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• FOREWORD; CONTENTS; Keynote Address; The Origin of Complexity (presented, but paper not submitted); Plenary Session; The Role of Field Coupling in Nano-Scale Cellular Nonlinear Networks; Session Theory I; On the Relationship Between CNNs and PDEs; New Spatial-Temporal Patterns and the First Programmable On-Chip Bifurcation Test-Bed; On Stability of Full Range and Polynomial Type CNNs; A Study on Limit Cycles in Nearly Symmetric Cellular Neural Networks; An Improved Global Stability Result for Cellular Neural Networks with Time Delay; Session Applications I.

• SC-CNNs for Chaotic Signals GenerationMoving Object Tracking on Panoramic Images; MPEG-4 Based Modifications for an CNN Segmentation Chip; Watermarking for the Authentication of Video on CNN-UM; An Analogic CNN-Algorithm of Pixel Level Snakes for Tracking and Surveillance Tasks; Session Theory II; On the Dynamics of a Class of Cellular Neural Networks; mL-CNN: A CNN Model for Reaction-Diffusion Processes in m-Component Systems; Emergence of Global Patterns in Connected Neural Networks; Influence of System Non-Uniformity on Dynamic Phenomena in Arrays of Coupled Nonlinear Networks.

• Boolean Design of Binary Initialized and Coupled CNN Image Processing OperatorsSession Physical Implementations I; ACE16K: A 128x128 Focal Plane Analog Processor with Digital I/O.; On the RTD Implementation of Simplicial Cellular Nonlinear Networks; Programmable Optical CNN Implementation Based on the Template Pixels' Angular Coding; Application Issues Of A Programmable Optical CNN Implementation; Configurable Multi-Layer CNN-UM Emulator on FPGA; Session Applications II (Poster); Object-Oriented Image Analysis via Analogic CNN Algorithms
• Part I: Motion Estimation.

• Object-Oriented Image Analysis via Analogic CNN Algorithms
• Part II: Image Synthesis and Consistency ObservationA CNN Path Planning for a Mobile Robot in an Environment with Obstacles; A CNN Based System to Blind Sources Separation of MEG Signals; Delay-Driven Contrast Enhancement Using a Cellular Neural Network with State-Dependant Delay; Plenary Session; Time as Coding Space for Information Processing in the Cerebral Cortex; Characterizing the Spatio-Temporal Dynamics of the Epileptogenic Process with Nonlinear EEG Analyses; Session Bionics and Biologically Relevant Models I.

• Analyzing Multidimensional Neural Activity via CNN-UMBasic Mammalian Retinal Effects on the Prototype Complex Cell CNN Universal Machine; Prediction of Epileptic Seizures by CNN with Linear Weight Functions; Biometric Authentication Based on Perceptual Resonance between CNN Emergent Patterns and Humans; CNN Based Central Pattern Generators with Sensory Feedback; Session Integrated Sensing and Processing; Mobile Sensor-Actuator Networks: Opportunities and Challenges; Visual Feedback by Using a CNN Chip Prototype System; Multi-Target Tracking with Stored Program Adaptive CNN Universal Machines.

This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000).

• Soft computing agents based distributed intelligent systems / R.A. Aliev and B. Fazlollahi
• Incremental synchronous learning for embedded agents operating in ubiquitous computing environments / H. Hagras [and others]
• Component based distributed multi-agent architecture for soft computing / R. Khosla
• Similarity-based knowledge discovery for web information retrieval / V. Loia, S. Senatore and M.I. Sessa
• Collaborative agents in data exploration / W. Pedrycz and G. Vukovich
• Semi-metric behavior in document networks and its application to recommendation systems / L.M. Rocha
• Modeling tool for intelligent-agent based systems / S. Rahimi [and others]
• Emergence of holonic enterprises from multi-agent systems / M. Ulieru
• Emergent coordination among fuzzy reinforcement learning agents / D. Vengerov, H.R. Berrenji and A. Vengerov
• On dynamic domination for fuzzy causal networks / J.Y. Zhang and Z.-Q. Liu.

A study of soft computing agents. It seeks to: explore the development of soft computing-based agents; examine the role of soft computing-based technology in facets of agent design; and cross-fertilise ideas on the soft computing perspective to the development of agent-based systems.

• Hardware and software aspects
• transputer topologies for parallel implementation
• comparison between serial and parallel implementation
• analysis and implementation for equal distribution of the training set in a homogeneous transputer array
• analysis and implementation for unequal distribution of the training set in a homogeneous transputer array
• analysis and implementation for unequal distribution of the training set in a heterogeneous transputer array
• conclusion.
• (source: Nielsen Book Data)

This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.
(source: Nielsen Book Data)

• Table of Contents Introduction to Software Architecture API Design Data Modeling The Data Layer The Twelve-Factor App Methodology Web Server Structures Event-Driven Structures Advanced Event-Driven Structures Microservices vs Monolith Testing and TDD Package Management Logging Metrics Profiling Debugging Ongoing Architecture.
• (source: Nielsen Book Data)

Make the best of your test suites by using cutting-edge software architecture patterns in Python Key Features Learn how to create scalable and maintainable applications Build a web system for micro messaging using concepts in the book Use profiling to find bottlenecks and improve the speed of the system Book DescriptionDeveloping large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented. Software developers, architects, and technical management teams rely on high-level software design patterns such as microservices architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD) to make their work easier. This book covers these proven architecture design patterns with a forward-looking approach to help Python developers manage application complexity-and get the most value out of their test suites. Starting with the initial stages of design, you will learn about the main blocks and mental flow to use at the start of a project. The book covers various architectural patterns like microservices, web services, and event-driven structures and how to choose the one best suited to your project. Establishing a foundation of required concepts, you will progress into development, debugging, and testing to produce high-quality code that is ready for deployment. You will learn about ongoing operations on how to continue the task after the system is deployed to end users, as the software development lifecycle is never finished. By the end of this Python book, you will have developed "architectural thinking": a different way of approaching software design, including making changes to ongoing systems. What you will learn Think like an architect, analyzing software architecture patterns Explore API design, data storage, and data representation methods Investigate the nuances of common architectural structures Utilize and interoperate elements of patterns such as microservices Implement test-driven development to perform quality code testing Recognize chunks of code that can be restructured as packages Maintain backward compatibility and deploy iterative changes Who this book is forThis book will help software developers and architects understand the structure of large complex systems and adopt architectural patterns that are scalable. Examples in the book are implemented in Python so a fair grasp of basic Python concepts is expected. Proficiency in any programming languages such as Java or JavaScript is sufficient.
(source: Nielsen Book Data)

• Part I: Artificial Intelligence and Optimization.- Stochastic Proximal Gradient Algorithm with Minibatches Application to Large Scale Learning Models.- The Human Mental Search Algorithm for Solving Optimisation Problems.- Reducing Redundant Association Rules Using Type-2 Fuzzy Logic.- Identifiability of Discrete Concentration Graphical Models with a Latent Variable.- An Automatic Classification of Genetic Mutations by Exploring Different Classifiers.- Towards Artificial Intelligence: Concepts, Applications, and Innovations.- Part II: Big Data and Artificial Intelligence Applications.- In Depth Analysis, Applications and Future Issues of Artificial Neural Network.- Big Data and Deep Learning in Plant Leaf Diseases Classification for Agriculture.- Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities.
• (source: Nielsen Book Data)

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.
(source: Nielsen Book Data)

• Introduction.- IoT Overview.- Emerging Trends in IoT.- IoT Use Cases.- Energy and Utilities.- Context Management in IoT.- Context and Context Management.- Context-aware Architecture.- Augmented Reality and IoT.- Augmented Reality and IoT design Issues.- Augmented Reality and IoT enabled Architecture.- Conclusions.
• (source: Nielsen Book Data)

This book discusses the use of converged technology, a rapidly growing area that enhancements smart devices, communication, Internet of things (IoT), and augmented reality (AR). The book also explores the need for convergence of IoT and AR for various purposes, like personalized services, context awareness, and bridging the gap between the physical and digital world. Furthermore, it examines the implementation of IoT and AR in use cases to define pathways that allow application developers to design modern solutions to satisfy requirements like scalability, abstraction and security. Featuring an introduction, and covering sensing techniques, and effective architecture in AR-based IoT real-time use cases, the book also addresses the issues and challenges in designing standard architecture and middleware to support diverse applications. Given its scope, it is a valuable resource for teachers and students in engineering, as well as researchers, developers, and users working in multi-disciplinary areas.
(source: Nielsen Book Data)

• Introduction.- Related Work: Machine Learning and Electronic Design Automation.- Unlabeled Data and Artificial Neural Networks.- Placement Loss: Placement Constraints Description and Satisfiability Evaluation.- Experimental Results in Industrial Case Studies.- Conclusions. .
• (source: Nielsen Book Data)

In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the placement task in analog integrated circuit layout design, by creating a generalized model that can generate valid layouts at push-button speed. Further, it exploits ANNs' generalization and push-button speed prediction (once fully trained) capabilities, and details the optimal description of the input/output data relation. The description developed here is chiefly reflected in two of the system's characteristics: the shape of the input data and the minimized loss function. In order to address the latter, abstract and segmented descriptions of both the input data and the objective behavior are developed, which allow the model to identify, in newer scenarios, sub-blocks which can be found in the input data. This approach yields device-level descriptions of the input topology that, for each device, focus on describing its relation to every other device in the topology. By means of these descriptions, an unfamiliar overall topology can be broken down into devices that are subject to the same constraints as a device in one of the training topologies. In the experimental results chapter, the trained ANNs are used to produce a variety of valid placement solutions even beyond the scope of the training/validation sets, demonstrating the model's effectiveness in terms of identifying common components between newer topologies and reutilizing the acquired knowledge. Lastly, the methodology used can readily adapt to the given problem's context (high label production cost), resulting in an efficient, inexpensive and fast model. .
(source: Nielsen Book Data)

• An Overview of Handwritten Character Recognition Systems for Historical Documents
• Supporting Autonomous Navigation of Visually Impaired People to Experience Cultural Heritage
• A Robotic Humanoid for Information Exploration on Cultural Heritage Domain
• Using Technology and Gamification as a Means of Enhancing Users Experience at Cultural Heritage Sites
• Win the City Game
• Reversing Heritage Destruction through Digital Technology: The Rekrei Project
• Game-based learning research at the Danube University Krems Center for Applied Game Studies
• A case study into the User Experience of an application of Virtual Reality at the Saint Pauls Catacombs, Malta
• Techniques of Filming and Audio Recording in 360-Degree Ambiences
• The Effectiveness of an Augmented Reality Guiding System in an Artum Museum
• Augmented Reality in Cultural Heritage: designing for mobile AR user experiences.

With the proliferation of technology, science became a medium used to create and interpret heritage in a way that redefines human achievements. The recent advances in technology are providing us with a variety of tools aimed at exploring, experiencing and interacting with heritage in a completely new way, which was unimaginable up until a few decades ago. Suddenly, heritage has become accessible and exciting to those who might not have previously considered it interesting. This book presents a selection of approaches in various topics such as artificial intelligence, gamification, and virtual and augmented reality, and uses practical examples to show how they can be deployed in real-world scenarios. As such, it inspires a wide variety of stakeholders and helps them experience our common heritage through a new lens.

• Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps.- Shape-aware Deep Convolutional Neural Network for Vertebrae Segmentation.- Automated Characterization of Body Composition and Frailty with Clinically Acquired CT.- Unfolded cylindrical projection for rib fracture diagnosis.- 3D Cobb Angle Measurements from Scoliotic Mesh Models with Varying Face-Vertex Density.- Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features.- Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients.- Reconstruction of 3D muscle fiber structure using high resolution cryosectioned volume.- Segmentation of Pathological Spines in CT Images Using a Two-Way CNN and a Collision-Based Model.- Attention-driven deep learning for pathological spine segmentation.- Automatic Full Femur Segmentation from Computed Tomography Datasets using an Atlas-Based Approach.- Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling.- DSMS-FCN: A Deeply Supervised Multi-Scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th International Workshop and Challenge on Computational Methods and Clinical Applications for Musculoskeletal Imaging, MSKI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. Topics of interest include all major aspects of musculoskeletal imaging, for example: clinical applications of musculoskeletal computational imaging; computer-aided detection and diagnosis of conditions of the bones, muscles and joints; image-guided musculoskeletal surgery and interventions; image-based assessment and monitoring of surgical and pharmacological treatment; segmentation, registration, detection, localization and visualization of the musculoskeletal anatomy; statistical and geometrical modeling of the musculoskeletal shape and appearance; image-based microstructural characterization of musculoskeletal tissue; novel techniques for musculoskeletal imaging.
(source: Nielsen Book Data)

• Table of Contents Data science: Bird's-eye view Data Modeling in Action - The Titanic Example Feature Engineering and Model Complexity - The Titanic Example Revisited Get Up and Running with TensorFlow Tensorflow in Action - Some Basic Examples Deep Feed-forward Neural Networks - Implementing Digit Classification Introduction to Convolutional Neural Networks Object Detection - CIFAR-10 Example Object Detection - Transfer Learning with CNNs Recurrent-Type Neural Networks - Language modeling Representation Learning - Implementing Word Embeddings Neural sentiment Analysis Autoencoders - Feature Extraction and Denoising Generative Adversarial Networks in Action - Generating New Images Face Generation and Handling Missing Labels Appendix - Implementing Fish Recognition.
• (source: Nielsen Book Data)

Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner Key Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples Book DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learn Understand the fundamentals of deep learning and how it is different from machine learning Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning Increase the predictive power of your model using feature engineering Understand the basics of deep learning by solving a digit classification problem of MNIST Demonstrate face generation based on the CelebA database, a promising application of generative models Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation Who this book is forThis book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.
(source: Nielsen Book Data)

• Chapter 1 Memristor Emulators A Note on Modeling.-
• Chapter 2 A Simple Oscillator using Memristor.-
• Chapter 3 A Hyperjerk Memristive System with Hidden Attractors.-
• Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application.-
• Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based.-
• Chapter 6 Modern System Design using Memristors.-
• Chapter 7 RF/Microwave Applications of Memristors.-
• Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits.-
• Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors.-
• Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki's System.
• (source: Nielsen Book Data)

This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
(source: Nielsen Book Data)

• Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles
• Credits
• About the Authors
• About the Reviewer
• Customer Feedback
• Table of Contents
• Preface
• Chapter 1: Neural Network and Artificial Intelligence Concepts
• Chapter 2: Learning Process in Neural Networks
• Chapter 3: Deep Learning Using Multilayer Neural Networks
• Chapter 4: Perceptron Neural Network Modeling
• Basic Models
• Chapter 5: Training and Visualizing a Neural Network in R
• Chapter 6: Recurrent and Convolutional Neural Networks
• Chapter 7: Use Cases of Neural Networks
• Advanced Topics
• Index.

Uncover the power of artificial neural networks by implementing them through R code. About This Book * Develop a strong background in neural networks with R, to implement them in your applications * Build smart systems using the power of deep learning * Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn * Set up R packages for neural networks and deep learning * Understand the core concepts of artificial neural networks * Understand neurons, perceptrons, bias, weights, and activation functions * Implement supervised and unsupervised machine learning in R for neural networks * Predict and classify data automatically using neural networks * Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
(source: Nielsen Book Data)

• Intro; Preface; Contents; List of Figures; List of Tables; 1 On Using Fuzzy Logic to Control a Simulated Hexacopter Carrying an Attached Pendulum; 1.1 Introduction; 1.2 Description of the Controlled Plant: The Hexacopter; 1.3 Proposed Multi-layer Controller; 1.3.1 Pre-processing Phase; 1.3.2 Post-processing Phase; 1.4 The Fuzzy Controllers; 1.4.1 The Fuzzy Method; 1.4.2 Roll Stabilization; 1.4.3 Pitch Stabilization; 1.4.4 Heading to Goal (Yaw Controller); 1.4.5 The Horizontal Navigation; 1.4.6 The Vertical Navigation; 1.5 Experiment and Results; 1.5.1 Overview of the Experiment; 1.5.2 Results

• 1.5.3 Discussion1.6 Conclusions and Future Work; References; 2 Monocular Pose Estimation for an Unmanned Aerial Vehicle Using Spectral Features; 2.1 Introduction; 2.2 Homography-Based Pose Estimation; 2.2.1 Review of Plane-Induced Homography; 2.2.2 Homography Decomposition; 2.3 Spectral Features Correspondence; 2.3.1 Corresponding Points; 2.4 Implementation and Results; 2.4.1 Spectral Features Evaluation; 2.4.2 Pose Estimation in Simulated Quadcopter; 2.5 Conclusions; References; 3 Simultaneous Navigation and Mapping in an Autonomous Vehicle Based on Fuzzy Logic; 3.1 Introduction

• 3.2 Virtual Robot Experimentation Platform3.3 Autonomous Vehicle; 3.4 Navigation and Mapping; 3.4.1 Trajectory Planning; 3.4.2 Navigation; 3.4.3 Decision Tree; 3.5 Simulation; 3.6 Case Study; 3.7 Conclusions; References; 4 Fully Scalable Parallel Hardware for Wheeled Robot Navigation Using Fuzzy Control; 4.1 Introduction; 4.2 Fuzzy Controlllers; 4.3 Fuzzy Models for Wheeled Robot Navigation; 4.4 The Proposed Macro-architecture; 4.5 Micro-architecture of the Functional Units; 4.5.1 Membership Function Unit; 4.5.2 Membership Function Memory; 4.5.3 Fuzzification Unit; 4.5.4 Inference Unit

• 4.5.5 Defuzzification Unit4.6 Performance Results; 4.7 Conclusion; References; 5 Nonlinear Correction for an Energy Estimator Operating at Severe Pile-Up Conditions; 5.1 Introduction; 5.2 High-Energy Signal Pile-Up; 5.3 Online Linear Estimation; 5.3.1 Minimum Variance Linear Unbiased Estimator; 5.3.2 Maximum Likelihood Estimation; 5.4 Nonlinear Correction; 5.5 Performance Comparisons; 5.5.1 Data Set; 5.5.2 Nonlinear Corrector Design; 5.5.3 Efficiency Tests; 5.5.4 Bias and Linearity; 5.5.5 Performance at Different Occupancy Levels; 5.5.6 Exploring the ANN Generalization; 5.6 Conclusions

• 8 Human
• Machine Musical Composition in Real-Time Based on Emotions Through a Fuzzy Logic Approach

This book discusses a number of real-world applications of computational intelligence approaches. Using various examples, it demonstrates that computational intelligence has become a consolidated methodology for automatically creating new competitive solutions to complex real-world problems. It also presents a concise and efficient synthesis of different systems using computationally intelligent techniques.

• Automated Negotiation Strategy and Model
• Applications of Automated Negotiations
• Automated Negotiating Agent Competition 2014.

This book covers recent advances in Complex Automated Negotiations as a widely studied emerging area in the field of Autonomous Agents and Multi-Agent Systems. The book includes selected revised and extended papers from the 7th International Workshop on Agent-Based Complex Automated Negotiation (ACAN2014), which was held in Paris, France, in May 2014. The book also includes brief introductions about Agent-based Complex Automated Negotiation which are based on tutorials provided in the workshop, and brief summaries and descriptions about the ANAC'14 (Automated Negotiating Agents Competition) competition, where authors of selected finalist agents explain the strategies and the ideas used by them. The book is targeted to academic and industrial researchers in various communities of autonomous agents and multi-agent systems, such as agreement technology, mechanism design, electronic commerce, related areas, as well as graduate, undergraduate, and PhD students working in those areas or having interest in them.

• A Stylized Software Model to Explore the Free Market Equality/Efficiency Tradeoff / Hugues Bersini, Nicolas van Zeebroeck
• Towards a Dynamic Negotiation Mechanism for QoS-Aware Service Markets / Claudia Di Napoli, Paolo Pisa, Silvia Rossi
• D3S -- A Distributed Storage Service / Rui Pedro Lopes, Pedro Sernadela
• Evaluation of the Color-Based Image Segmentation Capabilities of a Compact Mobile Robot Agent Based on Google Android Smartphone / Dani Martínez, Javier Moreno, Davinia Font
• Distributed and Specialized Agent Communities / Jesús Ángel Román, Sara Rodríguez
• A Gateway Protocol Based on FIPA-ACL for the New Agent Platform PANGEA / Alejandro Sánchez, Gabriel Villarrubia
• Applying Classifiers in Indoor Location System / Gabriel Villarubia, Francisco Rubio
• Geo-localization System for People with Cognitive Disabilities / João Ramos, Ricardo Anacleto, Paulo Novais
• Adding Sense to Patent Ontologies: A Representation of Concepts and Reasoning / Maria Bermudez-Edo, Manuel Noguera
• Representation of Clinical Practice Guideline Components in OWL / Tiago Oliveira, Paulo Novais, José Neves
• Dynamically Maintaining Standards Using Incentives / Ramón Hermoso, Henrique Lopes Cardoso.

• Self-organizing Prediction in Smart Grids through Delegate Multi-Agent Systems / Leo Rutten, Paul Valckenaers
• Holonic Recursiveness with Multi-Agent System Technologies / Sonia Suárez, Paulo Leitao, Emmanuel Adam
• Intelligent Energy Management System for the Optimization of Power Consumption / Vicente Botón-Fernández, Máximo Pérez Romero
• Find It -- An Assistant Home Agent / Ângelo Costa, Ester Martinez-Martin
• Efficient People Counting from Indoor Overhead Video Camera / Juan Serrano-Cuerda, José Carlos Castillo
• Modeling Intelligent Agents to Integrate a Patient Monitoring System / Gabriel Pontes, Carlos Filipe Portela
• Towards Self-Explaining Agents / Johannes Fähndrich, Sebastian Ahrndt
• Semi-automated Generation of Semantic Service Descriptions / Nils Masuch, Philipp Brock, Sahin Albayrak
• Patience in Group Decision-Making with Emotional Agents / Denis Mušić
• Analysis of Web Usage Data for Clustering Based Recommender System / Shafiq Alam, Gillian Dobbie, Patricia Riddle
• Multi-label Classification for Recommender Systems / Dolly Carrillo, Vivian F. López
• If It's on Web It's Yours! / Abdul Mateen Rajput
• User Assistance Tool for a WebService ERP / Israel Carlos Rebollo Ruiz
• TV-SeriesRec: A Recommender System Based on Fuzzy Associative Classification and Semantic Information / Diego Sánchez-Moreno, Ana Belén Gil.

This book brings together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on the application of multi-agent systems, to discuss and debate the major issues, and to showcase the latest systems using agent based technology.

• Information: The New Kid on the Block
• Shannon (1916-2001)
• Little Boxes that Reason and Learn
• Networks with Internal States
• Information Integration: The Measure of Consciousness
• Automata and Information Integration
• The Philosophy of Information
• The Structure of the Informational Mind
• Language and Information
• The Secret State: Freud and Automata
• Detractors and Open Minds
• Conclusion: Aristotle's Laptop.
• (source: Nielsen Book Data)

Aristotle's convincing philosophy is likely to have shaped (even indirectly) many of our current beliefs, prejudices and attitudes to life. This includes the way in which our mind (that is, our capacity to have private thoughts) appears to elude a scientific description. This book is about a scientific ingredient that was not available to Aristotle: the science of information. Would the course of the philosophy of the mind have been different had Aristotle pronounced that the matter of mind was information? This "mind is information" assertion is often heard in contemporary debates, and this book explores the verities and falsehoods of this proposition.
(source: Nielsen Book Data)

• Ensemble Learning
• Boosting Algorithms: A Review of Methods, Theory, and Applications
• Boosting Kernel Estimators
• Targeted Learning
• Random Forests
• Ensemble Learning by Negative Correlation Learning
• Ensemble Nyström
• Object Detection
• Classifier Boosting for Human Activity Recognition
• Discriminative Learning for Anatomical Structure Detection and Segmentation
• Random Forest for Bioinformatics.

Annotation It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed ensemble learning by researchers in computational intelligence and machine learning, it is known to improve a decision systems robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as boosting and random forest facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

• FOREWORD ix PREFACE xi CONTRIBUTORS xiii PART I INTRODUCTION 1
• 1 Mobile Agents and Applications in Networking and Distributed Computing 3 PART II PRINCIPLES OF APPLYING MOBILE AGENTS 17
• 2 Mobile Agent Communications 19
• 3 Distributed Security Algorithms for Mobile Agents 41
• 4 Mobile Agent Coordination 71
• 5 Cooperating Mobile Agents 93 PART III MOBILE AGENT BASED TECHNIQUES AND APPLICATIONS 127
• 6 Network Routing 129
• 7 Resource and Service Discovery 161
• 8 Distributed Control 189
• 9 Distributed Databases and Transaction Processing 219
• 10 Mobile Agents in Mobile and Wireless Computing 243 PART IV DESIGN AND EVALUATION 263
• 11 Naplet: Microkernel and Pluggable Design of Mobile Agent Systems 265
• 12 Performance Evaluation of Mobile Agent Platforms and Comparison with Client--Server Technologies 299 Index 323.
• (source: Nielsen Book Data)

The book focuses on mobile agents, which are computer programs that can autonomously migrate between network sites. This text introduces the concepts and principles of mobile agents, provides an overview of mobile agent technology, and focuses on applications in networking and distributed computing.
(source: Nielsen Book Data)

• Fuzzy relational calculus and its wide range of applications / Etienne E. Kerre
• Decision support and warning systems for business intelligence / Jie Lu
• A review of developments from fuzzy rule bases to fuzzy functions / I. Burhan Turksen
• Can fuzzy logic formalism bring complex environmental issues into focus? / Ashok Deshpande
• Evaluation of manufacturing technology of photovoltaic cells / E. Cables Perez ... [et al.]
• A multicriteria dynamic flow model for relief operations / G. Tirado, B. Vitoriano, M. T. Ortuno
• Future oriented positioning analysis with Bayesian networks / Burak Dereli, Umut Asan, Cigdem Kadaifci
• Aggregation procedure based on Choquet Integral for evaluating company's environmental practices / R. De Andres Calle, T. Gonzalez-Arteaga
• A fuzzy linguistic evaluation model of digital libraries based on the LibQUAL+ methodology / F. J. Cabrerizo ... [et al.]
• A location-aware tourism recommender system based on mobile devices / Jose M. Noguera ... [et al.]
• Web based support system for integral performance appraisal / M. Espinilla ... [et al.]
• Consensus in preference-approvals: a weighted distance approach / J. L. Garcia-Lapresta ... [et al.]
• A fuzzy envelope for hesitant fuzzy linguistic term sets based on Choquet integral / R. De Andres Calle ... [et al.]
• Analysis of the energy service market in Turkey using the fuzzy DEMATEL method / Ecem Basak, Umut Asan, Cigdem Kadaifci
• Replacement decisions for reliability centered maintenance / D. Ozgur-Unluakin, T. Bilgic
• A new project planning model for better handling vagueness and uncertainty of real world tasks with AHP and fuzzy numbers / Sibar Kaan Manga
• Employing cardinal rank ordering of criteria in multi-criteria decision analysis / Mona Riabacke ... [et al.]
• Fuzzy inference system for net present value analysis of construction projects / Alp Ustundag, Emre Cevikcan
• Combining different inference methods for medical decision support systems / Guven Kose ... [et al.]
• Decision making for concrete evaluation using fusion of NDT techniques / Z. M Sbartai, V. Garnier, M. A. Ploix
• Multi-period decision making with one-shot decision theory / Peijun Guo, Yonggang Li
• Preference intensity in MCDM when an additive utility function represents DM preferences / A. Mateos ... [et al.]
• Fuzzy TOPSIS with linguistic evaluations using [symbol]-cuts / Seza Ozge Gonen, Serdar Baysan
• An evolutionary algorithm for integer multicriteria optimization (EVALIMCO) / Vassil Guliashki, Leoneed Kirilov, Krasimira Genova
• The applications of energy alternatives in turkey using multicriteria decision making processes / Abit Balin, Pelin Alcan, Huseyin Basligil
• A MCDM model for energy policy evaluation / Mehmet Kabak, Metin Dagdeviren, Serhat Burmaoglu
• Project selection by using fuzzy TOPSIS method: a real application in construction sector / Bersam Bolat ... [et al.].

• Location selection of emergency logistics centers using an integrated DEMATEL-ANP approach / Umut Rifat Tuzkaya, Kadriye Busra Yilmazer, Gulfem Tuzkaya
• Garage location selection for public transportation system in Istanbul using fuzzy AHP and fuzzy axiomatic design techniques / Ozge Nalan Alp ... [et al.]
• Evaluation of criteria to select appropriate candidates for surgical sciences / Ipek Nur Aksu, F. Tunc Bozbura, Ahmet Beskese
• An extended method for fuzzy multiple attribute group decision-making based on interval type-2 fuzzy sets / Zafer Eren, C. Erhan Bozdag
• Group MCDM method with alternative voting system based on multi-information environment / Chen-Tung Chen ... [et al.]
• A ranking methodology using hierarchical grey relational analysis: the case of Turkish banking system / Mehmet Emin Baysal, Busra Aksu
• An artificial bee colony algorithm for solving nurse scheduling problems / Ahmet Sarucan, Kadir Buyukozkan
• A greedy algorithm for multiobjective fuzzy flow-shop scheduling problem / Orhan Engin ... [et al.]
• A group decision making model with intuitionistic fuzzy preference relations / Hulya Behret
• Fuzzy analytic hierarchy process with type-2 fuzzy sets / Cengiz Kahraman, Irem Ucal Sari, Ebru Turanoglu
• Fuzzy DEA approach to solve the location selection of wind energy / Nihal Erginel, Merve Yalcinkaya, Sevil Senturk
• An integrated methodology combining fuzzy goal programming and fuzzy AHP: case of ELT in Turkey / Yesim Kop, Mujde Erol Genevois, Hakki Ziya Ulukan
• Prioritizing the stakeholder requirements in QFDE with FAHP / Ilke Bereketli, Mujde Erol Genevois
• Fuzzy multi attribute valuation of software projects in banking via fuzzy real options using balanced scorecard / Ozlem Afacan, A. Cagri Tolga
• A cognitive group decision support system for projects evaluation / Fahimeh Ramezani, Jie Lu
• Fuzzy forecasting of interest rates in investment decisions / Irem Ucal Sari, Cengiz Kahraman
• Performance evaluation of Turkish retail firms using the fuzzy AHP, PROMETHEE, ELECTRE and VIKOR methods / Ufuk Bolukbas, Erkan Celik, Ali Fuat Guneri
• Fuzzy axiomatic design simulation for evaluating the conceptual aircraft designs / Ahmet Kandakoglu, Cengiz Kahraman, Ilker Topcu
• Criteria weighing and 4P's planning in marketing using a fuzzy metric distance and AHP hybrid method / Tuncay Gurbuz, Y. Esra Albayrak, Elif Alaybeyoglu
• Evaluation of urban regeneration strategies using VIKOR Method: the case of Istanbul
• Galata district / Nihan C. Demirel, Omer Onur Kaya, Tufan Demirel
• Weighting the criteria of green supply chain management for supplier selection in automotive industry using fuzzy ANP / Serhat Tuzun, Tufan Demirel
• Modelling a pay structure by using grey relations analysis integrated with fuzzy AHP / Ahmet Can Kutlu
• An integrated multi-criteria decision making methodology for facility location selection problem under fuzzy environment: application in cement sector / Irem Otay, Ferhan Cebi.

• A decision support system for two-echelon service supply chains / Baris Selcuk
• A fuzzy analytic hierarchy process methodology for the supplier selection problem / Pinar Mizrak Ozfirat, Gokcecicek Tuna, Gonca Tuncel
• Fuzzy modeling and optimization / Angel Garrido, Piedad Yuste
• Analyzing maturity levels of Turkish SMEs in respect to ERP and lean manufacturing applications: a relational model / Ufuk Cebeci, Cagatay Iris
• A possibilistic mathematical programming approach for backbone selection and demand allocation problem in telecom networks / H. H. Turan, N. Sener
• A modified QFD technique for product R&D / Selcuk Cebi, Emrullah Demirci
• An incremental collaborative filtering algorithm for recommender systems / Maytiyanin Komkhao ... [et al.]
• A judgement method for earthquake early warning information / Hongchang An, Jie Lu, Guangquan Zhang
• Medical skills assessment in training based on virtual reality using a possibilistic approach / Liliane S. Machado, Ronei M. Moraes
• A novel class-attribute interdependency discretization algorithm / Hongliang Zheng, Li Zou, Deqin Yan
• The task of minimum cost flow finding in transportation networks in fuzzy conditions / Alexandr Bozhenyuk, Evgeniya Gerasimenko, Igor Rozenberg
• Path identifying in points-to analysis for Java with answer set programming / Yang Bo, Zhang Ying, Zhang Ming-Yi
• A heuristic based on multi objective linear programming under fuzziness for the vehicle routing problem / Gulcin Dinc Yalcin, Nihal Erginel
• Rank reversals and testing of pairwise comparisons based non- numerical rankings / Ryszard Janicki, Yun Zhai
• Modeling a boiling process by means of a Takagi-Sugeno-fuzzy-model / Daniel Fiss, Michael Wagenknecht, Rainer Hampel
• A new improved bat algorithm for fuzzy interactive multi-objective economic/emission dispatch with load and wind power uncertainty / Rasoul Azizipanah-Abarghooee, Taher Niknam
• Handling nonlinear large-scale problems with modified firefly algorithm / Bahman Bahmanifirouzi, Abdollah Kavousi Fard, Taher Niknam
• An efficient fuzzy programming approach to the vehicle routing problem with soft time windows / Sezgin Kilic, Sezgin Kaplan
• Enhance green supply chain management capability to develop an eco-friendly enterprise: a fuzzy QFD approach / Ching-Torng Lin
• Agent-based modelling and simulation through video observation analysis / Shahrol Mohamaddan, Keith Case
• A Bayesian framework for uncertainty formulation of engineering design process / M. Rajabalinejad, C. Spitas
• Quantum gates and active set theory of agents / Germano Resconi, Chris Hinde
• Mixture augmented Lagrange multiplier method for tensor recovery / Huachun Tan ... [et al.]
• Asymptotic results for the ergodic distribution of an inventory model with fuzzy demands / Tahir Khaniyev , Cihan Aksop, Basak Gever
• Process chain model for corporate social responsibility projects / Funda Cinar, Esra Bas.

• Multi mode resource constrained project scheduling problems with solving taboo search / Omer Atli, Cengiz Kahraman
• A power model of enterprise interests' allocation / Xiaohong Liu
• Evaluation of airport passenger terminals using fuzzy cognitive mapping / Cigdem Kadaifci, Ilker Topcu
• Implementation of maximin and maximal solutions for linear programming problems under uncertainty / Nathan Huntley ... [et al.]
• A New C x K
• nearest neighbor linkage approach to the classification problem / Gozde Ulutagay, Efendi Nasibov
• Analyzing competitiveness of automotive industry through cumulative belief degrees / Ozgur Kabak ... [et al.]
• Using surrogate models for process design and optimization / A. Shokry, A. D. Bojarski, A. Espuna
• A chance constrained programming model for shortest path problem with fuzzy constraints / Pinar Dursun, Erhan Bozdag
• Proactive and reactive scheduling with fuzzy activity times / Marek Janczura, Dorota Kuchta
• Using fuzzy set theory for water quality evaluation of Wulihu Lake, China / Yuankun Wang ... [et al.]
• Newsvendor problem with one-shot decision theory / Peijun Guo, Xiuyan Ma
• A two-phased additive approach for multiple objective supplier selection with fuzzy demand level / Feyzan Arikan
• Selection of reverse logistics provider for a paper firm / Jihene Jlassi, Abederrahman El Mhamedi, Habib Chabchoub
• A fuzzy cognitive map application for causes of bullwhip effect in supply chains / Secil Ercan ... [et al.]
• Uncertainties and handling methods in supply chain: a literature review / Erhan Yazici, Murat Baskak, Gulcin Buyukozkan
• Uncertainty modelling in supply chain management: the trend in the use of fuzzy set theory / Ozgur Kabak
• Collaborative partner evaluation with multi-criteria decision making techniques / Jbid Arsenyan, Gulcin Buyukozkan
• The real algebraic normal form: fuzzy boolean logic satisfying [symbol]f = 0 / Michael Vielhaber
• Multi-expert decision making using logical aggregation / Ana Poledica, Aleksandar Rakicevic, Dragan Radojevic
• On evaluations of propositional formulas whose range is a subset of some fixed countable ordered field / Dragan Doder ... [et al.]
• Logical aggregation for modular neural networks in currency exchange rate forecasting / Ivan Nesic ... [et al.]
• Extending AHP with boolean consistent fuzzy logic and its application in web service selection / Ivana Dragovic, Nina Turajlic, Dragan Radojevic
• Cut systems in [symbol]-sets and reflections / Jiri Mockor
• Duality principles in residuated lattices / Irina Perfilieva
• Interpolativity of "at least-at most" models of monotone fuzzy rule bases: multiple-input case / Martin Stepnicka, Balasubramaniam Jayaram
• A method for solving fuzzy relation equations in complete Brouwerian lattices / Xiaobing Qu, Xue-Ping Wang, Feng Sun
• Fuzzy transform for image reconstruction / Irina Perfilieva, Pavel Vlasanek, Michaela Wrublova.

• Remarks to model theory in higher order fuzzy logic / Vilem Novak
• Interval-valued fuzzy relational equations with min-implication / Qing-Quan Xiong, Xue-Ping Wang
• Fuzzy lattice reasoning (FLR) classifier for human facial expression recognition / S. E. Papadakis, V. G. Kaburlasos, G. A. Papakostas
• Fuzzy cosets of fuzzy subgroups relative to fuzzy subgroups / Lan Shu, Zhaohao Wang
• Two topological operators of fuzzy finite automata / Zhiwen Mo, Na Feng
• On fuzzy rough concept lattices / Chang Shu, Zhi Wen Mo
• Study on the N-inequality in the lattice implication algebra*II / Long Xiqing, Xu Yang, He Xingxing
• The product of quantum automata / Mo Zhi-Wen, Chen Qing-Lei, Liu Jun
• Image matching based on medium similarity measure / Ningning Zhou, Long Hong
• Feature-based off-line recognition of degraded numeral characters / Weiqing Cheng ... [et al.]
• On measure method to the two-phase uncertainty and its application / Yulong Deng, Long Hong
• Filters and deductive systems on residuated multilattices / I. P. Cabrera ... [et al.]
• A-multi ary semantic resolution methods based on lattice-valued propositional logic LP(X) / Yi Liu, Xiaoyan Qin, Yang Xu
• An optimal spectrum allocation scheme based on Stackelberg game in Macro/Femtocell hierarchical networks / Peng Xu, Yang Xu
• A study on fuzzy D-implications / A. G. Hatzimichailidis, G. A. Papakostas, V. G. Kaburlasos
• Research advances on resolution automated reasoning in lattice-valued logic based on lattice implication algebra / Yang Xu ... [et al.]
• General form of A-linear resolution method based on lattice-valued propositional logic system LP(X) / Weitao Xu ... [et al.]
• N-truth degrees of formulae in two-valued predicate logic / Qin Xiaoyan, Liu Yi, Xu Yang
• Auto-associative memories based on complete inf-semilattices / Peter Sussner, Carlos Renato Medeiros
• An extended variable consistency model under dominance-based rough sets / Dun Liu, Tianrui Li
• Lifting quasi-filters of lattice implication algebras / Xiaodong Pan, Xiaohong Liu
• Intuitionistic fuzzy propositional system based on linguistic truth-valued intuitionistic fuzzy lattice / Li Zou, Xin Liu, Dengen Huang
• Fuzzy membership function construction based on multi-valued evaluation / Vasile Patrascu
• Interval identification of uncertain nonlinear systems using belief rule-based systems / Yu-Wang Chen, Dong-Ling Xu, Jian-Bo Yang
• Construction of strong equality index from implication operators / H. Bustince ... [et al.]
• On derivations of lattice implication algebras / Hua Zhu ... [et al.]
• Some remarks on a numerical representation theorem by Newman and Read / J. C. Candeal, E. Indurain, G. B. Mehta
• Conditional Choquet expectation / Hongxia Wang, Shoumei Li, Juan Gao
• Ascribing strong causes from observational and interventional data under the belief function framework / Imen Boukhris, Salem Benferhat, Zied Elouedi.

• SMOTE-FRST: a new resampling method using fuzzy rough set theory / E. Ramentol ... [et al.]
• Dealing with natural language interfaces in a geolocation context / Mohammed-Amine Abchir, Isis Truck, Anna Pappa
• Induced and heavy aggregation operators / Jose M. Merigo, Yejun Xu
• Forecasting high frequency financial data: statistical and fuzzy logic RBF ANN approach / Dusan Marcek
• Statistical models, SV regression models and soft neural networks: an application to the input-output function estimation of sales processes / Dusan Marcek
• A hybrid approach to selecting informative constraints for semi-supervised clustering / Xianhua Ni, Yan Yang
• Analysis of the impacts of the data length on the uncertainty of the Archimedean copula modeling / Tong Xin, Wang Dong, Chen Yuanfang
• An initial research to segment a crowdsourcing project participants using text mining / Basar Oztaysi, Lara Cemcem
• Spare part demand forecasting with Bayesian model / Sinan Apak
• Decision making in complex environments with uncertain generalized unified aggregation operators / Jose M. Merigo
• Software cost estimation by fuzzy analogy for ISBSG repository / Ali Idri, Fatima Azzahra Amazal
• Probabilistic kernel ranking / Bernd-Jurgen Falkowski
• Probabilistic operation management for a microgrid based on point estimate method and modified gravitational search algorithm / Masoud Jabbari, Faranak Golestaneh, Taher Niknam
• Effectiveness evaluation of complex networks: a simulation-based algorithm / Wuhong Wang ... [et al.]
• A new "implicit" parameter estimation for conditional Gaussian Bayesian networks / Aida Jarraya, Afif Masmoudi, Philippe Leray
• Dissimilarity-based bipolar supervised classification / J. Tinguaro Rodriguez, Javier Montero, Begona Vitoriano
• Using group method of data handling for brain computer interface / Ravikanth Varaganti, Vitaly Schetinin
• UApriori: an algorithm for finding sequential patterns in probabilistic data / Metanat Hooshsadat ... [et al.]
• Seasonal variation of Turkey: a consensus clustering approach / Ayca Yetere Kursun ... [et al.]
• Spatial analysis based method for detection of data traffic problems in computer networks / Grzegorz Kolaczek
• Uncertainty and a new measure for classification uncertainty / Haluk Damgacioglu, Cem Iyigun
• A new process monitoring control chart / Su-Fen Yang, Chung-Ming Yang
• Forecasting future enrollments to universities of North Cyprus through trend centrality based on fuzzy time series / Ebru Turanoglu, Ozlem Senvar
• Fuzzy expert system for forecasting return amount in reverse logistics / Gul Tekin Temur, Muhammet Balcilar, Bersam Bolat
• Rainfall-runoff modeling using artificial neural networks and adaptive neuro-fuzzy inference system models / Gholamali Ghafari, Mehdi Vafakhah
• Fuzzy rule-based demand forecasting for dynamic pricing / Ozlem Cosgun, Yeliz Ekinci, Seda Ugurlu.

• Neural network and parameter production modeling: metaheuristics clustering methods / A. F. Bernate Lara ... [et al.]
• In-core sensors readings diagnostics based on neuro-fuzzy techniques / Stefan Figedy
• An approach for attribute reduction in set-valued ordered information systems based on information entropy / Chuan Luo ... [et al.]
• Hybrid simulated annealing/genetic algorithm approach to buffer allocation problem in unreliable production lines / Simge Yelkenci Kose, Ozcan Kilincci
• A substitute keyword suggestion method for search engine advertising / Qiang Wei ... [et al.]
• Supporting situation awareness using neural network and expert system / Mohsen Naderpour, Jie Lu
• Making intelligent decisions on noisy images / Gonzalo Farias ... [et al.]
• SME clustering based on innovation needs / Ayca Altay ... [et al.]
• Artificial neural network for discrete model order reduction with frequency selectivity / Othman M. K. Alsmadi, Zaer. S. Abo-Hammour, Adnan M. Al-Smadi
• Response of soil-structure interaction system using artificial neural network (ANN) / Badreddine Sbartai, Kamel Goudjil
• Recognition of individuals based on hand geometry / Silarbi Samiya, Bendahmane Abderrahmane, Benyettou Abdelkader
• An new belief rule base representation scheme and its generation by learning from examples / J. Liu ... [et al.]
• Estimation of prediction intervals of neural network models by a multi-objective genetic algorithm / Ronay Ak, Yanfu Li, Enrico Zio
• Load average forecast: a new monitor to analyse instability in computer systems / Victoria Lopez, Guadalupe Minana
• Computation of component importance measures for reliability of complex systems / Qamar Mahboob ... [et al.]
• On the revision to the result of finance early-warning logit model from the perspective of efficiency / Shuangjie Li, Shao Wang
• Fuzzy AHP/DEA approach for relative efficiency of state university in Turkey / Ahmet Calik, Nimet Yapici Pehlivan, Ahmet Pekgor
• Improving the computational complexity and weights dispersion in fuzzy DEA / Saber Saati, Adel Hatami-Marbini
• General DEA with fuzzy data / Hilda Saleh, Mohsen Rostamy-Malkhalifeh, Zohreh Moghaddas
• Efficiency analysis under imprecise inputs and outputs reduction / Kobra Gholami ... [et al.]
• Resource allocation and target setting in interval data envelopment analysis / Zahra Ghelej Beigi ... [et al.]
• Project risk management taking into consideration the influence of various risk levels based on linguistic approach / Dorota Kuchta, Dariusz Skorupka
• Risk assessment of the Spanish national railway system / A. Mateos, A. Jimenez, H. J. Pichardo
• Performance evaluation of lot sizing strategy via discrete event simulation / Serdar Baysan, Cagatay Iris, Mehmet Mutlu Yenisey
• A heuristic approach for provider selection and task allocation model in telecommunication networks under stochastic QOS guaranties / Hasan Huseyin Turan, Nihat Kasap.

• A new architecture for assessment of multiple users in collaborative medical training environments based on virtual reality / Ronei M. Moraes, Liliane S. Machado
• Large-scale spatial grassland fire spread simulation based on GIS / Xingpeng Liu, Jiquan Zhang, Zhijun Tong
• Decomposing relation schemes of group-buying aggregators in E-commerce: a reverse engineering approach / Ming Ren, Qiang Wei
• A bibliometric exploration of the evolution of 3D technology / Gizem Intepe, Tufan Koc
• ANFIS model for vibration signals of an induction motor based on aging process decreasing the nonlinearity / D. Bayram, S. Seker
• Modelling the solid fraction transport of a fiber suspension with cellular automata / Stefan Kittan, Wolfgang Kaestner
• Reducing EMI in multiple-output DC converters with chaos control / Yuhong Song ... [et al.]
• An application of filled function method to the hardness property of Fe-Mn binary alloys / Ahmet Sahiner, Havva Gokkaya
• Shedding light on the underlying long-run price leadership: a study of US gasoline price data / Takamitsu Kurita
• Individual assessment model of the public spreading in the environmental pollution accident / Bin Luo, Xiaohong Liu, Xiaodong Pan
• Neutron power control of a TRIGA Mark III reactor using stable adaptive fuzzy control / Benitez-Read ... [et al.]
• A framework proposal upon the estimation of electronic-waste products in Istanbul / Tugba Efendigil ... [et al.]
• Variance reduction for financial options pricing / Semih Yon, Cafer Erhan Bozdag
• Learning capabilities and their effect on organizational capabilities: an empirical approach / Sezi Cevik Onar
• Ensemble of neural networks for detection and classification of faults in nuclear power systems / Roozbeh Razavi-Far, Piero Baraldi, Enrico Zio
• An area defuzzification technique and essential fuzzy rules for defuzzifying nuclear event failure possibilities into reliability data / Julwan Hendry Purba, Jie Lu, Guangquan Zhang
• Multivariable robust control system: application to a plasma reactor / Karima Chaker, Abdelkrim Moussaoui
• Application of genetic algorithms to the optimization of the robustness of controllers / Matilde Santos, Nicolas Antequera
• A hybrid ensemble approach for process parameter estimation in offshore oil platforms / Piero Baraldi ... [et al.]
• Heuristic GP optimization technique for design synthesis of monotonic objectives / Sayed M. Metwalli
• Quadcopter see and avoid using a fuzzy controller / M. A. Olivares-Mendez ... [et al.]
• Decision fuzzy system on the state of delayed vehicles / Matilde Santos, Manuel G. Romana
• Visual interpretation of fabric tactile properties using fuzzy inclusion degree and ANFIS / Zhebin Xue ... [et al.]
• Fuzzy logic assisted helicopter UAV flight control / A. J. Van Der Wal
• Making presentations using fuzzy logic in a tour-guide robot / J. Javier Rainer, Ramon Galan, Agustin Jimenez.

• A small world optimization for multiple objects optimization of mixed-model assembly line problem with parallel machine / Sukran Seker, Mesut Ozgurler
• A hybrid bees algorithm for solving a robotic assembly line balancing problem / Slim Daoud ... [et al.]
• Throughput analysis of two-machine-one-buffer line model: a comparative study / Yassine Ouazene, Alice Yalaoui, Hicham Chehade
• Mixed model assembly line balancing with subassembly selection in automotive industry / Rifat Gurcan Ozdemir, Ufuk Kula
• Matheuristics for the multi-item lot-sizing with vehicle routing problem / Heitor Liberalino, Christophe Duhamel, Alain Quilliot
• Scheduling job and preventive maintenance to obtain trade-off between Cmax and availability of 2-level flow-shop / F. Hnaien, F. Yalaoui
• A multi-objective method to solve a container terminal problem / Farah Belmecheri-Yalaoui, Farouk Yalaoui, Lionel Amodeo
• Fuzzy chance constrained programming for warehouse location problem with imprecise customer demand / Ceren Buket Kayi, Erhan Bozdag.

FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 10th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view.

• Title Page; Preface; BICA 2011 Conference Committees; Contents; Conference Papers and Extended Abstracts; Biologically Inspired Cognitive Architectures: One More Step Forward; Evaluating the Contribution of Top-Down Feedback and Post-Learning Reconstruction; Computational Modeling of Therapies Related to Cognitive Vulnerability and Coping; Natural Human Robot Meta-Communication Through the Integration of Android's Sensors with Environment Embedded Sensors; Towards a Biologically-Inspired Model for Relational Mapping Using Spiking Neurons.

This book presents the proceedings of the Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2011, which is also the Second Annual Meeting of the BICA Society, held in November 2011 in Arlington, Virginia, USA. A Biologically Inspired Cognitive Architecture (BICA) is a computational framework for the design of intelligent agents that incorporates formal mechanisms of human or animal cognition. Biology currently provides the only physical examples of cognitive systems at the level of robustness, flexibility, scalability and consciousness that artificial intel.

• Part I: Preliminaries and Literature Review.- Preliminaries.- Overview of Recent Research in Distributed Multi-agent Coordination.- Part II: Emergent Problems in Distributed Multi-agent Coordination.- Collective Periodic Motion Coordination.- Collective Tracking with a Dynamic Leader.- Containment Control with Multiple Leaders.- Part III: Emergent Models in Distributed Multi-agent Coordination.- Networked Lagrangian Systems.- Networked Fractional-order Systems.- Part IV Emergent Issues in Distributed Multi-agent Coordination.- Sampled-data Setting.- Optimality Aspect.- Time Delay.
• (source: Nielsen Book Data)

Distributed Coordination of Multi-agent Networks introduces problems, models, and issues such as collective periodic motion coordination, collective tracking with a dynamic leader, and containment control with multiple leaders, and explores ideas for their solution. Solving these problems extends the existing application domains of multi-agent networks; for example, collective periodic motion coordination is appropriate for applications involving repetitive movements, collective tracking guarantees tracking of a dynamic leader by multiple followers in the presence of reduced interaction and partial measurements, and containment control enables maneuvering of multiple followers by multiple leaders.
(source: Nielsen Book Data)

• Preface; Acknowledgements; 1. Introduction; 2. Statistical physics and phase transitions; 3. The satisfiability problem; 4. Constraint satisfaction problems; 5. Machine learning; 6. Searching the hypothesis space; 7. Statistical physics and machine learning; 8. Learning, SAT, and CSP; 9. Phase transition in FOL covering test; 10. Phase transitions and relational learning; 11. Phase transitions in grammatical inference; 12. Relationships with complex systems; 13. Phase transitions in natural systems; 14. Discussions and open issues; Appendix A. Phase transitions detected in two real cases; Appendix B. An intriguing idea; References; Index.

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.
(source: Nielsen Book Data)

• Prediction and Forecasting
• Fuzzy Neural Networks
• Optimization and Planning
• Pattern Recognition
• Signal and Image Processing
• Robotics and Control
• Transportation Systems
• Industrial Applications
• Real-World Applications.

This book is a part of the Proceedings of the Seventh International Symposium on Neural Networks (ISNN 2010), held on June 6-9, 2010 in Shanghai, China. ISNN 2010 received numerous submissions from about thousands of authors in about 40 countries and regions across six continents . Based on the rigorous peer-reviews by the program committee members and the reviewers, 108 high-quality papers were selected for publications in Lecture Notes in Electrical Engineering (LNEE) Proceedings. These papers cover all major topics of the engineering designs and applications of neural network research. In addition to the contributed papers, the ISNN 2010 technical program included four plenary speeches by Andrzej Cichocki (RIKEN Brain Science Institute, Japan), Chin-Teng Lin (National Chiao Tung University, Taiwan), DeLiang Wang (Ohio State University, USA), Gary G. Yen (Oklahoma State University, USA).

• Title page; Preface; BICA 2010 Conference Committees; Contents; Conference Papers and Extended Abstracts; Manifesto; Review; Subject Index; Author Index.

This book presents the proceedings of the First International Conference on Biologically Inspired Cognitive Architectures (BICA 2010), which is also the First Annual Meeting of the BICA Society. A cognitive architecture is a computational framework for the design of intelligent, even conscious, agents. It may draw inspiration from many sources, such as pure mathematics, physics or abstract theories of cognition. A biologically inspired cognitive architecture (BICA) is one which incorporates formal mechanisms from computational models of human and animal cognition, which currently provide the o.

• Table of Contents; Foreword; Preface; Who This Book Is For; How This Book Is Organized; What You Need to Use This Book; This Book's Website; Conventions Used in This Book; Using Code Examples; Safari® Books Online; Comments and Questions; Acknowledgments; Author Note for Third Printing, August 2011; Entity Framework 4.1 (Code First and DbContext) Has Released; Entity Framework June 2011 CTP;
• Chapter 1. Introducing the ADO.NET Entity Framework; The Entity Relationship Model: Programming Against a Model, Not the Database; The Entity Data Model: A Client-Side Data Model

• Entities: Blueprints for Business ClassesThe Backend Database: Your Choice; Database Providers; Access and ODBC; Entity Framework Features: APIs and Tools; Metadata; Entity Data Model Design Tools; Database-first design; Model-first design; Code generation; Object Services; POCO Support; Change Tracking; Relationship Management and Foreign Keys; Data Binding; n-Tier Development; EntityClient; The Entity Framework and WCF Services; What About ADO.NET DataSets and LINQ to SQL?; DataSets; LINQ to SQL; Entity Framework Pain Points Are Fading Away; Programming the Entity Framework

• Chapter
• 2. Exploring the Entity Data ModelWhy Use an Entity Data Model?; The EDM Within the Entity Framework; Walkthrough: Building Your First EDM; Inspecting the EDM in the Designer Window; Entity Container Properties; Entity Properties; Entity Property Properties; The Model's Supporting Metadata; Viewing the Model in the Model Browser; Viewing the Model's Raw XML; CSDL: The Conceptual Schema; EntityContainer; EntitySet; EntityType; The Key element; The Property elements; The navigation properties; Associations; Navigation Property; Navigation Properties That Return Collections

• SSDL: The Store SchemaMSL: The Mappings; Database Views in the EDM; Summary;
• Chapter 3. Querying Entity Data Models; Query the Model, Not the Database; Your First EDM Query; Where Did the Context and Classes Come From?; The ObjectContext class, SampleEntities; The entity classes; Querying with LINQ to Entities; Writing Your First LINQ to Entities Query; Querying with Object Services and Entity SQL; Why Another Way to Query?; Entity SQL; Entity SQL canonical functions; The Parameterized ObjectQuery; Querying with Methods; Querying with LINQ Methods; Chaining methods

• Querying with Query Builder Methods and Entity SQLSpecifying the control variable; The Shortest Query; ObjectQuery, ObjectSet, and LINQ to Entities; Querying with EntityClient to Return Streamed Data; EntityConnection and the Connection String; EntityCommand; ExecuteReader; Forward-Only Access to the Fields; Translating Entity Queries to Database Queries; Pay Attention to the .NET Method's Impact on Generated SQL; Avoiding Inadvertent Query Execution; Summary;
• Chapter 4. Exploring LINQ to Entities in Greater Depth; Getting Ready with Some New Lingo; Projections in Queries

Get a thorough introduction to ADO.NET Entity Framework 4 -- Microsoft's core framework for modeling and interacting with data in .NET applications. The second edition of this acclaimed guide provides a hands-on tour of the framework latest version in Visual Studio 2010 and .NET Framework 4. Not only will you learn how to use EF4 in a variety of applications, you'll also gain a deep understanding of its architecture and APIs. Written by Julia Lerman, the leading independent authority on the framework, Programming Entity Framework covers it all -- from the Entity Data Model and Object Services to WCF Services, MVC Apps, and unit testing. This book highlights important changes for experienced developers familiar with the earlier version. * Understand the core concepts you need to make the best use of the EF4 in your applications * Learn to query your data, using either LINQ to Entities or Entity SQL * Create Windows Forms, WPF, ASP.NET Web Forms, and ASP.N ET MVC applications * Build and consume WCF Services, WCF Data Services, and WCF RIA Services * Use Object Services to work directly with your entity objects * Create persistent ignorant entities, repositories, and write unit tests * Delve into model customization, relationship management, change tracking, data concurrency, and more * Get scores of reusable examples -- written in C# (with notes on Visual Basic syntax) -- that you can implement right away.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Concepts
• 3. Models
• 4. Quality
• 5. Agent programming platforms and languages
• 6. View point framework
• 7. Agent-oriented methodologies
• 8. Industry-related applications
• 9. Intelligent lifestyle applications
• 10. An e-learning application.

A new approach for conceptualizing and modeling multi-agent systems that consist of people, devices, and software agents. Today, when computing is pervasive and deployed over a range of devices by a multiplicity of users, we need to develop computer software to interact with both the ever-increasing complexity of the technical world and the growing fluidity of social organizations. The Art of Agent-Oriented Modeling presents a new conceptual model for developing software systems that are open, intelligent, and adaptive. It describes an approach for modeling complex systems that consist of people, devices, and software agents in a changing environment (sometimes known as distributed sociotechnical systems). The authors take an agent-oriented view, as opposed to the more common object-oriented approach. Thinking in terms of agents (which they define as the human and man-made components of a system), they argue, can change the way people think of software and the tasks it can perform. The book offers an integrated and coherent set of concepts and models, presenting the models at three levels of abstraction corresponding to a motivation layer (where the purpose, goals, and requirements of the system are described), a design layer, and an implementation layer. It compares platforms by implementing the same models in four different languages; compares methodologies by using a common example; includes extensive case studies; and offers exercises suitable for either class use or independent study.
(source: Nielsen Book Data)

• Foreword by Emile Aarts Foreword by Gary T. Marx Acknowledgements Preface An Executive Summary for Hasty Readers 1 Introduction 1.1 From ubiquitous computing to ambient intelligence 1.2 Challenges from the deployment of ambient intelligence 1.3 Challenges from ambient intelligence for EUpolicy-making 1.4 The challenges of this book 2 The brave new world of ambient intelligence 2.1 Enabling technologies 2.1.1 Ubiquitous computing 2.1.2 Ubiquitous communications 2.1.3 User-friendly interfaces 2.1.4 mbedded intelligence 2.1.5 Sensors and actuators 2.2 AmI visions 2.3 Scenarios 2.4 Roadmaps 2.5 Strategic research agendas 2.6 Platforms 2.7 Projects 2.8 Prospects 3 Dark scenarios 3.1 Creating and analysing dark scenarios 3.1.1 Framing the scenario 3.1.2 Identifying the technologies and/or devices 3.1.3 Identifying the applications 3.1.4 Drivers 3.1.5 Issues 3.1.6 Legal synopsis 3.1.7 Conclusions 3.2 Scenario
• 1: The AmI family 3.2.1 The scenario script 3.2.2 Analysis 3.2.3 The context 3.2.4 AmI technologies and devices 3.2.5 AmI applications 3.2.6 Drivers 3.2.7 Issues 3.2.8 Legal synopsis 3.2.9 Conclusions 3.3 Scenario
• 2: A crash in AmI space 3.3.1 The scenario script 3.3.2 Analysis 3.3.3 The context 3.3.4 AmI technologies and devices 3.3.5 AmI applications 3.3.6 Drivers 3.3.7 Issues 3.3.8 Legal synopsis 3.3.9 Conclusions 3.4 Scenario
• 3: What's an AmI data aggregator to do? 3.4.1 The scenario script 3.4.2 Analysis 3.4.3 The context 3.4.4 AmI technologies and devices 3.4.5 AmI applications 3.4.6 Drivers 3.4.7 Issues 3.4.8 Legal synopsis 3.4.9 Conclusions 3.5 Scenario
• 4: An early morning TV programme reports on AmI 3.5.1 The scenario script 3.5.2 Analysis 3.5.3 The context 3.5.4 AmI technologies and devices 3.5.5 Applications 3.5.6 Drivers 3.5.7 Issues Contents 3.5.8 Legal synopsis 3.5.9 Conclusions 4 Threats and vulnerabilities 4.1 Privacy under attack 4.2 Identity: Who goes there? 4.3 Can I trust you? 4.4 An insecure world 4.5 The looming digital divide 4.6 Threats today and tomorrow too 4.6.1 Hackers and malware 4.6.2 Identity theft 4.6.3 Penetration of identity management systems 4.6.4 Function creep 4.6.5 Exploitation of linkages by industry and government 4.6.6 Surveillance 4.6.7 Profiling 4.6.8 Authentication may intrude upon privacy 4.7 Lots of vulnerabilities 4.7.1 System complexity, false positives and unpredictable failures 4.7.2 Lack of user-friendly security and configuration software 4.7.3 Personal devices: networking with limited resources 4.7.4 Lack of transparency 4.7.5 High update and maintenance costs 4.7.6 Uncertainties about what to protect and the costs of protection 4.7.7 Misplaced trust in security mechanisms 4.7.8 Lack of public awareness or concern about privacy rights 4.7.9 Lack of enforcement and erosion of rights 4.7.10 People do not take adequate security precautions 4.7.11 Loss of control and technology paternalism 4.7.12 Dependency 4.7.13 Unequal access and voluntary exclusion 5 Safeguards 5.1 Technological safeguards 5.1.1 Research on overcoming the digital divide 5.1.2 Minimal data collection, transmission and storage Contents 5.1.3 Data and software security 5.1.4 Privacy protection in networking (transfer of identity and personal data) 5.1.5 Authentication and access control 5.1.6 Generic architecture-related solutions 5.1.7 Artificial intelligence safeguards 5.1.8 Recovery means 5.1.9 Conclusions and recommendations 5.2 Socio-economic safeguards 5.2.1 Standards 5.2.2 Audits 5.2.3 Open standards 5.2.4 Codes of practice 5.2.5 Trust marks and trust seals 5.2.6 Reputation systems and trust-enhancing mechanisms 5.2.7 Service contracts 5.2.8 Guidelines for ICT research 5.2.9 Public procurement 5.2.10 Accessibility and social inclusion 5.2.11 Raising public awareness 5.2.12 Education 5.2.13 Media attention, bad publicity and public opinion 5.2.14 Cultural safeguards 5.2.15 Conclusion and recommendation 5.3 Legal and regulatory safeguards 5.3.1 Introduction 5.3.2 General recommendations 5.3.3 Preserving the core of privacy and other human rights 5.3.4 Specific recommendations regarding data protection 5.3.5 Specific recommendations regarding security 5.3.6 Specific recommendations regarding consumer protection law 5.3.7 Specific recommendations regarding electronic commerce 5.3.8 Specific recommendation regarding liability law 5.3.9 Specific recommendation regarding equality law Contents 5.3.10 Specific recommendations regarding interoperability and IPR 5.3.11 Specific recommendations regarding international co-operation 6 Recommendations for stakeholders 6.1 Adopting a risk assessment/risk management approach to AmI 6.2 Recommendations for the European Commission 6.2.1 Research and development 6.2.2 Internal market and consumer protection 6.2.3 Privacy and security policy framework 6.2.4 Correcting the lacunae that exist in legislation, regulation 6.2.5 Socio-economic measures 6.3 Recommendations for the Member States 6.4 Recommendations for industry 6.5 Recommendations for civil society organisations. 6.6 Recommendations for academia. 6.7 Recommendations for individuals 7 Conclusions 7.1 User control and enforceability 7.2 The top six References Contributors Index.
• (source: Nielsen Book Data)

This book is a warning. It aims to warn policy-makers, industry, academia, civil society organisations, the media and the public about the threats and vulnerabilities facing our privacy, identity, trust, security and inclusion in the rapidly approaching world of ambient intelligence (AmI). In the near future, every manufactured product - our clothes, money, appliances, the paint on our walls, the carpets on our floors, our cars, everything - will be embedded with intelligence, networks of tiny sensors and actuators, which some have termed 'smart dust'. The AmI world is not far off. We already have surveillance systems, biometrics, personal communicators, machine learning and more. AmI will provide personalised services - and know more about us - on a scale dwarfing anything hitherto available. In the AmI vision, ubiquitous computing, communications and interfaces converge and adapt to the user. AmI promises greater user-friendliness in an environment capable of recognising and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way. While most stakeholders paint the promise of AmI in sunny colours, there is a dark side to AmI. This book aims to illustrate the threats and vulnerabilities by means of four 'dark scenarios'. The authors set out a structured methodology for analysing the four scenarios, and then identify safeguards to counter the foreseen threats and vulnerabilities. They make recommendations to policy-makers and other stakeholders about what they can do to maximise the benefits from ambient intelligence and minimise the negative consequences.
(source: Nielsen Book Data)

• Title page; Preface; Contents; Presented Papers; Poster Presentations; Invited Talks; Subject Index; Author Index.

The Scandinavian Conference on Artificial Intelligence is one of the regional AI conferences in Europe. This title includes contributions that exemplify the diversity of research in artificial intelligence and confirm the achievement and magnitude of 25 years AI research in Scandinavia.

• Title page; AmI Revs Up; Introduction; Contents; Cyber Assist Project for Ambient Intelligence; Ambient Intelligence
• From Personal Assistance to Intelligent Megacities; Towards a Context Theory for Context-Aware Systems; An Active Classification System for Context Representation and Acquisition; Ambiance: A Mobile Agent Platform for End-User Programmable Ambient Systems; A Human Activity Aware Learning Mobile Music Player; Two Issues for an Ambient Reminding System: Context-Awareness and User Feedback; Help from Strangers
• Media Arts in Ambient Intelligence Research.

Contains contributions which provide a window into the visions and trends dominating the area of Ambient Intelligence. This book addresses the core technology of the field and provides an analysis of elements of the area which will demand special consideration during the future development of the area.

• Title page; Preface; Contents; Introduction: Aspects of Artificial General Intelligence; A Collection of Definitions of Intelligence; Four Contemporary AGI Designs: A Comparative Treatment; A Foundational Architecture for Artificial General Intelligence; A Working Hypothesis for General Intelligence; From NARS to a Thinking Machine; Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence; Cognitive Map Dimensions of the Human Value System Extracted from Natural Language; Program Evolution for General Intelligence.

Examines the creation of software programs displaying broad, deep, human-style general intelligence. This work features papers presented at the 2006 AGIRI (Artificial General Intelligence Research Institute) workshop, which illustrates that it is a fit and proper subject for serious science and engineering exploration.

• Concepts and Techniques.- Data Mining and Knowledge Discovery: A Brief Overview.- Intelligent Agents and Multi-Agent Systems.- Methodology.- Exploiting Data Mining on Mas.- Coupling Data Mining with Intelligent Agents.- Knowledge Diffusion: Three Representative Test Cases.- Data Mining on the Application Level of a Mas.- Mining Agent Behaviors.- Mining Knowledge for Agent Communities.- Extensions....- Agent Retraining and Dynamical Improvement of Agent Intelligence.- Areas of Application & Future Directions.
• (source: Nielsen Book Data)

This book addresses the use of data mining for smarter, more efficient agents, as well as the challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. Following a brief review of data mining and agent technology fields, the book presents a methodology for developing multi-agent systems, describes available open-source tools, and demonstrates the application of the methodology on three different cases.
(source: Nielsen Book Data)

• A Review of Search and Resource Discovery Techniques in Peer-to-Peer Networks (S Botros & S Waterhouse)
• Adaptive Content Mapping for Internet Navigation (R W Brause & M Ueberall)
• Flexible Queries to XML Information (E Damiani et al.)
• Agent-Based Hypermedia Models (W Balzano et al.)
• Self-Organizing Neural Networks Application for Information Organization (R Rizzo)
• Emotion-Orientated Intelligent Systems (T Ichimura et al.)
• Public Opinion Channel: A Network-Based Interactive Broadcasting System for Supporting a Knowledge-Creating Community (T Fukuhara et al.)
• A New Era of Intelligent E-Commerce Based on Intelligent Java Agent-Based Development Environment (iJADE) (R S T Lee)
• Automated Internet Trading Based on Optimized Physics Models of Markets (L Ingber & R P Mondescu)
• Implementing and Maintaining a Web Case-Based Reasoning System for Heating Ventilation and Air Conditioning Systems Sales Support (I Watson).
• (source: Nielsen Book Data)

The Internet/WWW has made it possible to easily access quantities of information never available before. However, both the amount of information and the variation in quality pose obstacles to the efficient use of the medium. Artificial intelligence techniques can be useful tools in this context. Intelligent systems can be applied to searching the Internet and data-mining, interpreting Internet-derived material, the human-Web interface, remote condition monitoring and many other areas.This volume presents the latest research on the interaction between intelligent systems (neural networks, adaptive and connectionist paradigms, fuzzy and rule-based systems, intelligent agents) and the Internet/WWW. It surveys both the employment of intelligent systems to facilitate and enhance the use of the Internet, and applications where the Internet is a channel through which intelligent techniques are applied.
(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)

• Automatic detection of microcalcifications in mammograms using a fuzzy classifier, A.P. Drijarkara et al
• predictive fuzzy model for control of an artificial muscle, P.B. Petrovi
• evolutionary computation for information retrieval based on user preference, H.-G. Kim and S.-B. Cho
• fuzzy logic and neural networks approach - a way to improve overall performance of integrated heating systems, E. Entchev
• design and tuning a neurofuzzy power system stabilizer using genetic algorithms, A. Afzalian and D.A. Linkens
• an application of logic programmes with soft computing aspects to fault diagnosis in digital circuits, H. Sakai et al
• determination of the motion parameters from the perspective projection of a triangle, M.M. Sein and H. Hama. (Part contents).
• (source: Nielsen Book Data)

Soft computing has been presented not only with the theoretical developments but also with a large variety of realistic applications to consumer products and industrial systems. Application of soft computing has provided the opportunity to integrate human-like vagueness and real-life uncertainty into an otherwise hard computer program.This book highlights some of the recent developments in practical applications of soft computing in engineering problems. All the chapters have been sophisticatedly designed and revised by international experts to achieve wide but in-depth coverage.
(source: Nielsen Book Data)

• 1. Rational Agents
• 2. The Belief-Desire-Intention Model
• 3. Introduction to LORA
• 4. LORA Defined
• 5. Properties of Rational Agents
• 6. Collective Mental States
• 7. Communication
• 8. Cooperation
• 9. Logic and Agent Theory.

One goal of modern computer science is to engineer computer programs that can act as autonomous, rational agents; software that can independently make good decisions about what actions to perform on our behalf and execute those actions. Applications range from small programs that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This book focuses on the belief-desire-intention (BDI) model of rational agents, which recognizes the primacy of beliefs, desires, and intentions in rational action. The BDI model has three distinct strengths: an underlying philosophy based on practical reasoning in humans, a software architecture that is implementable in real systems, and a family of logics that support a formal theory of rational agency.The book introduces a BDI logic called LORA (Logic of Rational Agents). In addition to the BDI component, LORA contains a temporal component, which allows one to represent the dynamics of how agents and their environments change over time, and an action component, which allows one to represent the actions that agents perform and the effects of the actions. The book shows how LORA can be used to capture many components of a theory of rational agency, including such notions as communication and cooperation.
(source: Nielsen Book Data)

• Preface
• 1. The Machine That Could Learn Anything
• 2. Consider Thy Neighbor
• 3. Kepler on Mars
• 4. The Information Chicane
• 5. Fence-and-Fill Learning
• 6. Turing and the Submarines
• 7. The Relational Gulf
• 8. The Supercharged Learner
• 9. David Hume and the Crash of '87
• 10. Phases of Compression
• 11. Protorepresentational Learning
• 12. The Creativity Continuum
• References
• Index.

Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. This study of learning in autonomous agents offers a bracing intellectual adventure. Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. Although theorists of machine learning tell us that all learning methods contribute some form of bias and thus involve a degree of creativity, Thornton carries the idea much further. He describes an incremental process, recursive relational learning, in which the results of one learning step serve as the basis for the next. Very high-level recodings are then substantially the creative artifacts of the learner's own processing. Lower-level recodings are more "objective" in that their properties are more severely constrained by the source data. Thornton sees consciousness as a process at the outer fringe of relational learning, just prior to the onset of creativity. According to this view, we cannot assume consciousness to be an exclusively human phenomenon, but rather the expected feature of any cognitive mechanism able to engage in extended flights of relational learning. Thornton presents key background material in an entertaining manner, using extensive mental imagery and a minimum of mathematics. Anecdotes and dialogue add to the text's informality.
(source: Nielsen Book Data)

• Approximate reasoning about comlpex objects in distributed systems - rough mereological formulation
• FOOD - fuzzy object-oriented design
• approximating block access in database systems
• the computer zoo in a box
• expert system design
• automating creation of computer programs for design circuits using genetic programming
• self-organizing maps
• knowledge-based techniques for software quality management
• object networks in developing intelligent systems
• application of genetic programming in software quality prediction
• neural networks for software quality prediction
• fuzzy Petri nets and Choquet integral in software cost estimation
• non-Cartesian approach to software development
• inductive programming.
• (source: Nielsen Book Data)

This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of software engineering.The book addresses a number of crucial issues: what is CI, what role does it play in software development, how are CI elements built into successive phases of the software life cycle, and what is the role played by CI in quantifying fundamental features of software artifacts?With contributions from leading researchers and practitioners, the book provides the reader with a wealth of new concepts and approaches, complete algorithms, in-depth case studies, and thought-provoking exercises. The topics coverage include neurocomputing, granular as well as evolutionary computing, object-oriented analysis and design in software engineering. There is also an extensive bibliography.
(source: Nielsen Book Data)

• PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents.

• 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS.

• B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS;
• 1. Introduction; 1
• .1. Reminiscing; 1
• .2. The 1961 Model; 1
• .3. Notation;
• 2. Linear Separable NE; 2
• .1. Neuronic Equations; 2
• .2. Polygonal Inequalities; 2
• .3. Computation of the n-expansion of arbitrary l.s. functions; 2
• .4. Continuous versus discontinuous behaviour: transitions;
• 3. General Boolean NE; 3
• .1. Linearization in tensor space; 3
• .2. Next-state matrix; 3
• .3. Normal modes, attractors; 3
• .4. Synthesis of nets: the inverse problem; 3
• .5. Separable versus Boolean nets.

• Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS;
• 1. INTRODUCTION;
• 2. MODELING THE NOISY NEURON; 2
• .1. Empirical Properties of Neuron and Synapse;
• 22. Model of Shaw and Vasudevan; 2
• .3. Model of Little; 2
• .4. Model of Taylor.

• 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion
• Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS.

This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.
(source: Nielsen Book Data)

• Chapter 1. Double hierarchy linguistic term set and its extensions.-
• Chapter 2. Measure methods for DHHFLTSs.-
• Chapter 3. Consistency theory framework of DHHFLPRs.-
• Chapter 4. Group consensus decision-making methods with DHHFLPRs, LPOs and self-confident DHLPRs.-
• Chapter 5. Large-scale group consensus decision-making methods with DHHFLPRs.
• (source: Nielsen Book Data)

This book presents the concept of the double hierarchy linguistic term set and its extensions, which can deal with dynamic and complex decision-making problems. With the rapid development of science and technology and the acceleration of information updating, the complexity of decision-making problems has become increasingly obvious. This book provides a comprehensive and systematic introduction to the latest research in the field, including measurement methods, consistency methods, group consensus and large-scale group consensus decision-making methods, as well as their practical applications. Intended for engineers, technicians, and researchers in the fields of computer linguistics, operations research, information science, management science and engineering, it also serves as a textbook for postgraduate and senior undergraduate university students.
(source: Nielsen Book Data)

• Section
• -1: Data Science - The "What" Chapter
• -1: IntroductionFirst chapter will set the basic foundation of the subject for students. Like many other books, this introductory level chapter will comprise of the basic concepts. Introduction of the following concepts will be discussed:* Data Science* Importance of data science* Applications of data science* Data Driven Decision Making* Data analysisChapter
• -2: Widely used techniques in data scienceThis chapter will discuss the concepts required for one to start working on data analysis. Chapter will comprise of the concepts that student should know before performing any task on data analysis and some of the tasks that can be performed as part of data analysis. Following concepts will be discussed.* Supervised vs Unsupervised data* Data understanding* Data preparation* Modeling* Overfitting* Random sampling* Cross Validation* Feature selection* Outlier detection* Rule extractionSection
• -2: Data science: The "How" Chapter
• -3: Statistical InferenceEvery part of data analysis involves statistics and statistical inference to properly utilize data and perform decision making. This chapter will provide statistical concepts to support the data analysis tasks performed by students for decision making with real life data. Following topics will be discussed:* Probability theory* Transformations and expectations* Common families of distribution* Random variables* Preparation of random samples* Asymptotic evaluations* Regression and regression models Chapter
• -4: Supervised Learning In real world, we come across two types of data, supervised and unsupervised. In this chapter, we will discuss the concepts, tools and techniques related to processing of supervised data with examples and decision making out of it. The following concepts will be discussed:* Supervised Learning* Classification and Regression* Generalization, Overfitting and Underfitting* Evaluation models* Supervised learning algorithmsChapter
• -5: Unsupervised LearningThe unsupervised data forms the other half of the data available in real world applications. Like previous chapter, this chapter will include the concepts, tools and techniques related to unsupervised data with examples. Following contents will be included:* Challenges of unsupervised learning* Processing and scaling* Clustering* Dimensionality reduction, feature extraction and manifold learning* Unsupervised learning algorithmsChapter
• -6: Natural language processingIn this chapter, we will focus on one particular sort of data that has become extremely common i.e. text data. We will see in this chapter the fundamental principles of natural language processing and will look at one of the common application of NLP that is sentiment analysis. Following contents will be discussed:* Why Text Is Important* Why Text Is Difficult* Representation* Sentiment Analysis* Lexicon-based Approaches for Text MiningSection
• -3: Data Science - The "Where" Chapter
• -7: Customers AnalyticsIn this chapter, we will introduce he use of analytics for understanding customers and predicting their behaviour in different situations. This includes the understanding of loyalty programs, market research, understanding customer lifetime value, predicting churn, and identifying potential defaulters. These are few examples of what will be contained in this chapter. Chapter
• -8: Operations AnalyticsIn this chapter, we will prepare our readers to understand and acknowledge the use of data science for improving business operations. For example, we will discuss how analyzing data can help avoid service outages, or at least predict the service outage in order to prepare contingency plans. Analyzing data can also help in identifying redundancies which can be removed in order to significantly reduce operational costs. We will give examples on how various manufacturing and service industries are using real-time sensor data to track their systems wear and tear. This helps them improve their mean time to repair by forecasting breakdown of different components well ahead in time.
• (source: Nielsen Book Data)

This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.
(source: Nielsen Book Data)

• Chapter 1: An Overview of Artificial Intelligence
• Chapter 2: An Overview of Machine Learning
• Chapter 3: Introduction to Deep Learning
• Chapter 4: Machine Learning Versus Deep Learning
• Chapter 5: Machine Learning with Python
• Chapter 6: Introduction to Jupyter Notebooks
• Chapter 7: Python Programming on the Jupyter Notebook
• Chapter 8: The Tensorflow Machine Learning Library
• Chapter 9: Programming with Tensorflow 1.0
• Chapter 10: Introducing TensorFlow 2.0
• Chapter 11: Machine Learning Programming with TensorFlow 2.0.

Create, execute, modify, and share machine learning applications with Python in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebooks instead of a text editor or a regular IDE. Youll start by learning fundamental concepts in Python necessary for working with machine learning application development. Then use Jupyter Notebooks to improve the way you program with Python. After grounding your skills in working with Python in Jupyter Notebooks, youll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can start in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. You will: Program machine learning models in Python Tackle basic machine learning obstacles Develop in the Jupyter Notebooks environment.

• Learning for vision.- Computational photography.- Human analysis.- Human sensing.- Stereo and reconstruction.- Optimization.- Matching and recognition.- Video attention.- Poster sessions.
• (source: Nielsen Book Data)

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
(source: Nielsen Book Data)

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book * Apply R to simplify predictive modeling with short and simple code * Use machine learning to solve problems ranging from small to big data * Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn * Create and inspect transaction datasets and perform association analysis with the Apriori algorithm * Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm * Compare differences between each regression method to discover how they solve problems * Detect and impute missing values in air quality data * Predict possible churn users with the classification approach * Plot the autocorrelation function with time series analysis * Use the Cox proportional hazards model for survival analysis * Implement the clustering method to segment customer data * Compress images with the dimension reduction method * Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.
(source: Nielsen Book Data)

• Lightweight Metric Computation for Distributed Massive Data Streams.- Performance Analysis of Object Store Systems in a Fog and Edge Computing Infrastructure.- Scientific Workflow Scheduling with Provenance Data in a Multisite Cloud.- Cost Optimization of Data Flows Based on Task Re-ordering.- Fusion Strategies for Large-Scale Multi-Modal Image Retrieval.
• (source: Nielsen Book Data)

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 33rd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include distributed massive data streams, storage systems, scientific workflow scheduling, cost optimization of data flows, and fusion strategies.
(source: Nielsen Book Data)

• Detection, recognition and retrieval
• Scene understanding
• Optimization
• Image and video processing
• Learning
• Action activity and tracking
• 3D
• Poster sessions.

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.

• Detection, recognition and retrieval
• Scene understanding
• Optimization
• Image and video processing
• Learning
• Action activity and tracking
• 3D
• Poster sessions.

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.

• Detection, recognition and retrieval
• Scene understanding
• Optimization
• Image and video processing
• Learning
• Action activity and tracking
• 3D
• Poster sessions.

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.

• Detection, recognition and retrieval
• Scene understanding
• Optimization
• iImage and video processing
• Learning
• Action activity and tracking
• 3D
• Poster sessions.

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.

• Detection, recognition and retrieval
• Scene understanding
• Optimization
• iImage and video processing
• Learning
• Action activity and tracking
• 3D
• Poster sessions.

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.

• Intro; Contents; Introduction; 1 Flow Tasks in Networks in Crisp Conditions; 1.1 Basic Concepts of the Flow Theory; 1.2 Selecting of the Arc Capacities; 1.2.1 Factors Leading to the Problem Statements in Fuzzy Conditions; 1.3 Fuzzy Logic as the Main Tool of Dealing with Uncertainty; 1.4 Flow Tasks in Transportation Networks; 1.4.1 Maximum Flow Finding in a Network; 1.4.2 Maximum Flow Finding in a Network with Nonzero Lower Flow Bounds; 1.4.3 Minimum Cost Flow Finding in a Network; 1.4.4 Minimum Cost Flow Finding in a Network with Nonzero Lower Flow Bounds; 1.5 Flow Tasks in Dynamic Networks

• 1.5.1 Maximum Flow Finding in Dynamic Networks with Zero and Nonzero Lower Flow Bounds1.5.2 Minimum Cost Flow Finding in Dynamic Network with Zero and Nonzero Lower Flow Bounds; 1.6 Summary; References; 2 Maximum and Minimum Cost Flow Finding in Networks in Fuzzy Conditions; 2.1 Maximum Flow Finding in a Network with Fuzzy Arc Capacities; 2.2 Method of Fuzzy Calculations with Fuzzy Numbers; 2.3 Maximum Flow Finding in a Network with Fuzzy Nonzero Lower and Upper Flow Bounds; 2.4 Minimum Cost Flow Finding in a Network with Fuzzy Arc Capacities and Transmission Costs

• 2.4.1 Potential Method for the Minimum Cost Flow Finding in a Network with Fuzzy Arc Capacities and Transmission2.5 Minimum Cost Flow Finding in a Network with Fuzzy Nonzero Lower, Upper Flow Bounds and Transmission Costs; 2.6 Summary; References; 3 Flow Tasks Solving in Dynamic Networks with Fuzzy Lower, Upper Flow Bounds and Transmission Costs; 3.1 Definition of the Fuzzy Dynamic Network; 3.2 Maximum Flow Finding in Dynamic Network with Fuzzy Transit Arc Capacities; 3.3 Maximum Flow Finding in Dynamic Network with Fuzzy Transit Nonzero Lower and Upper Flow Bounds

• 3.4 Minimum Cost Flow Finding in Dynamic Network with Fuzzy Transit Arc Capacities and Transmission Costs3.5 Minimum Cost Flow Finding in Dynamic Network with Fuzzy Nonzero Lower, Upper Flow Bounds and Transmission Costs; 3.6 Summary; References; 4 Implementing the Program for Solving the Flow Tasks in Networks in Fuzzy Conditions; 4.1 Functional Purpose of Proposed Program Complex; 4.2 Description of the Logical Structure of the Software Mode; 4.3 Preparing of Input Data Using GIS ObjectLand; 4.4 Summary; References; Conclusion

This book offers a comprehensive introduction to fuzzy methods for solving flow tasks in both transportation and networks. It analyzes the problems of minimum cost and maximum flow finding with fuzzy nonzero lower flow bounds, and describes solutions to minimum cost flow finding in a network with fuzzy arc capacities and transmission costs. After a concise introduction to flow theory and tasks, the book analyzes two important problems. The first is related to determining the maximum volume for cargo transportation in the presence of uncertain network parameters, such as environmental changes, measurement errors and repair work on the roads. These parameters are represented here as fuzzy triangular, trapezoidal numbers and intervals. The second problem concerns static and dynamic flow finding in networks under fuzzy conditions, and an effective method that takes into account the network's transit parameters is presented here. All in all, the book provides readers with a practical reference guide to state-of-the art fuzzy methods for solving flow tasks and offers a valuable resource for all researchers and postgraduate students in the fields of network theory, fuzzy models and decision-making. .

• Intro; Preface; Contents; 1 Fuzzy Statistical Decision-Making; Abstract; 1 Introduction; 2 Literature Review: Graphical Illustrations; 2.1 Classification with Respect to Publication Years; 2.2 Classification with Respect to Affiliations; 2.3 Classification with Respect to Authors; 2.4 Classification with Respect to Countries; 2.5 Classification with Respect to Document Type; 2.6 Classification with Respect to Subject Areas; 3 Books on Fuzzy Statistics; 4 Fuzzy Statistical Decision-Making: An Example; 5 Conclusion; References; 2 Fuzzy Probability Theory I: Discrete Case; Abstract

• 1 Introduction2 Fuzzy Random Variables; 2.1 The FRV Model by Puri and Ralescu; 2.2 The FRV Model by Buckley Based on Kwakernaak; 3 Alpha-Cuts; 4 Fuzzy Probability; 5 Fuzzy Discrete Expectation; 6 Fuzzy Conditional Probability; 7 Fuzzy Independency; 8 Fuzzy Bayes Formula; 9 Fuzzy Hypergeometric Distribution; 10 Fuzzy Binomial Distribution; 11 Fuzzy Poisson Distribution; 12 Conclusion; References; 3 Fuzzy Probability Theory II: Continuous Case; Abstract; 1 Introduction; 2 Fuzzy Continuous Expectation; 3 Fuzzy Uniform Distribution; 4 Fuzzy Exponential Distribution; 5 Fuzzy Laplace Distribution

• 6 Fuzzy Normal Distribution7 Fuzzy Lognormal Distribution; 8 Conclusion; References; 4 On Fuzzy Bayesian Inference; Abstract; 1 Introduction; 2 Fuzzy Numbers and Fuzzy Vectors; 3 Fuzzy A-priori Distributions; 4 Fuzzy Data; 5 Generalized Bayes' Theorem; 6 Fuzzy Predictive Distributions; 7 Example; 8 Final Remark; References; 5 Fuzzy Central Tendency Measures; Abstract; 1 Introduction; 2 Literature Review; 3 Central Tendency Measures; 3.1 Mean; 3.2 Median; 3.3 Mode; 3.4 Geometric Mean; 3.5 Harmonic Mean; 3.6 Quadratic Mean; 3.7 Graphical Representation of Data; 3.7.1 Stem-and-Leaf Plots

• 3.7.2 Frequency Distribution Tables3.7.3 Histograms; 3.7.4 Frequency Polygons; 3.7.5 Bar Charts; 3.7.6 Pie Charts; 4 Central Tendency Measures Under Fuzziness; 4.1 Fuzzy Arithmetic Mean; 4.2 Fuzzy Median; 4.3 Fuzzy Mode; 5 Fuzzy Frequency Distribution; 6 Fuzzy Graphics; 6.1 Fuzzy Stem-and-Leaf Display; 6.2 Fuzzy Histograms; 6.3 Fuzzy Frequency Polygons; 7 Conclusion; References; 6 Fuzzy Dispersion Measures; Abstract; 1 Introduction; 2 Classical Dispersion Measures; 2.1 Standard Deviation and Variance; 2.2 Mean Absolute Deviation; 2.3 Coefficient of Variation; 2.4 Range

• 2.5 The Interquartile Range3 Dispersion Measures Under Fuzziness; 3.1 Fuzzy Variance and Fuzzy Standard Deviation; 3.2 Fuzzy Mean Absolute Deviation; 3.3 Fuzzy Coefficient of Variation; 3.4 Fuzzy Range; 3.5 Fuzzy Interquartile Range; 4 Conclusion; References; 7 Sufficiency, Completeness, and Unbiasedness Based on Fuzzy Sample Space; Abstract; 1 Introduction and Motivation; 2 Literature Survey; 3 Preliminaries; 4 Sufficiency and Completeness; 5 Unbiasedness; 6 Numerical Examples; 7 Conclusion; References; 8 Fuzzy Confidence Regions; Abstract; 1 Introduction; 2 Fuzzy Data

• Preface
• Fuzzy Statistical Decision Making
• Fuzzy Probability Theory I: Discrete Case
• Fuzzy Probability Theory II: Continuous Case
• On Fuzzy Bayesian Inference
• Fuzzy Central Tendency Measures
• Fuzzy Dispersion Measures
• Sufficiency, Completeness, and Unbiasedness based on Fuzzy Sample Space
• Fuzzy Confidence Regions
• Fuzzy Extensions of Confidence Intervals: Estimation for µ, σ2, and p
• Testing Fuzzy Hypotheses: A New p-value-based Approach
• Fuzzy Regression Analysis : An Actuarial Perspective
• Fuzzy Correlation and Fuzzy Non-Linear Regression Analysis
• Fuzzy Decision Trees
• Fuzzy Shewhart Control Charts
• Fuzzy EWMA and Fuzzy CUSUM Control Charts
• Linear Hypothesis Testing Based on Unbiased Fuzzy Estimators and Fuzzy Significance Level
• A Practical Application of Fuzzy Analysis of Variance in Agriculture
• A Survey of Fuzzy Data Mining Techniques.

This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.

• Hesitant Fuzzy Multiple Criteria Decision Analysis Based on TOPSIS.- Hesitant Fuzzy Multiple Criteria Decision Analysis Based on TODIM.- Hesitant Fuzzy Multiple Criteria Decision Analysis Based on QUALIFLEX.- Hesitant Fuzzy Multiple Criteria Decision Analysis Based on LINMAP.- Consensus Model-Based Hesitant Fuzzy Multiple Criteria Group Decision Analysis.
• (source: Nielsen Book Data)

The book offers a comprehensive introduction to methods for solving multiple criteria decision making and group decision making problems with hesitant fuzzy information. It reports on the authors' latest research, as well as on others' research, providing readers with a complete set of decision making tools, such as hesitant fuzzy TOPSIS, hesitant fuzzy TODIM, hesitant fuzzy LINMAP, hesitant fuzzy QUALIFEX, and the deviation modeling approach with heterogeneous fuzzy information. The main focus is on decision making problems in which the criteria values and/or the weights of criteria are not expressed in crisp numbers but are more suitable to be denoted as hesitant fuzzy elements. The largest part of the book is devoted to new methods recently developed by the authors to solve decision making problems in situations where the available information is vague or hesitant. These methods are presented in detail, together with their application to different type of decision-making problems. All in all, the book represents a valuable reference guide for graduate students and researchers in the both fields of fuzzy logic and decision making. .
(source: Nielsen Book Data)

• Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons.- Navigation of Distinct Euclidean Particles via Hierarchical Clustering.- Coalition Formation Games for Dynamic Multirobot Tasks.- Active Control Strategies for Discovering And Localizing Devices with Range-Only Sensors.- Aggressive Moving Obstacle Avoidance Using a Stochastic Reachable Set Based Potential Field.- Distributed Range-Based Relative Localization of Robot.- Computing Large Convex Regions of Obstacle-Free Space through Semidefinite Programming.- A Region-Based Strategy for Collaborative Roadmap Construction.- Efficient Sampling-based Approaches to Optimal Path Planning in Complex Cost Spaces.- Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions.- FFRob: An efficient heuristic for task and motion planning.- Fast Nearest Neighbor Search in SE(3) for Sampling-Based Motion Planning.- Trackability with Imprecise Localization.- Kinodynamic RRTs with Fixed Time Step and Best-Input Extension are not Probabilistically Complete.-Featureless Motion Vector-based Simultaneous Localization, Planar Surface Extraction, and Moving Obstacle Tracking.- Sparse Methods for Efficient Asymptotically Optimal KinodynamicPlanning.- Adaptive Informative Path Planning in Metric Spaces.- The Feasible Transition Graph: Encoding Topology and Manipulation Constraints for Multirobot Push-Planning.- Predict Collision Among Rigid and Articulated Obstacles with Unknown Motion.- Asymptotically Optimal Stochastic Motion Planning with Temporal Goals.- Resolution-Exact Algorithms for Link Robots.- Optimal Trajectories for Planar Rigid Bodies with Switching Costs.- Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective.- Optimal Path Planning in Cooperative Heterogeneous Multi-robot Delivery Systems.- Composing Dynamical Systems to Realize Dynamic Robotic Dancing.-The Lion and Man Game on Convex Terrains.- RRT-X: Real-Time Motion Planning/Replanning for Environments with Unpredictable Obstacles.- Orienting Parts with Shape Variation.- Smooth and Dynamically Stable Navigation of Multiple Human-Like Robots.- Scaling up Gaussian Belief Space Planning through Covariance-Free Trajectory Optimization and Automatic Differentiation.- Planning Curvature and Torsion Constrained Ribbons in 3D with Application to Intracavitary Brachytherapy.- A Quadratic Programming Approach to Quasi-Static Whole-Body Manipulation.- On-Line Coverage of Planar Environments by a Battery Powered Autonomous Mobile Robot.- Finding a needle in an exponential haystack: Discrete RRT for Exploration of Implicit Roadmaps in Multi-Robot Motion Planning.- Stochastic Extended LQR: Optimization-Based Motion Planning Under Uncertainty.- An Approximation Algorithm for Time Optimal Multi-Robot Routing Decidability of Robot Manipulation Planning: Three Disks in the Plane.- A Topological Perspective on Cycling Robots for Full Tree Coverage.- Towards arranging and tightening knots and unknots with fixtures.- Asymptotically Optimal Feedback Planning: FMM Meets Adaptive Mesh Refinement.- Online Task Planning and Control for Aerial Robots with Fuel Constraints in Winds.-Pebble Motion on Graphs with Rotations: Efficient Feasibility Tests and Planning Algorithms.
• (source: Nielsen Book Data)

This carefully edited volume is the outcome of the eleventh edition of the Workshop on Algorithmic Foundations of Robotics (WAFR), which is the premier venue showcasing cutting edge research in algorithmic robotics. The eleventh WAFR, which was held August 3-5, 2014 at Bogazici University in Istanbul, Turkey continued this tradition. This volume contains extended versions of the 42 papers presented at WAFR. These contributions highlight the cutting edge research in classical robotics problems (e.g. manipulation, motion, path, multi-robot and kinodynamic planning), geometric and topological computation in robotics as well novel applications such as informative path planning, active sensing and surgical planning. This book - rich by topics and authoritative contributors - is a unique reference on the current developments and new directions in the field of algorithmic foundations.
(source: Nielsen Book Data)

• Introduction to Optimization.- Probability Collectives: A Distributed Optimization Approach.- Constrained Probability Collectives: A Heuristic Approach.- Constrained Probability Collectives with a Penalty Function Approach.- Constrained Probability Collectives With Feasibility-Based Rule I.- Probability Collectives for Discrete and Mixed Variable Problems.- Probability Collectives with Feasibility-Based Rule II.
• (source: Nielsen Book Data)

This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
(source: Nielsen Book Data)

• Back to Basics and Forward to Novelty in Machine Consciousness / Igor Aleksander and Helen Morton
• Characterizing and Assessing Human-Like Behavior in Cognitive Architectures / Raúl Arrabales, Agapito Ledezma and Araceli Sanchis
• Architects or Botanists? The Relevance of (Neuronal) Trees to Model Cognition / Giorgio Ascoli
• Consciousness and the Quest for Sentient Robots / Pentti O.A. Haikonen
• Biological Fluctuation "Yuragi" as the Principle of Bio-inspired Robots / Hiroshi Ishiguro
• Active Learning by Selecting New Training Samples from Unlabelled Data / Ho Gyeong Kim, Cheong-An Lee and Soo-Young Lee
• Biologically Inspired beyond Neural: Benefits of Multiple Modeling Levels / Christian Lebiere
• Turing and de Finetti Ganes: Machines Making Us Think / Ignazio Licata
• How to Simulate the Brain without a Computer / Karlheinz Meier
• Odor Perception through Network Self-organization: Large Scale Realistic Simulations of the Olfactory Bulb / Michele Migliore
• Extending Cognitive Architectures / Alexei V. Samsonovich.

• Babies and Baby-Humanoids to Study Cognition / Giulio Sandini
• Towards Architectural Foundations for Cognitive Self-aware Systems / Ricardo Sanz and Carlos Hernández
• Achieving AGI within My Lifetime: Some Progress and Some Observations / Kristinn R. Thórisson
• Learning and Creativity in the Global Workspace / Geraint A. Wiggins
• Multimodal People Engagement with iCub / Salvatore M. Anzalone, Serena Ivaldi, Olivier Sigaud and Mohamed Chetouani
• Human Action Recognition from RGB-D Frames Based on Real-Time 3D Optical Flow Estimation / Gioia Ballin, Matteo Munaro and Emanuele Menegatti
• Modality in the MGLAIR Architecture / Jonathan P. Bona and Stuart C. Shapiro
• Robotics and Virtual Worlds: An Experiential Learning Lab / Barbara Caci, Antonella D'Amico and Giuseppe Chiazzese
• Comprehensive Uncertainty Management in MDPs / Vincenzo Cannella, Roberto Pirrone and Antonio Chella
• A New Humanoid Architecture for Social Interaction between Human and a Robot Expressing Human-Like Emotions Using an Android Mobile Device as Interface / Antonio Chella, Rosario Sorbello, Giovanni Pilato, Giorgio Vassallo and Marcello Giardina.

• The Concepts of Intuition and Logic within the Frame of Cognitive Process Modeling / O.D. Chernavskaya, A.P. Nikitin and J.A. Rozhilo
• Do Humanoid Robots Need a Body Schema? / Dalia De Santis, Vishwanathan Mohan, Pietro Morasso and Jacopo Zenzeri
• Simulation and Anticipation as Tools for Coordinating with the Future / Haris Dindo, Giuseppe La Tona, Eric Nivel, Giovanni Pezzulo and Antonio Chella, et al.
• Solutions for a Robot Brain / Walter Fritz
• Exemplars, Prototypes and Conceptual Spaces / Marcello Frixione and Antonio Lieto
• The Small Loop Problem: A Challenge for Artificial Emergent Cognition / Olivier L. Georgeon and James B. Marshall
• Crowd Detection Based on Co-occurrence Matrix / Stefano Ghidoni, Arrigo Guizzo and Emanuele Menegatti
• Development of a Framework for Measuring Cognitive Process Performance / Wael Hafez
• I Feel Blue: Robots and Humans Sharing Color Representation for Emotional Cognitive Interaction / Ignazio Infantino, Giovanni Pilato, Riccardo Rizzo and Filippo Vella.

• Investigating Perceptual Features for a Natural Human -- Humanoid Robot Interaction Inside a Spontaneous Setting / Hiroshi Ishiguro, Shuichi Nishio, Antonio Chella, Rosario Sorbello and Giuseppe Balistreri, et al.
• Internal Simulation of an Agent's Intentions / Magnus Johnsson and Miriam Buonamente
• A Model of Primitive Consciousness Based on System-Level Learning Activity in Autonomous Adaptation / Yasuo Kinouchi and Yoshihiro Kato
• Decision-Making and Action Selection in Two Minds / Muneo Kitajima and Makoto Toyota
• Cognitive Chrono-Ethnography: A Methodology for Understanding Users for Designing Interactions Based on User Simulation with Cognitive Architectures / Muneo Kitajima and Makoto Toyota
• Emotional Emergence in a Symbolic Dynamical Architecture / Othalia Larue, Pierre Poirier and Roger Nkambou
• An Integrated, Modular Framework for Computer Vision and Cognitive Robotics Research (icVision) / Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster and Jürgen Schmidhuber
• Insertion Cognitive Architecture / Alexander Letichevsky.

• A Parsimonious Cognitive Architecture for Human-Computer Interactive Musical Free Improvisation / Adam Linson, Chris Dobbyn and Robin Laney
• Cognitive Integration through Goal-Generation in a Robotic Setup / Riccardo Manzotti, Flavio Mutti, Giuseppina Gini and Soo-Young Lee
• A Review of Cognitive Architectures for Visual Memory / Michal Mukawa and Joo-Hwee Lim
• A Model of the Visual Dorsal Pathway for Computing Coordinate Transformations: An Unsupervised Approach / Flavio Mutti, Hugo Gravato Marques and Giuseppina Gini
• Multiagent Recursive Cognitive Architecture / Zalimkhan V. Nagoev
• A Biologically-Inspired Perspective on Commonsense Knowledge / Pietro Perconti
• Coherence Fields for 3D Saliency Prediction / Fiora Pirri, Matia Pizzoli and Arnab Sinha
• Principles of Functioning of Autonomous Agent-Physicist / Vladimir G. Red'ko
• Affect-Inspired Resource Management in Dynamic, Real-Time Environments / W. Scott Neal Reilly, Gerald Fry and Michael Reposa
• An Approach toward Self-organization of Artificial Visual Sensorimotor Structures / Jonas Ruesch, Ricardo Ferreira and Alexandre Bernardino.

• Biologically Inspired Methods for Automatic Speech Understanding / Giampiero Salvi
• Modeling Structure and Dynamics of Selective Attention / Hecke Schrobsdorff, Matthias Ihrke and J. Michael Herrmann
• How to Engineer Biologically Inspired Cognitive Architectures / Valeria Seidita, Massimo Cossentino and Antonio Chella
• An Adaptive Affective Social Decision Making Model / Alexei Sharpanskykh and Jan Treur
• A Robot Uses an Evaluation Based on Internal Time to Become Self-aware and Discriminate Itself from Others / Toshiyuki Takiguchi and Junichi Takeno
• Why Neurons Are Not the Right Level of Abstraction for Implementing Cognition / Claude Touzet
• Intertemporal Decision Making: A Mental Load Perspective / Jan Treur
• A Non-von-Neumann Computational Architecture Based on in Situ Representations: Integrating Cognitive Grounding, Productivity and Dynamics / Frank van der Velde
• A Formal Model of Neuron That Provides Consistent Predictions / E.E. Vityaev.

• Safely Crowd-Sourcing Critical Mass for a Self-improving Human-Level Learner/"Seed AI" / Mark R. Waser
• Unconscious Guidance of Pedestrians Using Vection and Body Sway / Norifumi Watanabe and Takashi Omori
• The Analysis of Amodal Completion for Modeling Visual Perception / Liliana Albertazzi, James Dadam, Luisa Canal and Rocco Micciolo
• Naturally Biased Associations between Colour and Shape: A Brentanian Approach / Liliana Albertazzi and Michela Malfatti
• Architecture to Serve Disabled and Elderly / Miriam Buonamente and Magnus Johnsson
• Bio-inspired Sensory Data Aggregation / Alessandra De Paola and Marco Morana
• Clifford Rotors for Conceptual Representation in Chatbots / Agnese Augello, Salvatore Gaglio, Giovanni Pilato and Giorgio Vassallo
• Neurogenesis in a High Resolution Dentate Gyrus Model / Craig M. Vineyard, James B. Aimone and Glory R. Emmanuel
• A Game Theoretic Model of Neurocomputation / Craig M. Vineyard, Glory R. Emmanuel, Stephen J. Verzi and Gregory L. Heileman.

The challenge of creating a real-life computational equivalent of the human mind requires that we better understand at a computational level how natural intelligent systems develop their cognitive and learning functions. In recent years, biologically inspired cognitive architectures have emerged as a powerful new approach toward gaining this kind of understanding (here "biologically inspired" is understood broadly as "brain-mind inspired"). Still, despite impressive successes and growing interest in BICA, wide gaps separate different approaches from each other and from solutions found in biology. Modern scientific societies pursue related yet separate goals, while the mission of the BICA Society consists in the integration of many efforts in addressing the above challenge. Therefore, the BICA Society shall bring together researchers from disjointed fields and communities who devote their efforts to solving the same challenge, despite that they may "speak different languages". This will be achieved by promoting and facilitating the transdisciplinary study of cognitive architectures, and in the long-term perspective - creating one unifying widespread framework for the human-level cognitive architectures and their implementations. This book is a proceedings of the Third Annual Meeting of the BICA Society, which was hold in Palermo-Italy from October 31 to November 2, 2012. The book describes recent advances and new challenges around the theme of understanding how to create general-purpose humanlike artificial intelligence using inspirations from studies of the brain and the mind

• Modules and Linear Spaces
• Matrices
• MATLAV
• Determinants
• Norms on Linear Spaces
• Inner Product Spaces
• Convexity
• Eigenvalues
• Similarity and Spectra
• Singular Values
• Graphs and Matrices
• Data Sample Matrices
• Least Squares Approximation and Data Mining
• Dimensionality Reduction Techniques
• The K-Means Clustering Algorithm
• Spectral Properties of Graphs and Spectral Clustering.
• (source: Nielsen Book Data)

This comprehensive volume presents the foundations of linear algebra ideas and techniques applied to data mining and related fields. Linear algebra has gained increasing importance in data mining and pattern recognition, as shown by the many current data mining publications, and has a strong impact in other disciplines like psychology, chemistry, and biology. The basic material is accompanied by more than 550 exercises and supplements, many accompanied with complete solutions and MATLAB applications.
(source: Nielsen Book Data)

• ch. 1: Introduction to Reinforcement and Systemic Machine Learning
• 1.1. Introduction
• 1.2. Supervised, Unsupervised, and Semisupervised Machine Learning
• 1.3. Traditional Learning Methods and History of Machine Learning
• 1.4. What is Machine Learning?
• 1.5. Machine-Learning Problem
• 1.6. Learning Paradigms
• 1.7. Machine-Learning Techniques and Paradigms
• 1.8. What is Reinforcement Learning?
• 1.9. Reinforcement Function and Environment Function
• 1.10. Need of Reinforcement Learning
• 1.11. Reinforcement Learning and Machine Intelligence
• 1.12. What is Systemic Learning?
• 1.13. What Is Systemic Machine Learning?
• 1.14. Challenges in Systemic Machine Learning
• 1.15. Reinforcement Machine Learning and Systemic Machine Learning
• 1.16. Case Study Problem Detection in a Vehicle
• 1.17. Summary
• Reference.

• ch. 2: Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning
• 2.1. Introduction
• 2.2. What is Systemic Machine Learning?
• 2.3. Generalized Systemic Machine-Learning Framework
• 2.4. Multiperspective Decision Making and Multiperspective Learning
• 2.5. Dynamic and Interactive Decision Making
• 2.6. The Systemic Learning Framework
• 2.7. System Analysis
• 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry
• 2.9. Summary.

• ch. 3. : Reinforcement Learning
• 3.1. Introduction
• 3.2. Learning Agents
• 3.3. Returns and Reward Calculations
• 3.4. Reinforcement Learning and Adaptive Control
• 3.5. Dynamic Systems
• 3.6. Reinforcement Learning and Control
• 3.7. Markov Property and Markov Decision Process
• 3.8. Value Functions
• 3.9. Learning An Optimal Policy (Model-Based and Model-Free Methods)
• 3.10. Dynamic Programming
• 3.11. Adaptive Dynamic Programming
• 3.12. Example: Reinforcement Learning for Boxing Trainer
• 3.13. Summary
• Reference.

• ch. 4: Systemic Machine Learning and Model
• 4.1. Introduction
• 4.2. A Framework for Systemic Learning
• 4.3. Capturing THE Systemic View
• 4.4. Mathematical Representation of System Interactions
• 4.5. Impact Function
• 4.6. Decision-Impact Analysis
• 4.7. Summary.

• ch. 5: Inference and Information Integration
• 5.1. Introduction
• 5.2. Inference Mechanisms and Need
• 5.3. Integration of Context and Inference
• 5.4. Statistical Inference and Induction
• 5.5. Pure Likelihood Approach
• 5.6. Bayesian Paradigm and Inference
• 5.7. Time-Based Inference
• 5.8. Inference to Build a System View
• 5.9. Summary.

• ch. 6: Adaptive Learning
• 6.1. Introduction
• 6.2. Adaptive Learning and Adaptive Systems
• 6.3. What is Adaptive Machine Learning?
• 6.4. Adaptation and Learning Method Selection Based on Scenario
• 6.5. Systemic Learning and Adaptive Learning
• 6.6. Competitive Learning and Adaptive Learning
• 6.7. Examples
• 6.8. Summary.

• ch. 7: Multiperspective and Whole-System Learning
• 7.1. Introduction
• 7.2. Multiperspective Context Building
• 7.3. Multiperspective Decision Making and Multiperspective Learning
• 7.4. Whole-System Learning and Multiperspective Approaches
• 7.5. Case Study Based on Multiperspective Approach
• 7.6. Limitations to a Multiperspective Approach
• 7.7. Summary.

• ch. 8: Incremental Learning and Knowledge Representation
• 8.1. Introduction
• 8.2. Why Incremental Learning?
• 8.3. Learning from What Is Already Learned
• 8.4. Supervised Incremental Learning
• 8.5. Incremental Unsupervised Learning and Incremental Clustering
• 8.6. Semisupervised Incremental Learning
• 8.7. Incremental and Systemic Learning
• 8.8. Incremental Closeness Value and Learning Method
• 8.9. Learning and Decision-Making Model
• 8.10. Incremental Classification Techniques
• 8.11. Case Study: Incremental Document Classification
• 8.12. Summary.

• ch. 9 Knowledge Augmentation: A Machine Learning Perspective
• 9.1. Introduction
• 9.2. Brief History and Related Work
• 9.3. Knowledge Augmentation and Knowledge Elicitation
• 9.4. Life Cycle of Knowledge
• 9.5. Incremental Knowledge Representation
• 9.6. Case-Based Learning and Learning with Reference Knowledge Loss
• 9.7. Knowledge Augmentation: Techniques and Methods
• 9.8. Heuristic Learning
• 9.9. Systemic Machine Learning and Knowledge Augmentation
• 9.10. Knowledge Augmentation in Complex Learning Scenarios
• 9.11. Case Studies
• 9.12. Summary.

• ch. 10: Building a Learning System
• 10.1. Introduction
• 10.2. Systemic Learning System
• 10.3. Algorithm Selection
• 10.4. Knowledge Representation
• 10.4.1. Practical Scenarios and Case Study
• 10.5. Designing a Learning System
• 10.6. Making System to Behave Intelligently
• 10.7. Example-Based Learning
• 10.8. Holistic Knowledge Framework and Use of Reinforcement Learning
• 10.9. Intelligent Agents Deployment and Knowledge Acquisition and Reuse
• 10.10. Case-Based Learning: Human Emotion-Detection System
• 10.11. Holistic View in Complex Decision Problem
• 10.12. Knowledge Representation and Data Discovery
• 10.13. Components
• 10.14. Future of Learning Systems and Intelligent Systems
• 10.15. Summary
• Appendix A: Statistical Learning Methods
• Appendix B: Markov Processes.

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)

• Invited Speakers.- Past, Present and Future of Ambient Intelligence and Smart Environments.- I: Artificial Intelligence.- Modelling Social Learning of Adolescence-Limited Criminal Behaviour.- How Do Emotions Induce Dominant Learners' Mental States Predicted from Their Brainwaves?.- A Multiagent Semantics for the Game Description Language.- Verifying Context-Dependent Reduction Relations for Knowledge Specifications.- Combining Artificial Intelligence Techniques for the Training of Power System Control Centre Operators.- Adaptive State Space Abstraction Using Neuroevolution.- Goal-Based Game Tree Search for Complex Domains.- Generating Incomplete Data with DataZapper.- Extending Learning Vector Quantization for Classifying Data with Categorical Values.- Action Knowledge Acquisition with Opmaker2.- Application of Hidden Topic Markov Models on Spoken Dialogue Systems.- Gossip Galore: An Embodied Conversational Agent for Collecting and Sharing Pop Trivia from the Web.- Biosignal Based Discrimination between Slight and Strong Driver Hypovigilance by Support-Vector Machines.- II: Agents.- Tiered Logic for Agents in Contexts.- HomeManager: Testing Agent-Oriented Software Engineering in Home Intelligence.- Developing Multi-Agent Systems through Integrating Prometheus, INGENIAS and ICARO-T.- An Efficient Winner Approximation for a Series of Combinatorial Auctions.- How to Integrate Personalization and Trust in an Agent Network.- Modeling Two Stage Preventive Medical Checkup Systems with Social Science Approaches.- Translating Discrete Multi-Agents Systems into Cellular Automata: Application to Diffusion-Limited Aggregation.- Using Values to Turn Agents into Characters.
• (source: Nielsen Book Data)

The present book includes a set of selected papers from the First International Conf- ence on Agents and Artificial Intelligence (ICAART 2009), held in Porto, Portugal, during January 19-21, 2009. The conference was organized in two simultaneous tracks: "Artificial Intelligence and Agents." The book is based on the same structure. ICAART 2009 received 161 paper submissions, from more than 37 different co- tries in all continents. After a blind review process, only 26 where accepted as full papers, of which 21 were selected for inclusion in this book, based on the classifi- tions provided by the Program Committee. The selected papers reflect the interdis- plinary nature of the conference. The diversity of topics is an important feature of this conference, enabling an overall perception of several important scientific and tech- logical trends. These high-quality standards will be maintained and reinforced at ICAART 2010, to be held in Valencia, Spain, and in future editions of this conf- ence. Furthermore, ICAART 2009 included five plenary keynote lectures given by Juan Carlos Augusto (University of Ulster), Marco Dorigo (IRIDIA, Free University of Brussels), Timo Honkela (Helsinki University of Technology), Edward H. Shortliffe (Arizona State University) and Paulo Urbano (University of Lisbon). We would like to express our appreciation to all of them and in particular to those who took the time to contribute with a paper to this book.
(source: Nielsen Book Data)

• Ensembles Discovered Predictive Learning and Decision Trees Model Complexity, Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity.
• (source: Nielsen Book Data)

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
(source: Nielsen Book Data)

• Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.
• (source: Nielsen Book Data)

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
(source: Nielsen Book Data)

• 1. Speech hyphenation segmentation by means of blind source separation / Harold Szu and Charles Hsu
• 2. Higher-order moments based synthesis of supervised fourier demixing filter / Eiji Uchino, Noriaki Suetake, and Takeshi Yamakawa
• 3. Design and application of an acoustic database navigator for the intractive analysis of psychoacoustic sound archives and sound engineerint / Andreas Konig, Friedrich E. Blutner, Michael Eberhardt, and Robert Wenzel
• 4. Multilayer perceptron networks with adaptive centroid transformation / Mikko Lehtokangas
• 5. Identification and analysis for transiently evoked otoacoustic emission / Liming Li, Jinsen Lin, and Zhengguo Zhang
• 6. New reliability models based on imprecise probabilities / Lev V. Utkin and Sergey V. Gurov.

• 7. Multi-modular neural network for breast cancer detection / Huai Li and K.J. Ray Liu
• 8. Advanced neural nets for visual image communication / Harold Szu and Charles Hsu
• 9. Continuous valued techniques based on the Lagrangian method for the wire routing problem / Shakeel Ismail, Masahiro Nagamatu and Torao Yanaru
• 10. Chaotic neural networks for information processing / Charles Hsu and Harold Szu
• 11. Wavelet encoding for interactive genetic algorithm in emotional image retrieval / Joo-Young Lee and Sung-Bae Cho
• 12. Call admission control using interval arithmetic coulomb energy network / Won Don Lee, Kyunghee Lee and Hong-kee Kim.

This book presents worldwide outstanding research and recent progress in the applications of neural networks, fuzzy logic, chaos, independent component analysis, etc to fields related to speech recognition enhancement, supervised Fourier demixing noise elimination, acoustic databases, the human hearing system, cancer detection, image processing, and visual communications.
(source: Nielsen Book Data)

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• Outline of a computational theory of perceptions based on computing with words / L.A. Zadeh
• Introduction to soft computing and intelligent control systems / N.K. Sinha and M.M. Gupta
• Computational issues in intelligent control / X.D. Koutsoukos and P.J. Antsaklis
• Neural networks
• a guided tour / S. Haykin
• On generating variable structure organization using a genetic algorithm / A.K. Zaidi and A.H. Levis
• Evolutionary algorithms and neural networks / R.G.S. Asthana
• Neural networks and fuzzy systems / P. Musilek and M.M. Gupta
• Fuzzy neural networks / P. Musilek and M.M. Gupta
• A cursory look at parallel and distributed architectures and biologically inspired computing / S.K. Basu
• Developments in learning control systems / J.X. Xu [and others]
• Techniques for genetic adaptive control / W.K. Lennon and K.M. Passino
• Cooperative behavior of intelligent agents : theory and practice / L. Vlacic, A. Engwirda, and M. Kajitani
• Expert systems in process diagnosis and control / D. Popovic
• Neural networks for identification of nonlinear systems : an overview / P. Gupta and N.K. Sinha
• Sensor fusion system using recurrent fuzzy inference / F. Kobayashi [and others]
• Neurofuzzy state estimators / C.J. Harris, X. Hong, and Q. Gan.

• Soft computing paradigms for artificial vision / K.K. Shukla
• Intelligent control with neural networks / D. Popovic
• Knowledge-based adaptation of neurofuzzy models in predictive control of a heat exchanger / M. Fischer, O. Nelles, and R. Isermann
• Neural network approximation of piecewise continuous functions : application to friction compensation / R.R. Selmic and F.L. Lewis
• Fuzzy adaptive and predictive control of a thermic process / I. Skrjanc and D. Matko
• An intelligent approach to positive target identification / R.-N.P. Singh
• Adaptive agents and artificial life : insights for the power industry / S.A. Harp and T. Samad
• Truck backer-upper control using dynamic neural network / M.M Gupta and D.H. Rao
• Toward intelligent machines : future perspectives / M.M. Gupta and N.K. Sinha.

The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science. This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply "fuzzy" engineering into a wide array of devices and systems.
(source: Nielsen Book Data)

• 1. Learning, goals, and learning goals / by Ashwin Ram and David B. Leake
• 2. Planning to learn / by Lawrence Hunter
• 3. Quantitative results concerning the utility of explanation-based learning / by Steven Minton
• 4. The use of explicit goals for knowledge to guide inference and learning / by Ashwin Ram and Lawrence Hunter
• 5. Deriving categories to achieve goals / by Lawrence W. Barsalou
• 6. Harpoons and long sticks : the interaction of theory and similarity in rule induction / by Edward J. Wisniewski and Douglas L. Medin
• 7. Introspective reasoning using meta-explanations for multistrategy learning / by Ashwin Ram and Michael T. Cox
• 8. Goal-directed learning : a decision-theoretic model for deciding what to learn next / by Marie desJardins
• 9. Goal-based explanation evaluation / by David B. Leake
• 10. Planning to perceive / by Louise Pryor and Gregg Collins
• 11. Planning and learning in PRODIGY : overview of an integrated architecture / by Jaime Carboneil [and others]
• 12. A learning model for the selection of problem-solving strategies in continuous physical systems / by Xiaodong Xia and Dif-Yan Yeung
• 13. Explicitly biased generalization / by Diana Gordon and Donald Perlis
• 14. Three levels of goal orientation in learning / by Evelyn Ng and Carl Bereiter
• 15. Characterizing the application of computer simulations in education : instructional criteria / by Jos J.A. van Berkum [and others]
• 16. Goal-driven learning : fundamental issues and symposium report / by David B. Leake and Ashwin Ram
• 17. Storage side effects : studying processing to understand learning / by Lawrence W. Barsalou
• 18. Goal-driven learning in multistrategy reasoning and learning systems / by Ashwin Ram, Michael T. Cox and S. Narayanan
• 19. Inference to the best plan : a coherence theory of decision / by Paul Thagard and Elija Millgram
• 20. Toward goal-driven integration of explanation and action / by David B. Leake
• 21. Learning as goal-driven inference / by Ryszard Michalski and Ashwin Ram.

In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book.
(source: Nielsen Book Data)
In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations.
(source: Nielsen Book Data)

Building a person has been an elusive goal in artificial intelligence. This failure, John Pollock argues, is because the problems involved are essentially philosophical; what is needed for the construction of a person is a physical system that mimics human rationality. Pollock describes an exciting theory of rationality and its partial implementation in OSCAR, a computer system whose descendants will literally be persons.In developing the philosophical superstructure for this bold undertaking, Pollock defends the conception of man as an intelligent machine and argues that mental states are physical states and persons are physical objects as described in the fable of Oscar, the self conscious machine.Pollock brings a unique blend of philosophy and artificial intelligence to bear on the vexing problem of how to construct a physical system that thinks, is self conscious, has desires, fears, intentions, and a full range of mental states. He brings together an impressive array of technical work in philosophy to drive theory construction in AI. The result is described in his final chapter on "cognitive carpentry." John Pollock is Professor of Philosophy and Cognitive Science at the University of Arizona. A Bradford Book.
(source: Nielsen Book Data)

• Image Processing for Practical Applications.- New Methods of Forming and Measurement of Sub-pixel Shift of Digital Images.- The Characteristics of the Phase-energy Image Spectrum.- Detectors Fields.- Comparative Evaluation of Algorithms for Trajectory Filtering.
• (source: Nielsen Book Data)

This book attempts to improve algorithms by novel theories and complex data analysis in different scopes including object detection, remote sensing, data transmission, data fusion, gesture recognition, and medical image processing and analysis. The book is directed to the Ph.D. students, professors, researchers, and software developers working in the areas of digital video processing and computer vision technologies.
(source: Nielsen Book Data)

Cut through the noise and get real results with this workshop for beginners. Use a project-based approach to exploring machine learning with TensorFlow and Keras. Key Features Understand the nuances of setting up a deep learning programming environment Gain insights into the common components of a neural network and its essential operations Get to grips with deploying a machine learning model as an interactive web application with Flask Book Description Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you'll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you've understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you'll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you'll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you'll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects. What you will learn Familiarize yourself with the components of a neural network Understand the different types of problems that can be solved using neural networks Explore different ways to select the right architecture for your model Make predictions with a trained model using TensorBoard Discover the components of Keras and ways to leverage its features in your model Explore how you can deal with new data by learning ways to retrain your model Who this book is for If you are a data scientist or a machine learning and deep learning enthusiast, who is looking to design, train, and deploy TensorFlow and Keras models into real-world applications, then this workshop is for you. Knowledge of computer science and machine learning concepts and experience in ...

• 1: Introduction
• 2: Fundamentals of Edge Computing
• 2.1 Paradigms of Edge Computing
• Cloudlet and Micro Data Centers
• Fog Computing
• Mobile (Multi-access) Edge Computing (MEC)
• Definition of Edge Computing Terminologies
• Collaborative End-Edge-Cloud Computing
• 2.2 Hardware for Edge Computing
• AI Hardware for Edge Computing
• Integrated Commodities Potentially for Edge Nodes
• Edge Computing Frameworks
• 2.3 Virtualizing the Edge
• Virtualization Techniques
• Network Virtualization
• Network Slicing
• 3. Fundamentals of Deep Learning
• 3.1 Neural Networks in Deep Learning
• Fully Connected Neural Network (FCNN)
• Auto-Encoder (AE)
• Convolutional Neural Network (CNN)
• Generative Adversarial Network (GAN)
• Recurrent Neural Network (RNN)
• Transfer Learning (TL)
• 3.2 Deep Reinforcement Learning (DRL)
• Value-based DRL
• Policy-gradient-based DRL
• 3.3 Distributed DL Training
• 3.4 Potential DL Libraries for Edge
• 4. Deep Learning Applications on Edge
• 4.1 Real-time Video Analytic
• 4.2 Autonomous Internet of Vehicles (IoVs)
• 4.3 Intelligent Manufacturing
• 4.4 Smart Home and City
• 5. Deep Learning Inference in Edge
• 5.1 Optimization of DL Models in Edge
• General Methods for Model Optimization
• Model Optimization for Edge Devices
• 5.2 Segmentation of DL Models
• 5.3 Early Exit of Inference (EEoI)
• 5.4 Sharing of DL Computation
• 6. Edge Computing for Deep Learning
• 6.1 Edge Hardware for DL
• Mobile CPUs and GPUs
• FPGA-based Solutions
• 6.2 Communication and Computation Modes for Edge DL
• Integral Offloading
• Partial Offloading
• Vertical Collaboration
• Horizontal Collaboration
• 6.3 Tailoring Edge Frameworks for DL
• 6.4 Performance Evaluation for Edge DL
• 7. Deep Learning Training at Edge
• 7.1 Distributed Training at Edge
• 7.2 Vanilla Federated Learning at Edge
• 7.3 Communication-efficient FL
• 7.4 Resource-optimized FL
• 7.5 Security-enhanced FL
• 8. Deep Learning for Optimizing Edge
• 8.1 DL for Adaptive Edge Caching
• 8.2 DL for Optimizing Edge Task Offloading
• 8.3 DL for Edge Management and Maintenance
• Edge Communication
• Edge Security
• Joint Edge Optimization
• 9. Lessons Learned and Open Challenges
• 9.1 More Promising Applications
• 9.2 General DL Model for Inference
• 9.3 Complete Edge Architecture for DL
• 9.4 Practical Training Principles at Edge
• 9.5 Deployment and Improvement of Intelligent Edge
• .
• (source: Nielsen Book Data)

As an important enabler for changing people's lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
(source: Nielsen Book Data)

• Comprehensive Guide to Implementation of Data Warehouse in Education.- Computational Biology Tool Towards Studying the Interaction Between Azadirachtin Plant Compound with Cervical Cancer Proteins.- Intelligent Agent Based Organization For Studying the Big Five Personality Traits.- Automatic Pruning of Rules Through Multi-Objective Optimization - A Case Study With a Multi-Objective Cultural Algorithm.- Knowledge Genesis And Dissemination: Impact On Performance In Information Technology Services.- Artificial Intelligence Based Load Balancing In Cloud Computing Environment: A Study.- Implementation of Statistical Data Analytics in Data Science Life Cycle.- A Big Data Analytics-Based Design for Viable Evolution of Retail Sector.- Document Content Analysis Based on Random Forest Algorithm.- Sensing The Prostatectomy in Neuroendocrine Metastatic Active Surveillance in Data Mining Techniques.- Work Load Forecasting Based On Big Data Characteristics In Cloud Systems.- Phrase Extraction Using Pattern Based Bootstrapping Approach.- IOT Based Trash Collection Bin Using Arduino.
• (source: Nielsen Book Data)

This book covers both basic and high-level concepts relating to the intelligent computing paradigm and data sciences in the context of distributed computing, big data, data sciences, high-performance computing and Internet of Things. It is becoming increasingly important to develop adaptive, intelligent computing-centric, energy-aware, secure and privacy-aware systems in high-performance computing and IoT applications. In this context, the book serves as a useful guide for industry practitioners, and also offers beginners a comprehensive introduction to basic and advanced areas of intelligent computing. Further, it provides a platform for researchers, engineers, academics and industrial professionals around the globe to showcase their recent research concerning recent trends. Presenting novel ideas and stimulating interesting discussions, the book appeals to researchers and practitioners working in the field of information technology and computer science.
(source: Nielsen Book Data)

• Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction.- EIFEL - a New Approach for Digital Education.- FINMINA: a French National Project Dedicated to Educational Innovation in Microelectronics to Meet the Challenges of a Digital Society.- Effect of International Student Competition Experience on Smart Education.- Knowledge-Based Model Representation for a Modern Digital University.- Effects on Girls' Emotions During Gamification-Tasks with Male Priming in STEM Subjects via Eye tracking.- Relevancy of the MOOC about Teaching Methods in Multilingual Classroom.- e-learning Tools for Informal Caretakers of Patients with Dementia - a Review Study.- Automation of e-Workshop Project Control for Knowledge-Intensive Areas.- Assessment of Student Work and the Organization of Individual Learning Paths in Electronic Smart-Learning Systems.
• (source: Nielsen Book Data)

This book contains the contributions presented at the 7th international KES conference on Smart Education and e-Learning (KES SEEL-2020), which being held as a virtual conference on June 17-19, 2020. It contains fifty three high quality peer-reviewed papers that are grouped into several interconnected parts: Part 1 - Smart Education, Part 2 - Smart e-Learning, Part 3 - Smart Pedagogy, Part 4 - Smart Education: Systems and Technology, Part 5 - Smart Education: Case Studies and Research, Part 6 - Smart University Development: Organizational and Managerial Issues, Part 7 - Smart Education and Smart Universities and their Impact on Students with Disabilities, Part 8 - Mathematical Models in Smart Education and e-Learning, and Part 9 - Models of Professional Practice in Higher Education. Smart education and smart e-learning are emerging and rapidly growing areas with the potential to transform existing teaching strategies, learning environments, and educational activities and technology in the classroom. Smart education and smart e-learning focus on enabling instructors to develop new ways of achieving excellence in teaching in highly technological smart classrooms, and providing students with new opportunities to maximize their success and select the best options for their education, location and learning style, as well as the mode of content delivery. This book serves as a useful source of research data and valuable information on current research projects, best practices and case studies for faculty, scholars, Ph.D. students, administrators, and practitioners - all those who are interested in smart education and smart e-learning.
(source: Nielsen Book Data)

• Foreword.- Should anthropomorphic systems be"redundant"?.- From Biomechanics to Robotics.- Multibody optimisations: from kinematic constraints to knee contact forces and ligament forces.- Creating personalized dynamic models.- Optimality and modularity in human movement: from optimal control to muscle synergies.- Human Movements: Synergies, Stability, and Agility.- Motor compositionality and timing: combined geometrical and optimization approaches.- Review of Anthropomorphic Head Stabilisation and Verticality Estimation in Robots.- The Physics and Control of Balancing on a Point in the Plane.- Design and Control of a passive noise rejecting Variable Stiffness Actuator.- Bipedal locomotion: a continuous tradeoff between robustness and energy-efficiency.- An overview of humanoid robots technologies.
• (source: Nielsen Book Data)

Mechanical laws of motion were applied very early for better understanding anthropomorphic action as suggested in advance by Newton "For from hence are easily deduced the forces of machines, which are compounded of wheels, pullies, levers, cords, and weights, ascending directly or obliquely, and other mechanical powers; as also the force of the tendons to move the bones of animals". In the 19th century E.J. Marey and E. Muybridge introduced chronophotography to scientifically investigate animal and human movements. They opened the field of motion analysis by being the first scientists to correlate ground reaction forces with kinetics. Despite of the apparent simplicity of a given skilled movement, the organization of the underlying neuro-musculo-skeletal system remains unknown. A reason is the redundancy of the motor system: a given action can be realized by different muscle and joint activity patterns, and the same underlying activity may give rise to several movements. After the pioneering work of N. Bernstein in the 60's on the existence of motor synergies, numerous researchers "walking on the border" of their disciplines tend to discover laws and principles underlying the human motions and how the brain reduces the redundancy of the system. These synergies represent the fundamental building blocks composing complex movements. In robotics, researchers face the same redundancy and complexity challenges as the researchers in life sciences. This book gathers works of roboticists and researchers in biomechanics in order to promote an interdisciplinary research on anthropomorphic systems at large and on humanoid robotics in particular.
(source: Nielsen Book Data)

• Introduction.- First approach: sentence analysis using rewriting rules.- Second approach: constituent grammars.- Third approach: dependency trees.- Evaluation of the dependency parser.- Applications.- Prepositional phrase attachment disambiguation.- The unsupervised approach: grammar induction.- Multiple argument handling.- The need for full co-occurrence.
• (source: Nielsen Book Data)

This book describes effective methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it. To tackle ambiguities, the authors use selectional preferences (SP), which measure how well two words fit together semantically in a sentence. Today, many disciplines require automatic text analysis based on the syntactic and semantic characteristics of language and as such several techniques for parsing sentences have been proposed. Which is better? In this book the authors begin with simple heuristics before moving on to more complex methods that identify nouns and verbs and then aggregate modifiers, and lastly discuss methods that can handle complex subordinate and relative clauses. During this process, several ambiguities arise. SP are commonly determined on the basis of the association between a pair of words. However, in many cases, SP depend on more words. For example, something (such as grass) may be edible, depending on who is eating it (a cow?). Moreover, things such as popcorn are usually eaten at the movies, and not in a restaurant. The authors deal with these phenomena from different points of view.
(source: Nielsen Book Data)

• Learning for vision
• Computational photography
• Human analysis
• Human sensing
• Stereo and reconstruction
• Optimization
• Matching and recognition
• Video attention
• Poster sessions.

The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018. The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

• Introduction.- Radon Transform based Automatic Posture Recognition in Ballet Dance.- Fuzzy Image Matching Based Posture Recognition in Ballet Dance.- Gesture Driven Fuzzy Interface System For Car Racing Game.- Type-2 Fuzzy Classifier based Pathological Disorder Recognition.- Probabilistic Neural Network based Dance Gesture Recognition.- Differential Evolution based Dance Composition.- EEG-Gesture based Artificial Limb Movement for Rehabilitative Applications.- Conclusions and Future Directions.- Index.
• (source: Nielsen Book Data)

This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.
(source: Nielsen Book Data)

• SYMMETRIC KEY ENCRYPTION.- CHAM: A Family of Lightweight Block Ciphers for Resource-Constrained Devices.- Improved Meet-in-the-Middle Attacks on Reduced Round Kuznyechik.- Security of Stateful Order-Preserving Encryption.- HOMOMORPHIC ENCRYPTION.- Cryptanalysis of Tran-Pang-Deng Verifiable Homomorphic Encryption.- Multi-Party (Leveled) Homomorphic Encryption on Identity-Based and Attribute-Based Settings.- Improved Key Generation Algorithm for Gentry's Fully Homomorphic Encryption Scheme.- SIDE CHANNEL ANALYSIS AND IMPLEMENTATION.- Novel Leakage against Realistic Masking and Shuffling Countermeasures - case study on PRINCE and SEED.- Detecting Similar Code Segments through Side Channel Leakage in Microcontrollers.- Secure Number Theoretic Transform and Speed Record for Ring-LWE Encryption on Embedded Processors.- BROADCAST ENCRYPTION.- Recipient Revocable Broadcast Encryption Schemes Without Random Oracles.- Recipient Revocable Broadcast Encryption with Dealership.- ELLIPTIC CURVE.- Solving 114-bit ECDLP for a Barreto-Naehrig Curve.- On the computational complexity of ECDLP for elliptic curves in various forms using index calculus.- SIGNATURE AND PROTOCOL.- Two Mutual Authentication Protocols Based on Zero-Knowledge Proofs For RFID Systems.- On New Zero-Knowledge Arguments for Attribute-Based Group Signatures from Lattices.- Security Analysis of Improved Cubic UOV Signature Schemes.- NETWORK AND SYSTEM SECURITY.- Evaluating the Impact of Juice Filming Charging Attack in Practical Environments.- Reading Network Packets as a Natural Language for Intrusion Detection.- Friend-safe Adversarial Examples in an Evasion Attack on a Deep Neural Network.
• (source: Nielsen Book Data)

This book constitutes revised selected papers from the 20th International Conference on Information Security and Cryptology, ICISC 2017, held in Seoul, South Korea, in November/December 2017. The total of 20 papers presented in this volume were carefully reviewed and selected from 70 submissions. The papers were organized in topical sections named: symmetric key encryption; homomorphic encryption, side channel analysis and implementation; broadcast encryption; elliptic curve; signature and protocol; and network and system security. .
(source: Nielsen Book Data)

• Table of Contents A Gentle Introduction to Machine Learning Important Elements in Machine Learning Feature Selection and Feature Engineering Regression Algorithms Linear Classification Algorithms Naive Bayes and Discriminant Analysis Support Vector Machines Decision Trees and Ensemble Learning Clustering Fundamentals Advanced Clustering Hierarchical Clustering Introducing Recommendation Systems Introducing Natural Language Processing Topic Modeling and Sentiment Analysis in NLP Introducing Neural Networks Advanced Deep Learning Models Creating a Machine Learning Architecture.
• (source: Nielsen Book Data)

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book DescriptionMachine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is forMachine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.
(source: Nielsen Book Data)

• Table of Contents Machine Learning Model Fundamentals Introduction to Semi-Supervised Learning Graph-based Semi-Supervised Learning Bayesian Networks and Hidden Markov Models EM algorithm and applications Hebbian Learning Advanced Clustering and Feature Extraction Ensemble Learning Neural Networks for Machine Learning Advanced Neural Models Auto-Encoders Generative Adversarial Networks Deep Belief Networks Introduction to Reinforcement Learning Policy estimation algorithms.
• (source: Nielsen Book Data)

Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book DescriptionMachine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is forThis book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
(source: Nielsen Book Data)

• Theory of Complex Networks
• Multilayer Networks
• Controllability of Networks
• Algorithms for Networks
• Community Detection
• Dynamics and Spreading Phenomena on Networks
• Applications of Networks.

The last decades have seen the emergence of Complex Networks as the language with which a wide range of complex phenomena in fields as diverse as Physics, Computer Science, and Medicine (to name just a few) can be properly described and understood. This book provides a view of the state of the art in this dynamic field and covers topics ranging from network controllability, social structure, online behavior, recommendation systems, and network structure. This book includes the peer-reviewed list of works presented at the 7th Workshop on Complex Networks CompleNet 2016 which was hosted by the Université de Bourgogne, France, from March 23-25, 2016. The 28 carefully reviewed and selected contributions in this book address many topics related to complex networks and have been organized in seven major groups: (1) Theory of Complex Networks, (2) Multilayer networks, (3) Controllability of networks, (4) Algorithms for networks, (5) Community detection, (6) Dynamics and spreading phenomena on networks, (7) Applications of Networks.

• Introduction.- Coordination of Distributed Systems.- Coordination of Selforganising Systems.- Coordination of Pervasive Systems.- Coordination of Sociotechnical Systems.- Molecules of Knowledge: Model.- Molecules of Knowledge: Technology.-. Molecules of Knowledge: Simulation.- Molecules of Knowledge: Case Studies.- Conclusions.
• (source: Nielsen Book Data)

The book discusses the main issues of coordination in complex sociotechnical systems, covering distributed, self-organising, and pervasive systems. A chemistry-inspired model of coordination, a situated architecture and coordination language, and a cognitive model of interaction are the ingredients of the Molecules of Knowledge (MoK) model for self-organisation of knowledge presented in this book. The MoK technology is discussed, along with some case studies in the fields of collaborative systems, academic research, and citizen journalism. The target audience includes researchers and practitioners in the field of complex software systems engineering. The book is also appropriate for graduate and late undergraduate students in computer science and engineering.
(source: Nielsen Book Data)

• A Plantar Inclinometer Based Approach to Fall Detection in Open Environments
• Using Fuzzy Evidential Reasoning for Multiple Assessment Fusion in Spondylarthropathic Patient Self-Management
• Rescue System with Sensor Network for Vital Sign Monitoring and Rescue Simulations by Taking into Account Triage with Measured Vital Signs
• An Approach for Detecting Traffic Events using Social Media
• Applying Supervised & Unsupervised Learning Techniques on Dental Patients? Records
• Technology in Primary Schools: Teachers? Perspective towards the Use of Mobile Technology in Children Education
• Designing, Implementing and Testing an Automated Trading Strategy Based on Dynamic Bayesian Networks, the Limit Order Book Information and the Random Entry Protocol
• An Adaptive Multi Agent Service Discovery for Peer to Peer Cloud Services
• Modelling and Detection of User Activity Patterns for Energy Saving in Buildings
• e QoS using PSO in Fixed WiMAX Networks
• Colour-Preserving Contrast Enhancement Algorithm for Images
• Sequential Pattern Discovery for Weather Prediction Problem
• A Class-based Strategy to User Behavior Modeling in Recommender Systems
• Feature Correspondence in Low Quality CCTV Videos
• Automatic Detection and Severity Assessment of Crop Diseases using Image Pattern Recognition
• Image Complexity and Visual Working Memory Capacity
• Immersive Brain Entrainment in Virtual Worlds: Actualizing Meditative States
• A Real-Time Stereo Vision Based Obstacle Detection
• Depth and Thermal Image Fusion for Human Detection with Occlusion Handling under Poor Illumination from Mobile Robot
• Exploiting the Retinal Vascular Geometry in Identifying the Progression to Diabetic Retinopathy Using Penalized Logistic Regression and Random Forests
• A New Method for Improving the Detection Capability of RADAR in the Presence of Noise.

This book is a collection of extended chapters from the selected papers that were published in the proceedings of Science and Information (SAI) Conference 2015. It contains twenty-one chapters in the field of Computational Intelligence, which received highly recommended feedback during SAI Conference 2015 review process. During the three-day event 260 scientists, technology developers, young researcher including PhD students, and industrial practitioners from 56 countries have engaged intensively in presentations, demonstrations, open panel sessions and informal discussions.

• Flower Pollination Algorithm and its Applications in Engineering
• An Evolutionary Discrete Firefly Algorithm with Novel Operators for Solving the Vehicle Routing Problem with TimeWindows
• The Plant Propagation Algorithm for Discrete Optimisation: The Case of the Travelling Salesman Problem
• Enhancing Cooperative Coevolution with Surrogate-Assisted Local Search
• Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization
• Clustering Optimization for WSN based on Nature-Inspired Algorithms
• Discrete Firefly Algorithm for Recruiting Task in a Swarm of Robots
• Nature-Inspired Swarm Intelligence for Data Fitting in Reverse Engineering: Recent Advances and FutureTrends
• A Novel Fast Optimisation Algorithm Using Differential Evolution Algorithm Optimisation and Meta- Modelling Approach
• A Hybridization of Runner-Based and Seed-Based Plant Propagation Algorithm
• Gravitational Search Algorithm Applied to Cell Formation Problem
• Parameterless Bat Algorithm and its Performace Study.

This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.

• Autonomous mobile robots
• Tele-operated and telepresence robots
• Human-robot interaction
• Trajectory control for mobile robots
• Autonomous vehicles
• Service-oriented robotic systems
• Semantic mapping
• Environment mapping
• Visual odometry
• Applications of RGB-D sensors
• Humanoid and biped robots
• Robocup soccer robots
• Robot control
• Path planning
• Multiple vehicles and teams of robots.

This book constitutes the refereed proceedings of the 12th Latin American Robotics Symposium and Third Brazilian Symposium on Robotics, LARS 2015 / SBR 2015, held in Uberlândia, Brazil, in October/November 2015. The 17 revised full papers presented were carefully reviewed and selected from 80 submissions. The selected papers present a complete and solid reference of the state-of-the-art of intelligent robotics and automation research, covering the following areas: autonomous mobile robots, tele-operated and telepresence robots, human-robot interaction, trajectory control for mobile robots, autonomous vehicles, service-oriented robotic systems, semantic mapping, environment mapping, visual odometry, applications of RGB-D sensors, humanoid and biped robots, Robocup soccer robots, robot control, path planning, multiple vehicles and teams of robots.

• Vocabularies, Schemas, Ontologies
• Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment
• RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration
• VocBench: a Web Application for Collaborative Development of Multilingual Thesauri
• Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning
• Reasoning
• A Context-Based Semantics for SPARQL Property Paths over the Web
• Distributed and Scalable OWL EL Reasoning
• Large scale rule-based Reasoning using a Laptop
• RDF Digest: Efficient Summarization of RDF/S KBs
• Linked Data
• A Comparison of Data Structures to Manage URIs on the Web of Data
• Heuristics for Fixing Common Errors in Deployed schema.org Microdata
• Semantic Web and Web Science
• LOD-based Disambiguation of Named Entities in @tweets through Context enrichment
• Knowledge Enabled Approach to Predict the Location of Twitter Users
• Semantic Data Management, Big data, Scalability
• A Compact In-Memory Dictionary for RDF data
• Quality Assessment of Linked Datasets using Probabilistic Approximations
• Cooperative Techniques for SPARQL Query Relaxation in RDF Databases
• HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce
• Processing Aggregate Queries in a Federation of SPARQL Endpoints
• A survey of HTTP caching implementations on the open Semantic Web
• Query Execution Optimization for Clients of Triple Pattern Fragments
• Natural Language Processing and Information Retrieval LIME: the Metadata Module for OntoLex
• Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text
• HAWK Hybrid Question Answering using Linked Data
• Machine Learning
• Automating RDF Dataset Transformation and Enrichment
• Semi-supervised Instance Matching Using Boosted Classifiers
• Assigning Semantic Labels to Data Sources
• Inductive Classification through Evidence-based Models and their Ensembles
• Mobile Web, Internet of Things and Semantic Streams
• Standardized and Efficient RDF Encoding for Constrained Embedded Networks
• Services, Web APIs, and the Web of Things SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks
• Data as a Service: The Semantic Web Redeployed
• Cognition and Semantic Web
• On Coherent Indented Tree Visualization of RDF Graphs
• Gagg: A Graph Aggregation Operator
• FrameBase: Representing N-ary Relations using Semantic Frames
• Human Computation and Crowdsourcing
• Towards hybrid NER: a study of content and crowdsourcing-related performance factors
• Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets
• In-Use Industrial Track
• Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry
• Using Ontologies For Modeling Virtual Reality Scenarios
• Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes
• Crowdmapping Digital Social Innovation with Linked data
• Desperately searching for travel offers? Formulate better queries with some help from Linked Data
• Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing
• From Symptoms to Diseases
• Creating the Missing Link
• Using semantic web technologies for enterprise architecture analysis
• PADTUN
• Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain.

This book constitutes the refereed proceedings of the 12th Extended Semantic Web Conference, ESWC 2014, held in Anissaras, Portoroz, Slovenia, in May/June 2015. The 43 revised full papers presented together with three invited talks were carefully reviewed and selected from 164 submissions. This program was completed by a demonstration and poster session, in which researchers had the chance to present their latest results and advances in the form of live demos. In addition, the PhD Symposium program included 12 contributions, selected out of 16 submissions. The core tracks of the research conference were complemented with new tracks focusing on linking machine and human computation at web scale (cognition and Semantic Web, Human Computation and Crowdsourcing) beside the following subjects Vocabularies, Schemas, Ontologies, Reasoning, Linked Data, Semantic Web and Web Science, Semantic Data Management, Big data, Scalability, Natural Language Processing and Information Retrieval, Machine Learning, Mobile Web, Internet of Things and Semantic Streams, Services, Web APIs, and the Web of Things, Cognition and Semantic Web, Human Computation and Crowdsourcing, and In-Use Industrial Track as well.

• Towards More Realistic Human Behaviour Simulation: Modelling Concept, Deriving Ontology and Semantic Framework / Ladislav Hluchý, Marcel Kvassay, Štefan Dlugolinský, Bernhard Schneider and Holger Bracker, et al.
• 2-DOF and Fuzzy Control Extensions of Symmetrical Optimum Design Method: Applications and Perspectives / Stefan Preitl, Alexandra-Iulia Stînean, Radu-Emil Precup, Claudia-Adina Dragoş and Mircea-Bogdan Rădac
• Mixed Multidimensional Risk Aversion / Irina Georgescu and Jani Kinnunen
• Strong Berge and Strong Berge Pareto Equilibrium Detection Using an Evolutionary Approach / Noémi Gaskó, Rodica Ioana Lung and Dan Dumitrescu
• A Proposal of the Information Retrieval System Based on the Generalized One-Sided Concept Lattices / Peter Butka, Jana Pócsová and Jozef Pócs
• Visualization and Simulation Tool for Analyzing P-Graph Based Workflow Systems / József Tick
• Benchmark Based Comparison of Two Fuzzy Rule Base Optimization Methods / Zsolt Csaba Johanyák and Olga Papp
• Three Evolutionary Optimization Algorithms in PI Controller Tuning / Radu-Codruţ David, Radu-Emil Precup, Stefan Preitl, József K. Tar and János Fodor
• Statistical Analysis of Next Generation Network Traffics Based on Wavelets and Transformation ON/(ON+OFF) / Zoltan Gal and Gyorgy Terdik
• Data Cleaning and Anomaly Detection for an Intelligent Greenhouse / Peter Eredics and Tadeusz P. Dobrowiecki
• Clustering of Interval Data Using Self-Organizing Maps -- Application to Meteorological Data / Chantal Hajjar and Hani Hamdan
• A Set of Java Metrics for Software Quality Tree Based on Static Code Analyzers / Ciprian-Bogdan Chirilă and Vladimir Creţu.

• VLearning, a New Direction for eLearning Challenges / Andrea Tick
• A Receding Horizon Control Approach to Navigation in Virtual Corridors / Domokos Kiss and Gábor Tevesz
• High Speed Stereo Vision Based Automotive Collision Warning System / Adrian Leu, Dorin Aiteanu and Axel Gräser
• Multithreaded Peripheral Processor for a Multicore Embedded System / Horia V. Caprita and Mircea Popa
• Using Cycle-Approximate Simulation for Bus Based Multi-Processor System-On Chip Analysis / Alexandru Amaricai, Alin Dobre, Oana Boncalo, Andrei Tanase and Camelia Valuch
• A Case Study on Hardware/Software Codesign in Embedded Artificial Neural Networks / Jonathan Parri, John-Marc Desmarais, Daniel Shapiro, Miodrag Bolic and Voicu Groza
• Pragmatic Method to Obtain Optimal Control Laws for Small Windgenerators / Radu Boraci, Octavian Prostean, Nicolae Budisan and Cosmin Koch-Ciobotaru
• Applicability of Asymptotic Tracking in Case of Type 1 Diabetes / Péter Szalay and Levente Kovács
• Optimal Energetic Conditions for Cell Seeding of Scaffolds / Andreea Robu, Lacramioara Stoicu-Tivadar and Adrian Neagu
• Ideas on a Pattern of Human Knowledge / Claudiu Pozna and Radu-Emil Precup
• Structural Improvements of the OpenRTM-aist Robot Middleware / Zoltán Krizsán and Szilveszter Kovács
• Decision Support at a New Global Level Definition of Products in PLM Systems / László Horváth and Imre J. Rudas
• Autocollimator Calibration Using a Tangent Bar / Gyula Hermann and Kálmán Tomanyiczka
• Gesture Control: A New and Intelligent Man-Machine Interface / Dan Ionescu, Bogdan Ionescu, Cristian Gadea and Shahid Islam.

This book highlights the potential of getting benefits from various applications of computational intelligence techniques. The present book is structured such that to include a set of selected and extended papers from the 6th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2011, held in Timisoara, Romania, from 19 to 21 May 2011. After a serious paper review performed by the Technical Program Committee only 116 submissions were accepted, leading to a paper acceptance ratio of 65 %. A further refinement was made after the symposium, based also on the assessment of the presentation quality. Concluding, this book includes the extended and revised versions of the very best papers of SACI 2011 and few invited papers authored by prominent specialists. The readers will benefit from gaining knowledge of the computational intelligence and on what problems can be solved in several areas; they will learn what kind of approaches is advised to use in order to solve these problems. A very important benefit for the readers is an understanding of what the major difficulties are and the cost-effective solutions to deal with them. This book will offer a convenient entry for researchers and engineers who intend to work in the important fields of computational intelligence.

• Part 1. Introductory Part / Reinforcement Learning and Markov Decision Processes / Martijn van Otterlo and Marco Wiering
• Part 2. Efficient Solution Frameworks / Batch Reinforcement Learning / Sascha Lange, Thomas Gabel and Martin Riedmiller
• Least-Squares Methods for Policy Iteration / Lucian Buşoniu, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos and Robert Babuška, et al.
• Learning and Using Models / Todd Hester and Peter Stone
• Transfer in Reinforcement Learning: A Framework and a Survey / Alessandro Lazaric
• Sample Complexity Bounds of Exploration / Lihong Li
• Part 3. Constructive-Representational Directions / Reinforcement Learning in Continuous State and Action Spaces / Hado van Hasselt
• Solving Relational and First-Order Logical Markov Decision Processes: A Survey / Martijn van Otterlo
• Hierarchical Approaches / Bernhard Hengst
• Evolutionary Computation for Reinforcement Learning / Shimon Whiteson
• Part 4. Probabilistic Models of Self and Others / Bayesian Reinforcement Learning / Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor and Pascal Poupart
• Partially Observable Markov Decision Processes / Matthijs T.J. Spaan
• Predictively Defined Representations of State / David Wingate
• Game Theory and Multi-agent Reinforcement Learning / Ann Nowé, Peter Vrancx and Yann-Michaël De Hauwere
• Decentralized POMDPs / Frans A. Oliehoek
• Part 5. Domains and Background / Psychological and Neuroscientific Connections with Reinforcement Learning / Ashvin Shah
• Reinforcement Learning in Games / István Szita
• Reinforcement Learning in Robotics: A Survey / Jens Kober and Jan Peters
• Conclusions, Future Directions and Outlook / Marco Wiering and Martijn van Otterlo.

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.

• Computational Intelligence and Expert Systems
• Data Mining and Data Analysis
• Intelligent Decision Support Systems
• Intelligent Information Processing
• Knowledge Representation and Learning.
• (source: Nielsen Book Data)

ISKE2009 is the fourth in a series of conferences on Intelligent Systems and Knowledge Engineering. The ISKE2009 proceedings covers state-of-the-art research and development in various areas of Intelligent Systems and Knowledge Engineering, particularly of Intelligent Decision Making Systems.
(source: Nielsen Book Data)

• About the Authors. Foreword. Preface.
• 1 Teamwork in Multi-Agent Environments. 1.1 Autonomous Agents. 1.2 Multi-Agent Environments as a Pinnacle of Interdisciplinarity. 1.3 Why Teams of Agents? 1.4 The Many Flavors of Cooperation. 1.5 Agents with Beliefs, Goals and Intentions. 1.6 From Individuals to Groups. 1.7 Group Attitudes. 1.8 A Logical View on Teamwork: TEAMLOG. 1.9 Teamwork in Times of Change. 1.10 Our Agents are Planners. 1.11 Temporal or Dynamic? 1.12 From Real-World Data to Teamwork. 1.13 How Complex are Models of Teamwork?
• 2 Beliefs in Groups. 2.1 Awareness is a Vital Ingredient of Teamwork. 2.2 Perception and Beliefs. 2.3 Language and Models for Beliefs. 2.4 Axioms for Beliefs. 2.5 Axioms for Knowledge. 2.6 Relations between Knowledge and Belief. 2.7 Levels of Agents Awareness.
• 3 Collective Intentions. 3.1 Intentions in Practical Reasoning. 3.2 Language and Models for Goals and Intentions. 3.3 Goals and Intentions of Individual Agents. 3.4 Collective Intention Constitutes a Group. 3.5 Definitions of Mutual and Collective Intentions. 3.6 Collective Intention as an Infinitary Concept. 3.7 Alternative Definitions. 3.8 The Logic of Mutual Intention TeamLogmint is Complete. 3.9 Related Approaches to Intentions in a Group.
• 4 A Tuning Machine for Collective Commitments. 4.1 Collective Commitment. 4.2 The Language and Kripke Semantics. 4.3 Building Collective Commitments. 4.4 Tuning Collective Commitments. 4.5 Different Notions of Collective Commitment. 4.6 Topologies and Group Commitments. 4.7 Summing up TeamLog: The Static Part of the Story.
• 5 Reconfiguration in a Dynamic Environment. 5.1 Dealing with Dynamics. 5.2 The Four Stages of Teamwork. 5.3 The Reconfiguration Method. 5.4 Case Study of Teamwork: Theorem Proving.
• 6 The Evolution of Commitments during Reconfiguration. 6.1 A Formal View on Commitment Change. 6.2 Individual Actions and Social Plan Expressions. 6.3 Kripke Models. 6.4 Dynamic Description of Teamwork. 6.5 Evolution of Commitments During Reconfiguration. 6.6 TeamLog Summary.
• 7 A Case Study in Environmental Disaster Management. 7.1 A Bridge from Theory to Practice. 7.2 The Case Study: Ecological Disasters. 7.3 Global Plans. 7.4 Adjusting the TeamLog Definitions to the Case Study. 7.5 Conclusion.
• 8 Dialogue in Teamwork. 8.1 Dialogue as a Synthesis of Three Formalisms. 8.2 Dialogue Theory and Dialogue Types. 8.3 Zooming in on Vital Aspects of Dialogue. 8.4 Information Seeking During Potential Recognition. 8.5 Persuasion During Team Formation. 8.6 Deliberation During Planning. 8.7 Dialogues During Team Action. 8.8 Discussion.
• 9 Complexity of Teamlog. 9.1 Computational Complexity. 9.2 Logical Background. 9.3 Complexity of TeamLogind. 9.4 Complexity of the System TeamLog. 9.5 Discussion and Conclusions. A Appendix A. A.1 Axiom Systems. A.2 An Alternative Logical Framework for Dynamics of Teamwork: Computation Tree Logic. Bibliography. Index.
• (source: Nielsen Book Data)

What makes teamwork tick? Cooperation matters, in daily life and in complex applications. After all, many tasks need more than a single agent to be effectively performed. Therefore, teamwork rules! Teams are social groups of agents dedicated to the fulfilment of particular persistent tasks. In modern multiagent environments, heterogeneous teams often consist of autonomous software agents, various types of robots and human beings. Teamwork in Multi-agent Systems: A Formal Approach explains teamwork rules in terms of agents' attitudes and their complex interplay. It provides the first comprehensive logical theory, TeamLog, underpinning teamwork in dynamic environments. The authors justify design choices by showing TeamLog in action. The book guides the reader through a fascinating discussion of issues essential for teamwork to be successful: * What is teamwork, and how can a logical view of it help in designing teams of agents? * What is the role of agents' awareness in an uncertain, dynamic environment? * How does collective intention constitute a team? * How are plan-based collective commitments related to team action? * How can one tune collective commitment to the team's organizational structure and its communication abilities? * What are the methodological underpinnings for teamwork in a dynamic environment? * How does a team and its attitudes adjust to changing circumstances? * How do collective intentions and collective commitments arise through dialogue? * What is the computational complexity of TeamLog? * How can one make TeamLog efficient in applications? This book is an invaluable resource for researchers and graduate students in computer science and artificial intelligence as well as for developers of multi-agent systems. Students and researchers in organizational science, in particular those investigating teamwork, will also find this book insightful. Since the authors made an effort to introduce TeamLog as a conceptual model of teamwork, understanding most of the book requires solely a basic logical background.
(source: Nielsen Book Data)

• Metrics for Multiagent Systems.- Evaluation Criteria for Human-Automation Performance Metrics.- Performance Evaluation Methods for Assistive Robotic Technology.- Issues in Applying Bio-Inspiration, Cognitive Critical Mass and Developmental-Inspired Principles to Advanced Intelligent Systems.- Evaluating Situation Awareness of Autonomous Systems.- From Simulation to Real Robots with Predictable Results: Methods and Examples.- Cognitive Systems Platforms using Open Source.- Assessing Coordination Demand in Cooperating Robots.- Measurements to Support Performance Evaluation of Wireless Communications in Tunnels for Urban Search and Rescue Robots.- Quantitative Assessment of Robot-Generated Maps.- Mobile Robotic Surveying Performance for Planetary Surface Site Characterization.- Performance Evaluation and Metrics for Perception in Intelligent Manufacturing.- Quantification of Line Tracking Solutions for Automotive Applications.
• (source: Nielsen Book Data)

To design and develop capable, dependable, and affordable intelligent systems, their performance must be measurable. Scienti?c methodologies for standardization and benchmarking are crucial for quantitatively evaluating the performance of eme- ing robotic and intelligent systems' technologies. There is currently no accepted standard for quantitatively measuring the performance of these systems against user-de?ned requirements; and furthermore, there is no consensus on what obj- tive evaluation procedures need to be followed to understand the performance of these systems. The lack of reproducible and repeatable test methods has precluded researchers working towards a common goal from exchanging and communic- ing results, inter-comparing system performance, and leveraging previous work that could otherwise avoid duplication and expedite technology transfer. Currently, this lack of cohesion in the community hinders progress in many domains, such as m- ufacturing, service, healthcare, and security. By providing the research community with access to standardized tools, reference data sets, and open source libraries of solutions, researchers and consumers will be able to evaluate the cost and be- ?ts associated with intelligent systems and associated technologies. In this vein, the edited book volume addresses performance evaluation and metrics for intel- gent systems, in general, while emphasizing the need and solutions for standardized methods. To the knowledge of the editors, there is not a single book on the market that is solely dedicated to the subject of performance evaluation and benchmarking of intelligent systems.
(source: Nielsen Book Data)

• Title page; Conference Organization; Preface; Contents; Invited Talks; Web Mining or the Wisdom of the Crowds; AI at 50: From Programs to Solvers
• Models and Techniques for General Intelligence; Agents; Scalable and Efficient Multiagent Platform Closer to the Operating System; Designing Norms in Virtual Organizations; Distributed Barter-Based Directory Services; Trading Paper Clips
• An Analysis of "Trading Up" in Artificial Societies Without Altruists; Validation and Experimentation of a Tourism Recommender Agent Based on a Graded BDI Model; Agent Negotiation Dissolution.

• On Partial Deduction and Conversational AgentsRobustness in Recurrent Auctions for Resource Allocation; Using Electronic Institutions for Hospitals Chronic Disease Management and Purchasing System; Categorization and Social Norms Support; Constraints, Satisfiability, and Search; How Hard Is a Commercial Puzzle: The Eternity II Challenge; Random SAT Instances à la Carte; Privacy in Distributed Meeting Scheduling; Solving the Response Time Variability Problem by Means of Multi-Start and GRASP Metaheuristics; An Algorithm Based on Structural Analysis for Model-Based Fault Diagnosis.

• Knowledge and Information SystemsKnowledge Discovery with Explained Case-Based Reasoning; Improving Pseudobagging Techniques; Knowledge Discovery on the Response to Neurorehabilitation Treatment of Patients with Traumatic Brain Injury Through an AI & Stats and Graphical Hybrid Methodology; Using Ensemble-Based Reasoning to Help Experts in Melanoma Diagnosis; Ergonomic Advice Through Case-Based Reasoning to Avoid Dangerous Positions Adopted Using the Computer; Knowledge Representation and Logic; Probabilistic Dynamic Belief Logic for Image and Reputation; Aggregation Operators and Ruled Surfaces.

• New Challenges: Group Decision Systems by Means of Entropy Defined Through Qualitative Reasoning StructuresMachine Learning; Voltage Sag Source Location from Extracted Rules Using Subgroup Discovery; Statistical Monitoring of Injection Moulds; On the Dimensions of Data Complexity Through Synthetic Data Sets; Can Evolution Strategies Improve Learning Guidance in XCS? Design and Comparison with Genetic Algorithms Based XCS; Intersection and Signed-Intersection Kernels for Intervals; Multidisciplinary Topics and Applications; Representation of Discrete Quasi-Copulas Through Non-Square Matrices.

• A System to Extract Social Networks Based on the Processing of Information Obtained from InternetA Negotiation Styles Recommenders Approach Based on Computational Ecology; CABRO: Winner Determination Algorithm for Single-Unit Combinatorial Auctions; Nearest Neighbor Technique and Artificial Neural Networks for Short-Term Electric Consumptions Forecast; Pattern Discovery in Melanoma Domain Using Partitional Clustering; Reasoning About Plans, Processes, and Actions; Using Ant Colony Systems with Pheromone Dispersion in the Traveling Salesman Problem; Building Policies for Scrabble; Robotics.

There was a time when AI was seen by many as science fiction, i.e., the healthy endeavour of speculating about the future. Now the future is here. AI has passed from being a visionary discipline to lying at the core of many commercial enterprises. AI programs scattered through the web influence nowadays our lives: by extracting profiles and offering tailored advertisement, helping us in our searches, establishing social networks, providing entertainment ... And not just in the net, but also in the physical wold. In Japan there are robots that guide customers through marketplaces advising them where to find the product matching their needs, and realistic replicas of university professors allow them to teach their lectures a hundred kilometres away from the clasroom. Not to speak about intelligent prostheses and remote high-precision surgery. In the Catalan-speaking world there are no robots in marketplaces yet, but it is coming. Recently, the first commercial humanoid robot was built. Since AI technology is beoming reasonably mature, companies are progressively relying on it. The Catalan Association for Artificial Intelligence (ACIA) tries to promote synergies within the research community and also between the different actors playing a role in the developmentof AI: from universities to industry, from governmental departments to the information society, from entertainment enterprises to citizen services.

• Ch. 1. Modeling agent epistemic states: an informal overview. 1.1. Models of agent epistemic states. 1.2. Propositional epistemic model. 1.3. Probabilistic epistemic model. 1.4. Possible world epistemic model. 1.5. Comparisons of models. 1.6. P3 model for decision-making agents
• ch. 2. Mathematical preliminaries. 2.1. Usage of symbols. 2.2. Sets, relations, and functions. 2.3. Graphs and trees. 2.4. Probability. 2.5. Algorithmic complexity
• ch. 3. Classical logics for the propositional epistemic model. 3.1. Propositional logic. 3.2. First-order logic. 3.3. Theorem proving procedure. 3.4. Resolution theorem proving. 3.5. Refutation procedure. 3.6. Complexity analysis
• ch. 4. Logic programming. 4.1. The concept. 4.2. Program clauses and goals. 4.3. Program semantics. 4.4. Definite programs. 4.5. Normal programs. 4.6. Prolog. 4.7. Prolog systems. 4.8. Complexity analysis
• ch. 5. Logical rules for making decisions. 5.1. Evolution of rules. 5.2. Bayesian probability theory for handling uncertainty. 5.3. Dempster-Shafer theory for handling uncertainty. 5.4. Measuring consensus. 5.5. Combining sources of varying confidence. 5.6. Advantages and disadvantages of rule-based systems
• ch. 6. Bayesian belief networks. 6.1. Bayesian belief networks. 6.2. Conditional independence in belief networks. 6.3. Evidence, belief, and likelihood. 6.4. Prior probabilities in networks without evidence. 6.5. Belief revision. 6.6. Evidence propagation in polytrees. 6.7. Evidence propagation in directed acyclic graphs. 6.8. Complexity of inference algorithms. 6.9. Acquisition of probabilities. 6.10. Advantages and disadvantages of belief networks. 6.11. Belief network tools
• ch. 7. Influence diagrams for making decisions. 7.1. Expected utility theory and decision trees. 7.2. Influence diagrams. 7.3. Inferencing in influence diagrams. 7.4. Compilation of influence diagrams. 7.5. Inferencing in strong junction tress
• ch. 8. Modal logics for the possible world epistemic model. 8.1. Historical development of modal logics. 8.2. Systems of modal logic. 8.3. Deductions in modal systems. 8.4. Modality. 8.5. Decidability and matrix method. 8.6. Relationships among modal systems. 8.7. Possible world semantics. 8.8. Soundness and completeness results. 8.9. Complexity and decidability of modal systems. 8.10. Modal first-order logics. 8.11. Resolution in modal first-order logics. 8.12. Modal epistemic logics. 8.13. Logic of agents beliefs (LAB)
• ch. 9. Symbolic argumentation for decision-making. 9.1. Toulmin's model of argumentation. 9.2. Domino decision-making model for P3. 9.3. Knowledge representation syntax of P3. 9.4. Formalization of P3 via LAB. 9.5. Aggregation via Dempster-Shafer theory. 9.6. Aggregation via Bayesian belief networks.

This self-contained book provides three fundamental and generic approaches (logical, probabilistic, and modal) to representing and reasoning with agent epistemic states, specifically in the context of decision making. Each of these approaches can be applied to the construction of intelligent software agents for making decisions, thereby creating computational foundations for decision-making agents. In addition, the book introduces a formal integration of the three approaches into a single unified approach that combines the advantages of all the approaches. Finally, the symbolic argumentation approach to decision making developed in this book, combining logic and probability, offers several advantages over the traditional approach to decision making which is based on simple rule-based expert systems or expected utility theory.

• Language of algorithms
• Knowledge Handling
• Search
• Learning and Decision Making
• Problem Solving and Planning.

Provides an overview of methods developed in artificial intelligence for search, learning, problem solving and decision making. This book also gives an overview of algorithms and architectures of artificial intelligence that have reached the degree of maturity when a method can be presented as an algorithm.

• Foreword; Contents; Researching Spontaneous Collaborative Play; Thesis Structure; Exploring Presence and Social Play; Design for Emergence; Early Design Sketches for Design for Emergence; Playground Interaction Online: The Bumper Car Game; CitiTag: Urban Space as a Large Group Playground; Designing for Spontaneous Collaborative Play Based on Presence; References; Acknowledgements.

Design for Emergence investigates spontaneous, unpredictable uses of technology that are driven by social contexts and collaborative processes, based on our ability to communicate our presence, both virtual and physical, in symbolic ways. In light of the fact that social dynamics and unexpected uses of technology can inspire innovation, this book proposes a research model of design for emergence, focusing on emergent phenomena as part of an iterative design process. By providing playful, technology-mediated experiences with minimal structure, unpredictable user behaviors can emerge through exp.

• Applications in Remote Sensing.- Multispectral Remote Sensing Image Classification Using Wavelet Based Features.- Images with Uncertainty: Efficient Algorithms for Shift, Rotation, Scaling, and Registration, and Their Applications to Geosciences.- Neural Networks in Multiple Classifier Systems for Remote-Sensing Image Classification.- Applications in Image Retrieval.- Linguistic Expression Based Image Description Framework and its Application to Image Retrieval.- Focusing Keywords to Automatically Extracted Image Segments Using Self-Organising Maps.- Applications in Image Analysis.- A Fuzzy Edge-Dependent Interpolation Algorithm.- Fuzzy Image Segmentation Based on Triangular Function and Its n-dimensional Extension.- Application of Neuro-Fuzzy Methods for Noise Filtering, Noise Detection and Edge Extraction in Digital Images Corrupted by Impulse Noise.- Other Applications.- Creating Photorealistic Models by Data Fusion with Genetic Algorithms.- The Use of Fuzzy Information Granular for Content Representation of Video Sequences.- Wavelets and Fuzzy Contours in 3D-CAD for Digital Breast Tomosynthesis.- Intelligent Biometric Information Fusion using Support Vector Machine.- Theoretical Contributions.- General Definition of Fuzzy Mathematical Morphology Operations. Image and Non-image Applications.- Intuitionistic Fuzzy Image Processing.- Integer Programming Methods in Image Processing and Bayes Estimation.- Colour Quantisation by Black-Box Optimisation.
• (source: Nielsen Book Data)

Images have always been very important in human life. Their applications range from primitive communication between humans of all ages to advanced technologies in the industrial, medical and military field. The increased possibilities to capture and analyze images have contributed to the largeness that the scientific field of "image processing" has become today. Many techniques are being applied, including soft computing. "Soft Computing in Image Processing: Recent Advances" follows the edited volumes "Fuzzy Techniques in Image Processing" (volume 52, published in 2000) and "Fuzzy Filters for Image Processing" (volume 122, published in 2003), and covers a wide range of both practical and theoretical applications of soft computing in image processing. The 16 excellent chapters of the book have been grouped into five parts: Applications in Remote Sensing, Applications in Image Retrieval, Applications in Image Analysis, Other Applications, and Theoretical Contributions. The focus of the book is on practical applications, which makes it interesting for every researcher that is involved with soft computing, image processing, or both scientific branches.
(source: Nielsen Book Data)

• Fundamentals.- From Autonomy to AOC.- AOC at a Glance.- Design and Engineering Issues.- A Formal Framework of AOC.- AOC in Depth.- AOC in Constraint Satisfaction.- AOC in Complex Systems Modeling.- AOC in Optimization.- Challenges and Opportunities.
• (source: Nielsen Book Data)

Autonomy Oriented Computing is a comprehensive reference for scientists, engineers, and other professionals concerned with this promising development in computer science. It can also be used as a text in graduate/undergraduate programs in a broad range of computer-related disciplines, including Robotics and Automation, Amorphous Computing, Image Processing, Programming Paradigms, Computational Biology, etc. Part One describes the basic concepts and characteristics of an AOC system and enumerates the critical design and engineering issues faced in AOC system development. Part Two gives detailed analyses of methodologies and case studies to evaluate AOC used in problem solving and complex system modeling. The final chapter outlines possibilities for future research and development. Numerous illustrative examples, experimental case studies, and exercises at the end of each chapter of Autonomy Oriented Computing help particularize and consolidate the methodologies and theories presented.
(source: Nielsen Book Data)

• FUZZY WEB INTELLIGENCE, ROUGH WEB INTELLIGENCE AND PROBABILISTIC WEB INTELLIGENCE. Recommender Systems Based on Representations
• Web Intelligence: Concept-Based Web Search
• A Fuzzy Logic Approach to Answer Retrieval from the World-Wide-Web
• Fuzzy Inference Based Server Selection in Content Distribution Networks
• Recommendation Based on Personal Preference
• Fuzzy Clustering and Intelligent Search for a Web-Based Fabric Database
• Web Usage Mining: Comparison of Conventional, Fuzzy and Rough Set Clustering
• Towards Web Search using Contextual Probabilistic Independencies.

• NEURAL WEB INTELLIGENCE. EVOLUTIONARY WEB INTELLIGENCE AND GRANULAR WEB INTELLIGENCE. Neural Expert System for Vehicle Fault Diagnosis via The WWW
• Dynamic Documents in the Wired World
• Proximity-Based Supervision for Flexible Web Page Categorization
• Web Usage Mining: Business Intelligence from Web Logs
• Intelligent Content-Based Audio Classification and Retrieval for Web Application.

• HYBRID WEB INTELLIGENCE AND E-APPLICATIONS. Developing an Intelligent Multi-Regional Chinese Medical Portal
• Multiplicative Adaptive User Preference Retrieval and its Applications to Web Search
• Scalable Learning Method to Extract Biological Information from Huge Online Biomedical Literature
• iMASS . An Intelligent Multi-resolution Agent-Based Surveillance System
• Networking Support for Neural Network-Based Web Monitoring and Filtering
• Web Intelligence: Web-Based BISC Decision Support System (WBIS-DSS)
• Content and Link Structure Analysis for Seraching Web
• Mobile Agent Technology for Web Applications
• Intelligent Virtual Agents and the Web
• Data Mining for Network Security
• Agent-supported WI Infrastructure: Case Studies in Peer-to-Peer Networks
• Intelligent Technology for Content Monitoring on the Web.

This review volume introduces the novel intelligent Web theory called computational Web intelligence (CWI) based on computational intelligence (CI) and Web technology (WT). It takes an in-depth look at hybrid Web intelligence (HWI), which is based on artificial biological and computational intelligence with Web technology and is used to build hybrid intelligent Web systems that serve wired and wireless users more efficiently. The basic principles of CWI and various e-applications of CWI and HWI are discussed. For completeness, six major CWI techniques - fuzzy Web intelligence, neural Web intelligence, evolutionary Web intelligence, granular Web intelligence, rough Web Intelligence and probabilistic Web intelligence - are described. With the huge potential for intelligent e-business applications of CWI and HWI, these techniques represent the future of intelligent Web applications.
(source: Nielsen Book Data)

• Introduction to Data Mining
• The Data Mining Process
• Data Mining Tools and Techniques
• Managing the Data Mining Project
• Modeling Data
• Deploying the Results
• The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Management
• Appendices: Glossary
• References
• Web Sites
• Data Mining and Knowledge Discovery Data Sets in the Public Domain
• Microsoft Solution Providers
• Summary of Knowledge Management Case Studies and Web Locations.
• (source: Nielsen Book Data)

Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management.
(source: Nielsen Book Data)

• Introduction.- Modelling Vehicle Systems.- Offset Models of Vehicles.- Design of Controllers.- Implementation of Controllers.- Conclusion.
• (source: Nielsen Book Data)

This book provides methodologies for designing and implementing guidance algorithms for autonomous vehicles. These algorithms make important decision regarding how to steer and drive a ground vehicle in order to safely stay on an intended path, thereby making the vehicle driverless. The design tools provided in this book enable the reader to develop highly practical and real-world implementable guidance algorithms that will deliver high-accuracy driving for field vehicles. (They are equally applicable for on-road vehicles.) The book covers a variety of vehicle types, including wheeled vehicles, tracked vehicles, wheeled and tracked vehicles towing trailers, and four-wheel-steer and four-wheel-drive vehicles. It also covers active trailers that are driven and steered. Vehicles used in agriculture, mining and road construction are subjected to unpredictable and significant disturbances. The robust control methodologies presented can successfully compensate for these disturbances, as confirmed by the experimental results presented. Though the majority of the methodologies presented are based on sliding-mode controllers, other robust control methodologies are also discussed. To help the reader decide which controller is best suited for his/her choice of vehicle, experimental results are presented in a comparative format. .
(source: Nielsen Book Data)

• Chapter 1: Improved Steganography using Odd Even substitution.-
• Chapter 2: A Tags Mining Approach for Automatic Image Annotation Using Neighbor Images Tree.-
• Chapter 3: A Survey: Implemented Architectures of 3D Convolutional Neural Networks.-
• Chapter 4: An approach for detection of dust on solar panels using CNN from RGB dust image to predict power loss.-
• Chapter 5: A Novel Method of Data Partitioning Using Genetic Algorithm Work Load Driven Approach Utilizing Machine Learning.-
• Chapter 6: Virtual Dermoscopy using Deep Learning Approach.-
• Chapter 7: Evaluating Robustness for Intensity Based Image Registration Measures Using Mutual Information and Normalized Mutual Information.-
• Chapter 8: A New Contrast Based Degraded Document Image Binarization. .
• (source: Nielsen Book Data)

This edited book designs the Cognitive Computing in Human Cognition to analyze to improve the efficiency of decision making by cognitive intelligence. The book is also intended to attract the audience who work in brain computing, deep learning, transportation, and solar cell energy. Due to this in the recent era, smart methods with human touch called as human cognition is adopted by many researchers in the field of information technology with the Cognitive Computing.
(source: Nielsen Book Data)

• Domain Adaptation for Visual Understanding Soumyadeep Ghosh, Richa Singh, Mayank Vatsa, Nalini Ratha, and Vishal M. Patel
• M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning Issam H. Laradji and Reza Babanezhad
• XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings Amelie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, and Kevin Murphy
• Improving Transferability of Deep Neural Networks Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois, and Matthew Hill
• Cross Modality Video Segment Retrieval with Ensemble Learning Xinyan Yu, Ya Zhang, and Rui Zhang
• On Minimum Discrepancy Estimation for Deep Domain Adaptation Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan
• Multi-Modal Conditional Feature Enhancement for Facial Action Unit Recognition Nagashri N. Lakshminarayana, Deen Dayal Mohan, Nishant Sankaran, Srirangaraj Setlur, and Venu Govindaraju
• Intuition Learning Anush Sankaran, Mayank Vatsa, and Richa Singh
• Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating Xiyu Kong, Qiping Zhou, Yunyu Lai, Muming Zhao, and Chongyang Zhang.
• (source: Nielsen Book Data)

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.
(source: Nielsen Book Data)