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• Cover
• Copyright
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Customer Feedback
• Table of Contents
• Preface
• Chapter 1: Getting Started with Microsoft Cognitive Services
• Cognitive Services in action for fun and life changing purposes
• Setting up boilerplate code
• Detecting faces with the Face API
• Overview of what we are dealing with
• Vision
• Computer Vision
• Emotion
• Face
• Video
• Speech
• Bing Speech
• Speaker Recognition
• Custom Recognition
• Language
• Bing Spell Check
• Language Understanding Intelligent Service (LUIS)
• Linguistic Analysis
• Text Analysis
• Web Language Model
• Knowledge
• Academic
• Entity Linking
• Knowledge Exploration
• Recommendations
• Search
• Bing Web Search
• Bing Image Search
• Bing Video Search
• Bing News Search
• Bing Autosuggest
• Getting feedback on detected faces
• Summary
• Chapter 2: Analyzing Images to Recognize a Face
• Learning what an image is about using Computer Vision API
• Setting up a chapter example project
• Generic image analysis
• Recognizing celebrities using domain models
• Utilizing Optical Character Recognition
• Generating image thumbnails
• Diving deep into the Face API
• Retrieving more information from the detected faces
• Deciding whether two faces belong to the same person
• Finding similar faces
• Grouping similar faces
• Adding identification to our Smart-House application
• Creating our Smart-House application
• Adding people to be identified
• Identifying a person
• Summary
• Chapter 3: Analyzing Videos
• Knowing your mood using the Emotion API
• Getting images from a web camera
• Letting the smart-house know your mood
• Diving into the Video API
• Video operations as common code
• Getting operation results
• Wiring up execution in the ViewModel.

• Detecting and tracking faces in videos
• Detecting motion
• Stabilizing shaky videos
• Generating video thumbnails
• Analyzing emotions in videos
• Summary
• Chapter 4: Letting Applications Understand Commands
• Creating language-understanding models
• Register an account and get a license key
• Creating an application
• Recognizing key data using entities
• Understanding what the user wants using intents
• Simplifying development using pre-built models
• Pre-built applications
• Training a model
• Training and publishing the model
• Connecting to the smart-house application
• Model improvement through active usage
• Visualizing performance
• Resolving performance problems
• Adding model features
• Adding labeled utterances
• Looking for incorrect utterance labels
• Changing the schema
• Active learning
• Executing operations based on commands
• Maintaining conversations from unclear utterances
• Completing actions from intents
• Action fulfillment
• Summary
• Chapter 5: Speak with Your Application
• Converting text to audio and vice versa
• Speaking to the application
• Letting the application speak back
• Audio output format
• Error codes
• Supported languages
• Utilizing LUIS based on spoken commands
• Knowing who is speaking
• Adding speaker profiles
• Enrolling a profile
• Identifying the speaker
• Verifying a person through speech
• Customizing speech recognition
• Creating a custom acoustic model
• Creating a custom language model
• Deploying the application
• Summary
• Chapter 6: Understanding Text
• Setting up a common core
• New project
• Web requests
• Data contracts
• Correcting spelling errors
• Natural Language Processing using the Web Language Model
• Breaking a word into several
• Generating the next word in a sequence of words.

• Learning if a word is likely to follow a sequence of words
• Learning if certain words is likely to appear together
• Extracting information through textual analysis
• Detecting language
• Extracting key phrases from text
• Learning if a text is positive or negative
• Exploring text using linguistic analysis
• Introduction to linguistic analysis
• Analyzing text from a linguistic viewpoint
• Summary
• Chapter 7: Extending Knowledge Based on Context
• Linking entities based on context
• Providing personalized recommendations
• Creating a model
• Importing catalog data
• Importing usage data
• Building a model
• Consuming recommendations
• Recommending items based on prior activities
• Summary
• Chapter 8: Querying Structured Data in a Natural Way
• Tapping into academic content using the Academic API
• Setting up an example project
• Interpreting natural language queries
• Finding academic entities from query expressions
• Calculating the distribution of attributes from academic entities
• Entity attributes
• Creating the backend using the Knowledge Exploration Service
• Defining attributes
• Adding data
• Building the index
• Understanding natural language
• Local hosting and testing
• Going for scale
• Hooking into Microsoft Azure
• Deploying the service
• Answering FAQs using QnA Maker
• Creating a knowledge base from frequently asked questions
• Training the model
• Publishing the model
• Improving the model
• Summary
• Chapter 9: Adding Specialized Searches
• Searching the Web from the Smart-House application
• Preparing the application for web searches
• Searching the Web
• Getting the news
• News from queries
• News from categories
• Trending news
• Searching for images and videos
• Using a common user interface
• Searching for images
• Searching for videos.

• Helping the user with auto suggestions
• Adding Autosuggest to the user interface
• Suggesting queries
• Search commonalities
• Languages
• Pagination
• Filters
• Safe search
• Freshness
• Errors
• Summary
• Chapter 10: Connecting the Pieces
• Connecting the pieces
• Creating an intent
• Updating the code
• Executing actions from intents
• Searching news on command
• Describing news images
• Real-life applications using Microsoft Cognitive Services
• Uber
• DutchCrafters
• CelebsLike.me
• Pivothead
• wearable glasses
• Zero Keyboard
• The common theme
• Where to go from here
• Summary
• Appendix A: LUIS Entities and Intents
• LUIS pre-built intents
• LUIS pre-built entities
• Appendix B: Additional Information on Linguistic Analysis
• Part-of-Speech Tags
• Phrase types
• Appendix C: License Information
• Video Frame Analyzer
• OpenCvSharp3
• Newtonsoft. Json
• NAudio
• Definitions
• Grant of Rights
• Conditions and Limitations
• Index.

Learn to build interactive and efficient applications by leveraging 24 effective cognitive services APIs powered by Microsoft About This Book * Explore the capabilities of 24 of the APIs released as part of the Cognitive Services platform * Build intelligent apps that combine the power of computer vision, speech recognition, and language processing * Give your apps human-like cognitive intelligence with this hands-on guide Who This Book Is For .NET developers who want to add AI capabilities to their applications will find this book useful. No knowledge of machine learning or AI is necessary to work through this book. What You Will Learn * Identify a person through visual inspection and audio * Reduce user effort by utilizing AI-like capabilities * Understand how to analyze images and text in different ways * Find out how to analyze images using Vision APIs * Add video analysis to applications using Vision APIs * Utilize Search to find anything you want * Analyze text to extract information and explore text structure In Detail Microsoft has revamped its Project Oxford to launch the all new Cognitive Services platform-a set of 30 APIs to add speech, vision, language, and knowledge capabilities to apps. This book will introduce you to 24 of the APIs released as part of Cognitive Services platform and show you how to leverage their capabilities. More importantly, you'll see how the power of these APIs can be combined to build real-world apps that have cognitive capabilities. The book is split into three sections: computer vision, speech recognition and language processing, and knowledge and search. You will be taken through the vision APIs at first as this is very visual, and not too complex. The next part revolves around speech and language, which are somewhat connected. The last part is about adding real-world intelligence to apps by connecting them to Knowledge and Search APIs. By the end of this book, you will be in a position to understand what Microsoft Cognitive Service can offer and how to use the different APIs. Style and approach This book takes you through essential API capabilities and shows how to utilize them to suit the needs of your application.
(source: Nielsen Book Data)

• Cooperative Games.- Fuzzy Coalitions and Fuzziness of Games.- Games with a Fuzzy Bilateral Relation among the Players.- Fuzzy Communication.- A Priori Fuzzy Unions
• Fuzzy Permission.
• (source: Nielsen Book Data)

This book offers a comprehensive introduction to cooperative game theory and a practice-oriented reference guide to new models and tools for studying bilateral fuzzy relations among several agents or players. It introduces the reader to several fuzzy models, each of which is first analyzed in the context of classical games (crisp games) and subsequently in the context of fuzzy games. Special emphasis is given to the value of Shapley, which is presented for the first time in the context of fuzzy games. Students and researchers will find here a self-contained reference guide to cooperative fuzzy games, characterized by a wealth of examples, descriptions of a wide range of possible situations, step-by-step explanations of the basic mathematical concepts involved, and easy-to-follow information on axioms and properties.
(source: Nielsen Book Data)

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book * Skill up and implement tricky neural networks using Google's TensorFlow 1.x * An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. * Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn * Install TensorFlow and use it for CPU and GPU operations * Implement DNNs and apply them to solve different AI-driven problems. * Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. * Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. * Use different regression techniques for prediction and classification problems * Build single and multilayer perceptrons in TensorFlow * Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. * Learn how restricted Boltzmann Machines can be used to recommend movies. * Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. * Master the different reinforcement learning methods to implement game playing agents. * GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
(source: Nielsen Book Data)

• Part I Literature Review and State of the Art
• Part II Model for Adaptive Semantics Visualization
• Part III Proof of the Conceptual Model.

This book introduces a novel approach for intelligent visualizations that adapts the different visual variables and data processing to human's behavior and given tasks. Thereby a number of new algorithms and methods are introduced to satisfy the human need of information and knowledge and enable a usable and attractive way of information acquisition. Each method and algorithm is illustrated in a replicable way to enable the reproduction of the entire "SemaVis" system or parts of it. The introduced evaluation is scientifically well-designed and performed with more than enough participants to validate the benefits of the methods. Beside the introduced new approaches and algorithms, readers may find a sophisticated literature review in Information Visualization and Visual Analytics, Semantics and information extraction, and intelligent and adaptive systems. This book is based on an awarded and distinguished doctoral thesis in computer science

• Advances in Business ICT: New Ideas from Ongoing Research.- Verification of Temporal Knowledge Bases as an Important Aspect of Knowledge Management Processes in Organization.- The role of simulation performance in software-in-the-loop simulations.- Cognitum Ontorion: Knowledge Representation and Reasoning System.- Overview of Selected Business Process Semantization Techniques.- Selected Approaches Towards Taxonomy of Business Process Anomalies.- Hybrid framework for investment project portfolio selection.- Towards Predicting Stock Price Moves with Aid of Sentiment Analysis of Twitter Social Network Data and Big Data Processing Environment.- On a Property of Phase Correlation and Possibilities to Reduce the Walsh Function System.
• (source: Nielsen Book Data)

This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.
(source: Nielsen Book Data)

• Artificial Neural Networks Applications: An Introduction
• Order in the Black Box: Consistency And Robustness Of Neuron Activation Of Feed Forward Neural Networks And Its Use In Efficient Optimization Of Network Structure
• Artificial Neural Networks as Models of Robustness in Development And Regeneration: Stability of Memory During Morphological Remodeling
• A Structure Optimization Algorithm of Neural Networks For Pattern Learning from Educational Data
• Stochastic Neural Networks for Modelling Random Processes from Observed Data
• Curvelet Interaction with Artificial Neural Networks
• Hybrid Wavelet Neural Network Approaches.-Quantification of Prediction Uncertainty in Artificial Neural Network Models
• Classifying Calpain Inhibitors for The Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery
• Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Using Neural Networks Incorporating a Novel Effective Feature
• Degree of Central Regularity of Mass.-SOM Clustering and Modelling of Australian Railway Drivers' Sleep, Wake, Duty Profiles
• A Neural Approach to Electricity Demand Forecasting
• Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia
• Artificial Neural Networks in Precipitation Nowcasting: An Australian Case Study
• Construction of Pmx Concentration Surfaces Using Neural Evolutionary Fuzzy Models of Semi Physical Class
• Application of Artificial Neural Network in Social Media Data Analysis: A Case of Lodging Business in Philadelphia
• Sentiment Analysis on Morphologically Rich Languages
• An Artificial Neural Network (ANN) Approach
• Predicting Stock Price Movements with News Sentiment: An Artificial Neural Networks Approach
• Modelling Mode Choice of Individual In Linked Trips with Artificial Neural Networks and Fuzzy Representation
• Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies
• A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-Spatial Attribute Data Mining: A Case Study Experience.

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

• Aquatic I
• Vision I
• Planetary
• Vision II
• Poster Session
• Aerial
• Aquatic II
• Underground
• Systems.

This book contains the proceedings of the 10th FSR, (Field and Service Robotics) which is the leading single-track conference on applications of robotics in challenging environments. The 10th FSR was held in Toronto, Canada from 23-26 June 2015. The book contains 42 full-length, peer-reviewed papers organized into a variety of topics: Aquatic, Vision, Planetary, Aerial, Underground, and Systems. The goal of the book and the conference is to report and encourage the development and experimental evaluation of field and service robots, and to generate a vibrant exchange and discussion in the community. Field robots are non-factory robots, typically mobile, that operate in complex and dynamic environments: on the ground (Earth or other planets), under the ground, underwater, in the air or in space. Service robots are those that work closely with humans to help them with their lives. The first FSR was held in Canberra, Australia, in 1997. Since that first meeting, FSR has been held roughly every two years, cycling through Asia, Americas, Europe.

• Fuzzy Logic and the Linz Seminar:Themes and some Personal Reminiscences
• How I Saw and How I See Fuzzy Sets
• Modules in the Category
• A Geometric Approach to MV-algebras
• On the Equational Characterization of Continuous t-norms
• The Semantics of Fuzzy Logics: Two Approaches to Finite Tomonoids
• Structure of Uninorms with Continuous Diagonal Functions
• The Notions of Overlap and Grouping Functions
• Asymmetric Copulas and Their Application in Design of Experiments
• Copulæ of Processes Related to the Brownian Motion: a Brief Survey
• Extensions of Capacities
• Multi-source Information Fusion Using Measure Representations
• Bases and Transforms of Set Functions
• Conditioning for Boolean Subsets, Indicator Functions, and Fuzzy Subsets
• Multivalued Functions Integration from Additive to Arbitrary Non-Negative Set Function.

The book is a collection of contributions by leading experts, developed around traditional themes discussed at the annual Linz Seminars on Fuzzy Set Theory. The different chapters have been written by former PhD students, colleagues, co-authors and friends of Peter Klement, a leading researcher and the organizer of the Linz Seminars on Fuzzy Set Theory. The book also includes advanced findings on topics inspired by Klement's research activities, concerning copulas, measures and integrals, as well as aggregation problems. Some of the chapters reflect personal views and controversial aspects of traditional topics, while others deal with deep mathematical theories, such as the algebraic and logical foundations of fuzzy set theory and fuzzy logic. Originally thought as an homage to Peter Klement, the book also represents an advanced reference guide to the mathematical theories related to fuzzy logic and fuzzy set theory with the potential to stimulate important discussions on new research directions in the field.

• 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.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Joint Conference on Robotics, LARS, SBR, Robocontrol 2014, held in Sao Carlos, Brazil, in October 2014. The 8 revised full papers presented were carefully reviewed and selected from 76 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.
(source: Nielsen Book Data)

• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
• (source: Nielsen Book Data)

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
(source: Nielsen Book Data)

• Cover; Title; EUL; Contents; Foreword; Preface; List of Contributors; Keywords; Chapter 01; Chapter 02; Chapter 03; Chapter 04; Chapter 05; Chapter 06; Chapter 07; Chapter 08; Chapter 09; Chapter 10; Chapter 11; Index.

This book dives into the heart of how to design distributed control architectures for heterogeneous teams of humans, robots, and automated systems, enabling them to achieve greater cooperation and autonomy through the use of network technologiesReaders can envision a realistic view of what can be expected from a networked human robot cooperative environment in the next decade.

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Foundations of machine learning
• Using AdaBoost to minimize training error
• Direct bounds on the generalization error
• The margins explanation for boosting's effectiveness
• Game theory, online learning, and boosting
• Loss minimization and generalizations of boosting
• Boosting, convex optimization, and information geometry
• Using confidence-rated weak predictions
• Multiclass classification problems
• Learning to rank
• Attaining the best possible accuracy
• Optimally efficient boosting
• Boosting in continuous time.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
(source: Nielsen Book Data)

• Preface
• Progressive Organization of Co-Operating Colonies/Collections of Ants/Agents (POOCA) for Competent Pheromone-Based Navigation & Multi-Agent Learning
• Ant Colony Solution to the Optimal Transformer Sizing & Efficiency Problem in Power Systems
• Distributed Decisions: New Insights from Radio-Tagged Ants
• Ant Colony Optimization used in No Wavefront Sensor Adaptive Optics Systems for Solid-State Lasers
• Any Colony Optimization Agents & Path Routing: The Cases of Construction Scheduling & Urban Water Distribution Pipe Networks
• KANTS: A Self-Organized Ant System for Pattern Clustering & Classification
• A Hybrid System Based in Ant Colony & Paraconsistent Logic
• Ant Colony Optimization: A Powerful Strategy for Biomarker Feature Selection
• Any Colony Optimization Based Message Authentication for Wireless Networks
• Index.
• (source: Nielsen Book Data)

Ant colony optimisation is a population-based general-search technique that can be used for the solution of difficult combinatorial problems. Ant colony optimisation uses the knowledge-reinforcing mechanism used by real-life ants while traversing possible paths in search of food, depositing a chemical substance (called pheromone), en-route and subsequently forming trails which can then be followed by other ants in the colony. With its strong mathematical foundation and its simplicity of use, ant colony optimisation provides an alternative method to routing optimisations with a wide range of applications. This book presents topical research in the study of ant colonies, their behaviour and application in computer technologies.
(source: Nielsen Book Data)

• Defeasible Planning through Multi-Agent Argumentation .-Operator behavior modelling in a submarine .-Automatic Wrapper Adaptation by Tree Edit Distance Matching
• Representing Temporal Knowledge in the Semantic Web: The Extended 4D Fluents Approach
• Combining a Multi-Document Update Summarization System -CBSEAS- with a Genetic Algorithm
• Extraction of Essential Events with Application to Damage Evaluation on Fuel Cells
• Detecting car accidents based on traffic flow measurements using machine learning techniques
• Next Generation Environments for Context-aware Learning Design
• Neurules-A Type of Neuro-Symbolic Rules: An Overview.

The combination of different intelligent methods is a very active research area in Artificial Intelligence (AI). The aim is to create integrated or hybrid methods that benefit from each of their components. Some of the existing efforts combine soft computing methods either among themselves or with more traditional AI methods such as logic and rules. Another stream of efforts integrates machine learning with soft-computing or traditional AI methods. Yet another integrates agent-based approaches with logic and also non-symbolic approaches. Some of the combinations have been quite important and more extensively used, like neuro-symbolic methods, neuro-fuzzy methods and methods combining rule-based and case-based reasoning. However, there are other combinations that are still under investigation, such as those related to the Semantic Web. The 2nd Workshop on?Combinations of Intelligent Methods and Applications? (CIMA 2010) was intended to become a forum for exchanging experience and ideas among researchers and practitioners who are dealing with combining intelligent methods either based on first principles or in the context of specific applications. CIMA 2010 was held in conjunction with the 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010). Also, a special track was organized in ICTAI 2010, under the same title. This volume includes revised versions of the papers presented in CIMA 2010 and one of the short papers presented in the corresponding ICTAI 2010 special track. It also includes a paper of the editors as invited.

• Theoretical Foundations of Both SI and ANN
• Advances of SI and ANN
• Hybridization of SI and ANN
• Applications.
• (source: Nielsen Book Data)

This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.
(source: Nielsen Book Data)

• Chapter 1 Introduction 1
• 1.1 Logic, foundations of computer science, and applications of logic to computer science 1
• 1.2 On the utility of logic for computer engineers 3
• Chapter 2 A Few Thoughts Before the Formalization 7
• 2.1 What is logic? 7
• 2.1.1 Logic and paradoxes 8
• 2.1.2 Paradoxes and set theory 9
• l2.1.2.1 The answer 10
• 2.1.3 Paradoxes in arithmetic and set theory 13
• 2.1.3.1 The halting problem 13
• 2.1.4 On formalisms and well-known notions 15
• 2.1.4.1 Some "well-known" notions that could turn out to be difficult to analyze 19
• 2.1.5 Back to the definition of logic 23
• 2.1.5.1 Some definitions of logic for all 24
• 2.1.5.2 A few more technical definitions 24
• 2.1.5.3 Theory and meta-theory (language and meta-language) 30
• 2.1.6 A few thoughts about logic and computer science 30
• 2.2 Some historic landmarks 32
• Chapter 3 Propositional Logic 39
• 3.1 Syntax and semantics 40
• 3.1.1 Language and meta-language 43
• 3.1.2 Transformation rules for cnf and dnf 49
• 3.2 The method of semantic tableaux 54
• 13.2.1 A slightly different formalism: signed tableaux 58
• 3.3 Formal systems 64
• 3.3.1 A capital notion: the notion of proof 64
• 3.3.2 What do we learn from the way we do mathematics? 72
• 3.4 A formal system for PL (PC) 78
• 3.4.1 Some properties of formal systems 84
• 3.4.2 Another formal system for PL (PC) 86
• 3.4.3 Another formal system 86
• 3.5 The method of Davis and Putnam 92
• 3.5.1 The Davis-Putnam method and the SAT problem 95
• 3.6 Semantic trees in PL 96
• 3.7 The resolution method in PL 101
• 3.8 Problems, strategies, and statements 109
• 3.8.1 Strategies 110
• 3.9 Horn clauses 113
• 3.10 Algebraic point of view of propositional logic 114
• Chapter 4 First-order Terms 121
• 4.1 Matching and unification 121
• 4.1.1 A motivation for searching for a matching algorithm 121
• 4.1.2 A classification of trees 123
• 4.2 First-order terms, substitutions, unification 125
• Chapter 5 First-Order Logic (FOL) or Predicate Logic (PL1, PC1) 131
• 5.1 Syntax 133
• 5.2 Semantics 137
• 5.2.1 The notions of truth and satisfaction 139
• 5.2.2 A variant: multi-sorted structures 150
• 5.2.2.1 Expressive power, sort reduction 150
• 5.2.3 Theories and their models 152
• 5.2.3.1 How can we reason in FOL? 153
• 5.3 Semantic tableaux in FOL 154
• 5.4 Unification in the method of semantic tableaux 166
• 5.5 Toward a semi-decision procedure for FOL 169
• 5.5.1 Prenex normal form 169
• 5.5.1.1 Skolemization 174
• 5.5.2 Skolem normal form 176
• 5.6 Semantic trees in FOL 186
• 5.6.1 Skolemization and clausal form 188
• 5.7 The resolution method in FOL 190
• 5.7.1 Variables must be renamed 201
• 5.8 A decidable class: the monadic class 202
• 5.8.1 Some decidable classes 205
• 5.9 Limits: Gödel's (first) incompleteness theorem 206
• Chapter 6 Foundations of Logic Programming 213
• 6.1 Specifications and programming 213
• 6.2 Toward a logic programming language 219
• 6.3 Logic programming: examples 222
• 6.3.1 Acting on the execution control: cut"/" 229
• 6.3.1.1 Translation of imperative structures 231
• 6.3.2 Negation as failure (NAF) 232
• 6.3.2.1 Some remarks about the strategy used by LP and negation as failure 238
• 6.3.2.2 Can we simply deduce instead of using NAF? 239
• 6.4 Computability and Horn clauses 241
• Chapter 7 Artificial Intelligence 245
• 7.1 Intelligent systems: AI 245
• 7.2 What approaches to study AI? 249
• 7.3 Toward an operational definition of intelligence 249
• 7.3.1 The imitation game proposed by Turing 250
• 7.4 Can we identify human intelligence with mechanical intelligence? 251
• 7.4.1 Chinese room argument 252
• 7.5 Some history 254
• 7.5.1 Prehistory 254
• 7.5.2 History 255
• 7.6 Some undisputed themes in AI 256
• Chapter 8 Inference 259
• 8.1 Deductive inference 260
• 8.2 An important concept: clause subsumption 266
• 8.2.1 An important problem 268
• 8.3 Abduction 273
• 8.3.1 Discovery of explanatory theories 274
• 8.3.1.1 Required conditions 275
• 8.4 Inductive inference 278
• 8.4.1 Deductive inference 279
• 8.4.2 Inductive inference 280
• 8.4.3 Hempel's paradox (1945) 280
• 8.5 Generalization: the generation of inductive hypotheses 284
• 8.5.1 Generalization from examples and counter examples 288
• Chapter 9 Problem Specification in Logical Languages 291
• 9.1 Equality 291
• 9.1.1 When is it used? 292
• 9.1.2 Some questions about equality 292
• 9.1.3 Why is equality needed? 293
• 9.1.4 Whatis equality? 293
• 9.1.5 How to reason with equality? 295
• 9.1.6 Specification without equality 296
• 9.1.7 Axiomatization of equality 297
• 9.1.8 Adding the definition of = and using the resolution method 297
• 9.1.9 By adding specialized rules to the method of semantic tableaux 299
• 9.1.10 By adding specialized rules to resolution 300
• 9.1.10.1 Paramodulation and demodulation 300
• 9.2 Constraints 309
• 9.3 Second Order Logic (SOL): a few notions 319
• 9.3.1 Syntax and semantics 324
• 9.3.1.1 Vocabulary 324
• 9.3.1.2 Syntax 325
• 9.3.1.3 Semantics 325
• Chapter 10 Non-classical Logics 327
• l0.l Many-valued logics 327
• 10.1.1 How to reason with p-valued logics? 334
• 10.1.1.1 Semantic tableaux for p-valued logics 334
• 10.2 Inaccurate concepts: fuzzy logic 337
• 10.2.1 Inference in FL 348
• 10.2.1.1 Syntax 349
• 10.2.1.2 Semantics 349
• 10.2.2 Herbrand's method in FL 350
• 10.2.2.1 Resolution andFL 351
• 10.3 Modal logics 353
• 10.3.1 Toward a semantics 355
• 10.3.1.1 Syntax (language of modal logic) 357
• 10.3.1.2 Semantics 358
• 10.3.2 How to reason with modallogics? 360
• 10.3.2.1 Formal systems approach 360
• 10.3.2.2 Translation approach 361
• 10.4 Some elements of temporal logic 371
• 10.4.1 Temporal operators and semantics 374
• 10.4.1.1 A famous argument 375
• 10.4.2 A temporal logic 377
• 10.4.3 How to reason with temporal logics? 378
• 10.4.3.1 The method of semantic tableaux 379
• 10.4.4 An example of a PL for linear and discrete time; PTL (or PLTL) 381
• 10.4.4.1 Syntax 331
• 10.4.4.2 Semantics 382
• 10.4.4.3 Method of semantic tableaux for PLTL (direct method) 333
• Chapter 11 Knowledge and Logic: Some Notions 385
• 11.1 What is knowledge? 335
• 11.2 Knowledge and modal logic 389
• 11.2.1 Toward a formalization 389
• 11.2.2 Syntax 339
• 11.2.2.1 What expressive power? An example 389
• 11.2.2.2 Semantics 339
• 11.2.3 New modal operators 391
• 11.2.3.1 Syntax (extension) 391
• 11.2.3.2 Semantics (extension) 391
• 11.2.4 Application examples 392
• 11.2.4.1 Modeling the muddy children puzzle 392
• 11.2.4.2 Corresponding Kripke worlds 392
• 11.2.4.3 Properties of the (formalization chosen for the) knowledge 394
• Chapter 12 Solutions to the Exercises 395.

Logic and its components (propositional, first-order, non-classical) play a key role in Computer Science and Artificial Intelligence. While a large amount of information exists scattered throughout various media (books, journal articles, webpages, etc.), the diffuse nature of these sources is problematic and logic as a topic benefits from a unified approach. Logic for Computer Science and Artificial Intelligence utilizes this format, surveying the tableaux, resolution, Davis and Putnam methods, logic programming, as well as for example unification and subsumption. For non-classical logics, the translation method is detailed. Logic for Computer Science and Artificial Intelligence is the classroom-tested result of several years of teaching at Grenoble INP (Ensimag). It is conceived to allow self-instruction for a beginner with basic knowledge in Mathematics and Computer Science, but is also highly suitable for use in traditional courses. The reader is guided by clearly motivated concepts, introductions, historical remarks, side notes concerning connections with other disciplines, and numerous exercises, complete with detailed solutions, The title provides the reader with the tools needed to arrive naturally at practical implementations of the concepts and techniques discussed, allowing for the design of algorithms to solve problems.
(source: Nielsen Book Data)

• Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces.- Renyi's Entropy, Divergence and Their Nonparametric Estimators.- Adaptive Information Filtering with Error Entropy and Error Correntropy Criteria.- Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems.- Nonlinear Adaptive Filtering with MEE, MCC, and Applications.- Classification with EEC, Divergence Measures, and Error Bounds.- Clustering with ITL Principles.- Self-Organizing ITL Principles for Unsupervised Learning.- A Reproducing Kernel Hilbert Space Framework for ITL.- Correntropy for Random Variables: Properties and Applications in Statistical Inference.- Correntropy for Random Processes: Properties and Applications in Signal Processing.
• (source: Nielsen Book Data)

This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld, this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi'sde?nitionofinformation.Thebasicapproac h is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic Learning (ITL). In the course of these studies, we discovered that the main idea enabling a synergistic view as well as algorithmic implementations, does not involve the conventional central moments of the data (mean and covariance). Rather, the core concept is the ?-norm of the PDF, in part- ular its expected value (? = 2), which we call the information potential. This operator and related nonparametric estimators link information theory, optimization of adaptive systems, and reproducing kernel Hilbert spaces in a simple and unconventional way.
(source: Nielsen Book Data)

• Investigating Universal Computability of Conventional Cellular Automata Problems on an Organic Molecular Matrix Noise-Based Logic and Computing: From Boolean Logic Gates to Brain Circuitry and Its Possible Hardware Realization Models and Mechanisms for Artificial Morphogenesis Biologically-Inspired Network Architecture for Future Networks Foraging Behaviors and Potential Computational Ability of Problem-Solving in an Amoeba Two Molecular Information Processing Systems Based on Catalytic Nucleic Acids The Effect of Community on Distributed Bio-inspired Service Composition Efficient Computation in Brownian Cellular Automata A Molecular Communication System Properties of Threshold Coupled Chaotic Neuronal Maps Implementation of Rotary Element with Quantum Cellular Automata Universal 2-State Asynchronous Cellular Automaton with Inner-Independent Transitions Effect of Population Size in Extended Parameter-Free Genetic Algorithm Temperature Effects on Olive Fruit Fly Infestation in the FlySim Cellular Automata Model Computing by Observing Changes Robustness of the Critical Behaviour in a Discrete Stochastic Reaction-Diffusion Medium Quantifying the Severity of the Permutation Problem in Neuroevolution Extending the Geometrical Design of DNA Nanostructures An Optical Solution for the Subset Sum Problem Design of True Random One-Time Pads in DNA XOR Cryptosystem On Designing Gliders in Three-Dimensional Larger than Life Cellular Automata Instability of Collective Flow in Two-Dimensional Optimal Velocity Model A New Differential Evolution for Multiobjective Optimization by Uniform Design and Minimum Reduce Hypervolume Noise Effects on Chaos in Chaotic Neuron Model Application of Improved Grammatical Evolution to Santa Fe Trail Problems Limit Theorem for a Time-Dependent Coined Quantum Walk onthe Line Top-Predator Survivor Region Is Affected by Bottom-Prey Mortality Rate on the Monte-Carlo Simulation in Lattice Model Simulation and Theoretical Comparison between 'Zipper' and 'Non-Zipper' Merging Universality of 2-State 3-Symbol Reversible Logic Elements - A Direct Simulation Method of a Rotary Element Pump Current as a Signal Transformation Evaluation of Generation Alternation Models in Evolutionary Robotics Photonic Switching of DNA's Position That Represents the Internal State in Photonic DNA Automaton Fluctuation Induced Structure in Chemical Reaction with Small Number of Molecules Parallel Retrieval of Nanometer-Scale Light-Matter Interactions for Nanophotonic Systems A Compressible Fluid Model for Traffic Flow and Nonlinear Saturation of Perturbation Growth Functional Sized Population Magnetic Optimization Algorithm Emergence and Collapse of Order in Ad Hoc Cellular Automata A Transition Rule Set for the First 2-D Optimum-Time Synchronization Algorithm A Two-Dimensional Optimum-Time Firing Squad Synchronization Algorithm and Its Implementation Quaternion Based Thermal Condition Monitoring System Firing Correlation in Spiking Neurons with Watts-Strogatz Rewiring Methods for Shortening Waiting Time in Walking-Distance Introduced Queueing Systems Effect of Mutation to Distribution of Optimum Solution in Genetic Algorithm.
• (source: Nielsen Book Data)

This book is the refereed proceedings of the Fourth International Workshop on Natural Computing, IWNC 2009, held in Himeji International Exchange Center, HIMEJI, JAPAN on September 2009. IWNC aims to bring together computer scientists, biologists, mathematicians, electronic engineers, physicists, and humanitarians, to critically assess present findings in the field, and to outline future developments in nature-inspired computing.
(source: Nielsen Book Data)

• Part 1 Keynote Speakers
• Part 2 Quantitative Logic
• Part 3 Fuzzy Sets and Systems
• Part 4 Soft Computing.

This book is the proceedings of the 2nd International Conference on Quantitative Logic and Soft Computing (QL & SC 2010) from Oct. 22-25, 2010 in Xiamen, China. Only high-quality papers are included. The 2nd International Conference on Quantitative Logic and Soft Computing (QL & SC 2010), built on the success of previous conferences, the QL & QS 2009 (Shanghai, China), is a major symposium for scientists, engineers and practitioners in China to present their updated results, ideas, developments and applications in all areas of quantitative logic and soft computing. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in quantitative logic and soft computing fields as follows: 1) Quantitative Logic. 2) Fuzzy Sets and Systems. 3) Soft Computing.

• 1. Programming-by-Demonstration of Robot Motions
• 2. Grasp Recognition by Fuzzy Modeling and Hidden Markov Models
• 3. Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks
• 4. A New Framework for View-invariant Human Action Recognition
• 5. Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions
• 6. Obstacle Detection using Cross-ratio and Disparity Velocity
• 7. Learning and Vision-based Obstacle Avoidance and Navigation
• 8. A Fraction Distortion Model for Accurate Camera Calibration and Correction
• 9. A Leader-follower Flocking System Based on Estimated Flocking Center
• 10. A Behavior Based Control System for Surveillance
• 11. Hierarchical Composite Anti-Disturbance Control for Robotic Systems Using Robust Disturbance Observer
• 12. Autonomous Navigation for Mobile Robots with Human-Robot Interaction
• 13. Prediction-based Perceptual System of a Partner Robot for Natural Communication
• Index.

Robot Intelligence is an exciting interdisciplinary field including engineering, information technology, machine learning, biological science and psychology. Its dramatic growth in practical applications is driven by both real-world requirements and maturity of related disciplines such as intelligent algorithms. It is expected that perception, understanding and reasoning capabilities play a crucial role in robot-assisted tasks and enable robots to exhibit similar performance on executing various tasks in both constrained and unconstrained environments. Robot Intelligence is a rare collection of chapters reflecting recent robotics developments from the advanced knowledge processing perspective. It also provides a comprehensive introduction and methodology to selected robotics topics including human robot interaction, human motion analysis, robot vision and advanced control. The robot intelligence methods presented enable readers to address many complex problems involving a wide range of robot sensors for the perception and understanding of the environment and the reasoning of the subsequent actions. The book's comprehensive coverage will prove invaluable to senior undergraduates, PhD students, researchers and practitioners.

• Introduction to Approaches in the Development of Heterogeneous Catalysts
• Methods of Searching for Optimal Catalytic Materials
• Analysis and Mining of Data Gathered in Catalytic Experiments
• Artificial Neural Networks in the Study of Catalytic Performance.
• (source: Nielsen Book Data)

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
(source: Nielsen Book Data)

• Introduction to Approaches in the Development of Heterogeneous Catalysts
• Methods of Searching for Optimal Catalytic Materials
• Analysis and Mining of Data Gathered in Catalytic Experiments
• Artificial Neural Networks in the Study of Catalytic Performance.
• (source: Nielsen Book Data)

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.
(source: Nielsen Book Data)

• Humanoid Robotics.- Time-Varying Affective Response for Humanoid Robots.- The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink.- From RoboNova to HUBO: Platforms for Robot Dance.- BunnyBot: Humanoid Platform for Research and Teaching.- Teen Sized Humanoid Robot: Archie.- Interdisciplinary Construction and Implementation of a Human Sized Humanoid Robot by Master Students.- Human Robot Interaction.- Safety Aspects in a Human-Robot Interaction Scenario: A Human Worker Is Co-operating with an Industrial Robot.- Integration of a RFID System in a Social Robot.- A Practical Study on the Design of a User-Interface Robot Application.- Infrared Remote Control with a Social Robot.- BlogRobot: Mobile Terminal for Blog Browse Using Physical Representation.- An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction.- Devious Chatbots - Interactive Malware with a Plot.- Towards Better Human Robot Interaction: Understand Human Computer Interaction in Social Gaming Using a Video-Enhanced Diary Method.- Promotion of Efficient Cooperation by Sharing Environment with an Agent Having a Body in Real World.- Interaction Design for a Pet-Like Remote Control.- Experiences with a Barista Robot, FusionBot.- Mutually Augmented Cognition.- How Humans Optimize Their Interaction with the Environment: The Impact of Action Context on Human Perception.- Development of a Virtual Presence Sharing System Using a Telework Chair.- PLEXIL-DL: Language and Runtime for Context-Aware Robot Behaviour.- Ambient Intelligence in a Smart Home for Energy Efficiency and Eldercare.- Education and Entertainment.- Intelligent Technologies for Edutainment Using Multiple Robots.- Remote Education Based on Robot Edutainment.- Not Just "Teaching Robotics" but "Teaching through Robotics".- A Proposal of Autonomous Robotic Systems Educative Environment.- Mechatronics Education: From Paper Design to Product Prototype Using LEGO NXT Parts.- Fostering Development of Students' Collective and Self-efficacy in Robotics Projects.- From an Idea to a Working Robot Prototype: Distributing Knowledge of Robotics through Science Museum Workshops.- Teaching Electronics through Constructing Sensors and Operating Robots.- Learning from Analogies between Robotic World and Natural Phenomena.- Integrating Robot Design Competitions into the Curriculum and K-12 Outreach Activities.- Teamwork and Robot Competitions in the Undergraduate Program at the Copenhagen University College of Engineering.- Cooperative Robotics.- Multiagents System with Dynamic Box Change for MiroSot.- Multi Block Localization of Multiple Robots.- Soty-Segment: Robust Color Patch Design to Lighting Condition Variation.- Task-Based Flocking Algorithm for Mobile Robot Cooperation.- Analysis of Spatially Limited Local Communication for Multi-Robot Foraging.- AMiRESot - A New Robot Soccer League with Autonomous Miniature Robots.- Robotic System Design.- BeBot: A Modular Mobile Miniature Robot Platform Supporting Hardware Reconfiguration and Multi-standard Communication.- System Design for Semi-automatic AndroSot.- Learning, Optimization, Communication.- Extended TA Algorithm for Adapting a Situation Ontology.- An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem.- USAR Robot Communication Using ZigBee Technology.
• (source: Nielsen Book Data)

th This volume is an edition of the papers selected from the 12 FIRA RoboWorld C- gress, held in Incheon, Korea, August 16-18, 2009. The Federation of International Robosoccer Association (FIRA - www. fira. net) is a non-profit organization, which organizes robotic competitions and meetings around the globe annually. The RoboSoccer competitions started in 1996 and FIRA was - tablished on June 5, 1997. The Robot Soccer competitions are aimed at promoting the spirit of science and technology to the younger generation. The congress is a forum in which to share ideas and future directions of technologies, and to enlarge the human networks in robotics area. The objectives of the FIRA Cup and Congress are to explore the technical dev- opment and achievement in the field of robotics, and provide participants with a robot festival including technical presentations, robot soccer competitions and exhibits - der the theme "Where Theory and Practice Meet. " th Under the umbrella of the 12 FIRA RoboWorld Incheon Congress 2009, six int- national conferences were held for greater impact and scientific exchange: th * 6 International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS) th * 5 International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE) * International Conference on Social Robotics (ICSR) * International Conference on Advanced Humanoid Robotics Research (ICAHRR) * International Conference on Entertainment Robotics (ICER) * International Robotics Education Forum (IREF) This volume consists of selected quality papers from the six conferences.
(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)

• pt. I. Pattern Recognition
• Ch. 1. Multiple Classifier Systems
• Ch. 2. Building Decision Trees from the Fourier Spectrum of a Tree Ensemble
• Ch. 3. Clustering Large Data Sets
• Ch. 4. Multi-objective Variable String Genetic Classifier: Application to Remote Sensing Imagery
• pt. II. Image Processing and Vision
• Ch. 5. Dissimilarity Measures Between Fuzzy Sets or Fuzzy Structures
• Ch. 6. Early Vision: Concepts and Algorithms
• Ch. 7. Self-organizing Neural Network for Multi-level Image Segmentation
• Ch. 8. Geometric Transformation by Moment Method with Wavelet Matrix
• Ch. 9. New Computationally Efficient Algorithms for Video Coding.

• An introduction to artificial intelligence, N.G. Bourbakis
• fundamental methods for horn logic and AI applications, E. Kounalis and P. Marquis
• applications of genetic algorithms to permutation problems, F. Petry and B. Buckles
• extracting procedural knowledge from software systems using inductive learning in the PM system, R. Reynolds and E. Zannoni
• resource oriented parallel planning, S. Lee and K. Chung
• advanced parsing technology for knowledge based shells, J. Kipps
• analysis and synthesis of intelligent systems, W. Arden
• document analysis and recognition, S.N. Srihari et al
• signal understanding - an AI approach to modulation and classification, J.E. Whelchel et al
• and others.
• (source: Nielsen Book Data)

This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues.
(source: Nielsen Book Data)

• Table of Contents An Overview of Amazon EMR Exploring the Architecture and Deployment Options Common Use Cases and Architecture Patterns Big Data Applications and Notebooks Available in Amazon EMR Setting Up and Configuring EMR Clusters Monitoring, Scaling, and High Availability Understanding Security in Amazon EMR Understanding Data Governance in Amazon EMR Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Migrating On-Premises Hadoop Workloads to Amazon EMR Best Practices and Cost Optimization Techniques.
• (source: Nielsen Book Data)

Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governance, and monitoring capabilities of EMR Build applications for batch and real-time streaming data analytics solutions Perform interactive development with a persistent EMR cluster and Notebook Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow Who this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
(source: Nielsen Book Data)

• Table of Contents Getting Started with TinyML Prototyping with Microcontrollers Building a Weather Station with TensorFlow Lite for Microcontrollers Voice Controlling LEDs with Edge Impulse Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano Building a Gesture-Based Interface for YouTube Playback Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS Toward the Next TinyML Generation with microNPU.
• (source: Nielsen Book Data)

Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learning Key Features Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico Work with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge Impulse Explore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPU Book DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you'll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase. What you will learn Understand the relevant microcontroller programming fundamentals Work with real-world sensors such as the microphone, camera, and accelerometer Run on-device machine learning with TensorFlow Lite for Microcontrollers Implement an app that responds to human voice with Edge Impulse Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense Create a gesture-recognition app with Raspberry Pi Pico Design a CIFAR-10 model for memory-constrained microcontrollers Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM Who this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.
(source: Nielsen Book Data)

• Chapter 1
• Welcome to TensorFlow.js Headings
• What is TensorFlow.js?
• TensorFlow.js API
• Tensors Operations Variables
• How to install it
• Use cases Chapter 2 Building your First Model Headings
• Building a logistic regression classification model
• Building a linear regression model
• Doing unsupervised learning with k-means
• Dimensionality reduction and visualization with t-SNE and d3.js
• Our first neural network Chapter 3 Create a drawing app to predict handwritten digits using Convolutional Neural Networks and MNIST Headings
• Convolutional Neural Networks
• The MNIST Dataset
• Design the model architecture
• Train the model
• Evaluate the model
• Build the drawing app
• Integrate the model within the app
• Chapter 4 "Move your body!" A game featuring PoseNet, a pose estimator model Headings
• What is PoseNet?
• Loading the model
• Interpreting the result
• Building a game around it Chapter 5 Detect yourself in real-time using an object detection model trained in Google Cloud's AutoML Headings
• TensorFlow Object Detection API
• Google Cloud's AutoML
• Training the model
• Exporting the model and importing it in TensorFlow.js
• Building the webcam app Chapter 6 Transfer Learning with Image Classifier and Voice Recognition Headings
• What's Transfer Learning?
• MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)
• Transferring the knowledge
• Re-training the model
• Testing the model with a video Chapter 7 Censor food you do not like with pix2pix, Generative Adversarial Networks, and ml5.js Headings
• Introduction to Generative Adversarial Networks
• What is image translation?
• Training your custom image translator with pix2pix
• Deploying the model with ml5.js Chapter 8 Detect toxic words from a Chrome Extension using a Universal Sentence Encoder Headings
• Toxicity classifier
• Training the model
• Testing the model
• Integrating the model in a Chrome Extension Chapter 9 Time Series Analysis and Text Generation with Recurrent Neural Networks Headings
• Recurrent Neural Networks
• Example 1: Building an RNN for time series analysis
• Example 2: Building an RNN to generate text Chapter 10 Best practices, integrations with other platforms, remarks and final words Headings
• Best practices
• Integration with other platforms
• Materials for further practice
• Conclusion.
• (source: Nielsen Book Data)

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
(source: Nielsen Book Data)

• Introduction to Fuzzy Sets.- Interval-Valued Fuzzy Relations.- Applications.- Summary and Open Problem.
• (source: Nielsen Book Data)

This book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov's intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information. The theory and application of interval-values fuzzy sets to various decision making problems represent the central core of this book, which describes in detail aggregation operators and their use with imprecise data represented as intervals. Interval-valued fuzzy relations, compatibility measures of interval and the transitivity property are thoroughly covered. With its good balance between theoretical considerations and applications of originally developed algorithms to real-world problem, the book offers a timely, inspiring guide to mathematicians and engineers developing new decision making models or implementing/applying existing ones to a wide range of applications involving imprecise or incomplete data. .
(source: Nielsen Book Data)

• Part I: Type-2 Fuzzy Logic in Metaheuristics.- A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic.- Ensemble Neural Network optimization using a gravitational search algorithm with Interval Type-1 and Type-2 fuzzy parameter adaptation in pattern recognition applications.- Improved method based on type-2 fuzzy logic for the adaptive harmony search algorithm.- Comparison of bio-inspired methods with parameter adaptation through interval type-2 fuzzy logic.- Differential Evolution algorithm with Interval type-2 fuzzy logic for the optimization of the mutation parameter.- Part II: Neural Networks Theory and Applications.- Person recognition with modular deep neural network using the iris biometric measure.- Neuro-evolutionary Neural Network for the Estimation of Melting Point of Ionic Liquids.- A proposal to classify ways of walking patterns using spik-ing neural networks.- Partially-connected Artificial Neural Networks developed by Grammatical Evolution for pattern recognition problems.- Part III: Metaheuristics: Theory and Applications.- Bio-inspired Metaheuristics for Hyper-parameter Tuning of Support Vector Machine Classifiers.
• (source: Nielsen Book Data)

This book comprises papers on diverse aspects of fuzzy logic, neural networks, and nature-inspired optimization meta-heuristics and their application in various areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book is organized into seven main parts, each with a collection of papers on a similar subject. The first part presents new concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory and applications, and includes papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The third part addresses the theory and practice of meta-heuristics in different areas of application, while the fourth part describes diverse fuzzy logic applications in the control area, which can be considered as intelligent controllers. The next two parts explore applications in areas, such as time series prediction, and pattern recognition and new optimization and evolutionary algorithms and their applications respectively. Lastly, the seventh part addresses the design and application of different hybrid intelligent systems.
(source: Nielsen Book Data)

• Co-occurrence Relation" and "Ingredient Category" Recommend Alternative-ingredients.- Design and Initial Evaluation of Bluetooth Low Energy Separate Channel Fingerprinting.- Toward Sustainable Smart Mobility Information Infrastructure Platform :-Project Overview-.- Model-based methodology Establishing Traceability between Requirements, Design and Operation Information in Lifecycle-Oriented Architecture.- Sports game summarization based on sub-events and game-changing phrases.- Headline Generation with Recurrent Neural Network.- Customer State Analysis with Enthusiasm Analysis.
• (source: Nielsen Book Data)

This book examines theoretical and practical aspects of emerging technologies in e-service and artificial intelligence from an academic and professional viewpoint. To do so, it focuses on three major areas: the development of novel user support systems; development of smart mobility; and emerging technologies in Artificial Intelligence (AI). With regard to the development of novel user support systems, Chapter 1 introduces alternative ingredients recommendation using data on co-occurrence relation and ingredients categories to support cooking, while Chapter 2 introduces a study on location information inference using data acquired by low-energy Bluetooth devices. Turning to the development of smart mobility, Chapter 3 highlights a sustainable information infrastructure project for smart mobility systems. In addition, Chapter 4 presents a lifecycle-oriented development process to improve requirements and design in terms of uncertainties to provide sustainable information architectures for smart mobility. In the book's third and last part - emerging technologies in AI - Chapter 5 presents a summarization task for sports events on Twitter, focusing on an abstractive approach based on sub-events during the sports event. Chapter 6 discusses the generation of headlines using a recurrent neural network based on a machine translation approach. Lastly, Chapter 7 explores customer behavior analysis using enthusiasm analysis, an approach that estimates customers' activation levels. The book gathers a selection of the highest-quality papers presented at the 4th International Congress on Advanced Applied Informatics, held on July 12-16, 2015 in Okayama, Japan. Given the breadth of its coverage, it offers a valuable resource for practitioners, researchers and students alike.
(source: Nielsen Book Data)

• Phraseology in translation and contrastive studies.- Lexicography and terminography.- Exploitation of corpora in phraseological studies.- Development of corpora for phraseological studies.- Phraseology and language learning.- Cognitive and cultural aspects of phraseology.- Theoretical and descriptive approaches to phraseology.- Computational approaches to phraseology.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the International Conference on Computational and Corpus-Based Phraseology, Europhras 2017, held in London, UK, in November 2017. The 31 full papers presented were carefully reviewed and selected from numerous submissions and are organized into the following thematic sessions: Phraseology in translation and contrastive studies, Lexicography and terminography, Exploitation of corpora in phraseological studies, Development of corpora for phraseological studies, Phraseology and language learning, Cognitive and cultural aspects of phraseology, Theoretical and descriptive approaches to phraseology, and Computational approaches to phraseology. The chapter 'Frequency Consolidation Among Word N-Grams' is available open access under a CC BY 4.0 license.
(source: Nielsen Book Data)

• Preface.- A Genetic Programming Approach to Cost-Sensitive Control in Resource Constrained Sensor Systems.- A Study on Performance of the Hill Climbing Heuristic Method for Router Placement in Mesh Networks.- An Automated Irrigation System Based on a Low- Cost Micro-controller for Tomato Production in South India.- Artificial-Neural-Network-based Real-Time Urban Road Traffic State Estimation Framework.- Attack Detection using Evolutionary Computation.- Computational Intelligence based Security in WSNs - Technologies and Design Challenges.- Efficient Anomaly Detection System for Video Surveillance Application in WVSN with Particle Swarm Optimization.- Planning Robust Sensor Relocation Trajectories for a Mobile Robot with Evolutionary Multi-Objective Optimization.
• (source: Nielsen Book Data)

This book emphasizes the increasingly important role that Computational Intelligence (CI) methods are playing in solving a myriad of entangled Wireless Sensor Networks (WSN) related problems. The book serves as a guide for surveying several state-of-the-art WSN scenarios in which CI approaches have been employed. The reader finds in this book how CI has contributed to solve a wide range of challenging problems, ranging from balancing the cost and accuracy of heterogeneous sensor deployments to recovering from real-time sensor failures to detecting attacks launched by malicious sensor nodes and enacting CI-based security schemes. Network managers, industry experts, academicians and practitioners alike (mostly in computer engineering, computer science or applied mathematics) benefit from th e spectrum of successful applications reported in this book. Senior undergraduate or graduate students may discover in this book some problems well suited for their own research endeavors.
(source: Nielsen Book Data)

Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book * An overview of modern Data Science and Machine Learning libraries available in Java * Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. * Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn * Get a solid understanding of the data processing toolbox available in Java * Explore the data science ecosystem available in Java * Find out how to approach different machine learning problems with Java * Process unstructured information such as natural language text or images * Create your own search engine * Get state-of-the-art performance with XGBoost * Learn how to build deep neural networks with DeepLearning4j * Build applications that scale and process large amounts of data * Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.
(source: Nielsen Book Data)

• Remote Material Handling systems
• Industrial Robotics
• Mobile Robotics
• Adaptive control
• Vision system
• Smart materials
• Tele-operation.

This book constitutes the thoroughly refereed proceedings of the First International Conference of Advancements in Automation, Robotics and Sensing, ICAARS 2016, held in Coimbatore, India, in June 2016. The 83 revised selected papers were selected from 159 submissions and focus on industrial robotics, mobile robotics, adaptive control, vision system, smart materials, and teleoperation.

• Part I Fundamental Theory.- Part II Applications.
• (source: Nielsen Book Data)

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
(source: Nielsen Book Data)

• Intro; Preface; Contents; 1 The Role of Computational Intelligence in Quantitative Software Engineering; 1 Introduction
• Software Development and the Art of Cappuccino; 2 Persistent Problems in Software Development; 3 Uncertainty; 4 Irreversibility; 5 Complexity; 6 Handling Uncertainty, Irreversibility, and Complexity 2026 and Cappuccino!; 7 The Pivotal Role of Computational Intelligence in Quantitative Software Engineering; 8 Conclusions; References; 2 Computational Intelligence: An Introduction; Abstract; 1 Introduction

• 2 Computational Intelligence: An Agenda of Synergy of Algorithms of Learning, Optimization and Knowledge Representation3 Neural Networks and Neurocomputing; 4 Evolutionary and Biologically Inspired Computing: Towards a Holistic View at Global Optimization; 5 Information Granularity and Granular Computing; 6 Formal Platforms of Information Granularity; 6.1 Information Granules of Higher Type and Higher Order; 6.2 Hybrid Models of Information Granules; 7 The Concept of Information Granulation-Degranulation; 8 Clustering as a Means of Design of Information Granules

• 8.1 Unsupervised Learning with Fuzzy Sets8.2 Fuzzy C-Means as an Algorithmic Vehicle of Data Reduction Through Fuzzy Clusters; 8.3 Knowledge-Based Clustering; 9 Computational Intelligence and Software Engineering; 10 Conclusions; References; 3 Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction; Abstract; 1 Introduction; 2 Related Work; 3 Feature Subset Selection (FSS) Methods; 3.1 Information Gain (IG) Attribute Ranking; 3.2 Relief (RLF); 3.3 Principal Component Analysis (PCA); 3.4 Correlation-Based Feature Selection (CFS)

• 3.5 Consistency-Based Subset Evaluation (CNS)3.6 Wrapper Subset Evaluation (WRP); 3.7 Genetic Programming (GP); 4 Experimental Setup; 5 Results and Analysis; 6 Validity Evaluation; 7 Conclusions; References; 4 Evolutionary Computation for Software Product Line Testing: An Overview and Open Challenges; Abstract; 1 Introduction; 2 Background; 2.1 SPL Foundations
• Feature Models and Running Example; 2.2 Basics of Evolutionary Algorithms; 3 Overview of SPL Testing; 4 Combinatorial Interaction Testing for Software Product Lines; 4.1 Basic Terminology; 4.2 SPL Genetic Solver (SPLGS)

• 4.3 State of the Art CIT for SPL Testing5 Multi-objective SPL Testing; 5.1 Multi-objective Optimization Formalization; 5.2 An Example Scenario; 5.3 Computation of Exact Pareto Fronts; 5.4 Sate of the Art in Evolutionary Multi-objective Optimization for SPL Testing; 6 Evolutionary Testing of SPLs in Practice; 7 Open Challenges and Questions; 8 Conclusions; Acknowledgments; References; 5 Metaheuristic Optimisation and Mutation-Driven Test Data Generation; Abstract; 1 Introduction; 2 Test Data Generation; 3 Mutation Analysis; 4 Metaheuristic Optimisation

In a down-to-the earth manner, the volume lucidly presents how the fundamental concepts, methodology, and algorithms of Computational Intelligence are efficiently exploited in Software Engineering and opens up a novel and promising avenue of a comprehensive analysis and advanced design of software artifacts. It shows how the paradigm and the best practices of Computational Intelligence can be creatively explored to carry out comprehensive software requirement analysis, support design, testing, and maintenance. Software Engineering is an intensive knowledge-based endeavor of inherent human-centric nature, which profoundly relies on acquiring semiformal knowledge and then processing it to produce a running system. The knowledge spans a wide variety of artifacts, from requirements, captured in the interaction with customers, to design practices, testing, and code management strategies, which rely on the knowledge of the running system. This volume consists of contributions written by widely acknowledged experts in the field who reveal how the Software Engineering benefits from the key foundations and synergistically existing technologies of Computational Intelligence being focused on knowledge representation, learning mechanisms, and population-based global optimization strategies. This book can serve as a highly useful reference material for researchers, software engineers and graduate students and senior undergraduate students in Software Engineering and its sub-disciplines, Internet engineering, Computational Intelligence, management, operations research, and knowledge-based systems.

This book constitutes the proceedings of the 10th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2015, held in Hefei, China, in September 2015. The 63 revised full papers presented were carefully reviewed and selected from 182 submissions. The papers deal with the following main topics: evolutionary computing, neural computing, DNA computing, and membrane computing.

• Intro; Preface; Organization; Contents; Utilizing Permission Norms in BDI Practical Normative Reasoning; 1 Introduction; 2 Background; 2.1 Jason; 2.2 Event Calculus; 3 Norm Representation; 3.1 Norms; 4 BDI Agent Normative Reasoning; 5 Experiments; 5.1 Gold and Silver Mining Society; 6 Summary; References; Holonic Institutions for Multi-scale Polycentric Self-governance; 1 Introduction; 2 Background and Motivation; 2.1 Formalising Ostrom's Principles; 2.2 The Eighth Principle: Nested Enterprises; 2.3 An Example; 3 Institutions and Holons; 3.1 Institutions: An Informal Overview

• 3.2 Self-organising Electronic Institutions3.3 Holonic Systems; 4 Holonic Institutions; 5 Case Study for Community Energy Systems; 5.1 Smart Houses; 5.2 Decentralised Community Energy Systems; 5.3 Holonic Institutions; 5.4 Polycentric Self-Governance; 6 Related and Future Work; 7 Summary and Conclusions; References; Contextualized Planning Using Social Practices; 1 Introduction; 2 Background; 2.1 2APL; 2.2 Case-Based Reasoning; 2.3 Work Practice Simulation; 3 Social Practices; 3.1 Social Intelligence and Social Practices; 3.2 Characteristics of Social Practices

• 3.3 Social Practices in Deliberation4 Scenario; 5 Discussion and Conclusion; References; Modelling the Impact of Role Specialisation on Cooperative Behaviour in Historic Trader Scenarios; 1 Introduction; 2 Dynamic Deontics; 2.1 Characteristics; 2.2 Operationalisation; 3 Historic Trader Scenarios; 4 Model; 5 Simulation Results; 5.1 Role Unification Without Norm Enforcement; 5.2 Role Unification with Norm Enforcement; 5.3 Role Specialisation Without Norm Enforcement; 5.4 Role Specialisation with Norm Enforcement; 6 Discussion, Conclusions and Outlook; References

• Severity-Sensitive Robustness Analysis in Normative Systems1 Introduction; 2 ISR Scenario; 3 Formalization; 4 Normative Ranking of Possible Worlds; 4.1 Computing Pw; 4.2 Computing the Ranking; 5 Detailed Example; 6 Checking Robustness; 7 Discussion; 8 Conclusions; Building an Artificial Primitive Human Society: An Agent-Based Approach; Abstract; 1 Introduction; 2 Background; 2.1 Simulation Environment and Settings; 3 Experimental Settings and Results; 4 Discussion; 5 Conclusion and Future Direction; References; Designing for Planned Emergence in Multi-agent Systems; 1 Introduction

• 2 OJAzzIC Overview3 Design Considerations; 3.1 Scenario; 3.2 Design Questions; 3.3 Designing an OJAzzIC Based System; 4 Incident Response Demonstration System; 4.1 Define the Goal Model; 4.2 Define the Organizational Model; 4.3 Define the Agent Capabilities Model; 4.4 Define the Role Model; 4.5 Establish Social Policies; 5 Observations; 6 Related Work; 7 Conclusion; References; Supporting Request Acceptance with Use Policies; 1 Introduction; 2 Related Work; 3 Overview; 4 Requests and Use Policies; 4.1 Representations; 4.2 Conflict; 5 The Event Calculus Normative Model; 5.1 Event Calculus

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Workshops on Coordination, Organizations, Institutions and Norms in Agent Systems, COIN 2014. The workshops were co-located with AAMAS 2014, held in Paris, France, in May 2014, and with PRICAI 2014, held in Gold Coast, QLD, Australia, in December 2014. The 16 full papers were carefully reviewed and selected for inclusion in this volume. The papers cover a wide range of topics from work on formal aspects of normative and team based systems, to software engineering with organisational concepts, to applications of COIN based systems, and to philosophical issues surrounding socio-technical systems. They highlight not only the richness of existing work in the field, but also point out the challenges and exciting research that remains to be done in the area.
(source: Nielsen Book Data)

• Malware and Machine Learning
• Soft Computing Based Epidemical Crisis Prediction
• An ACP-Based Approach to Intelligence and Security Informatics
• Microfiles as a Potential Source of Confidential Information Leakage
• Decision Support in Open Source Intelligence
• Information Fusion Process Design Issues for Hard and Soft Information: Developing an Initial Prototype
• Intrusion Detection with Type-2 Fuzzy Ontologies and Similarity Measures
• A multi-objective genetic algorithm based approach for effective intrusion detection using neural networks
• Cyber Insider Mission Detection for Situation Awareness
• A Game Theoretic Engine for Cyber Warfare
• Mission Impact Assessment for Cyber Warfare
• Uncertainty modeling: the Computational Economists' View on Cyberwarfare.

Cyberwarfare has become an important concern for governmental agencies as well businesses of various types. This timely volume, with contributions from some of the internationally recognized, leaders in the field, gives readers a glimpse of the new and emerging ways that Computational Intelligence and Machine Learning methods can be applied to address problems related to cyberwarfare. The book includes a number of chapters that can be conceptually divided into three topics: chapters describing different data analysis methodologies with their applications to cyberwarfare, chapters presenting a number of intrusion detection approaches, and chapters dedicated to analysis of possible cyber attacks and their impact. The book provides the readers with a variety of methods and techniques, based on computational intelligence, which can be applied to the broad domain of cyberwarfare.

• Improvement Model of Business Continuity Management on E-Learning / Gang Chen
• Using Computer Simulation to Improve Teaching and Learning Effectiveness on DC/DC Converter / LiPing Fan
• The Improved Diagonal-Matrix-Weight IMM Algorithm Based on LDU Factorization / Liang Gao, Jianping Xing, Fan Shi
• Performance Analysis of VCN and IVCN Localization Algorithms in WSN / Yang Liu, Jianping Xing, Hua Wu, Xiaoming Wu
• Impact Analysis of Environment Factors on Iterative Calculation of Secondary Grid Division (ICSGD) Localization Scheme for Wireless Sensor Networks / Yuxin Ren, Jianping Xing, Yang Liu, Can Sun
• Research on Data-Driven in Business Workflow System Design / ChuanSheng Zhou
• Research on XML Data-Driven Based Web Page Design / ChuanSheng Zhou
• Research and Application of Data-Driven in Web System Design / ChuanSheng Zhou
• Cogitation of Information Technology Construction in University Library / Ning Xu.

• Study on E-Commerce Application in Manufacturing Enterprises: A Case Study of Ruijie Network Co., Ltd. / Ling Tian, Xiaohong Wang, Liwei Li
• Discussion on Occupation Moral Education in Higher Vocational Colleges / Jushun Li
• Dynamic Analysis and System Design Based on Diluted Asymmetric Discrete-Time Hopfield Network / Li Tu, Chi Zhang
• Evaluation Method on the Scientific Research of Local University / Xiaomin Huang
• A Random Endogenous Economic Growth Model and Equilibrium Analysis / Zhongcheng Zhang
• Try to Talk about the Background of the Information Network Moral Construction / LiYan Yang
• Construction of Library Information Resources Depending on Internet Environment / LiYan Yang
• A General Fuzzified CMAC and Its Function Approximation / Zhipeng Shen, Chen Guo
• Discussion on Young Teachers' Teaching Method / Peng Qin, Yinghua Wang, Zhihai Qin
• Reform of Basic Computer Teaching in Universities and Its Measures under New Circumstances / Jianguo Yu, Shuanghong Liu.

• Exploration of the Building Model about Theme_Based Learning and Shared Community Based Python Add Django / Xiang Li, Mei Zhu, Xuan Zhan
• Principles and Methods in Teaching English with Multimedia / YiFei Zhu
• Research on the Difference of Attitudinal Changes between Ambivalent Consumers / Minxue Huang, Xiaoliang Feng, Chao Wang
• Study on Chinese Error Checking / Chongwen Wang, Bo Yuan
• Double-Sources Dijkstra Algorithm within Typical Urban Road Networks / Shiming Wang, Jianping Xing, Yong Wu
• Development and Practice of Knowledge Service Platform Based on DSpace / Lu Han, Yi Ding
• Application of Hopfield Network in Grayscale Image Recognition / Li Tu, Zhiping Liao, Chi Zhang
• Design of Mobile Learning-Recommendation Services Supporting Small-Screen Display Interfaces / Hong-Ren Chen
• The Effect of Perceived Organizational Support on Employee Work Engagement: A Case Study Analysis / TingJun Kou
• Web-Based Data Mining in Computer Culture Basic Course in College Using an Improved GA / Jianxin Bi.

• Teaching Mode Innovation and Practice of "Foundation of Computer Programming" / Yaqing Shi, Meijuan Wang, Cheng Hu
• MBR Phrase Scoring and Pruning for SMT / Nan Duan
• Application of Computer Aided Instruction (CAI) in English Teaching for Non-English-Major Postgraduates Based on Constructivism / Xueyao Chen
• Discussion on Digital Storytelling in Comprehensive Practice Courses Application / JiangYan Zheng, Hao Zhang, Liang Hong
• GCM Data Analysis Using Dimensionality Reduction / Zuoling Li, Guirong Weng
• Application of the BP Neural Network PID Algorithm in Heat Transfer Station Control / Yang Yu, Dongmei Yin
• The Influence of Human Resource Management Information System on Human Resource Management / Yan-Hong Yu
• The Empirical Study of Investment Opportunities in the Value-Relevance of Accounting Earnings / Yuanyang Fu, He Guo, Danping Wang
• On Software Professional Studio Construction in Higher Vocational Schools and Its Meaning / ShuYan Yu.

• The Research on Teaching Ideas of "Data Structure and Algorithm" in Non-computer Major / Zhao Wang
• Application-Oriented Intercultural Engineering Education in Automotive Engineering and Service / Yanfen Mao, Ming Chen, Hans Wiedmann
• Research of Private Courier Enterprise Based on E-Commerce / Bo Zhang
• The Considering about Chinese Enterprises to Establish E-Training Platform / Jianlin Qiu
• Empirical Study on Undergraduate Employability and Training Mechanism in Chinese Independent Institutes / Yong Chen
• Research and Practice on the Open Teaching for the Authentication of AutoCAD Senior Application Engineers / Qibing Wang
• The Application Research on Determination Method of Poor University Students Based on the Interval Density Cluster / Huang Zhenzhen
• Course Design and Practices of Teaching Undergraduate's Database System / Hongji Wang, Qingqin Yan
• The Impact of Use Context on Mobile Payment Acceptance: An Empirical Study in China / Luzhuang Wang, Yongzheng Yi.

• An Empirical Research on Correlation of Communication Companies' Compensation Gap with Enterprise's Performance / Luzhuang Wang, Hui Yu
• On the Principles of College Chinese Textbooks Compilation / Zhonghua Li
• The Secondary Hash MM Algorithm Based on Probability Factor / Yan Niu, Lele Huan
• An Optimal Model of Selecting Players for Mathematics Competition / Ying Wei
• Gestalt Psychology Theory Based Design of User's Experience on Online Trade Websites / Peng Bian, Nairen Zhang
• Exploration and Practice on Construction of Curriculum Group of Numerical Analysis / Datian Niu, Xuegang Yuan, Jia Jiao, Wei Zhao
• College Chinese and Humanities Education / Zhonghua Li
• Distributed Detecting and Real-Time Advertising System of Equipment Failure Based on MANET / Xia Qin, Tian Yi
• Strengthen Study Motivations of College Students via Learning Environment Improvement / Xin Zhang, Libo Huang.

• The Mediating Role of Entrepreneurial Efficiency on the Relationship between Human Resource Management and Corporate Entrepreneurship / ChunLi Liu, HongLi Wang
• M-Learning Activity Design for College English Courses / Lin Hu, Honghua Xu, Shuying Zhuang
• A Novel Approach to Online Image Education Resource Retrieval by Semantic Network and Interactive Filtering / Xianlin Peng, Ling Gao, Yuwen Ning
• A Study on the Establishment of Interpretation Teaching Model Based on Multi-media Network / JinBao Cai, Jing Xiao, Ying Lin
• Inter-domain Communication Mechanism Design and Implementation for High Performance / Tan Jie, Huang Wei
• The Study on College Library Electronic Resources Integration Based on Internet / Min Ren
• A Multimedia Based Natural Forest Park for Ecological Education / Zhen Liu, YanJie Chai
• Web-Based Interactive Animation for Children's Safety Education: From 2D to 3D / Zhen Liu, YanJie Chai
• The Evaluation on Ideological and Political Teachers' Competency / Yongfang Liu.

• The Application of Set Pair Analysis in College Ideological and Political Education / Yongfang Liu, Zhikai Yun
• The Improvement of Enterprise-Oriented Scale-Free Network Model / Yongxian Chen, Dichong Wu, Canlin Mo
• Research on Model of Ontology-Based Semantic Information Retrieval / Yu Cheng, Ying Xiong
• Research of ICE-Based Unified Identity Authentication Model / Yue Zhang, BingBing Xia
• Research of Key Technologies in Development in Thesis Management System / Huadong Wang
• The Localization Algorithm for Wireless Sensor Networks Based on Distance Clustering / Xiaohui Chen, Jinpeng Chen, Bangjun Lei
• Research and Analysis of the Social Access to College Sports Venues in Wuhan / Han Jiang
• Prediction of Zenith Tropospheric Delay Based on BP Neural Network / Yong Wang, Lihui Zhang, Jing Yang
• Advance in Scence Change-Based H.264 Rate Control Methods / Feifei Lu, Xiao Chen
• Optimizing the Network Topology in Gnutella P2P Networks / Jin Bo.

CSE2011 is an integrated conference concentration its focus on computer science and education. In the proceeding, you can learn much more knowledge about computer science and education of researchers from all around the world. The main role of the proceeding is to be used as an exchange pillar for researchers who are working in the mentioned fields. In order to meet the high quality of Springer, AISC series, the organization committee has made their efforts to do the following things. Firstly, poor quality paper has been refused after reviewing course by anonymous referee experts. Secondly, periodically review meetings have been held around the reviewers about five times for exchanging reviewing suggestions. Finally, the conference organizers had several preliminary sessions before the conference. Through efforts of different people and departments, the conference will be successful and fruitful.

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

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

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

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

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

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

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

• 1. Introduction
• 2. Relational knowledge
• 3. From data to hypotheses
• 4. Clustering
• 5. Information gain
• 6. Knowledge and relations
• 7. Rough set theory
• 8. Inductive logic learning
• 9. Ensemble learning
• 10. The logic of knowledge
• 11. Indexes and bibliography
• Bibliography
• Index.
• (source: Nielsen Book Data)

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.
(source: Nielsen Book Data)

• pt. 1. A world of annotations
• pt. 2. Leaving a mark ...
• pt. 3. Peeking at the future.

Did you ever read something on a book, felt the need to comment, took up a pencil and scribbled something on the books' text'? If you did, you just annotated a book. But that process has now become something fundamental and revolutionary in these days of computing. Annotation is all about adding further information to text, pictures, movies and even to physical objects. In practice, anything which can be identified either virtually or physically can be annotated. In this book, we will delve into what makes annotations, and analyse their significance for the future evolutions of the web. We will explain why it was thought to be unreasonable to annotate documents manually and how Web 2.0 is making us rethink our beliefs. We will have a look at tools which make use of Artificial Intelligence techniques to support people in the annotation task. Behind these tools, there exists an important property of the web known as redundancy; we will explain what it is and show how it can be exploited. Finally we will gaze into the crystal ball and see what we might expect to see in the future. Until people understand what the web is all about and its grounding in annotation, people cannot start appreciating it. And until they do so, they cannot start creating the web of the future.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)