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• Conversational Bot/QA/IR
• Knowledge graph/IE
• Machine Learning for NLP
• Machine Translation
• NLP Applications.

This two volume set of LNAI 11108 and LNAI 11109 constitutes the refereed proceedings of the 7th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2018, held in Hohhot, China, in August 2018. The 55 full papers and 31 short papers presented were carefully reviewed and selected from 308 submissions. The papers of the first volume are organized in the following topics: conversational Bot/QA/IR; knowledge graph/IE; machine learning for NLP; machine translation; and NLP applications. The papers of the second volume are organized as follows: NLP for social network; NLP fundamentals; text mining; and short papers.

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator
• Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition
• Face Recognition Benchmark with ID Photos
• Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition
• Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion
• A Demand-based Allocation Mechanism for Virtual Machine
• A Joint Hierarchy Model for Action Recognition Using Kinect
• QoS-Based Medical Program Evolution
• An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry
• The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography
• Combining CNN and MRF for Road Detection
• The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis
• A Diffeomorphic Demons Approach to Statistical Shape Modeling.

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.

• Java deep learning cookbook: train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
• Dedication
• About Packt
• Contributors
• Table of Contents
• Preface
• 1. Introduction to Deep Learning in Java
• 2. Data Extraction, Transformation, and Loading
• 3. Building Deep Neural Networks for Binary Classification
• 4. Building Convolutional Neural Networks
• 5. Implementing Natural Language Processing
• 6. Constructing an LSTM Network for Time Series
• 7. Constructing an LSTM Neural Network for Sequence Classification
• 8. Performing Anomaly Detection on Unsupervised Data
• 9. Using RL4J for Reinforcement Learning
• 10. Developing Applications in a Distributed Environment
• 11. Applying Transfer Learning to Network Models
• 12. Benchmarking and Neural Network Optimization
• Other Books You May Enjoy
• Index.

Deep Learning is a trending topic in AI currently, as it allows you to make faster and more accurate predictions using the power of neural networks. This book will teach you the process of neural network design, and show you how to develop efficient deep learning applications using Deeplearning4j through practical and easy to implement recipes.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Chapter 1: Machine Learning for Cybersecurity
• Technical requirements
• Train-test-splitting your data
• Getting ready
• How to do it ...
• How it works ...
• Standardizing your data
• Getting ready
• How to do it ...
• How it works ...
• Summarizing large data using principal component analysis
• Getting ready
• How to do it ...
• How it works ...
• Generating text using Markov chains
• Getting ready
• How to do it ...
• How it works ...

• Performing clustering using scikit-learn
• Getting ready
• How to do it ...
• How it works ...
• Training an XGBoost classifier
• Getting ready
• How to do it ...
• How it works ...
• Analyzing time series using statsmodels
• Getting ready
• How to do it ...
• How it works ...
• Anomaly detection with Isolation Forest
• Getting ready
• How to do it ...
• How it works ...
• Natural language processing using a hashing vectorizer and tf-idf with scikit-learn
• Getting ready
• How to do it ...
• How it works ...
• Hyperparameter tuning with scikit-optimize
• Getting ready
• How to do it ...

• How it works ...
• Chapter 2: Machine Learning-Based Malware Detection
• Technical requirements
• Malware static analysis
• Computing the hash of a sample
• Getting ready
• How to do it ...
• How it works ...
• YARA
• Getting ready
• How to do it ...
• How it works ...
• Examining the PE header
• Getting ready
• How to do it ...
• How it works ...
• Featurizing the PE header
• Getting ready
• How to do it ...
• How it works ...
• Malware dynamic analysis
• Getting ready
• How to do it ...
• How it works ...
• Using machine learning to detect the file type

• Scraping GitHub for files of a specific type
• Getting ready
• How to do it ...
• How it works ...
• Classifying files by type
• Getting ready
• How to do it ...
• How it works ...
• Measuring the similarity between two strings
• Getting ready
• How to do it ...
• How it works ...
• Measuring the similarity between two files
• Getting ready
• How to do it ...
• How it works ...
• Extracting N-grams
• Getting ready
• How to do it ...
• How it works ...
• Selecting the best N-grams
• Getting ready
• How to do it ...
• How it works ...
• Building a static malware detector
• Getting ready

• How to do it ...
• How it works ...
• Tackling class imbalance
• Getting ready
• How to do it ...
• How it works ...
• Handling type I and type II errors
• Getting ready
• How to do it ...
• How it works ...
• Chapter 3: Advanced Malware Detection
• Technical requirements
• Detecting obfuscated JavaScript
• Getting ready
• How to do it ...
• How it works ...
• Featurizing PDF files
• Getting ready
• How to do it ...
• How it works ...
• Extracting N-grams quickly using the hash-gram algorithm
• Getting ready
• How to do it ...
• How it works ...
• See also
• Building a dynamic malware classifier

This book helps data scientists and cybersecurity experts on implementing the latest AI techniques in cybersecurity. Concrete and clear steps for implementing ML security systems are provided, saving you months in research and development. By the end of this book, you will be able to build defensive systems to curb cybersecurity threats.

• Vehicle Navigation Systems
• Localization and Mapping
• Path Planning
• Sensing and Tracking Systems
• Identification and Motion Control of Robotic Vehicles
• Coordinated and Cooperative Control of Multi-Vehicle Systems.

This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

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

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

• Frontmatter
• Remerciements
• Sommaire
• Préface
• 1. Qu'est-ce au juste que l'intelligence artificielle?
• 2. Avec l'intelligence artificielle générale pour Graal
• 3. Langage, créativité et émotions
• 4. Les réseaux de neurones artificiels
• 5. Les robots et la vie artificielle (A-life)
• 6. Mais, est-ce réellement de l'intelligence?
• 7. La Singularité
• Références
• Lectures complémentaires
• Index

L'intelligence artificielle, qui consiste à faire faire aux ordinateurs ce que peut faire l'esprit humain et ses applications touchent tous les aspects de notre vie et se trouvent tout autour de nous. Ce petit livre nous rappelle que les résultats de l'intelligence artificielle ont été inestimables pour les biologistes, les psychologues et les linguistes en les aidant à comprendre les processus de la mémoire, de l'apprentissage et du langage sous un angle nouveau. L'intelligence artificielle a aussi alimenté et aiguisé les débats philosophiques concernant la nature de l'esprit, l'intelligence et le caractère unique des êtres humains. Ainsi, Margaret Boden, leader dans le domaine de la recherche en intelligence artificielle, passe en revue ici tous les défis philosophiques et technologiques soulevés par l'intelligence artificielle, et tente de répondre à la question si complexe : les programmes pourront-ils être un jour réellement intelligents, créatifs, ou même conscients ?

• Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
• (source: Nielsen Book Data)

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
(source: Nielsen Book Data)

• Introduction The Query Process and Barriers to Finding Information Online Online Search: An Evolution Search and Discovery Technologies: An Overview Information Access: A Spectrum of Needs and Uses Future Tense: The Next Era in Information Access and Discovery Answer Machines.
• (source: Nielsen Book Data)

The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's DeepQA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
(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)

• The Lovelace Test
• Three types of creativity
• Ready steady go
• Algorithms, the secret to modern life
• From top-down to bottom-up
• Algorithmic evolution
• Painting by numbers
• Learning from the masters
• The art of mathematics
• The mathematician's telescope
• Music: the process of sounding mathematics
• The song-writing formula
• Deepmathematics
• Language games
• Let AI tell you a story
• Why we create: a meeting of minds.

Most books on AI focus on the future of work. But now that algorithms can learn and adapt, does the future of creativity also belong to well-programmed machines? To answer this question, Marcus du Sautoy takes us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines.-- Provided by publisher.

• Table of Contents Understanding Rewards-Based Learning Dynamic Programming and the Bellman Equation Monte Carlo Methods Temporal Difference Learning Exploring SARSA Going Deep with DQN Going Deeper with DDQN Policy Gradient Methods Optimizing for Continuous Control All about Rainbow DQN Exploiting ML-Agents DRL Frameworks 3D Worlds From DRL to AGI.
• (source: Nielsen Book Data)

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features Get to grips with the different reinforcement and DRL algorithms for game development Learn how to implement components such as artificial agents, map and level generation, and audio generation Gain insights into cutting-edge RL research and understand how it is similar to artificial general research Book DescriptionWith the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent's productivity. As you advance, you'll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you'll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learn Understand how deep learning can be integrated into an RL agent Explore basic to advanced algorithms commonly used in game development Build agents that can learn and solve problems in all types of environments Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem Develop game AI agents by understanding the mechanism behind complex AI Integrate all the concepts learned into new projects or gaming agents Who this book is forIf you're a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Related Learning Paradigms Lifelong Supervised Learning Continual Learning and Catastrophic Forgetting Open-World Learning Lifelong Topic Modeling Lifelong Information Extraction Continuous Knowledge Learning in Chatbots Lifelong Reinforcement Learning Conclusion and Future Directions Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
(source: Nielsen Book Data)

• 1. Models for mobile agent computing
• Introduction
• What is a mobile agent
• Why mobile agents
• An algorithmic model for mobile agents
• Mobile agents
• Distributed networks
• Resource measures
• Mobile agent rendezvous
• Outline of the book
• Comments and bibliographic remarks.

• 2. Deterministic rendezvous in a ring
• Introduction
• A single stationary token
• The feasibility of rendezvous
• The time complexity of rendezvous
• Memory tradeoff for rendezvous with detection
• Limits to the memory trade-off
• Movable tokens
• Comments and bibliographic remarks.

• 3. Multiple agent rendezvous in a ring
• Introduction
• Impossibility of rendezvous
• Rendezvous with detection
• Conditional solutions
• Comments and bibliographic remarks.

• 4. Randomized rendezvous in a ring
• Introduction
• Random walk algorithm
• Randomization and tokens
• Time/memory trade-offs
• Coin half tour algorithm
• Approximate counting algorithm
• Comments and bibliographic remarks.

• 5. Other models
• Introduction
• Leader election and rendezvous
• Rendezvous with failing tokens
• Rendezvous when tokens fail upon release
• Rendezvous when tokens can fail at any time
• The cost of token failure
• Flickering tokens
• Asynchronous rendezvous
• Look-compute-move
• Model and terminology
• Impossibility results
• Gathering configurations with a single multiplicity
• Gathering rigid configurations
• Gathering an odd number of robots
• Dangerous networks
• Black-hole search in an asynchronous ring
• Rendezvous in asynchronous rings in spite of a black-hole
• Comments and bibliographic remarks.

• 6. Other topologies
• Introduction
• Synchronous torus
• Memory lower bounds for rendezvous
• Rendezvous algorithms
• Trees
• Arbitrary graphs
• Comments and bibliographic remarks.

• Bibliography
• Glossary
• Authors' biographies
• Index.

Mobile agent computing is being used in fields as diverse as artificial intelligence, computational economics and robotics. Agents' ability to adapt dynamically and execute asynchronously and autonomously brings potential advantages in terms of fault-tolerance, flexibility and simplicity. This monograph focuses on studying mobile agents as modelled in distributed systems research and in particular within the framework of research performed in the distributed algorithms community. It studies the fundamental question of how to achieve rendezvous, the gathering of two or more agents at the same node of a network. Like leader election, such an operation is a useful subroutine in more general computations that may require the agents to synchronize, share information, divide up chores, etc. The work provides an introduction to the algorithmic issues raised by the rendezvous problem in the distributed computing setting. For the most part our investigation concentrates on the simplest case of two agents attempting to rendezvous on a ring network. Other situations including multiple agents, faulty nodes and other topologies are also examined. An extensive bibliography provides many pointers to related work not covered in the text. The presentation has a distinctly algorithmic, rigorous, distributed computing flavor and most results should be easily accessible to advanced undergraduate and graduate students in computer science and mathematics departments.

• Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
• (source: Nielsen Book Data)

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose ""queries, "" usually in the form of unlabeled data instances to be labeled by an ""oracle"" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or ""query selection frameworks."" We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
(source: Nielsen Book Data)

• Markov Decision Processes Value Prediction Problems Control For Further Exploration.
• (source: Nielsen Book Data)

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
(source: Nielsen Book Data)

From making faster, better decisions to automating rote work to enabling robots to respond to emotions, AI and machine learning are already reshaping business and society. What should you and your company be doing today to ensure that you're poised for success and keeping up with your competitors in the age of AI' Artificial Intelligence: The Insights You Need from Harvard Business Review brings you today's most essential thinking on AI and explains how to launch the right initiatives at your company to capitalize on the opportunity of the machine intelligence revolution. Business is changing. Will you adapt or be left behind' Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues-blockchain, cybersecurity, AI, and more-each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow. You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas-and prepare you and your company for the future.

• Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
• (source: Nielsen Book Data)

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
(source: Nielsen Book Data)

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

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

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

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

• Introduction Patterns in Static Graphs Patterns in Evolving Graphs Patterns in Weighted Graphs Discussion: The Structure of Specific Graphs Discussion: Power Laws and Deviations Summary of Patterns Graph Generators Preferential Attachment and Variants Incorporating Geographical Information The RMat Graph Generation by Kronecker Multiplication Summary and Practitioner's Guide SVD, Random Walks, and Tensors Tensors Community Detection Influence/Virus Propagation and Immunization Case Studies Social Networks Other Related Work Conclusions.
• (source: Nielsen Book Data)

What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints.
(source: Nielsen Book Data)

• Introduction to Statistical Machine Learning Overview of Semi-Supervised Learning Mixture Models and EM Co-Training Graph-Based Semi-Supervised Learning Semi-Supervised Support Vector Machines Human Semi-Supervised Learning Theory and Outlook.
• (source: Nielsen Book Data)

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field.
(source: Nielsen Book Data)

• Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
• (source: Nielsen Book Data)

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
(source: Nielsen Book Data)

• Introduction Markov Logic Inference Learning Extensions Applications Conclusion.
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Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system.
(source: Nielsen Book Data)

• Introduction Ranking-Based Clustering Classification of Heterogeneous Information Networks Meta-Path-Based Similarity Search Meta-Path-Based Relationship Prediction Relation Strength-Aware Clustering with Incomplete Attributes User-Guided Clustering via Meta-Path Selection Research Frontiers.
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Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions.
(source: Nielsen Book Data)

• Introduction MDPs Fundamental Algorithms Heuristic Search Algorithms Symbolic Algorithms Approximation Algorithms Advanced Notes.
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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems.
(source: Nielsen Book Data)

• Preface Sentiment Analysis: A Fascinating Problem The Problem of Sentiment Analysis Document Sentiment Classification Sentence Subjectivity and Sentiment Classification Aspect-Based Sentiment Analysis Sentiment Lexicon Generation Opinion Summarization Analysis of Comparative Opinions Opinion Search and Retrieval Opinion Spam Detection Quality of Reviews Concluding Remarks Bibliography Author Biography.
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Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.
(source: Nielsen Book Data)

• Introduction Preference Modeling and Reasoning Uncertainty in Preference Reasoning Aggregating Preferences Stable Marriage Problems.
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Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice.
(source: Nielsen Book Data)

• Introduction Example: Bidding on eBay Auction Fundamentals Continuous Double Auctions Interdependent Markets Conclusion.
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Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications. The lecture assumes no prior background in game theory or auction theory, or artificial intelligence.
(source: Nielsen Book Data)

• Understanding Web Analytics The Foundations of Web Analytics: Theory and Methods The History of Web Analytics Data Collection for Web Analytics Web Analytics Fundamentals Web Analytics Strategy Web Analytics as Competitive Intelligence Supplementary Methods for Augmenting Web Analytics Search Log Analytics Conclusion Key Terms Blogs for Further Reading References.
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This lecture presents an overview of the Web analytics process, with a focus on providing insight and actionable outcomes from collecting and analyzing Internet data. The lecture first provides an overview of Web analytics, providing in essence, a condensed version of the entire lecture. The lecture then outlines the theoretical and methodological foundations of Web analytics in order to make obvious the strengths and shortcomings of Web analytics as an approach. These foundational elements include the psychological basis in behaviorism and methodological underpinning of trace data as an empirical method. These foundational elements are illuminated further through a brief history of Web analytics from the original transaction log studies in the 1960s through the information science investigations of library systems to the focus on Websites, systems, and applications. Following a discussion of on-going interaction data within the clickstream created using log files and page tagging for analytics of Website and search logs, the lecture then presents a Web analytic process to convert these basic data to meaningful key performance indicators in order to measure likely converts that are tailored to the organizational goals or potential opportunities. Supplementary data collection techniques are addressed, including surveys and laboratory studies. The overall goal of this lecture is to provide implementable information and a methodology for understanding Web analytics in order to improve Web systems, increase customer satisfaction, and target revenue through effective analysis of user-Website interactions.
(source: Nielsen Book Data)

• Introduction to Web Page Recommender Systems Preprocessing for Web Page Recommender Models Pattern Extraction Evaluation Metrics.
• (source: Nielsen Book Data)

One of the application areas of data mining is the World Wide Web (WWW or Web), which serves as a huge, widely distributed, global information service for every kind of information such as news, advertisements, consumer information, financial management, education, government, e-commerce, health services, and many other information services. The Web also contains a rich and dynamic collection of hyperlink information, Web page access and usage information, providing sources for data mining. The amount of information on the Web is growing rapidly, as well as the number of Web sites and Web pages per Web site. Consequently, it has become more difficult to find relevant and useful information for Web users. Web usage mining is concerned with guiding the Web users to discover useful knowledge and supporting them for decision-making. In that context, predicting the needs of a Web user as she visits Web sites has gained importance. The requirement for predicting user needs in order to guide the user in a Web site and improve the usability of the Web site can be addressed by recommending pages to the user that are related to the interest of the user at that time. This monograph gives an overview of the research in the area of discovering and modeling the users' interest in order to recommend related Web pages. The Web page recommender systems studied in this monograph are categorized according to the data mining algorithms they use for recommendation.
(source: Nielsen Book Data)

• Table of Contents Preparing and Understanding Data Linear Regression Logistic Regression Advanced Feature Selection in Linear Models K-Nearest Neighbors and Support Vector Machines Tree-Based Classification Neural Networks and Deep Learning Creating Ensembles and Multiclass Methods Cluster Analysis Principal Component Analysis Association Analysis Time Series and Causality Text Mining Exploring the Machine Learning Landscape Predicting Employee Attrition Using Ensemble Models Implementing a Joke Recommendation Engine Sentiment Analysis of Amazon Reviews with NLP Customer Segmentation Using Wholesale Data Image Recognition Using Deep Neural Networks Credit Card Fraud Detection Using Autoencoders Automatic Prose Generation with Recurrent Neural Networks Winning the Casino Slot Machines with Reinforcement Learning.
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Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languages Key Features Gain expertise in machine learning, deep learning and other techniques Build intelligent end-to-end projects for finance, social media, and a variety of domains Implement multi-class classification, regression, and clustering Book DescriptionR is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You'll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood. By the end of this Learning Path, you'll be equipped with the skills you need to deploy machine learning techniques in your own projects. This Learning Path includes content from the following Packt products: R Machine Learning Projects by Dr. Sunil Kumar Chinnamgari Mastering Machine Learning with R - Third Edition by Cory Lesmeister What you will learn Develop a joke recommendation engine to recommend jokes that match users' tastes Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learning Implement NLP techniques for sentiment analysis and customer segmentation Produce simple and effective data visualizations for improved insights Use NLP to extract insights for text Implement tree-based classifiers including random forest and boosted tree Who this book is forIf you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
(source: Nielsen Book Data)

• Table of Contents Welcome to the Robot World Discover Your AI Toolkit Python Fundamentals - Learn How to Code in Python AI Foundation Techniques Your First AI Model - Beware the Bandits! AI for Sales and Advertising - Sell like the Wolf of AI Street Welcome to Q-Learning AI for Logistics - Robots in a Warehouse Going Pro with Artificial Brains - Deep Q-Learning AI for Autonomous Vehicles - Build a Self-Driving Car AI for Business - Minimize Costs with Deep Q-Learning Deep Convolutional Q-Learning AI for Games - Become the Master at Snake Recap and Conclusion.
• (source: Nielsen Book Data)

Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key Features Learn from friendly, plain English explanations and practical activities Put ideas into action with 5 hands-on projects that show step-by-step how to build intelligent software Use AI to win classic video games and construct a virtual self-driving car Book DescriptionWelcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learn Master the basics of AI without any previous experience Build fun projects, including a virtual-self-driving car and a robot warehouse worker Use AI to solve real-world business problems Learn how to code in Python Discover the 5 principles of reinforcement learning Create your own AI toolkit Who this book is forIf you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).
(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.
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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.
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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)

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

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

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

• Frontmatter
• Preface
• Contents
• List of Contributors
• 1. Feature selection in biomedical signal classification process and current software implementations / Jovic, Alan
• 2. An overview of skin lesion segmentation, features engineering, and classification / Sabri, My Abdelouahed / Filali, Youssef / Ennouni, Assia / Yahyaouy, Ali / Aarab, Abdellah
• 3. Brain tumor image segmentation and classification using SVM, CLAHE, and ARKFCM / Ishita, Banerjee / Madhumathy, P. / Kavitha, N.
• 4. Coronary Heart Disease prediction using genetic algorithm based decision tree / Sivaranjani, Reddi / Naresh, Vankamamidi S. / Murthy, Nistala V.E.S.
• 5. Intelligent approach for retinal disease identification / Rajyaguru, Vipul C. / Vithalani, Chandresh H. / Thanki, Rohit M.
• 6. Speech separation for interactive voice systems / Wiem, Belhedi / Anouar, Ben Messaoud Mohamed / Aïcha, Bouzid
• 7. Machine vision for human-machine interaction using hand gesture recognition / Verma, Varnita / Rajput, Anshuman / Chauhan, Piyush / Rathore, Harshit / Goyal, Piyush / Gupta, Mukul Kumar
• Index

Intelligent prediction and decision support systems are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).
(source: Nielsen Book Data)

• Title page; Preface; Contents; Part I. Foundations of Well-Being; Part II. Supporting the Well-Being Through Care Taking in Smart Environments; Part III. Improving the Well-Being Through Life-Style and Entertainment; Subject Index; Author Index.

The notion of well-being is one which is crucial to many aspects of our daily lives. In addition to providing one of the cornerstones to a healthy lifestyle, the concept of well-being extends to the selection of the type of environment we live in, our interaction with other people and the things we do to realize our plans for the future. Well-being is so intrinsic to our daily lives that it plays a fundamental role at all times and in all places, so it is important that it is taken into account when designing the ubiquitous computing technologies which pervade our lives nowadays. This book contains the papers presented at the 2009 Behaviour Monitoring and Interpretation (BMI) workshop, third in the annual series launched in 2007, and co-located with the German conference on Artificial Intelligence. The focus of the 2009 workshop on the topic of well-being reflects the significant interest in this area of research and the book offers state-of-the-art contributions in the application area of well-being from diverse disciplines such as engineering and philosophy. This overview of a wide range of research projects will be of interest to researchers and developers whose work necessitates a consideration of well-being, either as part of the task of implementing current applications or of designing the systems of the future.
(source: Nielsen Book Data)

• Table of Contents Introduction to Markov Process Hidden Markov Models State Inference: Predicting the states Parameter Inference using Maximum Likelihood Parameter Inference using Bayesian Approach Time Series: Predicting Stock Prices Natural Language Processing: Teaching machines to talk 2D-HMM for Image Processing Reinforcement Learning: Teaching a robot to cross a maze.
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Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key Features Build a variety of Hidden Markov Models (HMM) Create and apply models to any sequence of data to analyze, predict, and extract valuable insights Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation Book DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you'll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You'll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you'll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learn Explore a balance of both theoretical and practical aspects of HMM Implement HMMs using different datasets in Python using different packages Understand multiple inference algorithms and how to select the right algorithm to resolve your problems Develop a Bayesian approach to inference in HMMs Implement HMMs in finance, natural language processing (NLP), and image processing Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm Who this book is forHands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book.
(source: Nielsen Book Data)

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

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

• Introduction.- Part 1:Background and Methodology.- Discrete Consensus Achievement in Artificial Systems.- Modular Design of Strategies for the Best-of-n Problem.- Part 2:Mathematical Modeling and Analysis.- Indirect Modulation of Majority-Based Decisions.- Direct Modulation of Voter-Based Decisions.- Direct Modulation of Majority-Based Decisions.- Part 3:Robot Experiments.- A Robot Experiment in Site Selection.- A Robot Experiment in Collective Perception.- Part 4:Discussion and Annexes.- Conclusions.- Background on Markov Chains.
• (source: Nielsen Book Data)

This book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identifies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a finite set when optimizing benefits and costs. The book leverages the identification of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.
(source: Nielsen Book Data)

• Local Perturbation Analysis Of The Stochastic Matrix Riccati Equation With Applications In Finance.- An Embedded Compact Scheme For Biharmonic Problems In Irregular Domains.- Ant Colony Optimization Algorithm For 1D Cutting Stock Problem.- 2D Optimal Cutting Problem.- Error Estimate In The Iterative Numerical Method For Two-Dimensional Nonlinear Hammerstein-Fredholm Fuzzy Functional Integral Equations.- Noise Removal And Feature Extraction Of 2D CT Radiographic Images.- Representation Of Civilians And Police Officers By Generalized Nets For Describing Software Agents In The Case Of Protest.- Comparison Of NDT Techniques For Elastic Modulus Determination Of Laminated Composites.- Integer Codes For Flash Memories.- On The Strong Asymptotics Of Rows Of The Pade Table.- Total Phosphorus Removal In Horizontal Subsurface Flow Constructed Wetlands: A Computational Investigation For The Optimal Adsorption Model.- Some Results Involving Euler-Type Integrals And Dilogarithm Values.- Nonlinear Evolution Equation For Propagation Of Waves In An Artery With An Aneurysm: An Exact Solution Obtained By The Modified Method Of Simplest Equation.- Modelling Of Light Mg And Al Based Alloys As "In Situ" Composites.- Asymptotic Study Of The Nonlinear Velocity Problem For The Oscillatory Non-Newtonian Flow In A Straight Channel.- Competition For Resources And Space Contributes To The Emergence Of Drug Resistance In Cancer.- Efficient Error Based Metrics For Fuzzy-Neural Network Performance Evaluation.- Box Model Of Migration In Channels Of Migration Networks.- Modified Coordinates In Dynamics Simulation Of Multibody Systems With Elastic Bodies.
• (source: Nielsen Book Data)

This book presents recent research on Advanced Computing in Industrial Mathematics, which is one of the most prominent interdisciplinary areas, bringing together mathematics, computer science, scientific computations, engineering, physics, chemistry, medicine, etc. Further, the book presents the major tools used in Industrial Mathematics, which are based on mathematical models, and the corresponding computer codes, which are used to perform virtual experiments to obtain new data or to better understand previous experimental findings. The book gathers the peer-reviewed papers presented at the 11th Annual Meeting of the Bulgarian Section of SIAM (BGSIAM), from December 20 to 22, 2016 in Sofia, Bulgaria.
(source: Nielsen Book Data)

• Database and Distributed Machine Learning.- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures.- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks.- Discovering Group Skylines with Constraints by Early Candidate Pruning.- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data.- A Higher-Fidelity Frugal Quantile Estimator.- Recommender System.- Fair Recommendations Through Diversity Promotion.- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data.- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities.- Group Recommender Model Based on Preference Interaction.- Identification of Grey Sheep Users By Histogram Inter
• section In Recommender Systems.- Social Network and Social Media.- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks.- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach.- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises.- Efficient Revenue Maximization for Viral Marketing in Social Networks.- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage.- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia.- A Solution to Tweet-Based User Identification across Online Social Networks.- Machine Learning.- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning.- Mixed Membership Sparse Gaussian Conditional Random Fields.- Effects of Dynamic Subspacing in Random Forest.- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach.- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions.- Classification and Clustering Methods.- StruClus: Scalable Structural Graph Set Clustering with Representative Sampling.- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification.- Environmental Sound Recognition using Masked Conditional Neural Networks.- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure.- A Framework for Clustering and Dynamic Maintenance of XML Documents.- Language-independent Twitter Classification using Character-based Convolutional Networks.- Behavior Modeling and User Profiling.- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues.- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors.- An Approach for Identifying Author Profiles of Blogs.- Generating Topics of Interests for Research Communities.- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples.- Bioinformatic and Medical Data Analysis.- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks.- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties.- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets.- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers.- Spatio-temporal Data.- People-Centric Mobile Crowdsensing Platform for Urban Design.- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining.- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting.- Mobile Robot Scheduling with Multiple Trips and Time Windows.- Natural Language Processing and Text Mining.- Feature Analysis for Duplicate Detection in Programming QA Communities.- A Joint Human/Machine Process for Coding Events and Conflict Drivers.- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning.- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN.- Entity Recognition by Distant Supervision with Soft List Constraint.- Structured Sentiment Analysis.- Data Mining Applications.- Improving Real-Time Bidding Using a Constrained Markov Decision Process.- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network.- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments.- Color-sketch simulator: a guide for color-based visual known-item search.- Applications.- Making Use of External Company Data to Improve the Classification of Bank Transactions.- Mining Load Profile Patterns for Australian Electricity Consumers.- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing.- Privacy and Utility Preservation for Location Data Using Stay Region Analysis.- Location-aware Human Activity Recognition.- Demos.- SWYSWYK: a new Sharing Paradigm for the Personal Cloud.- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs.- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data.- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media.- A system for Querying and Analyzing Urban Regions.- Detect tracking behavior among trajectory data.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. .
(source: Nielsen Book Data)

• Information systems architecture.- Business process alignment.- User knowledge discovery.- Business process performance.- Big data exploration.- Process variability management.- Information systems transformation and evolution.- Business process modeling readability.- Business process adaption.- Data mining.- Process discovery.- Business process modeling notation.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 29th International Conference on Advanced Information Systems Engineering, CAiSE 2017, held in Essen, Germany, in June 2017. The 37 papers presented together with 3 keynote papers in this volume were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on information systems architecture; business process alignment; user knowledge discovery; business process performance; big data exploration; process variability management; information systems transformation and evolution; business process modeling readability; business process adaption; data mining; process discovery; business process modeling notation.
(source: Nielsen Book Data)

• Part I: From Machine Learning to Data Mining.- Analyzing Accident Prone Regions by Clustering.- Analyzing Life Insurance Data with Different Classification Techniques for Customers' Behavior Analysis.- Classification of Product Rating Using Data Mining Techniques.- MASS: a Semi-Supervised Multi-Label Classification Algorithm with Specific Feature.- Parallel Self-organizing Map Using Shared Virtual Memory Buffers.- Parametric Optimization of the Selected Classifiers in Binary Classification.- Prediction of Academic Performance During Adolescence Based on Socioeconomic, Psychological and Academic Factors.- A Super-Vector Deep Learning Coprocessor with High Performance-Power Ratio.- Part II: Big Data and Collaborative Decision Support Systems.- Bi-temporal Database Model for Legal Merger Transactions and Late-arriving Information Problem: the Case of Polish Merger Market.- Breathing Movement Analysis for Adjustment of Radiotherapy Planning.- Measuring Improvement in Access to Complete Data in Healthcare Collaborative Dabase Systems.- Rough Set Theory for Supporting Decision Making on Relevance in Browsing Multilingual Digital Resources.- The Social Influence on the Behavioral Intention to Use Mobile Electronic Medical Records.- Stock Prices Growth Pattern by the Emergency Demand After the Great East-Japan Earthquake.- Structure-Based Virtual Screening for Novel Modulators of Human Orexin 2 Receptor with Cloud Systems and Supercomputers.- Supply Chains of Cross-Border E-Commerce.- Towards Personalized Virtual Customer Experience.- Towards Big Management.- Part III: Computer Vision Analysis, Detection, Tracking and Recognition.- Combination of Collision Detection and Visibility Algorithms in Simulation of Effective Placement of Anti-Air Elements.- A Combination of Deep Learning and Hand-Designed Feature for Plant Identification based on Leaf and Flower Images.- An Efficient Defect Classification Algorithm for Ceramic Tiles.- Parking Assistant - Prediction of an Empty Parking Space in Time.- Recent Advances in the Field of Foreground Detection: An Overview.- Tracking of Bone Reparation Process with Using of Periosteal Callus Extraction based on Fuzzy C-Means Algorithm.- Part IV: Data-Intensive Text Processing.- Automatic Post-Editing of Kazakh Sentences Machine Translated from English.- Complex Technology of Machine Translation Resources Extension for the Kazakh Language.- Enhancing Latent Semantic Analysis by Embedding Tagging Algorithm in Retrieving Malay Text Documents.- Exploiting Distance Graph and Hidden Topic Models for Multi-label Text Classification.- Financial Reports and Financial News - An Information Content Gap Analysis.- The Great National Photocorpus of 20th-Century Vietnamese. Origins, assumptions and goals.- The Impact of User Sentiment Aroused by The-day-of-the-week on the Recommendation Effectiveness in Microblog.- Multi-Sentence Compression Using Word Graph and Integer Linear Programming.- A Performance Comparison of Feature Extraction Methods for Sentiment Analysis.- A Study of Plagiarism Checker System Based on Chinese Word Segmentation and SQL Intersection Operation Technique.- Text Summarization Based on Classification Using ANFIS.- Part V: Innovations in Web and Internet Technologies.- Analysis of Indoor Positioning Based on BLE.- A Fusion Technique of Schema and Syntax Rules for Validating Open Data.- Job Description Language for a Browser-Based Computing Platform - A Preliminary Report.- Measuring the Effectiveness of Knowledge Driven Web Applications.- Responsive Data Table Solution with New Scrolling Control Gesture for Better User Experience.- Part VI: New Methods and Applications in Software Engineering.- Adaptation of an ANN-Based Air Quality Forecasting Model to a New Application Area.- Checking Compliance of Program with SecureUML Model.- Generation of Test Data using Genetic Algorithm and Constraint Solver.- Investigating the Issues of using Agile Methods in Offshore Software Development in Sri Lanka.- A Method for Automated Test Cases Generation from UML Models with String ConstraintS.- Mobile Application for Calculation of Optimal Route Between Searched Points of Interest.- On Implementation of the Assumption Generation Method for Component-Based Software Verification.
• (source: Nielsen Book Data)

This book presents recent research in intelligent information and database systems. The carefully selected contributions were initially accepted for presentation as posters at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017) held from to 5 April 2017 in Kanazawa, Japan. While the contributions are of an advanced scientific level, several are accessible for non-expert readers. The book brings together 47 chapters divided into six main parts: * Part I. From Machine Learning to Data Mining.* Part II. Big Data and Collaborative Decision Support Systems, * Part III. Computer Vision Analysis, Detection, Tracking and Recognition, * Part IV. Data-Intensive Text Processing, * Part V. Innovations in Web and Internet Technologies, and* Part VI. New Methods and Applications in Information and Software Engineering. The book is an excellent resource for researchers and those working in algorithmics, artificial and computational intelligence, collaborative systems, decision management and support systems, natural language processing, image and text processing, Internet technologies, and information and software engineering, as well as for students interested in such research areas.
(source: Nielsen Book Data)

• Constraints, planning, and optimization.- Data mining and machine learning.- Sensors, signal processing, and data fusion.- Recommender systems.- Decision support systems.- Knowledge representation and reasoning.- Navigation, control, and autonome agents.- Sentiment analysis and social media.- Games, computer vision, and animation.- Uncertainty management.- Graphical models: from theory to applications.- Anomaly detection.- Agronomy and artificial intelligence.- Applications of argumentation.- Intelligent systems in healthcare and mhealth for health outcomes.- Innovative applications of textual analysis based on AI.
• (source: Nielsen Book Data)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI.
(source: Nielsen Book Data)

• Constraints, planning, and optimization.- Data mining and machine learning.- Sensors, signal processing, and data fusion.- Recommender systems.- Decision support systems.- Knowledge representation and reasoning.- Navigation, control, and autonome agents.- Sentiment analysis and social media.- Games, computer vision, and animation.- Uncertainty management.- Graphical models: from theory to applications.- Anomaly detection.- Agronomy and artificial intelligence.- Applications of argumentation.- Intelligent systems in healthcare and mhealth for health outcomes.- Innovative applications of textual analysis based on AI.
• (source: Nielsen Book Data)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI.
(source: Nielsen Book Data)

• 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)

• Bio-inspired Computing.- E-Health and Computational Biology.- Human Computer Interaction.- Image and Signal Processing.- Mathematics for Neural Networks.- Self-organizing Networks.- Spiking Neurons.- Artificial Neural Networks in Industry ANNI'17.- Computational Intelligence Tools and Techniques for Biomedical Applications.- Assistive Rehabilitation Technology.- Computational Intelligence Methods for Time Series.- Machine Learning Applied to Vision and Robotics.- Human Activity Recognition for Health and Well-Being Applications.- Software Testing and Intelligent Systems.- Real World Applications of BCI Systems.- Machine Learning in Imbalanced Domains.- Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles.- End-User Development for Social Robotics.- Artificial Intelligence and Games.- Supervised, Non-Supervised, Reinforcement and Statistical Algorithms. .
• (source: Nielsen Book Data)

This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, held in Cadiz, Spain, in June 2017. The 126 revised full papers presented in this double volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on Bio-inspired Computing; E-Health and Computational Biology; Human Computer Interaction; Image and Signal Processing; Mathematics for Neural Networks; Self-organizing Networks; Spiking Neurons; Artificial Neural Networks in Industry ANNI'17; Computational Intelligence Tools and Techniques for Biomedical Applications; Assistive Rehabilitation Technology; Computational Intelligence Methods for Time Series; Machine Learning Applied to Vision and Robotics; Human Activity Recognition for Health and Well-Being Applications; Software Testing and Intelligent Systems; Real World Applications of BCI Systems; Machine Learning in Imbalanced Domains; Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles; End-User Development for Social Robotics; Artificial Intelligence and Games; and Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.
(source: Nielsen Book Data)

• Bio-inspired Computing.- E-Health and Computational Biology.- Human Computer Interaction.- Image and Signal Processing.- Mathematics for Neural Networks.- Self-organizing Networks.- Spiking Neurons.- Artificial Neural Networks in Industry ANNI'17.- Computational Intelligence Tools and Techniques for Biomedical Applications.- Assistive Rehabilitation Technology.- Computational Intelligence Methods for Time Series.- Machine Learning Applied to Vision and Robotics.- Human Activity Recognition for Health and Well-Being Applications.- Software Testing and Intelligent Systems.- Real World Applications of BCI Systems.- Machine Learning in Imbalanced Domains.- Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles.- End-User Development for Social Robotics.- Artificial Intelligence and Games.- Supervised, Non-Supervised, Reinforcement and Statistical Algorithms. .
• (source: Nielsen Book Data)

This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, held in Cadiz, Spain, in June 2017. The 126 revised full papers presented in this double volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on Bio-inspired Computing; E-Health and Computational Biology; Human Computer Interaction; Image and Signal Processing; Mathematics for Neural Networks; Self-organizing Networks; Spiking Neurons; Artificial Neural Networks in Industry ANNI'17; Computational Intelligence Tools and Techniques for Biomedical Applications; Assistive Rehabilitation Technology; Computational Intelligence Methods for Time Series; Machine Learning Applied to Vision and Robotics; Human Activity Recognition for Health and Well-Being Applications; Software Testing and Intelligent Systems; Real World Applications of BCI Systems; Machine Learning in Imbalanced Domains; Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles; End-User Development for Social Robotics; Artificial Intelligence and Games; and Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.
(source: Nielsen Book Data)

• Natural Language Processing
• The relevance of the Named Entities in Authorship Attribution.- A compact representation for cross-domain short text clustering.- Characteristics of Most Frequent Spanish Verb-Noun Combinations.- Sentence Paraphrase Graphs: Classification Based on Predictive Models or Annotators Decisions.- Mathematical model of an ontological-semantic analyzer using basic ontological-semantic patterns.- CookingQA: A Question Answering System based on Cooking Ontology.- LEXIK. An Integrated System for Specialized Terminology.- Linguistic Restrictions in Automatic Translation from Written Spanish to MSL.- Intra-Document and Inter-Document Redundancy on Multi-Document Summarization.- Framework for searching judicial precedents based on Automatic summarization.- Discriminatory capacity of the most representative phonemes in Spanish: an evaluation for forensic speech recognition.- A Speech-Based Web Co-Authoring Platform for the Blind.- Social Networks and Opinion Mining.- Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets.- Additive Regularization for Topic Modeling in Sociological Studies of User-Generated Texts.- Effect of the inclusion of non-newsworthy messages in credibility assessment.- On the impact of neighborhood selection strategies for recommender systems in LBSNs.- Fuzzy Logic.- For Multi-Interval-Valued Fuzzy Sets, Centroid Defuzzification Is Equivalent to Defuzzifying Its Interval Hull: A Theorem.- Metric Spaces Under Interval Uncertainty: Towards an Adequate Definition.- A study of parameter dynamic adaptation with fuzzy logic for the grey wolf optimizer algorithm.- Interval Type-2 fuzzy logic for parameter adaptation in the gravitational search algorithm.- Water cycle algorithm with fuzzy logic for dynamic adaptation of parameters.- Off-line tuning of a PID controller using Type-2 Fuzzy Logic.- Detection of faults in induction motors using texture-based features and fuzzy inference.- Time Series Analysis and Forecasting.- Trend Detection in Gold Worth using Regression.- Queries to Internet as a Tool for Analysis of Regional Police Work and Forecast of Crimes in Regions.- Creating Collections of Descriptors of Events and Processes Based on Internet Queries.- Planning and Scheduling.- Using a Grammar Checker to Validate Compliance of Processes with Workflow Models.- On Verification of Workflow and Planning Domain Models using Attribute Grammars.- Tramp Ship Scheduling Problem with Berth Allocation Considerations and Time-dependent Constraints.- Hierarchical Task Model for Resource Failure Recovery in Production Scheduling.- A multi-objective hospital operating room planning and scheduling problem using compromise programming.- Solving Manufacturing Cell Design Problems using the Black Hole Algorithm.- Image Processing and Computer Vision.- Efficient Computation of the Euler Number of a 2-D Binary of Image.- Image filter based on block matching and principal component analysis.- Support to the diagnosis of the Pap test, using computer algorithms of digital image processing.- Implementation of Computer Vision Guided Peg-Hole Insertion Task Performed by Robot through LabVIEW.- Object Tracking Based on Modified TLD Framework Using Compressive Sensing Features.- Parameter characterization of complex wavelets and its use in 3D reconstruction.- Customer Analysis via Video Analytics: Customer Detection with Multiple Cues.- Methodology for Automatic Collection of Vehicle Traffic Data by Object Tracking.- Robotics.- GPS-based curve estimation for an adaptive pure pursuit algorithm.- Collective motion of a swarm of simulated quadrotors using repulsion, attraction and orientation rules.- Design and simulation of a new lower exoskeleton for rehabilitation of patients with paraplegia.- Sensorial system for obtaining the angles of the human movement in the coronal and sagittal anatomical planes.
• (source: Nielsen Book Data)

The two-volume set LNAI 10061 and 10062 constitutes the proceedings of the 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, held in Cancun, Mexico, in October 2016. The total of 86 papers presented in these two volumes was carefully reviewed and selected from 238 submissions. The contributions were organized in the following topical sections: Part I: natural language processing; social networks and opinion mining; fuzzy logic; time series analysis and forecasting; planning and scheduling; image processing and computer vision; robotics. Part II: general; reasoning and multi-agent systems; neural networks and deep learning; evolutionary algorithms; machine learning; classification and clustering; optimization; data mining; graph-based algorithms; and intelligent learning environments.
(source: Nielsen Book Data)

• Requirements analysis.-Business analysis:- Ambient Assisted Living:- Computing for Healthcare:- Data Science:-Data Analysis.- Business Intelligence.- Ontology.- Software Quality.- Model Quality.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of five workshops and a symposium, held at the 36th International Conference on Conceptual Modeling, ER 2017, in Valencia, Spain in November 2017. The 21 revised full papers were carefully reviewed and selected out of 47 submissions to the following events: AHA 2017 - 3rd International Workshop on Modeling for Ambient Assistance and Healthy Ageing MoBiD 2017 - 6th International Workshop on Modeling and Management of Big Data MREBA 2017 - 4th International Workshop on Conceptual Modeling in Requirements and Business Analysis OntoCom 2017 - 5th International Workshop on Ontologies and Conceptual Modeling QMMQ 2017 - 4th Workshop on Quality of Models and Models of Quality.
(source: Nielsen Book Data)

• PART 1, Data mining, machine learning, knowledge discovery, Tensor Networks for Dimensionality Reduction, Big Data and Deep Learning.- Local Data Characteristics in Learning Classifiers from Imbalanced Data.- Similarity dimensions of semantic ontologies.- Some interesting phenomenon occurring during self-learning process with its psychological interpretation.- PART 2, Neural networks and connectionist systems, On the interpretation and characterization of echo state networks dynamics: A complex systems perspective.- Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange.- Deep Neural Networks - A Brief History.- PART 3, Intelligent technologies in systems modeling, Techniques for Construction and Integration of Rule Bases.- New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling.- On the intuitionistic fuzzy sets of n-th type.-
• PART 4. Intelligent technologies in decision making, optimization and control, MCTS/UCT in solving real-life problems.- Interactive cone contraction for evolutionary mutliple objective optimization.- A review of fuzzy and mathematic methods for dynamic parameter adaptation in the firefly algorithm.-
• PART 5. Applications of intelligent technologies Computational Intelligence Methods in Personalized Pharmacotherapy.- Embodying Intelligence in Autonomous and Robotic Systems with the Use of Cognitive Psychology and Motivation Theories.- Evolutionary Approach for Automatic Design of PID Controllers.- Fuzzy-genetic approach to identity verification using a handwritten signature.- A method of design and optimization for SiC-based grid-connected AC-DC converters.
• (source: Nielsen Book Data)

This book is a tribute to Professor Jacek Zurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Zurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Zurada's dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world's largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.
(source: Nielsen Book Data)

• Improving Chairlift Security with Deep Learning.- Discovering motifs with variants in music databases.- Biclustering Multivariate Time Series.- Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects.- Regression tree for bandits models in A/B testing.- Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases.- Skin Cancer Detection in Dermoscopy Images Using Sub-Region Features.- Bucket Selection: A Model-Independent Diverse Selection Strategy for Widening.- Interactive Pattern Sampling for Characterizing Unlabeled Data.- Searching for Spatio-Temporal-Keyword Patterns in Semantic Trajectories.- Natural Language Analysis of Online Health Forums.- Freudian Slips: Analysing the Internal Representations of a Neural Network from its Mistakes.- KAPMiner: Mining ordered association rules with constraints.- A Dynamic Adaptive Questionnaire for Improved Disease Diagnostics.- ABIDE: Querying Time-Evolving Sequences of Temporal Intervals.- The Actors of History: Narrative Network Analysis Reveals the Institutions of Power in British Society Between 1800-1950.- Learning DTW-Preserving Shapelets.- Droplet Ensemble Learning on Drifting Data Streams.- Predictive clustering trees for hierarchical multi-target regression.- Identifying novel features from specimen data for the prediction of valuable field trips.- Adapting supervised classifiation algorithms to arbitrary weak label scenarios.- The Combination of Decision in Crowds When The Number of Reliable Annotator is Rare.- Estimating Sequence Similarity from Contig Sets.- Computational Topology Techniques for Characterizing Time-Series Data.- Improving Cold-Start Recommendations with Social-Media Trends and Reputations.- Hierarchical Novelty Detection.- Towards automatic evaluation of asphalt irregularity using smartphone's Sensors.- A structural benchmark for logical argumentation frameworks.
• (source: Nielsen Book Data)

This book constitutes the conference proceedings of the 16th International Symposium on Intelligent Data Analysis, which was held in October 2017 in London, UK. The 28 full papers presented in this book were carefully reviewed and selected from 66 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. IDA solicits papers on all aspects of intelligent data analysis, including papers on intelligent support for modelling and analyzing data from complex, dynamical systems.
(source: Nielsen Book Data)

• Classification and deep learning.- Social network and graph mining.- Privacy-preserving mining and security/risk applications.- Spatio-temporal and sequential data mining.- Clustering and anomaly detection.- Recommender system.- Feature selection.- Text and opinion mining.- Clustering and matrix factorization.- Dynamic, stream data mining.- Novel models and algorithms.- Behavioral data mining.- Graph clustering and community detection.- Dimensionality reduction.
• (source: Nielsen Book Data)

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
(source: Nielsen Book Data)

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

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

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

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

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

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

This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Conference on Practical Applications of Scalable Multi-Agent Systems, PAAMS 2017, held in Porto, Portugal, in June 2017. The 11 revised full papers, 11 short papers, and 17 Demo papers were carefully reviewed and selected from 63 submissions. The papers report on the application and validation of agent-based models, methods, and technologies in a number of key application areas, including day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources. .
(source: Nielsen Book Data)

• General.- Normality from Monte Carlo Simulation for Statistical Validation of Computer Intensive Algorithms.- Are Android Smartphones Ready to Locally Execute Intelligent Algorithms.- Reasoning and Multi-Agent Systems.- Large Scale Reasoning Using Allen's Interval Algebra.- Towards the Distributed Logic Programming of Intelligent Visual Surveillance Applications.- An efficient expert system for diabetes with a Bayesian inference engine.- iEnsemble: A Framework for Committee Machine based On Multiagent Systems with Reinforcement Learning.- Exploring Complex Networks with Failure-prone Agents.- On the Conception of Intelligent Power Plants Based on Multiple Agent Systems.- Neural Networks and Deep Learning.- Author Profiling with doc2vec Neural Network-Based Document Embeddings.- Neural network modelling for dissolved oxygen effects in extensive Litopenaeus vannamei culture.- Neural-network based algorithm for algae detection in automatic inspection of underwater pipelines.- Evolutionary Algorithms.- Study on the Development of Complex Network for Evolutionary and Swarm based Algorithms.- Estimation of Distribution Algorithms based on the Beta Distribution for Bounded Search Spaces.- Automated Analog Synthesis with an Estimation of the Distribution Algorithm.- Mathematical model of glucose metabolism by symbolic regression alpha beta.- Machine Learning.- Jensen inequality with subdifferential for Sugeno integral.- Towards a more general XCS: Classifier fusion and don't care in actions.- A Novel Artificial Hydrocarbon Networks Based Value Function Approximation in Hierarchical Reinforcement Learning.- Wind power forecasting for the Villonaco wind farm using AI techniques.- Predicting the need of mechanical ventilation in Guillain-Barre patients using machine learning algorithms with relevant features.- A Robust Machine learning Approach to Microprocessor Instructions Identification Final.- Classification and Clustering.- Stochastic Semantic-Based Multi-Objective Genetic Programming Optimisation for Classification of Imbalanced Data.- Consensus Clustering for Binning Metagenome Sequences.- Algorithm for Clustering of Web Search Results from a Hyper-Heuristic approach.- Grouping Business Process Models Based on Multimodal Search and Covering Arrays.- Optimization.- A new method to optimize dynamic environments with global changes using the chickens-hen' algorithm.- Transit Network Frequencies-Setting Problem Solved Using a new Multi-Objective Global-Best Harmony Search Algorithm and Discrete Event Simulation.- Optimal pricing model: case of study for convenience stores.- Method of musical composition for portfolio optimization problem.- Metaheuristic hybridized applied to solve the Capacity Vehicle Routing Problem.- ABC-PSO: An Efficient Bioinspired Metaheuristic for Parameter Estimation in Nonlinear Regression.- Data Mining.- Data mining in EEG wave trains in early stages of Parkinson's disease.- Data Mining in the Analysis of Ocean-Atmosphere Dynamics in Colombia's Central Caribbean Ocean.- Molecular docking based on ligand by Complexity LMC.- Integrating Information of Films by a Multi-Source Combining Framework.- Graph-Based Algorithms.- Computing the clique-width of Polygonal tree graphs.- A New Approach to Weakening and Destruction of Malicious Internet Networks.- Intelligent Learning Environments.- Toward Optimal Pedagogical Action Patterns by means of Partially Observable Markov Decision Process.- Data-driven construction of a student model using Bayesian networks in an electrical domain.- Strategic Learning Meta-model: A Selection Model of Learning Activities.- CodeTraining: An Authoring Tool for a Gamified Programming Learning Environment.- Generating a Logical Structure for Virtualizing Physiotherapy Instructions through NLP.- Building a Corpus and a Local Binary Pattern Recognizer for Learning-Centered Emotions.- Affective Learning System for Algorithmic Logic Applying Gamification. .
• (source: Nielsen Book Data)

The two-volume set LNAI 10061 and 10062 constitutes the proceedings of the 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, held in Cancun, Mexico, in October 2016. The total of 86 papers presented in these two volumes was carefully reviewed and selected from 238 submissions. The contributions were organized in the following topical sections: Part I: natural language processing; social networks and opinion mining; fuzzy logic; time series analysis and forecasting; planning and scheduling; image processing and computer vision; robotics. Part II: general; reasoning and multi-agent systems; neural networks and deep learning; evolutionary algorithms; machine learning; classification and clustering; optimization; data mining; graph-based algorithms; and intelligent learning environments. .
(source: Nielsen Book Data)

• Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation.- Multi-objective Whale Optimization Algorithm for Multi-level Thresholding Segmentation.- Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images.- Thermal Image Segmentation Using Evolutionary Computation Techniques.- News Videos Segmentation Using Dominant Colors Representation.
• (source: Nielsen Book Data)

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.
(source: Nielsen Book Data)

• Spatial and temporal databases.- Current research: Concepts, tools, techniques.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Symposium on Spatial and Temporal Databases, SSTD 2017, held in Arlington, VA, USA, in August 2017.The 19 full papers presented together with 8 demo papers and 5 vision papers were carefully reviewed and selected from 90 submissions. The papers are organized around the current research on concepts, tools, and techniques related to spatial and temporal databases. .
(source: Nielsen Book Data)

• Multi-objective Optimization.- A Parametric Study of Crossover Operators in Pareto-based Multiobjective Evolutionary Algorithm.- Non-dominated Sorting and Crowding Distance Based Multi-objective Chaotic Evolution.- On Performance Improvement based on Restart Meta-Heuristic Implementation for Solving Multi-Objective Optimization Problems.- Using multi-objective evolutionary algorithm to solve dynamic environment and economic dispatch with EVs.- Improved Interval Multi-objective Evolutionary Optimization Algorithm based on Directed Graph.- A Novel Linear Time Invariant Systems Order Reduction Approach based on Cooperative Multi-Objective Genetic Algorithm.- Solving Constrained Multi-objective Optimization Problems with Evolutionary Algorithms.- Portfolio Optimization.- Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Optimization.- Metaheuristics for Portfolio Optimization.- Community Detection.- Community Detection under Exponential Random Graph Model: A Metaheuristic Approach.- An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection.- The Design and Development of the Virtual Learning Community for Teaching Resources Personalized Recommendation.- The Effect of Event Sentiment on Product Recommendation in a Microblog Platform.- Multi-Agent Systems and Swarm Robotics.- Solar Irradiance Forecasting Based on the Multi-agent Adaptive Fuzzy Neuronet.- Passive Field Dynamics Method: an Advanced Physics-based Approach for Formation Control of Robot Swarm.- Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics.- Swarm-based Spreading Points.- A Survivability Enhanced Swarm Robotic Searching System Using Multi-Objective Particle Swarm Optimization.- Autonomous Coordinated Navigation of Virtual Swarm Bots in Dynamic Indoor Environments by Bat Algorithm.- Building Fractals with a Robot Swarm.- A Stigmergy Based Search Method for Swarm Robots.- Cooperative Control of Multi-Robot System Using Mobile Agent for Multi Source Localization.- Hybrid Optimization Algorithms and Applications.- Evolutionary Fuzzy Control of Three Robots Cooperatively Carrying an Object for Wall Following through the Fusion of Continuous ACO and PSO.- Optimal Operational Planning of Energy Plants by Multi-population Differential Evolutionary Particle Swarm Optimization.- A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony.- Study on Greedy Search to Improve Simulated Annealing Algorithm for Large-scale Traveling Salesman Problem.- A Hybrid Swarm based Composition for Chinese Music.- Fuzzy and Swarm Approach.- Fuzzy Logic Controller Design for Tuning the Cooperation of Bio-Inspired Algorithms.- Making Capital Budgeting Decisions for Project Abandonment by Fuzzy Approach.- An Imputation for Missing Data Features based on Fuzzy Swarm Approach in Heart Disease Classification.- Clustering and Forecast.- Total Optimization of Smart City Using Initial Searching Points Generation Based on k-means Algorithm.- Clustering Analysis of ECG Data Streams.- A Novel Multi-cell Multi-Bernoulli Tracking Method Using Local Fractal Feature Estimation.- An Improved Locality Preserving Projection Method for Dimensionality Reduction with Hyperspectral Image.- Applying a Classification Model for Selecting Postgraduate Programs.- University Restaurant Sales Forecast Based on BP neural Network
• InShanghai Jiao Tong University Case.- Classification and Detection.- Swarm ANN/SVR-based modeling method for warfarin dose prediction in Chinese.- A Novel HPSOSA for Kernel Function Type and Parameter Optimization of SVR in Rainfall Forecasting.- An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning.- Fast Pseudo Random Forest using Discrimination Hyper-space.- A Fast Video Vehicle Detection Approach based on Improved Adaboost Classifier.- Detection of Repetitive Forex Chart Patterns.- Damage Estimation from Cues of Image Change.- Identifying Deceptive Review Comments with Rumor and Lie Theories.- Identifying Fake Review Comments for Hostel Industry.- Planning and Routing Problems.- Multi-UAV Cooperative Path Planning for Sensor Placement Using Cooperative Coevolving Genetic Strategy.- Optimal Micro-Siting Planning Considering Long-Term Electricity Demand.- A Hyper-Heuristic Method for UAV Search Planning.- An Efficient MVMO-SH Method for Optimal Capacitor Allocation in Electric Power Distribution Systems.- A Method of New Workers' Accurate Acceptance of Tasks Using Capable Sensing.- A Genetic Mission Planner for Solving Temporal Multi-agent Problems with Concurrent Tasks.- Reformulation and Metaheuristic for the Team Orienteering Arc Routing Problem.- Application of Smell Detection Agent Based Algorithm for Optimal Path Identification by SDN Controllers.- A Comparison of Heuristic Algorithms for Bus Dispatch.- Simulation and Application of Algorithms CVRP to Optimize the Transport of Minerals Metallic and Nonmetallic by Rail for Export.- Dialog System Applications.- User Intention Classification in An Entities Missed In-vehicle Dialog System.- An Exploratory Study of Factors Affecting Number of Fans onFacebook Based on Dialogic Theory.- Assembling Chinese-Mongolian Speech Corpus via Crowdsourcing.- Robotic Control.- Developing Robot Drumming Skill with Listening-Playing Loop.- Evaluation of Parameters of Transactions When Remote Robot Control.- Desktop Gestures Recognition for Human Computer Interaction.- Approach to the Diagnosis and Configuration of Servo Drives in Heterogeneous Machine Control Systems.- Other Applications.- Gravitational Search Algorithm in Recommendation Systems.- A Driver Model Based on Emotions.- A Binaural Signal Synthesis Approach for Fast Rendering of Moving Sound.- Semantic Evolutionary Visualization.

This book and its companion volumes, LNCS volumes 10385 and 10386, constitute the proceedings of the 8th International Conference on Swarm Intelligence, ICSI 2017 held from July 27 to August 1, 2017, in Fukuoka, Japan. ICSI 2017 was held in conjunctionwith the Second International Conference on Data Mining and Big Data (DMBD 2017)at Fukuoka, Japan, to share common mutual ideas, promote transverse fusion, and stimulate innovation. ICSI 2017 received 267 submissions from about 512 authors in 35 countries and regions across six continents. Each submission was reviewed by at least two, and on average 2.9 reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 133 high-quality papers were selected for publication in this proceedings volume, with an acceptance rate of 49.81%. The papers are organized in 24 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development.

• Inquiry-Based Learning and Gamification.- Learning Analytics.- Social Media and Web 2.0-based Learning Environments.- Assessment and Accessibility in Higher Education.- Open Educational Resources and Recommender Systems.- Practice and Experience Sharing.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 16th International Conference on Web-Based Learning, ICWL 2017, held in Cape Town, South Africa, in September 2017. The 13 revised full papers presented together with 9 short papers and 3 poster papers were carefully reviewed and selected from 56 submissions. The papers are organized in topical sections on Inquiry-Based Learning and Gamification; Learning Analytics; Social Media and Web 2.0-based Learning Environments; Assessment and Accessibility in Higher Education; Open Educational Resources and Recommender Systems; and Practice and Experience Sharing.
(source: Nielsen Book Data)

• Urban Systems Modelling.- Towards an Agent-Based Simulation of Housing in Urban Beirut.- Simulating Urban Growth with Raster and Vector models: A case study for the city of Can Tho, Vietnam.- Integrating Behavior and Microsimulation Models.- Agent-Based Modelling for Urban Planning Current Limitations and Future Trends.- Traffic Simulation in Urban Modelling.- Software Architecture for a Transparent and Versatile Traffic Simulation.- A Generic Software Framework for Carsharing Modelling based on a Large-Scale Multi-Agent Traffic Simulation Platform.- Mapping bicycling patterns with an agent-based model, census and crowdsourced data.- Transportation in Agent-Based Urban Modelling.- Applications.- Simulation-aided Crowd Management: a Multi-scale Model for an Urban Case Study.- A National Heat Demand Model for Germany.- How Smart is the Smart City? Assessing the Impact of ICT on Cities.
• (source: Nielsen Book Data)

This book constitutes revised, selected, and invited papers from the First International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016, held in conjunction with AAMAS 2016 in Singapore in May 2016. The 11 papers presented in this volume were carefully reviewed and selected from 20 submissions. They were organized in topical sections named: urban systems modeling; traffic simulation in urban modeling; and applications. .
(source: Nielsen Book Data)

• Machine Learning.- Optimization.- Swarm Intelligence and Evolutionary Computing.- Text Mining and Linguistic analysis.- Vision and Image.- Other Areas in Articial Intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 30th Australasian Joint Conference on Artificial Intelligence, AI 2017, held in Melbourne, VIC, Australia, in August 2017. The 29 full papers were carefully reviewed and selected from 58 submissions. This volume covers a wide spectrum of research streams in artificial intelligence ranging from machine learning, optimization to big data science and their practical applications. .
(source: Nielsen Book Data)

• Preferences and multi-criteria decision aiding.- Decision making and voting.- Game theory and decision theory.- Allocation and matching. .
• (source: Nielsen Book Data)

This book constitutes the conference proceedings of the 5th International Conference on Algorithmic Decision Theory , ADT 2017, held in Luxembourg, in October 2017.The 22 full papers presented together with 6 short papers, 4 keynote abstracts, and 6 Doctoral Consortium papers, were carefully selected from 45 submissions. The papers are organized in topical sections on preferences and multi-criteria decision aiding; decision making and voting; game theory and decision theory; and allocation and matching. .
(source: Nielsen Book Data)

• Graphs that arise from the Web and various user activities on the Web.- The development of high performance algorithms and applications that exploit graphs.- Graph-theoretic and algorithmic aspects of related complex networks.- Social networks, citation networks, biological networks
• molecular networks.- Other networks arising from the Internet.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 14th International Workshop Algorithms and Models for the Web Graph, WAW 2017, held in Toronto, ON, Canada, in June 2017. The 7 full papers presented in this volume were carefully reviewed and selected from 14 submissions. The papers are organized around topics such as graphs that arise from the Web and various user activities on the Web; the development of high Performance algorithms and applications that exploit these graphs; graph-theoretic and algorithmic aspects of related complex networks; social networks, citation networks, biological networks; molecular networks, and other networks arising from the Internet.
(source: Nielsen Book Data)

• Multi-Message Broadcast in Dynamic Radio Networks.- Global Synchronization and Consensus Using Beeps in a Fault-Prone MAC 16.- Vertex Coloring with Communication and Local Memory Constraints in Synchronous Broadcast Networks.- A New Kind of Selectors, and Their Applications to Conflict Resolution in Wireless Multi-channels Networks.- The Impact of the Gabriel Sub-graph of the Visibility Graph on the Gathering of Mobile Autonomous Robots.- Search-and-Fetch with One Robot on a Disk.- A 2-Approximation Algorithm for Barrier Coverage by Weighted Non-uniform Sensors on a Line.- Flexible Cell Selection in Cellular Networks.- The Euclidean k-Supplier Problem in IR2.
• (source: Nielsen Book Data)

This book constitutes revised selected papers from the 12th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2016, held in Aarhus, Denmark, in August 2016. The 9 full papers presented in this volume were carefully reviewed and selected from 19 submissions. This year papers were solicited into three tracks: Distributed and Mobile, Experiments and Applications, and Wireless and Geometry.
(source: Nielsen Book Data)

• Reconciling Humans and Technology: The Role of Ambient Intelligence.- Engineering a Cyber-Physical Intersection Management.- Exploring Design Opportunities for Intelligent Worker Assistance: A new Approach Using Projection-Based AR and a Novel Hand-Tracking Algorithm.- AuthentiCap - A Touchless Vehicle Authentication and Personalization System.- Indoor Localization Based on Passive Electric Field Sensing.- Safety Services in Smart Environments using Depth Cameras.- Contextual Requirements Prioritization and its Application to Smart Homes.- Voices and Views of Informal Caregivers: Investigating Ambient Assisted Living Technologies.- Easy to Install Indoor Positioning System that Parasitizes Home Lighting.- Exploring the Use of Ambient WiFi Signals to Find Vacant Houses.- A Framework for Distributed Interaction in Intelligent Environments.- Which mobile health toolkit should a service provider choose? A comparative evaluation of Apple HealthKit, Google Fit, and Samsung Digital Health Platform.- Visual End-User Programming of Personalized AAL in the Internet of Things.- Opportunities for Biometric Technologies in Smart Environments.- New approach for optimizing the usage of situation recognition algorithms within IoT domains.- HUDConCap - Automotive Head-Up Display Controlled with Capacitive Proximity Sensing.- E-textile Couch: Towards Smart Garments integrated Furniture.- Context-Aware Monitoring Agents For Ambient Assisted Living Applications.- Mobility competencies of people with Down syndrome supported by technical assistance - Results of the Requirement Analysis in POSEIDON Project.- An Exploratory Study on Electric Field Sensing.- A Framework for Responsive Environments.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th European Conference on Ambient Intelligence, AmI 2017, held in Malaga, Spain, in April 2017. The 16 revised full papers presented together with 4 short papers and 1 keynote paper were carefully reviewed and selected from 48 submissions. The papers cover topics such as: Enabling technologies, methods and platforms; objectives and approaches of ambient intelligence and internet of things; from information design to interaction and experience design, and application areas of AmI and IoT.
(source: Nielsen Book Data)

• EvoSET: Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines.- On the Use of Smelly Examples to Detect Code Smells in JavaScript.- Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka.- Focusing Learning-based Testing away from Known Weaknesses.- Polytypic Genetic Programming.- Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach.- EvoSTOC: A New Multi-swarm Particle Swarm Optimization for Robust Optimization over Time.- The Static and Stochastic VRP with Time Windows and Both Random Customers and Reveal Times.- Pre-Scheduled Colony Size Variation in Dynamic Environments.- An Online Packing Heuristic for the Three-dimensional Container Loading Problem in Dynamic Environments and the Physical Internet.- Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology.- Road Traffic Rules Synthesis Using Grammatical Evolution.- Solving Dynamic Graph Coloring Problem Using Dynamic Pool Based Evolutionary Algorithm.- General: Meta-Heuristics for Improved RF Emitter Localization.- Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
• (source: Nielsen Book Data)

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments). .
(source: Nielsen Book Data)

• EvoSET: Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines.- On the Use of Smelly Examples to Detect Code Smells in JavaScript.- Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka.- Focusing Learning-based Testing away from Known Weaknesses.- Polytypic Genetic Programming.- Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach.- EvoSTOC: A New Multi-swarm Particle Swarm Optimization for Robust Optimization over Time.- The Static and Stochastic VRP with Time Windows and Both Random Customers and Reveal Times.- Pre-Scheduled Colony Size Variation in Dynamic Environments.- An Online Packing Heuristic for the Three-dimensional Container Loading Problem in Dynamic Environments and the Physical Internet.- Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology.- Road Traffic Rules Synthesis Using Grammatical Evolution.- Solving Dynamic Graph Coloring Problem Using Dynamic Pool Based Evolutionary Algorithm.- General: Meta-Heuristics for Improved RF Emitter Localization.- Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
• (source: Nielsen Book Data)

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments). .
(source: Nielsen Book Data)

• Sliding Mode Control Design for Some Classes of Chaotic Systems.- Sliding Mode Based Control and Synchronization of Chaotic Systems in Presence of Parametric Uncertainties.- Chattering Free Sliding Mode Controller Design for a Quadrotor Unmanned Aerial Vehicle.- Terminal Sliding Mode Controller Design for a Quadrotor Unmanned Aerial Vehicle.- Insensibility of the Second Order Sliding Mode Control via Measurement Noises: Application to a Robot Manipulator Surveillance Camera.- Robust Control of a Photovoltaic Battery System via Fuzzy Sliding Mode Approach.- Particle Swarm Optimization based Sliding Mode Control Design: Application to a Quadrotor Vehicle.- Global Stabilization of Nonlinear Systems via Novel Second Order Sliding Mode Control with an Application to a Novel Highly Chaotic System.- Complete Synchronization of Chaotic Systems via Novel Second Order Sliding Mode Control with an Application to a Novel Three-Scroll Chaotic System.- Novel Second-Order Sliding Mode Control Design for the Anti-Synchronization of Chaotic Systems with an Application to a Novel Four-Wing Chaotic System.- Control and Synchronization of a Novel Hyperchaotic Two-Disk Dynamo System via Adaptive Integral Sliding Mode Control.- Adaptive Integral Sliding Mode Controller Design for the Control and Synchronization of a Rod-Type Plasma Torch Chaotic System.- Adaptive Integral Sliding Mode Controller Design for the Regulation and Synchronization of a Novel Hyperchaotic Finance System with a Stable Equilibrium.- Adaptive Integral Sliding Mode Controller Design for the Control of a Novel 6-D Coupled Double Convection Hyperchaotic System.- A Memristor-Based Hyperchaotic System with Hidden Attractor and its Sliding Mode Control.- Adaptive Integral Sliding Mode Control of a Chemical Chaotic Reactor System.- Adaptive Integral Sliding Mode Controller Design for the Control and Synchronization of a Novel Jerk Chaotic System.- Sliding Mode Control Design for a Sensorless Sun Tracker.- Super-Twisting Sliding Mode Control of the Enzymes-Substrates Biological Chaotic System.- Super-Twisting Sliding Mode Control and Synchronization of Moore-Spiegel Thermo-Mechanical Chaotic System.
• (source: Nielsen Book Data)

Gathering 20 chapters contributed by respected experts, this book reports on the latest advances in and applications of sliding mode control in science and engineering. The respective chapters address applications of sliding mode control in the broad areas of chaos theory, robotics, electrical engineering, physics, chemical engineering, memristors, mechanical engineering, environmental engineering, finance, and biology. Special emphasis has been given to papers that offer practical solutions, and which examine design and modeling involving new types of sliding mode control such as higher order sliding mode control, terminal sliding mode control, super-twisting sliding mode control, and integral sliding mode control. This book serves as a unique reference guide to sliding mode control and its recent applications for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
(source: Nielsen Book Data)

• Meal-Assistance Robot Operated by Head Movement.- Decision Tree Analysis in Game Informatics.- Binary Blockchain: Solving the Mining Congestion Problem by Dynamically Adjusting the Mining Capacity.- An Efficient Signature Scheme for Anonymous Credentials.- Improve Example-Based Machine Translation Quality for Low-Resource Language Using Ontology.- A Fast Area Labeling Method using Auxiliary Lines.- Heuristic Test Case Generation Techniques Using Extended Place/Transistion Net.- Risk Assessment of Security Requirements of Banking Information Systems Based on Attack Patterns.- mCITYPASS: Privacy-preserving Secure Access to Federated Touristic Services with Mobile Devices.- Heuristic-Based Usability Evaluation Tool for Android Applications.- Automated Essay Scoring System Based on Rubric.- Mobile Development Tools and Method Integration.
• (source: Nielsen Book Data)

This book presents the scientific outcome of the 5th International Conference on Applied Computing and Information Technology (ACIT 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science, to share their experiences and to exchange new ideas and information in a meaningful way. The book includes research findings on all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered along the way and the solutions adopted to solve them. This book features 12 of the conference's most promising papers, written by authors who are expected to make important contributions to the field of computer and information science.
(source: Nielsen Book Data)

• Business Sustainability Conceptualization.- A Replicated Study on Relationship between Code Quality and Method Comments.- A Predictive Model for Standardized Test Performance in Michigan Schools.- A Development Technique for Mobile Applications Program.- Identification Method Applications Development using Adapting Component Model.- Development of Guiding Walking Support Device for Visually Impaired People with the GPS.- User Evaluation Prediction Models Based on Conjoint Analysis and Neural Networks for Interactive Evolutionary Computation.- Emotional Video Scene Retrieval Using Multilayer Convolutional Network.- Proactive Approach for the Prevention of DDoS Attacks in Cloud Computing Environments.- Practical Uses of Memory Storage Extension.- How to Build a High Quality Mobile Applications Based on Improved Process.- A New Hybrid Discrete Firefly Algorithm for Solving the Traveling Salesman Problem.
• (source: Nielsen Book Data)

This edited book presents scientific results of the 4th International Conference on Applied Computing and Information Technology (ACIT 2016) which was held on December 12-14, 2016 in Las Vegas, USA. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The aim of this conference was also to bring out the research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the Program Committee, and underwent further rigorous rounds of review. This book captures 11 of the conference's most promising papers, and the readers impatiently await the important contributions that they know these authors are going to bring to the field of computer and information science.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Symposium on Applied Reconfigurable Computing, ARC 2017, held in Delft, The Netherlands, in April 2017. The 17 full papers and 11 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They are organized in topical sections on adaptive architectures, embedded computing and security, simulation and synthesis, design space exploration, fault tolerance, FGPA-based designs, neural neworks, and languages and estimation techniques.
(source: Nielsen Book Data)

• Architectures.- Mathematical foundations.- Algorithms.- Safety.- Understanding.- Human cognition.- Philosophy.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th International Conference on Artificial General Intelligence, AGI 2017, held in Melbourne, VIC, Australia, in August 2017. The 24 regular papers presented in this book together with 1 short paper were carefully reviewed and selected from 35 submissions. They cover topics such as architectures; mathematical foundations; algorithms; safety; understanding; human cognition; and philosophy.
(source: Nielsen Book Data)

• Computer Vision for Ocean Observing
• Fault Diagnosis and Classification of Mine Motor based on RS and SVM
• Particle Swarm Optimization based Image Enhancement of Visual Cryptography Shares
• Fast Level set Algorithm for Extraction and Evaluation of Weld Defects in Radiographic Images
• Efficient Combination of Color, Texture and Shape Descriptor, using SLIC Segmentation for Image Retrieval
• DEPO: Detecting Events of Public Opinion in Microblog
• Hybrid Cuckoo Search based Evolutionary Vector Quantization for Image Compression
• Edge and Fuzzy Transform based Image Compression Algorithm: edgeFuzzy
• Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis
• Cross-modal Learning with Images, Texts and Their Semantics
• Light Field Vision for Artificial Intelligence
• Author Index.

This edited book presents essential findings in the research fields of artificial intelligence and computer vision, with a primary focus on new research ideas and results for mathematical problems involved in computer vision systems. The book provides an international forum for researchers to summarize the most recent developments and ideas in the field, with a special emphasis on the technical and observational results obtained in the past few years.

• Intro; Preface; Organization; Contents
• Part I; Contents
• Part II; Neural Networks and Their Applications; Author Profiling with Classification Restricted Boltzmann Machines; 1 Introduction; 2 Author Profile Dimensions; 3 Restricted Boltzmann Machines; 4 Probabilities and Gradients; 4.1 Discriminative Training; 4.2 Generative Training; 5 Evaluation Datasets; 6 Experiments and Results; 6.1 Overall Results; 7 Conclusions; References; Parallel Implementation of the Givens Rotations in the Neural Network Learning Algorithm; 1 Introduction; 2 Givens Elimination Step; 3 Givens QR Decomposition.

• 4 QR Decomposition in Neural Network Weights Update5 Parallel Implementation; 6 Simulation Results; 7 Conclusion; References; Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation; 1 Introduction; 2 Parallel Realisation; 2.1 Calculating the Weight Derivatives Without Error Backpropagation; 2.2 Calculating the A Matrix and the Gradient Vector; 2.3 The QR Decomposition Based on the Householser Reflections; 3 Computational Results; 4 Conclusions; References; Spectral Analysis of CNN for Tomato Disease Identification; Abstract; 1 Introduction; 2 Related Works.

• 3 Spectral Analysis of CNN for Tomato Disease3.1 Deep Visualization of CNN; 3.2 Color Sensitivity of RGB Images; 3.3 Sensitivity to Color with Different Wavelength Values; 3.3.1 Visible Spectrum of Images; 4 Experimental Results; 4.1 Dataset Description; 4.2 CNN Activations and Features Visualization; 4.2.1 Activations of Neurons; 4.2.2 RGB Color Sensitivity; 4.2.3 Feature Maps; 5 Conclusion and Future Work; Acknowledgments; References; From Homogeneous Network to Neural Nets with Fractional Derivative Mechanism; 1 Introduction; 2 Weight Distribution with Fractional Calculus.

• 3 Fractional Derivative Inside Neuron Transfer Function4 The Fractional Mechanism Within 2D Homogeneous Network; 5 Conclusion; References; Neurons Can Sort Data Efficiently; 1 Introduction; 2 Models of Neurons, Receptors, and the Senses; 2.1 Sensory Fields and Sensors; 2.2 Extreme, Sensory and Object Neurons; 3 Simplistic Sequential Neural Associative Sorting; 4 Conclusions and Remarks; References; Avoiding Over-Detection: Towards Combined Object Detection and Counting; 1 Introduction; 2 Related Work; 2.1 Deep Learning Methods for Object Detection; 2.2 Deep Learning Methods for Cell Detection.

• 3 Method3.1 Loss Function; 3.2 Model Architecture; 4 Results; 5 Conclusion; References; Echo State Networks Simulation of SIR Distributed Control; 1 Introduction; 2 Echo State Networks; 3 SIR Model with Delay and Spatial Diffusions; 3.1 Distributed Optimal Control Problem; 4 Discretisation and Adaptive Critic Neural Networks Solution of the Distributed Optimal Control; 4.1 Numerical Simulation; 5 Conclusion; References; The Study of Architecture MLP with Linear Neurons in Order to Eliminate the ``vanishing Gradient'' Problem; 1 Introduction; 2 Nonlinearity capabilities of deep neural networks.

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the second volume are organized in the following five parts: data mining; artificial intelligence in modeling, simulation and control; various problems of artificial intelligence; special session: advances in single-objective continuous parameter optimization with nature-inspired algorithms; special session: stream data mining.
(source: Nielsen Book Data)
The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; computer vision, image and speech analysis; and bioinformatics, biometrics and medical applications.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents
• Part II; Contents
• Part I; Data Mining; Computer Based Stylometric Analysis of Texts in Polish Language; 1 Introduction; 2 Features Used in Stylometry; 3 Performed Stylometric Analysis; 3.1 Data Sets and Classes; 3.2 Feature Generation Methods; 3.3 Classification; 4 Results; 4.1 Stylometric Features; 4.2 Training Set Size; 4.3 Classifiers Accuracy; 4.4 Statistical Features Importance; 4.5 Grammatical Classes Importance; 5 Summary; References; Integration Base Classifiers Based on Their Decision Boundary; 1 Introduction; 2 Basic Concept.

• 3 Proposed Method4 Experimental Studies; 5 Conclusion; References; Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm; 1 Introduction; 2 Incomplete Data Sets; 3 Probabilistic Approximations; 4 Rule Induction; 4.1 True MLEM2; 4.2 Emulated MLEM2; 5 Experiments; 6 Conclusions; References; Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System; 1 Introduction; 2 Related Work; 3 The Recommender System; 3.1 Datasets; 3.2 Cleansing; 3.3 Preprocessing and Feature Reduction; 3.4 Training the Recommender; 3.5 Implementation.

• 4 Results and Discussion4.1 Results of Dimensionality Reduction Techniques; 4.2 Choice of SVM Kernel; 4.3 Comparison with Previous Approaches and Scalability; 5 Conclusion; References; The Bag-of-Words Method with Dictionary Analysis by Evolutionary Algorithm; 1 Introduction; 2 Description of Proposed Methods; 3 Experimental Research; 4 Conclusions; References; The Novel Method of the Estimation of the Fourier Transform Based on Noisy Measurements; 1 Introduction; 2 Orthogonal Series Estimation of Regression Function and Its Spectrum.

• 3 Nonparametric Orthogonal Series Estimation of Fourier Transform4 Simulation Example; 5 Remarks and Extensions; References; A Complete Efficient FFT-Based Algorithm for Nonparametric Kernel Density Estimation; 1 Introduction; 2 Problem Demonstration; 3 The Improved FFT-Based Algorithm for Density Estimation; 4 Binning; 5 Experiments; 6 Conclusions; References; A Framework for Business Failure Prediction; 1 Introduction; 2 Related Work; 3 The Dataset and the Proposed Framework; 3.1 Details of Dataset; 3.2 Proposed Framework; 4 Performance Evaluation Results; 5 Conclusions; References.

• Fuzzy Clustering with -Hyperballs and Its Application to Data Classification1 Introduction; 2 Fuzzy Clustering with -Hyperballs; 3 FCH Based Classification; 4 Results and Discussion; 5 Conclusions; References; Two Modifications of Yinyang K-means Algorithm; 1 Introduction; 2 Yinyang K-means; 3 Modifications of the Algorithm; 3.1 Regrouping of Centroids; 3.2 Initial Grouping and Regrouping Using Same-Size K-means; 4 Experimental Results; 5 Conclusions; References; Detection of the Innovative Logotypes on the Web Pages; 1 Introduction; 2 Related Works vs. Proposed Approach.

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the second volume are organized in the following five parts: data mining; artificial intelligence in modeling, simulation and control; various problems of artificial intelligence; special session: advances in single-objective continuous parameter optimization with nature-inspired algorithms; special session: stream data mining.
(source: Nielsen Book Data)

• Artificial Life and Computational Intelligence.- Extending the Delaunay Triangulation Based Density Measurement to Many-objective Optimization.- Emotion, Trustworthiness and Altruistic Punishment in a Tragedy of the Commons Social Dilemma.- Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm.- Co-Evolving Line Drawings with Hierarchical Evolution.- Reliability estimation of individual multi-target regression predictions.- Feedback Modulated Attention Within a Predictive Framework.- A Batch Infill Strategy for Computationally Expensive Optimization Problems.- Automatic Clustering and Summarisation of Microblogs: A Multi-Subtopic Phrase Reinforcement Algorithm.- Generation and exploration of architectural form using a composite Cellular Automata.- Wrapper Feature Construction for Figure-ground Image Segmentation Using Genetic Programming.- Surrogate-assisted Multi-swarm Particle Swarm Optimization of Morphing Airfoils.- Applying Dependency Patterns in Causal Discovery of Latent Variable Models.- An Evolutionary Multi-criteria Journey Planning Algorithm for Multi-modal Transportation Networks.- Estimating Passenger Preferences Using Implicit Relevance Feedback for Personalized Journey.- Quantitative Assessment of Hearts Function: A Hybrid Mechanism for Left Ventricles Segmentation from Cine MRI Sequences.- A Hybrid feature selection scheme based on local compactness and global separability for improving roller bearing diagnostic performance.- Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN.- Towards Solving TSPN with Arbitrary Neighborhoods: A Hybrid Solution.- Detectable Genetic Algorithms-based techniques for solving Dynamic Optimisation Problem with Unknown Active Variables.- Neighbourhood analysis: a case study on Google Machine Reassignment Problem.- Optimisation Algorithms and Applications.- Multi-Objective Optimisation with Multiple Preferred Regions.- An Adaptive Memetic Algorithm for the Architecture Optimisation Problem.- Resource Constrained Job Scheduling with Parallel Constraint-based ACO.- An Iterated Local Search with Guided Perturbation for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints.- A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization.- Investigating the Generality of Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling with Machine Breakdown.- Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution.- A PSO-based Reference Point Adaption Method for Genetic Programming Hyper-heuristic in Many-Objective Job Shop Scheduling.- Optimal power allocation of wireless sensor networks with multi-operator based constrained differential evolution.- CEMAB: A Cross-Entropy-based Method for Large-Scale Multi-Armed Bandits.- Binary PSO for Web Service Location-Allocation.- A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017, held in Geelong, VIC, Australia, in January/February 2017. The 32 papers presented in this volume were carefully reviewed and selected from 47 submissions. They were organized in topical sections named: artificial life and computational intelligence and optimization algorithms and applications. .
(source: Nielsen Book Data)

• From Perception to Action.- From Neurons to Networks.- Brain Imaging
• Recurrent Neural Networks.- Neuromorphic Hardware.- Brain Topology and Dynamics.- Neural Networks Meet Natural and Environmental Sciences.- Convolutional Neural Networks.- Games and Strategy.- Representation and Classification.- Clustering.- Learning from Data Streams and Time Series.- Image Processing and Medical Applications.- Advances in Machine Learning.
• (source: Nielsen Book Data)

The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning. There are 63 short paper abstracts that are included in the back matter of the volume.
(source: Nielsen Book Data)

• From Perception to Action.- From Neurons to Networks.- Brain Imaging
• Recurrent Neural Networks.- Neuromorphic Hardware.- Brain Topology and Dynamics.- Neural Networks Meet Natural and Environmental Sciences.- Convolutional Neural Networks.- Games and Strategy.- Representation and Classification.- Clustering.- Learning from Data Streams and Time Series.- Image Processing and Medical Applications.- Advances in Machine Learning.
• (source: Nielsen Book Data)

The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning. There are 63 short paper abstracts that are included in the back matter of the volume.
(source: Nielsen Book Data)

The 2-volume set LNCS 10324 and 10325 constitutes the refereed proceedings of the 4th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics, AVR 2017, held in Ugento, Italy, in June 2017. The 54 full papers and 24 short papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in the following topical sections: virtual reality; augmented and mixed reality; computer graphics; human-computer interaction; applications of VR/AR in medicine; and applications of VR/AR in cultural heritage.
(source: Nielsen Book Data)

The 2-volume set LNCS 10324 and 10325 constitutes the refereed proceedings of the 4th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics, AVR 2017, held in Ugento, Italy, in June 2017. The 54 full papers and 24 short papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in the following topical sections: virtual reality; augmented and mixed reality; computer graphics; human-computer interaction; applications of VR/AR in medicine; and applications of VR/AR in cultural heritage.
(source: Nielsen Book Data)

• Automated deduction
• Including foundations
• Applications.-Implementations
• Practical experience.

The chapter 'Certifying Confluence of Quasi-Decreasing Strongly Deterministic Conditional Term Rewrite Systems' is published open access under a CC BY 4.0 license.

• Sequents systems.- Tableaux.- Transitive closure and cyclic proofs.- Formalization and complexity.
• (source: Nielsen Book Data)

This book contains the proceedings of the 26th International Conference on Automated Reasoning with Analytics Tableaux and Related Methods, TABLEAUX 2017, held in Brasilia, Bazil, in September 2017. The 19 contributed papers presented in this volume were carefully reviewed and selected from 27 submissions.They are organized in topical sections named: Sequent systems; tableaux; transitive closure and cyclic proofs; formalization and complexity. Also included are papers of three invited speakers.
(source: Nielsen Book Data)

• Invited Talk: Proving Absence of Starvation by Means of Abstract Interpretation and Model-checking.- Program Analysis.- Precise Null Pointer Analysis Through Global Value Numbering.- May-Happen-in-Parallel Analysis with Returned Futures.- JTDec: A Tool for Tree Decompositions in Soot.- Fixing the State Budget: Approximation of Regular Languages with Small DFAs.- An Equivalence Checking Framework for Array-Intensive Programs.-Loop Quasi-Invariant Chunk Detection.- SamaTulyata: An Efficient Path Based Equivalence Checking Tool.- Model Checking and Temporal Logics.- Tests and Refutation.- The Density of Linear-time Properties.- HyLeak: Hybrid Analysis Tool for Information Leakage.- Compositional Safety Refutation Techniques.- Gradient-based variable ordering of Decision Diagrams for systems with structural units.- Model Checking of C and C++ with DIVINE 4 (Tool Paper).- Dealing with priorities and locks for concurrent programs.- Knowledge transfer and information leakage in protocols.- Concurrent Program Verification With Invariant-guided Underapproximation.- Neural Networks.- Maximum Resilience of Artificial Neural Networks.- Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks.- Learning and Invariant Synthesis.- Liquid Types for Array Invariant Synthesis.- Lifting CDCL to Template-based Abstract Domains for Program Verification.- Synthesizing invariants by solving solvable loops.- Requirements and Specifications Exploiting Partial Knowledge for Efficient Model Analysis.- A Language-theoretic View on Network Protocols.- Probabilistic Systems Efficient Strategy Iteration for Mean Payoff in Markov Decision Processes.- Finding Polynomial Loop Invariants for Probabilistic Programs.- Synthesis of Optimal Resilient Control Strategies.- Hybrid Systems and Control.- ForFET: A Formal Feature Evaluation Tool for Hybrid Systems.- Attacking the V: On the Resiliency of Adaptive-Horizon MPC.- The Reach-Avoid Problem for Constant-Rate Multi-Mode Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Symposium on Automated Technology for Verification and Analysis, ATVA 2017, held in Pune, India, in October 2017. The 22 full and 7 short papers presented in this volume were carefully reviewed and selected from 78 submissions. The book also contains one invited talk in full-paper length. The contributions are organized in topical sections named: program analysis; model checking and temporal logics; neural networks; learning and invariant synthesis; and hybrid systems and control.
(source: Nielsen Book Data)

• Introduction.- Events and Times.- Temporal Relations.- Relation Labelling Analysis.- Using Temporal Signals.- Using a Framework of Tense and Aspect.- Conclusion.
• (source: Nielsen Book Data)

The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally. This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties. It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• New Generation Data Warehouses Design
• Evaluation of Data Warehouse Design Methodologies in the Context of Big Data
• Abstract
• 1 Introduction
• 2 Methodology Classification
• 3 Metrics for Design Evaluation of Methodologies
• 3.1 Metrics for Methodology Evaluation
• 3.2 Metrics for Schema Quality Evaluation
• 4 Experimental Results
• 4.1 Methodology Evaluation
• 4.2 Schema Evaluation
• 5 Conclusion
• References
• Optimal Task Ordering in Chain Data Flows: Exploring the Practicality of Non-scalable Solutions
• 1 Introduction

• 2 Preliminaries
• 2.1 Problem Complexity
• 2.2 Chains in TPC-DI
• 3 Accurate Algorithms for Linear Execution Plans
• 3.1 Backtracking
• 3.2 Dynamic Programming
• 3.3 Topological Sorting
• 4 Evaluation of the Time Overhead
• 5 Related Work
• 6 Conclusions
• References
• Exploiting Mathematical Structures of Statistical Measures for Comparison of RDF Data Cubes
• 1 Introduction
• 2 Model and Data Representation
• 3 Structural Comparison of RDF Data Cubes
• 3.1 Computability and Comparability
• 3.2 Comparison Functionalities
• 3.3 Experimentation
• 4 Conclusion
• References

• S2D: Shared Distributed Datasets, Storing Shared Data for Multiple and Massive Queries Optimization in a Distributed Data Warehouse
• 1 Introduction
• 2 Related Work
• 3 Overview of Shared Distributed Datasets
• 3.1 Phase 1: The Logical Representation
• 3.2 Phase 2: The Physical Representation
• 4 Experimental Evaluation
• 4.1 Experimental Setup
• 4.2 Experimental Results and Discussion
• 5 Conclusion and Future Work
• References
• Cloud and NoSQL Databases
• Enforcing Privacy in Cloud Databases
• 1 Introduction
• 2 Non-cryptographic Methods
• 2.1 Differential Privacy

• 2.2 Data Anonymization
• 2.3 Data Fragmentation
• 3 Secret Sharing-Based Methods
• 3.1 Verifiable Secret Sharing
• 3.2 Order-Preserving Secret Sharing
• 3.3 Discussion
• 4 Index-Based Methods
• 4.1 Bucketization-Based Indexing
• 4.2 Order-Preserving Indexing
• 4.3 Searchable Encryption
• 4.4 Discussion
• 5 Secure Databases
• 5.1 CryptDB
• 5.2 MONOMI
• 5.3 Multi-valued Order Preserving Encryption (MV-OPE)
• 5.4 Secure Trusted Hardware
• 5.5 Discussion
• 6 Conclusion
• 6.1 Security
• 6.2 Query Post-processing
• 6.3 Storage Overhead
• 6.4 Computational Overhead
• 6.5 Wrap-up

This book constitutes the refereed proceedings of the 19th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2017, held in Lyon, France, in August 2017. The 24 revised full papers and 11 short papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in the following topical sections: new generation data warehouses design; cloud and NoSQL databases; advanced programming paradigms; non-functional requirements satisfaction; machine learning; social media and twitter analysis; sentiment analysis and user influence; knowledge discovery; and data flow management and optimization. .
(source: Nielsen Book Data)

• Biomedical applications.- Mobile brain computer interaction.- Human robot interaction.- Deep learning.- Machine learning applied to big data analysis.- Computational intelligence in data coding and transmission.- Applications.
• (source: Nielsen Book Data)

The two volumes LNCS 10337 and 10338 constitute the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, held in Corunna, Spain, in June 2017. The total of 102 full papers was carefully reviewed and selected from 194 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on natural and artificial computation for biomedicine and neuroscience, addressing topics such as theoretical neural computation; models; natural computing in bioinformatics; physiological computing in affective smart environments; emotions; as well as signal processing and machine learning applied to biomedical and neuroscience applications. The second volume deals with biomedical applications, based on natural and artificial computing and addresses topics such as biomedical applications; mobile brain computer interaction; human robot interaction; deep learning; machine learning applied to big data analysis; computational intelligence in data coding and transmission; and applications.
(source: Nielsen Book Data)

• Analogy for Reuse.- Activity recognition.- Case-based maintenance.- Computational analogy.- Computer vision.- Distributed AI.- Game AI.- Machine learning.- Recommender systems.- Similarity measures.- Time series analysis.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 25th International Conference on Case-Based Reasoning Research and Development, ICCBR 2017, held in Trondheim, Norway, in June 2017. The 27 full papers presented together with 3 keynote presentations were carefully reviewed and selected from 38 submissions. The theme of ICCBR-2017, "Analogy for Reuse", was highlighted in several events. These papers, which are included in the proceedings, address many themes related to the theory and application of case-based reasoning, analogical reasoning, CBR and Deep Learning, CBR in the Health Sciences, Computational Analogy, and Process-Oriented CBR.
(source: Nielsen Book Data)

• A Hybrid M&S Methodology for Knowledge Discovery.- A Model-driven Visualization System Based on DVDL.- A Practical Energy Modeling Method for Industrial.- An Optimization Method for User Interface Components Based on Big Data.- Clustering-based Data Aggregation and Routing for Real-time Wireless HART Communication.- Constrained Semantic Grammar Enabled Question Answering System0.3.- Information Composition Analysis and Adaptation Access of CNC Lathes in Cloud Manufacturing.- Interactive Animation Editing Based on Sketch Interaction.- Manufacturing Service Reconfiguration Optimization using Hybrid Bees Algorithm in Cloud Manufacturing.- Mobile Phone-based tourist Spatial-Temporal Behavior Record and Analysis System.- Multi-Source Information Intelligent Collection and Monitoring of CNC Machine Tools Based on Multi-Agent Ontology Management and Ontology Reuse in Web Environment.- Research on the shortest path of two places in urban based on improved ant colony algorithm.- RUL Prediction of Bearings based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description.- Social Recommendation Terms:Probabilistic Explanation and Optimization.- Towards a Holistic Method for Business Process - Traffic Flow Prediction with improved SOPIO-SVR algorithm.- Workshop Multi-source Information IntelliSense Method based on IPv6 Intelligent Terminal.
• (source: Nielsen Book Data)

This book presents the thoroughly refereed and revised post-workshop proceedings of the 19th Monterey Workshop, held in Beijing, China, in Ocotber 2016. The workshop explored the challenges associated with the Development, Operation and Management of Large-Scale complex IT Systems. The 18 revised full papers presented were significantly extended and improved by the insights gained from the productive and lively discussions at the workshop, and the feedback from the post-workshop peer reviews. 2016 marks the 23rd anniversary for the Monterey Workshop series which started in 1993. For nearly a quarter of century, the Monterey Workshops have established themselves as an important international forum to foster, among academia, industry, and government agencies, discussion and exchange of ideas, research results and experience in developing software intensive systems, and have significantly advanced the field. The community of the workshop participants has grown to become an influential source of ideas and innovations and its impact on the knowledge economy has been felt worldwide.
(source: Nielsen Book Data)

• Fundamental theory and methods of computational linguistics.- Machine translation and multilingual information processing.- Knowledge graph and information extraction.- Language resource and evaluation.- Information retrieval and question answering.- Text classification and summarization.- Social computing and sentiment analysis.- NLP applications
• Minority language information processing.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 16th China National Conference on Computational Linguistics, CCL 2017, and the 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017, held in Nanjing, China, in October 2017. The 39 full papers presented in this volume were carefully reviewed and selected from 272 submissions. They were organized in topical sections named: Fundamental theory and methods of computational linguistics; Machine translation and multilingual information processing; Knowledge graph and information extraction; Language resource and evaluation; Information retrieval and question answering; Text classification and summarization; Social computing and sentiment analysis; NLP applications; Minority language information processing.
(source: Nielsen Book Data)

• From the Content: From Multi-valued Logics to Fuzzy Logic
• A Dialogue Concerning Contradiction And Reasoning
• Some Entertainments Dealing with Three Valued Logic
• Fuzziness as an Experimental Science: an Homage to Claudio Moraga
• Some reflections on the use of interval fuzzy sets for dealing with Fuzzy Deformable Prototypes
• The way to the BliZ.

The book is an authoritative collection of contributions by leading experts on the topics of fuzzy logic, multi-valued logic and neural network. Originally written as an homage to Claudio Moraga, seen by his colleagues as an example of concentration, discipline and passion for science, the book also represents a timely reference guide for advance students and researchers in the field of soft computing, and multiple-valued logic. .