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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.

• 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.

• 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 .
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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.
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• 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.
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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.
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• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
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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.
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• 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.
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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.
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• 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.
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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.
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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)

• 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)

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

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.
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• 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)

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

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

• 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)