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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.

• 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.

• 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.

• 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 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.
• (source: Nielsen Book Data)

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)

• 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.
(source: Nielsen Book Data)

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

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

• Table of Contents 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)

• 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)