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• 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)