Immobile Location-Allocation Problem; Analysis and Generation of Pseudo-Industrial MaxSAT Instances; A SAT-Based Approach to MinSAT; Multicast Session Protection Planner
• Tool to Plan and Deploy Protection Infrastructure: A SPEA Approach

• AI Applications to Real WorldA Case-Based Hybrid System for Injection Molding Sensorization; First Studies on Self-Preserving Digital Objects; Intelligent Building Energy Management Through Holistic Knowledge Based Approach; Quantitative and Qualitative Approaches for Stock Movement Prediction; Subject Index; Author Index

• Part I. Density Ratio Approach to Machine Learning: 1. Introduction
• Part II. Methods of Density Ratio Estimation: 2. Density estimation
• 3. Moment matching
• 4. Probabilistic classification
• 5. Density fitting
• 6. Density-ratio fitting
• 7. Unified framework
• 8. Direct density-ratio estimation with dimensionality reduction
• Part III. Applications of Density Ratios in Machine Learning: 9. Importance sampling
• 10. Distribution comparison
• 11. Mutual information estimation
• 12. Conditional probability estimation
• Part IV. Theoretical Analysis of Density Ratio Estimation: 13. Parametric convergence analysis
• 14. Non-parametric convergence analysis
• 15. Parametric two-sample test
• 16. Non-parametric numerical stability analysis
• Part V. Conclusions: 17. Conclusions and future directions.
• (source: Nielsen Book Data)

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.
(source: Nielsen Book Data)

• On the History of the Growth Optimal Portfolio (M M Christensen)
• Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.)
• Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk)
• Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban)
• Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak)
• Empirical Pricing American Put Options (L Gyorfi & A Telcs).
• (source: Nielsen Book Data)

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
(source: Nielsen Book Data)

• On the History of the Growth Optimal Portfolio (M M Christensen)
• Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.)
• Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk)
• Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban)
• Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak)
• Empirical Pricing American Put Options (L Gyorfi & A Telcs).
• (source: Nielsen Book Data)

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
(source: Nielsen Book Data)

• Tile Page; Preface; Organization; Contents; An Assistance Infrastructure for Open MAS; On the Modularity of Industrial SAT Instances; On 2SAT-MaxOnes with Unbalanced Polarity: From Easy Problems to Hard MaxClique Problems; Experimenting with the Instances of the MaxSAT Evaluation; Lazy Learning Methods for Quality of Life Assessment in People with Intellectual Disabilities; Probabilistic Appearance-Based Mapping and Localization Using the Feature Stability Histogram; Towards an Efficient Use of Resources in All-Optical Networks; Depth of Valleys Accumulation Algorithm for Object Detection.

This book is a collection of the papers accepted for presentation at the 14th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2011), held at the University of Lleida in October 2011. From the 28 papers submitted, 24 were accepted for inclusion in this book and for oral presentation at the conference after a careful reviewing process. The conference also featured invited talks by two outstanding international researchers: Christian Bessiere of CNRS, University of Montpellier, France, and Bart Selman of Cornell University, USA. The CCIA conference brings tog.

• 1. Introduction
• 2. Clusters and robots
• 3. Cluster programming
• 4. Robot motion
• 5. Sensors
• 6. Mapping and localization
• 7. Vision and tracking
• 8. Learning landmarks
• 9. Robot architectures
• Appendix I: Summary of OpenMPI man page for mpirun
• Appendix II: MPI datatypes
• Appendix III: MPI reduction operations
• Appendix IV: MPI application programmer interface.

In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, target tracking, action selection and landmark learning, are reviewed and cluster implementations for these are presented.The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.
(source: Nielsen Book Data)

• Machine generated contents note: Invited Talks
• Playing Games with Games / Michael Wooldridge
• User-Generated AI for Interactive Digital Entertainment / Ashwin Ram
• Perspectives on Artificial Intelligence in a Business Environment / Peter Nordin
• Tutorials
• Probabilistic Decision Graphs for Optimization Under Uncertainty / Finn Verner Jensen
• Coordinating Multi-Agent Systems Using Social Laws / Thomas Agotnes
• Full Papers
• Machine Learning
• User-Oriented Assessment of Classification Model Understandability / Niklas Lavesson
• Concurrent Learning of Large-Scale Random Forests / Henrik Bostrom
• Learning Multi-Label Predictors Under Sparsity Budget / Tapio Salakoski
• Machine Learning Methods for Spatial Clustering on Precision Agriculture Data / Rudolf Kruse
• Planning
• On Constraint Models for Parallel Planning: The Novel Transition Scheme / Roman Bartak
• Trajectory Planning on Grids: Considering Speed Limit Constraints / Lukas Chrpa
• Safe Reinforcement Learning for Continuous Spaces Through Lyapunov-Constrained Behavior / Erik Kyrkjebø
• Parallel Monte Carlo Tree Search on GPU / Reiji Suda
• Exploiting Global Properties in Path-Consistency Applied on SAT / Pavel Surynek
• Applications
• Incremental Stream Clustering for Anomaly Detection and Classification / Jan Ekman
• Respiratory Motion Prediction: A Fuzzy Logic Approach / Manish Kakar
• Case-Based Reasoning in a System Architecture for Intelligent Fish Farming / Agnar Aamodt
• Overview of Fault Detection Techniques in Automated Monitoring Systems / Shengtong Zhong
• Robotics and Cognition
• View-Independent Human Gait Recognition Using CBR and HMM / Odd Erik Gundersen
• Self-Exploration of Autonomous Robots Using Attractor-Based Behavior Control and ABC-Learning / Matthias Kubisch.

• pt. 1. Basic principles
• pt. 2. Continuous time swarms
• pt. 3. Discrete time swarms
• pt. 4. Swarm based optimization methods.

Swarming species such as flocks of birds or schools of fish exhibitfascinating collective behaviors during migration and predatoravoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles ("vehicle swarms") exhibit sophisticated collective behaviors while maneuvering. In this book we show how to model and control a wide range of suchmulti-agent dynamic systems and analyze their collective behaviorusing both stability theoretic and simulation-based approaches. Inparticular, we investigate problems such as group aggregat

• Preface
• Agent-Based Genetic Algorithm for Global Numerical Optimization & Feature Selection
• Multi-Agent Enterprise Sustainability Performance Measurement System
• A Modular Artificial Neural Network Based Decision Making in a Multi-Agent Robot Soccer System
• Security & Privacy in Track & Trace Infrastructures
• A Challenge to Develop Large-Scale Agent Simulation Software
• A Framework of an Agent-Based Model Using Social & Physical Interaction for Vulnerability Analysis on Flood Events
• An Investigation into the Issues of Multi-Agent Data Mining
• Agent-Based Discovery, Composition & Orchestration of Grid Web Services
• An Efficient Mobile-Agent-Based Platform for Dynamic Service Provisioning in 3G/UMTs
• Index.
• (source: Nielsen Book Data)

Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. In many applications, the individual and collective behaviour of the agents depends on the observed data from distributed sources. This book discusses a number of research issues concerned with the use of Multi-Agent Systems for Data Mining (MADM), also known as agent-driven data mining. In addition, optimisation algorithms are very important in modern research and industrial areas. This book examines one multi-population co-genetic algorithm (MPAGA) with double chain-like agent structure to realise parallel optimisation, combining chain-like agent structure and multi-population parallel searching. Furthermore, this book proposes an efficient modular artificial neural network (ANN) architecture for the intelligent decision making of a robot in a robot soccer systems with different team configurations. Other chapters review the use of radio frequency identification (RFID) technology with supply chain agents and then analyse the security requirements, describe how to design and implement a large-scale multi-agent simulation software, and provide a framework of evacuation simulation for urban hazards such as flooding with effective agent's interaction tools with other agents and the physical environment.
(source: Nielsen Book Data)

• Title page; Preface; Conference Organization; Contents; Invited Talks; Agents and Multi-Agents Systems; AI Real-World Applications; Data Mining, Machine Learning and Soft Computing; Logics, Constraint Satisfaction and Reasoning; Robotics, Vision and Perception; Subject Index; Author Index.

Artificial Intelligence (AI) is one of the hottest areas of research with hundreds of potentially real life applications and one of the disciplines that is most intertwined with all sorts of fields related to human experience. A wide range of disciplines, sectors and technologies could benefit from the results of AI breakthroughs; from robotics to search engines; healthcare or environment; screening, monitoring or supervision techniques. This book compiles the 33 accepted papers presented at the 13th International Conference of the Catalan Association for Artificial Intelligence. Although most o.

• New Hybrid Intelligent Systems to Solve Linear and Quadratic Optimization Problems and Increase Guaranteed Optimal Convergence Speed of Recurrent ANN
• A Novel Optimization Algorithm Based on Reinforcement Learning
• The Use of Opposition for Decreasing Function Evaluations in Population-Based Search
• Search Procedure Exploiting Locally Regularized Objective Approximation. A Convergence Theorem for Direct Search Algorithms
• Optimization Problems with Cardinality Constraints
• Learning Global Optimization Through a Support Vector Machine Based Adaptive Multistart Strategy
• Multi-Objective Optimization Using Surrogates
• A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization
• A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems
• Optimization with Clifford Support Vector Machines and applications
• A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets
• An Integrated Approach to Speed Up GA-SVM Feature Selection Model
• Computation in Complex Environments;
• Project Scheduling: Time-Cost Tradeoff Problems
• Systolic VLSI and FPGA Realization of Artificial Neural Networks
• Application of Coarse-Coding Techniques for Evolvable Multirobot Controllers.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

This book presents the proceedings of the 2018 International Conference on Security with Intelligent Computing and Big-data Services (SICBS 2018). With the proliferation of security with intelligent computing and big-data services, the issues of information security, big data, intelligent computing, blockchain technology, and network security have attracted a growing number of researchers. Discussing topics in areas including blockchain technology and applications; multimedia security; information processing; network, cloud and IoT security; cryptography and cryptosystems; as well as learning and intelligent computing and information hiding, the book provides a platform for researchers, engineers, academics and industrial professionals from around the globe to present their work in security-related areas. It not only introduces novel and interesting ideas, but also stimulates discussions and inspires new ideas.