Expert systems / knowledge-based systems Books

Book SynopsisThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.

Read more less

Book SynopsisThis comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.Trade Review'Although there have been a series of classical textbooks on database systems, the new dramatic advances call for an updated text covering the latest significant topics, such as big data analytics, No-SQL and much more. Fortunately, this is exactly what this book has to offer. It is highly desirable for training the next generation of data management professionals.' Jian Pei, Simon Fraser University, Canada'I haven't seen an as up-to-date and comprehensive textbook for Database Management as this one in many years. Principles of Database Management combines a number of classical and recent topics concerning Data Modeling, Relational Databases, Object-Oriented Databases, XML, Distributed Data Management, NoSQL and Big Data in an unprecedented manner. The authors did a great job in stitching these topics into one coherent and compelling story that will serve as an ideal basis for teaching both introductory and advanced courses.' Martin Theobald, University of Luxembourg'This is a very timely book with outstanding coverage of database topics and excellent treatment of database details. It not only gives very solid discussions of traditional topics like data modeling and relational databases but also contains refreshing contents on frontier topics such as XML databases, NoSQL databases, big data, and analytics. For those reasons, this will be a good book for database professionals who will keep using it for all stages of database studies and works.' J. Leon Zhao, City University of Hong Kong'This accessible, authoritative book introduces the reader the most important fundamental concepts of data management, while providing a practical view of recent advances. Both are essential for data professionals today.' Foster Provost, New York University, Stern School of Business'This guide to big and small data management addresses both fundamental principles and practical deployment. It reviews a range of databases and their relevance for analytics. The book is useful to practitioners because it contains many case studies, links to open-source software, and a very useful abstraction of analytics that will help them better choose solutions. It is important to academics because it promotes database principles which are key to successful and sustainable data science.' Sihem Amer-Yahia, Laboratoire d'Informatique de Grenoble and Editor-in-Chief the International Journal on Very Large DataBases'This book covers everything you will need to teach in a database implementation and design class. With some chapters covering big data, analytic models/methods, and No-SQL, it can keep our students up-to-date with these new technologies in data management related topics.' Han-fen Hu, University of Nevada, Las Vegas'As we are entering a new technological era of intelligent machines powered by data-driven algorithms, understanding fundamental concepts of data management and their most current practical applications has become more important than ever. This book is a timely guide for anyone interested in getting up to speed with the state of the art in database systems, big data technologies, and data science. It is full of insightful examples and case studies with direct industrial relevance.' Nesime Tatbul, Intel Labs and Massachusetts Institute of Technology'It is a pleasure to study this new book on database systems. The book offers a fantastically fresh approach to database teaching. The mix of theoretical and practical contents is almost perfect, the content is up-to-date and covers the recent ones, the examples are nice, and the database testbed provides an excellent way of understanding the concepts. Coupled with the authors 'expertise, this book is an important addition to the database field.' Arnab Bhattacharya, Indian Institute of Technology, Kanpur'Principles of Database Management is my favorite textbook for teaching a course on database management. Written in a well-illustrated style, this comprehensive book covers essential topics in established data management technologies and recent discoveries in data science. With a nice balance between theory and practice, it is not only an excellent teaching medium for students taking information management and/or data analytics courses, but also a quick and valuable reference for scientists and engineers working in this area.' Chuan Xiao, Graduate School of Informatics, Nagoya University'Data science success stories and big data applications are only possible because of advances in database technology. This book provides both a broad and deep introduction to databases. It covers the different types of database systems (from relational to noSQL) and manages to bridge the gap between data modeling and the underlying basic principles. The book is highly recommended for anyone that wants to understand how modern information systems deal with ever-growing volumes of data.' Wil van der Aalst, RWTH Aachen University'The database field has been evolving for several decades and the need for updated textbooks is continuous. Now, this need is covered by this fresh book by Lemahieu, van den Broucke and Baesens. It spans from traditional topics - such as the relational model and SQL - to more recent topics – such as distributed computing with Hadoop and Spark as well as data analytics. The book can be used as an introductory text and for graduate courses.' Yannis Manolopoulos, Data Science & Engineering Lab, Aristotle University of Thessaloniki'I like the way the book covers both traditional database topics and newer material such as big data, No-SQL databases, and data quality. The coverage is just right for my course and the level of the material is very appropriate for my students. The book also has clear explanations and good examples.' Barbara Klein, University of MichiganThis book provides a unique perspective on database management and how to store, manage, and analyze small and big data. The accompanying exercises and solutions, cases, slides, and YouTube lectures turn it into an indispensable resource for anyone teaching an undergraduate or postgraduate course on the topic.' Wolfgang Ketter, Erasmus University Rotterdam'This is a very modern textbook that fills the needs of current trends without sacrificing the need to cover the required database management systems fundamentals.' George Dimitoglou, Hood College, Maryland'This book is a much needed foundational piece on data management and data science. The authors successfully integrate the fields of database technology, operations research and big data analytics, which have often been covered independently in the past. A key asset is its didactical approach that builds on a rich set of industry examples and exercises. The book is a must-read for all scholars and practitioners interested in database management, big data analytics and its applications.' Jan Mendling, Institute for Information Business, ViennaTable of ContentsPreface; Part I. Databases and Database Design: 1. Fundamental concepts of database management; 2. Architecture and categorization of DBMSs; 3. Conceptual data modeling using the (E)ER model and UML class diagram; 4. Organizational aspects of data management; Part II. Types of Database Systems: 5. Legacy databases; 6. Relational databases: the relational model; 7. Relational databases: structured query language (SQL); 8. Object oriented databases and object persistence; 9. Extended relational databases; 10. XML databases; 11. NoSQL databases; Part III. Physical Data Storage, Transaction Management, and Database Access: 12. Physical file organization and indexing; 13. Physical database organization; 14. Basics of transaction management; 15. Accessing databases and database APIs; 16. Data distribution and distributed transaction management; Part IV. Data Warehousing, Data Governance and (Big) Data Analytics: 17. Data warehousing and business intelligence; 18. Data integration, data quality and data governance; 19. Big data; 20. Analytics; Appendix A. Cases and questions; Appendix B. Using the online environment; Appendix C. Answer key to select review questions; Glossary; Index.

Read more less

Book Synopsis

Read more less

Book SynopsisLearn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You''ll Learn Become fluent in the essential concepts and terminology of data science and data engineering  Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling ofTable of Contents

Read more less

Book Synopsis Childhood learning is now more screen-based than ever before, and app developers are flocking in droves to this lucrative and exciting market. The younger generation deserves the best, and growing up in a digital world has made them discerning and demanding customers. Creating a valuable user experience for a child is as complex and involved as when designing a typical app for an adult, if not more, and Designing Digital Products for Kids is here to be your guide. Author and designer Rubens Cantuni recognizes the societal importance of a high-quality and ethical app experience for children. There is room for significant improvement in this space, and Cantuni helps you optimize it. Designing Digital Products for Kids walks hopeful developers through digital product design-including research, concept, design, release, marketing, testing, analyzing, and iterating-all while aiming to build specifically for children. Industry experts and their reTable of Contents 1. Why Design Apps for Kids? 2. Before You Start, Know the Industry 3. Know Your Target Audience 4. Concept 5. Gamification 6. Safety Measures. 7. Interaction Design 8. UI Design 9. User Testing with Kids 10. Market Your Product 11. Beyond the Screen 12. Conclusion

Read more less

Book SynopsisSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

Read more less

Book SynopsisFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.

Read more less

Book SynopsisMaking use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.Table of ContentsIntroduction Practical Data Analysis: An Example Project Understanding Data Understanding Principles of Modeling Data Preparation Finding Patterns Finding Explanations Finding Predictors Evaluation and DeploymentThe Labelling Problem Appendix A: Statistics Appendix B: KNIME

Read more less

Book SynopsisThis textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.Trade Review“This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in the chapter. … a good reference for practitioners, or a good textbook for graduate or upper-class undergraduate students.” (Xiannong Meng, Computing Reviews, September, 2017)“This textbook on practical data analytics unites fundamental principles, algorithms, and data. … this book is devoted to upper-division undergraduate and graduate students in mathematics, statistics, and computer science. It is intended for a one- or two-semester course in data analytics and reflects the authors’ research experience in data science concepts and the teaching skills in various areas. … The text is eminently suitable for self-study and an exceptional resource for practitioners.” (Krzysztof J. Szajowski, zbMATH 1367.62005, 2017) Table of ContentsIntroduction.- Data Mapping and Data Dictionaries.- Scalable Algorithms and Associative Statistics.- Hadoop and MapReduce.- Data Visualization.- Linear Regression Methods.- Healthcare Analytics.- Cluster Analysis.- k-Nearest Neighbor Prediction Functions.- The Multinomial Naive Bayes Prediction Function.- Forecasting.- Real-time Analytics.

Read more less

Book Synopsis

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book SynopsisThis book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book’s primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the “spin-net,” as a dynamic form of auto-associative memory.Table of ContentsAn Introduction.- Artificial Neural Networks.- Auto-Contractive Maps.- Visualization of Auto-CM Output.- Dataset Transformations and Auto-CM.- Comparison of Auto-CM to Various Other Data Understanding Approaches.

Read more less

Book SynopsisThe vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as an accessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.Table of Contents1. What is a knowledge graph?.- 2. Information Extraction.- 3. Entity Resolution.- 4. Advanced Topic: Knowledge Graph Completion.- 5. Ecosystems

Read more less

Book SynopsisThe three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.

Read more less

Book SynopsisThis book constitutes the workshop proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019. The 14 full papers presented were carefully selected and reviewed from 26 submissions to the three following workshops: the 6th International Workshop on Big Data Management and Service, BDMS 2019; the 4th International Workshop on Big Data Quality Management, BDQM 2019; and the Third International Workshop on Graph Data Management and Analysis, GDMA 2019. This volume also includes the short papers, demo papers, and tutorial papers of the main conference DASFAA 2019.Table of ContentsThe 6th International Workshop on Big Data Management and Service (BDSM 2019).- A Probabilistic Approach for Inferring Latent Entity Associations in Textual Web Contents.- UHRP Uncertainty-Based Pruning Method for Anonymized Data Linear Regression.- Meta-path based MiRNA-disease Association Prediction.- Medical Question Retrieval based on Siamese Neural Network and Transfer learning method.- An adaptive Kalman filter based Ocean Wave Prediction Model using Motion Reference Unit Data.- ASLM: Adaptive Single Layer Model for Learned Index.- SparseMAAC: Sparse Attention for Multi-Agent Reinforcement Learning.- The 4th International Workshop on Big Data Quality Management (BDQM 2019).- Identifying Reference Relationship of Desktop Files Based on Access Logs.- Visualization of Photo Album: Selecting a Representative Photo of a Specific Event.- Data Quality Management in Institutional Research Output Data Center.- Generalized Bayesian Structure Learning from Noisy Datasets.- The Third International Workshop on Graph Data Management and Analysis (GDMA 2019).- ANDMC: An Algorithm for Author Name Disambiguation Based on Molecular Cross Clustering.- Graph Based Aspect Extraction and Rating Classification of Customer Review Data.- Streaming Massive Electric Power Data Analysis Based on Spark Streaming.- Short Papers.- Deletion Robust k-Coverage Queries.- Episodic Memory Network with Self-Attention for Emotion Detection.- Detecting Suicidal Ideation with Data Protection in Online Communities.- Hierarchical Conceptual Labeling.- Anomaly Detection in Time-Evolving Attributed Networks.- A Multi-task Learning Framework for Automatic Early Detection of Alzheimer’s.- Top-k Spatial Keyword Query with Typicality and Semantics.- Align Reviews with Topics in Attention Network for Rating Prediction.- PSMSP: A Parallelized Sampling-based Approach for Mining Top-k Sequential Patterns in Database Graphs.- Value-Oriented Ranking of Online Reviews Based on Reviewer-influenced Graph.- Ancient Chinese Landscape Painting Composition Classification by Using Semantic Variational Autoencoder.- Learning Time-Aware Distributed Representations of Locations from Spatio-Temporal Trajectories.- Hyper2vec: Biased Random Walk for Hyper-Network Embedding.- Privacy-preserving and dynamic spatial range aggregation query processing in wireless sensor networks.- Adversarial Discriminative Denoising for Distant Supervision Relation Extraction.- Nonnegative Spectral Clustering for Large-Scale Semi-Supervised Learning.- Distributed PARAFAC Decomposition Method based on In-Memory Big Data System.- GPU-Accelerated Dynamic Graph Coloring.- Relevance-based Entity Embedding.- An Iterative Map-Trajectory Co-Optimisation Framework Based on Map-Matching and Map Update.- Exploring Regularity in Traditional Chinese Medicine Clinical Data Using Heterogeneous Weighted Networks Embedding.- AGREE: Attentive Tour Group Recommendation with Multi-Modal Data.- Random Decision DAG: An Entropy Based Compression Approach for Random Forest.- Generating Behavior Features for Cold-Start Spam Review Detection.- TCL: Tensor-CNN-LSTM for Travel Time Prediction with Sparse Trajectory Data.- A Semi-supervised Classification Approach for Multiple Time-varying Networks with Total Variation.- Multidimensional Skylines Over Streaming Data.- A domain adaptation approach for multistream classification.- Gradient Boosting Censored Regression for Winning Price Prediction in Real-Time Bidding.- Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction.- SemiSync: Semi-supervised Clustering by Synchronization.- Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors.- MVS-match: An Efficient Subsequence Matching Approach Based on the Series Synopsis.- Temporal-Spatial Recommendation for On-demand Cinemas.- Finding the key influences on the house price by Finite Mixture Model based on the real estate data in Changchun.- Semi-supervised Clustering with Deep Metric Learning.- Spatial Bottleneck Minimum Task Assignment with Time-delay.- A Mimic Learning Method for Disease Risk Prediction with Incomplete Initial Data.- Hospitalization Behavior Prediction Based on Attention and Time Adjustment Factors in Bidirectional LSTM.- Modeling Item Category for Effective Recommendation.- Distributed Reachability Queries on Massive Graphs.- Edge-Based Shortest Path Caching in Road Networks.- Extracting Definitions and Hypernyms with a Two-Phase Framework.- Tag Recommendation by Word-Level Tag Sequence Modeling.- A New Statistics Collecting Method with Adaptive Strategy.- Word Sense Disambiguation with Massive Contextual Texts.- Learning DMEs from Positive and Negative Examples.- Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition.- DRGAN: A GAN-based Framework for Doctor Recommendation in Chinese On-line QA Communities.- Attention-based Abnormal-Aware Fusion Network for Radiology Report Generation.- LearningTour: A Machine Learning Approach for Tour Recommendation based on Users’ Historical Travel Experience.- TF-Miner: Topic-specific Facet Mining by Label Propagation.- Fast Raft Replication for Transactional Database Systems over Unreliable Networks.- Parallelizing Big De Bruijn Graph Traversal for Genome Assembly on GPU Clusters.- GScan: Exploiting Sequential Scans for Subgraph Matching.- SIMD Accelerates the Probe Phase of Star Joins in Main Memory Databases.- A Deep Recommendation Model Incorporating Adaptive Knowledge-based Representations.- BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation.- Spatiotemporal-Aware Region Recommendation with Deep Metric Learning.- On the Impact of the Length of Subword Vectors on Word Embeddings.- Using Dilated Residual Network to Model Distant Supervision Relation Extraction.- Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis.- A Sparse Matrix-based Join for SPARQL Query Processing.- Change Point Detection for Streaming High-Dimensional time series.- Demo Papers.- Distributed Query Engine for Multiple-Query Optimization over Data Stream.- Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case.- AgriKG: An Agricultural Knowledge Graph and Its Applications.- KGVis: An Interactive Visual Query Language for Knowledge Graphs.- OperaMiner: Extracting Character Relations from Opera Scripts using Deep Neural Networks.- GparMiner: A System to mine Graph Pattern Association Rules.- A Data Publishing System Based on Privacy Preservation.- Privacy as a Service: Publishing Data and Models.- Dynamic Bus Route Adjustment Based on Hot Bus Stop Pair Extraction.- DHDSearch: A Framework for Batch Time Series Searching on MapReduce.- Bus Stop Refinement based on Hot Spot Extraction.- Adaptive Transaction Scheduling for Highly Contended Workloads.- IMOptimizer: An Online Interactive Parameter Optimization System based on Big Data.- Tutorial Papers.- Cohesive Subgraphs with Hierarchical Decomposition on Big Graphs.- Tracking User Behaviours: Laboratory-Based and In-The-Wild User S.- Mining Knowledge Graphs for Vision Tasks.- Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management.- Knowledge Graph Data Management.- Deep learning for Healthcare Data Processing.

Read more less

Book SynopsisThis book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019. The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.Table of ContentsAdvanced Machine Learning.- Applications.- Data and Knowledge Representation.- Feature Importance.- Interpretable Machine Learning.- Networks.- Pattern Discovery.- Time Series.

Read more less

Book SynopsisText Mining with MATLAB® provides a comprehensive introduction to text mining using MATLAB. It is designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The book is structured in three main parts: The first part, Fundamentals, introduces basic procedures and methods for manipulating and operating with text within the MATLAB programming environment. The second part of the book, Mathematical Models, is devoted to motivating, introducing, and explaining the two main paradigms of mathematical models most commonly used for representing text data: the statistical and the geometrical approach. Eventually, the third part of the book, Techniques and Applications, addresses general problems in text mining and natural language processing applications such as document categorization, document search, content analysis, summarization, question answering, and conversational systems. This second edition includes updates in line with the recently released “Text Analytics Toolbox” within the MATLAB product and introduces three new chapters and six new sections in existing ones. All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation. Table of Contents1. Introduction.- PART I: FUNDAMENTALS.- 2. Handling Text Data.- 3. Regular Expressions.- 4. Basic Operations with Strings.- 5. Reading and Writing Files.- 6. The Structure of Language.- PART II: MATHEMATICAL MODELS.- 7. Basic Corpus Statistics.- 8. Statistical Models.- 9. Geometrical Models.- 10. Dimensionality Reduction.- PART III: METHODS AND APPLICATIONS.- 11. Document Categorization.- 12. Document Search.- 13. Content Analysis.- 14. Keyword Extraction and Summarization.- 15. Question Answering and Dialogue.

Read more less

Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the 24th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2021, held as a virtual event in May 2021 (due to the Covid-19 pandemic).The 10 revised full papers presented were carefully reviewed and selected from 17 submissions. In addition to this, one keynote paper was included in the workshop. The volume contains two sections: Open Scheduling Problems and Proposals and Technical Papers. The papers cover such topics as parallel computing, distributed systems, workload modeling, performance optimization, and others.Table of ContentsKeynote.- ​Resampling with Feedback: A New Paradigm of Using Workload Data for Performance Evaluation.- Open Scheduling Problems and Proposals.- Collection of Job Scheduling Prediction Methods.- Modular Workload Format: extending SWF for modular systems.- Technical Papers.- Measurement and Modeling of Performance of HPC Applications towards Overcommitting Scheduling Systems.- Scheduling Microservice Containers on Large Core Machines through Placement and Coalescing.- Learning-based Approaches to Estimate Job Wait Time in HTC Datacenters.- A HPC Co-Scheduler with Reinforcement Learning.- Performance-Cost Optimization of Moldable Scientific Workflows.- Temperature-Aware Energy-Optimal Scheduling of Moldable Streaming Tasks onto 2D-Mesh-Based Many-Core CPUs with DVFS.- Scheduling Challenges for Variable Capacity Resources.- GLUME: A Strategy for Reducing Workflow Execution Times on Batch-Scheduled Platforms.

Read more less

Book SynopsisThis book constitutes the post-conference proceedings of the 2nd International Conference on Modern Problems of Robotics, MPoR 2020, held in Moscow, Russia, in March 2020.The 16 revised full papers were carefully reviewed and selected from 21 submissions. The volume includes the following topical sections: Collaborative Robotic Systems, Robotic Systems Design and Simulation, and Robots Control. The papers are devoted to the most interesting today’s investigations in Robotics, such as the problems of the human–robot interaction, the problems of robot design and simulation, and the problems of robot and robotic complexes control. Table of ContentsCollaborative Robotic Systems.- Robotic Systems Design and Simulation.- Robots Control.

Read more less

Book SynopsisThe volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.*The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: artificial intelligence; mechatronics; image processing and computer vision; special purpose and application-based systems; user interfaces and human computer interaction.* The conference was held virtually due to the COVID-19 pandemic.Table of Contents​Algorithms.- A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning.- An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet.- CPG-Actor: Reinforcement Learning for Central Pattern Generators .- Deep semantic segmentation of 3D plant point clouds.- Grasp Stability Prediction for a Dexterous Robotic Hand combining RGB-D Vision and Haptic Bayesian Exploration.- Improving SLAM in Pipe Networks by Leveraging Cylindrical Regularity.- CRH*: A Deadlock Free Framework for Scalable Prioritised Path Planning in Multi-Robot Systems.- TASK-BASED AD-HOC TEAMWORK with ADVERSARY.- Human-Robot Cooperative Lifting using IMUs and Human Gestures.- Reinforcement Learning-based Mapless Navigation with Fail-safe Localisation.- Collaborative Coverage for a Network of Vacuum Cleaner Robots.- Network-Aware Genetic Algorithms for the Coordination of MALE UAV Networks.- Self-organised Flocking of Robotic Swarm in Cluttered Environments.- Exploring Feedback Modalities in a Mobile Robot for Telecare.- Demonstrating the Differential Impact of Flock Heterogeneity on Multi-Agent Herding.- Evaluation of an OpenCV Implementation of Structure from Motion on Open Source Data.- Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards.- Lidar-only localization in 3D Pose-Feature Map.- Toward robust visual odometry using prior 2D map information.- Comparison of Concentrated and Distributed Compliant Elements in a 3D Printed Gripper.- Perception of a humanoid robot as an interface for auditory testing.- Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments.- Systems.- Predicting Artist Drawing Activity via Multi-Camera Inputs for Co-Creative Drawing.- 3D printed mechanically modular two-degree-of-freedom robotic segment utilizing variable-stiffness actuators.- Design of a Multimaterial 3D-printed Soft Actuator with Bi-directional Variable Stiffness.- Designing a Multi-Locomotion Modular Snake Robot.- Deep robot path planning from demonstrations for breast cancer examination.- Priors inspired by Speed-Accuracy Trade-Offs for Incremental Learning of Probabilistic Movement Primitives.- Tactile Dynamic Behaviour Prediction Based on Robot Action.- State space analysis of variable-stiffness tendon drive with non-back-drivable worm-gear motor actuation.- Development of a ROS Driver and Support Stack for the KMR iiwa Mobile Manipulator.- Collision Avoidance with Optimal Path Replanning for Mobile Robots.- An Autonomous Mapping Approach for Confined Spaces using Flying Robots.- Maximising availability of transportation robots through intelligent allocation of parking spaces.- A Minimalist Solution to the Multi-Robot Barrier Coverage Problem.- Scheduling Multi-robot Missions with JointTasks and Heterogeneous Robot Teams.- Area Coverage in Two-Dimensional Grid Worlds Using Computation-Free Agents.- Online Scene Visibility Estimation as a Complement to SLAM in UAVs.- Statics Optimization of a Hexapedal Robot Modelled as a Stewart Platform.- EtherCAT implementation of a variable-stiffness tendon drive with non-back-drivable worm-gear motor actuation.- Growing Robotic Endoscope for early Breast Cancer Detection: Robot Motion Control.- Design and Charachterisation of a Variable-Stiffness Soft Actuator Based on Tendon Twisting.- WhiskEye: A biomimetic model of multisensoryspatial memory based on sensory reconstruction.- Equipment Detection based Inspection Robot for Industrial Plants.- Inference of Mechanical Properties of Dynamic Objects through Active Perception.

Read more less

Book SynopsisThis book constitutes the refereed proceedings of the 11th International Conference on Cloud Computing, CloudComp 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 full papers were carefully reviewed and selected from 40 submissions and detail cloud computing technologies for efficient and intelligent computing in secure and smart environments with distributed devices. The theme of CloudComp 2021 was “Cloud Computing for Secure and Smart Applications”. The book is organized in three general areas of data analytics for cloud systems with distributed applications, cloud architecture and challenges in real-world use, and security in cloud/edge platforms.Table of ContentsData Analytics for Cloud Systems with Distributed Applications 1 Load quality analysis and forecasting for power data set on cloud platform.- A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges.- A dynamic gesture recognition control file method based on deep learning.- A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment.- Triangle Coordinate Diagram Localization for Academic Literature Based on Line Segment Detection.- Optimizing Fund Allocation for Game-based Verifiable Computation Outsourcing.- A Survey of Face Image Inpainting Based on Deep Learning.- Cloud Architecture and Challenges in Real-World Use.- Layered Service Model Architecture for Cloud Computin.- KPG4Rec: Knowledge Property-aware Graph for Recommender Systems.- 10 ERP as Software-as-a-Service: Factors depicting large enterprises cloud adoption.- Design Of An Evaluation System Of Limb Motor Function Using Inertial Sensor.- Towards a GPU-accelerated Open Source VDI for OpenStack Manuel.- Security in Cloud/Edge Platforms.- Trustworthy IoT Computing Environment Based on Layered Blockchain Consensus Framework.- Heuristic Network Security Risk Assessment Based on Attack Graph.- Research on Network Security Automation and Orchestration Oriented to Electric Power Monitoring System.- Energy- and Reliability-aware Computation Offloading with Security Constraints.- A Review of Cross-Blockchain Solutions.

Read more less

Book SynopsisThis book constitutes the refereed proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021, Virtual Event, July 6–8, 2021. The conference was held virtually due to the COVID-19 crisis.The 10 full papers included in this book were carefully reviewed and selected from 117 submissions.Table of ContentsLinked Data as Medium for Stigmergy-based Optimization and Coordination.- Object Parsing Expressions for Unplanned, Unmodified, and Incremental Grammar Reuse.- A Methodology for Organizational Data Science towards Evidence-based Process Improvement.- Feedback Generation for Automatic User Interface Design Evaluation.- Tales from the Code #2: A Detailed Assessment of Code Refactoring's Impact on Energy Consumption.- Towards Power Consumption Optimization for Embedded Systems from a Model-driven Software Development Perspective.- Materializing Microservice-oriented Architecture from Monolithic Object-oriented Source Code.- A Personalized Code Formatter: Detection & Fixing.- Software Framework of Context-aware Reconfigurable Secure Smart Grids.- A Novel Neural Network-based Malware Severity Classification System.

Read more less

Book SynopsisThis book constitutes the post-conference proceedings of the 23rd International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2021, held in Moscow, Russia, in October 2021*.The 16 revised full papers were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: problem solving infrastructures, experiment organization, and machine learning applications; data analysis in astronomy; data analysis in material and earth sciences; information extraction from text* The conference was held virtually due to the COVID-19 pandemic.Table of ContentsProblem Solving Infrastructures, Experiment Organization, and Machine Learning Applications.- MLDev: Data Science Experiment Automation and Reproducibility Software.- Response to Cybersecurity Threats of Informational Infrastructure Based on Conceptual Models.- Social Network Analysis of the Professional Community Interaction - Movie Industry Case.- Data Analysis in Astronomy.- Cross-Matching of Large Sky Surveys and Study of Astronomical Objects Apparent in Ultraviolet Band Only.- The Diversity of Light Curves of Supernovae Associated with Gamma-Ray Bursts.- Application of Machine Learning Methods for Cross-Matching Astronomical Catalogs.- Pipeline for Detection of Transient Objects in Optical Surveys.- VALD in Astrophysics.- Data Analysis in Material and Earth Sciences.- Machine Learning Application to Predict New Inorganic Compounds – Results and Perspectives.- Interoperability and Architecture Requirements Analysis and Metadata Standardization for a Research Data Infrastructure in Catalysis.- Fast Predictions of Lattice Energies by Continuous Isometry Invariants of Crystal Structures.- Image Recognition for Large Soil Maps Archive Overview: Metadata Extraction and Georeferencing Tool Development.- Information Extraction from Text.- Cross-lingual Plagiarism Detection Method.- Methods for Automatic Argumentation Structure Prediction.- A System for Information Extraction from Scientific Texts in Russian.- Improving Neural Abstractive Summarization with Reliable Sentence Sampling.

Read more less

Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Coordination, Organizations, Institutions, and Norms for Governance of Multi-Agent Systems, COINE 2022, which was held in Auckland, New Zealand, on May 9, 2022.The 14 papers included in these proceedings were carefully reviewed and selected from 15 submissions. They deal with autonomous agents and multi-agent systems, focusing on the scientific and technological aspects of social coordination, organizational theory, artificial (electronic) institutions, and normative and ethical MAS.Table of ContentsDesigning International Humanitarian Law into Military Autonomous Devices.- Epistemic Diversity and Explanatory Adequacy in Distributed Information Processing.- The Complexity of Norm Synthesis and Revision.- Embracing AWKWARD! Real-Time Adjustment of Reactive Plans Using Social Norms.- Self-Learning Governance of Black-Box Multi-Agent Systems.- Computational Theory of Mind for Human-Agent Coordination.- Computational Discovery of Transaction-Based Financial Crime via Grammatical Evolution: The Case of Ponzi Schemes.- Centralized Norm Enforcement in Mixed-Motive Multiagent Reinforcement Learning.- Supporting the Reasoning about Environmental Consequences of Institutional Actions.- Social Motives and Social Contracts in Cooperative Survival Games.- Evaluating Human and Agent Task Allocators in Ad Hoc Human-Agent Teams.- Fleur: Social Values Orientation for Robust Norm Emergence.- Reasoning about Collective Action in Markov Logic: A Case Study from Classical Athens.- Design Heuristics for Ethical Online Institutions.

Read more less

Book SynopsisThis book constitutes the refereed post-conference proceedings of the 5th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2022, held in Nile University of Nigeria Abuja, Nigeria, in March 2022. The 26 papers presented were selected from 66 submissions and issue different problems in underserved and unserved areas. They face problems in almost all sectors such as energy, water, communication, climate change, food, education, transportation, social development, and economic growth.Table of ContentsSustainable Development for Underserved Areas.- Effects of Noise Pollution on learning in Schools of Bamenda II Municipality, Northwest Region of Cameroon.- Hydro-meteorological Trends and Thermal comfort of Khartoum Sudan.- Community Water Projects Sustainability for Climate Change Resilience and Adaptation in Suam Catchment Area of West Pokot County, Kenya.- The Nigerian HealthCare Facilities: Need for Adopting Evidence-Based Design as an Innovative Approach for Improved Health and Wellbeing.- Artificial Intelligence (AI) and Machine Learning (ML) for Development.- An E-Nose Using Metal Oxide Semiconductor Sensors Array to Recognize the Odors of Fall Armyworm Pest For Its Early Detection in the Farm.- Prediction of process failure approach using process mining.- An AI-based Model for the Prediction of a newborn’s Sickle Cell Disease Status.- Study of an approach based on the analysis of computer program execution traces for the detection of vulnerabilities.- ICT for Development.- Comparative Evaluation on Sentiment Analysis Algorithms.- A new wavelet based steganography method for Securing medical Data.- A dual ring architecture using controllers for better load balancing in a Fog Computing environment.- Recommendation System for Carbon Reduction.- Engineering Impact on Sustainability Development.- Assessment of Heavy Metal Concentration of Municipal open-air Dumpsite: A case study of Gosa Dumpsite, Abuja.- Ten years after the Deepwater Horizon disaster - lessons learned for a better cementing job.- Generating Bioelectricity From Traditional Food Processing Wastewater Using An Inoculum Of Return Activated Sewage Sludge.- Application of molasses in improving water purification efficiency of diatomaceous earth-waste ceramic membranes.- Community Engagements and Collaboration.- The levels of crop raiding by rodents and primates in a subsistence farming community, in South Africa.- Survey on crop disease detection and identification based on deep learning.- Pest Birds Detection Approach in Rice Crops Using Pre-trained YOLOv4 Model.- Pest Birds Detection Approach in Rice Crops Using Pre-trained YOLOv4 Model.- Truncation effect of a three-dimensional compound parabolic concentrator on the solar flux at the input of the receiver of a 30 kWe solar tower power plant.- Renewable Energy Transition: A Panacea to the Ravaging Effects of Climate Change in Nigeria.- Investigation on Concrete with Partial Replacement of Aggregate from Demolition Waste.- Valorization of the recovered lime in cement-typha concretes: thermal and mechanical behavior.- Engineering and Science Education in Underserved Areas.- The Need for Nigerian Universities to Collaborate for Quality Research Output.- SenTekki: online platform and Restful Web service for translation between Wolof and French.- Towards an optimal placement of learning ressources in a fog computing based e-learning system : The case of UVS.

Read more less

Book SynopsisThis book constitutes the refereed post-conference proceedings of the workshops held at the 16th Asian Conference on Computer Vision, ACCV 2022, which took place in Macao, China, in December 2022. The 25 papers included in this book were carefully reviewed and selected from 40 submissions. They have been organized in topical sections as follows: Learning with limited data for face analysis; adversarial machine learning towards advanced vision systems; computer vision for medical computing; machine learning and computing for visual semantic analysis; vision transformers theory and applications; and deep learning-based small object detection from images and videos.Table of ContentsLearning with Limited Data for Face Analysis.- FAPN: Face Alignment Propagation Network for Face Video Super-Resolution.- Micro-expression recognition using a shallow ConvLSTM-based network.- Adversarial Machine Learning towards Advanced Vision Systems.- ADVFilter: Adversarial Example Generated by Perturbing Optical Path.- Enhancing Federated Learning Robustness Through clustering Non-IID Features.- Towards Improving the Anti-attack Capability of the RangeNet++.- Computer Vision for Medical Computing.- Ensemble Model of Visual Transformer and CNN Helps BA Diagnosis for Doctors in Underdeveloped Areas.- Understanding Tumor Micro Environment using Graph theory.- Handling Domain Shift for Lesion Detection via Semi-Supervised Domain Adaptation.- Photorealistic Facial Wrinkles Removal.- Improving Segmentation of Breast Arterial Calcifications from Digital Mammography: Good Annotation Is All You Need.- Machine Learning and Computing for Visual Semantic Analysis.- Towards Scene Understanding for Autonomous Operations on Airport Aprons.- Lightweight Hyperspectral Image Reconstruction Network with Deep Feature Hallucination.- A Transformer-based Model for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Muti-p.- CaltechFN: Distorted and Partially Occluded Digits.- Temporal Extension Topology Learning for Video-based Person Re-Identification.- Deep RGB-driven Learning Network for Unsupervised Hyperspectral Image Super-resolution.- Gift from nature: Potential Energy Minimization for explainable dataset distillation.- Object Centric Point Sets Feature Learning with Matrix Decomposition.- Aerial Image Segmentation via Noise Dispelling and Content Distilling.- Vision Transformers Theory and Applications.- Temporal Cross-attention for Action Recognition.- Transformer Based Motion In-Betweening.- Convolutional point Transformer.- Cross-Attention Transformer for Video Interpolation.- Deep Learning-Based Small Object Detection from Images and Videos.- Evaluating and Bench-marking Object Detection Models for Traffic Sign and Traffic Light Datasets.- Exploring Spatial-temporal Instance Relationships In an Intermediate Domain For Image-to-video Object Detection.

Read more less

Book SynopsisMachine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.Table of ContentsMachine Learning for Health Informatics.- Bagging Soft Decision Trees.- Grammars for Discrete Dynamics.- Empowering Bridging Term Discovery for Cross-domain Literature Mining in the TextFlows Platform.- Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice.- Deep learning trends for focal brain pathology segmentation in MRI.- Differentiation between Normal and Epileptic EEG using K-Nearest-Neighbors Technique.- Survey on Feature Extraction and Applications of Biosignals.- Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning.- Machine Learning and Data mining Methods for Managing Parkinson’s Disease.- Challenges of Medical Text and Image Processing: Machine Learning Approaches.- Visual Intelligent Decision Support Systems in the medical field: design and evaluation.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book Synopsis

Read more less

Book SynopsisThis book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.Table of Contents1. What is Data Science (DS)?1.1 Major Families of Data Science Problems1.1.1 Classification Problems1.1.2 Prediction Problems1.1.3 Clustering Problems1.2 Data, Big Data and Pre-processing1.2.1 What is Data?1.2.2 Big data1.2.3 Data Cleansing1.2.4 Data Visualization1.2.5 Data Understanding1.3 Populations and Data Sampling1.3.1 Sampling1.3.2 Training, Testing and Validation1.4 Overview and Scope1.4.1 Prerequisites and Layout1.4.2 Data Science Methodology1.4.3 Scope of the Book2. Solutions to Data Science Problems2.1 Conventional Statistical Solutions2.1.1 Linear Multiple Regression Model: Continuous Response2.1.2 Logistic Regression: Categorical Response2.1.3 Variable Selection and Model Building2.1.4 Generalized Linear Model (GLM)2.1.5 Decision Trees2.1.6 Bayesian Learning2.2 Machine Learning Solutions: Supervised2.2.1 k-Nearest Neighbors (kNN)2.2.2 Ensemble Methods2.2.3 Support Vector Machines (SVMs)2.2.4 Neural Networks (NNs)2.3 Machine Learning Solutions: Unsupervised2.3.1 Hard Clustering2.3.2 Soft Clustering2.4 Controls, Evaluation and Assessment2.4.1 Evaluation Methods2.4.2 Metrics for Assessment3. What is Dimensionality Reduction (DR)?3.1 Dimensionality Reduction3.2 Major Approaches to Dimensionality Reduction3.2.1 Conventional Statistical Approaches3.2.2 Geometric Approaches3.2.3 Information-theoretic Approaches3.2.4 Molecular Computing Approaches3.3 The Blessings of Dimensionality4. Conventional Statistical Approaches4.1 Principal Component Analysis (PCA)4.1.1 Obtaining the Principal Components4.1.2 Singular value decomposition (SVD)4.2 Nonlinear PCA 4.2.1 Kernel PCA4.2.2 Independent component analysis (ICA)4.3 Nonnegative Matrix Factorization (NMF)4.3.1 Approximate Solutions4.3.2 Clustering and Other Applications4.4 Discriminant Analysis4.4.1 Linear discriminant analysis (LDA)4.4.2 Quadratic discriminant analysis (QDA)4.5 Sliced Inverse Regression (SIR)5. Geometric Approaches5.1 Introduction to Manifolds5.2 Manifold Learning Methods5.2.1 Multi-Dimensional Scaling (MDS)5.2.2 Isometric Mapping (ISOMAP)5.2.3 t-Stochastic Neighbor Embedding ( t-SNE )5.3 Exploiting Randomness (RND)6. Information-theoretic Approaches6.1 Shannon Entropy (H)6.2 Reduction by Conditional Entropy6.3 Reduction by Iterated Conditional Entropy6.4 Reduction by Conditional Entropy on Targets6.5 Other Variations7. Molecular Computing Approaches7.1 Encoding Abiotic Data into DNA7.2 Deep Structure of DNA Spaces7.2.1 Structural Properties of DNA Spaces7.2.2 Noncrosshybridizing (nxh) Bases7.3 Reduction by Genomic Signatures7.3.1 Background7.3.2 Genomic Signatures7.4 Reduction by Pmeric Signatures8. Statistical Learning Approaches8.1 Reduction by Multiple Regression8.2 Reduction by Ridge Regression8.3 Reduction by Lasso Regression 8.4 Selection versus Shrinkage8.5 Further refinements9. Machine Learning Approaches9.1 Autoassociative Feature Encoders9.1.1 Undercomplete Autoencoders 9.1.2 Sparse Autoencoders9.1.3 Variational Autoencoders9.1.4 Dimensionality Reduction in MNIST Images9.2 Neural Feature Selection9.2.1 Facial Features, Expressions and Displays9.2.2 The Cohn-Kanade Dataset9.2.3 Primary and Derived Features9.3 Other Methods10. Metaheuristics of DR Methods10.1 Exploiting Feature Grouping10.2 Exploiting Domain Knowledge10.2.1 What is Domain Knowledge?10.2.2 Domain Knowledge for Dimensionality Reduction10.3 Heuristic Rules for Feature Selection, Extraction and Number10.4 About Explainability of Solutions10.4.1 What is Explainability?10.4.2 Explainability in Dimensionality Reduction10.5 Choosing Wisely10.6 About the Curse of Dimensionality10.7 About the No-Free-Lunch Theorem (NFL)11. Appendices11.1 Statistics and Probability Background11.1.1 Commonly Used Discrete Distributions11.1.2 Commonly Used Continuous Distributions11.1.3 Major Results In Probability and Statistics11.2 Linear Algebra Background11.2.1 Fields, Vector Spaces and Subspaces11.2.2 Linear independence, Bases and Dimension11.2.3 Linear Transformations and Matrices11.2.4 Eigenvalues and Spectral Decomposition11.3 Computer Science Background11.3.1 Computational Science and Complexity11.3.2 Machine Learning11.4 Typical Data Science Problems11.5 A Sample of Common and Big Datasets11.6 Computing Platforms11.6.1 The Environment R11.6.2 Python environmentsReferences

Read more less

Book SynopsisTable of ContentsPart 1: Data Structures.- Chapter 1: Intro to DSA, Types and Big-O.- Chapter 2: Linear Data Structures.- Chapter 3: Tree Data Structures.- Chapter 4: Hash Data Structures.- Chapter 5: Graphs.- Part 2: Algorithms.- Chapter 6: Linear and Binary Search.- Chapter 7: Sorting Algorithms.- Chapter 8: Searching Algorithms.- Chapter 9: Clustering Algorithms.- Chapter 10: Randomness.- Chapter 11: Scheduling Algorithms.- Chapter 12: Algorithm Planning and Design.- Appendix A: Going Further.-

Read more less

Book SynopsisIntermediate-Advanced user levelTable of ContentsChapter 1: Getting Ready.- Chapter 2: Working with Table Design.- Chapter 3: Table Relationships and Working With Joins.- Chapter 4: Working with Calculated Data.- Chapter 5: Aggregating Data.- Chapter 6: Creating and Using Views and Friends.- Chapter 7: Working With Subqueries and Common Table Expressions.- Chapter 8: Working With Window Functions.-Chapter 9: More on Common Table Expressions.- Chapter 10: More Techniques with SQL: Triggers, Pivot Tables, and Variables.- Appendix A.

Read more less

Book Synopsis

Read more less

Book SynopsisHow do organizations become created? Entrepreneurship scholars have debated this question for decades, but only recently have they been able to gain insights into the non-linear dynamics that lead to organizational emergence, through the use of the complexity sciences. Written for social science researchers, Generative Emergence summarizes these literatures, including the first comprehensive review of each of the 15 complexity science disciplines. In doing so, the book makes a bold proposal for a discipline of Emergence, and explores one of its proposed fields, namely Generative Emergence. The book begins with a detailed summary of its underlying science, dissipative structures theory, and rigorously maps the processes of order creation discovered by that science to identify a 5-phase model of order creation in entrepreneurial ventures. The second half of the book presents the findings from an experimental study that tested the model in four fast-growth ventures through a year-long, weTable of ContentsChapter 1. Why Emergence ; Chapter 2. Prototypes of Emergence ; Chapter 3. Methods for Studying Emergence - 15 Fields of Complexity Science ; Chapter 4. Defining Emergence and Generative Emergence ; Chapter 5. Types of Emergence Studies ; Chapter 6. Dissipative Structures ; Chapter 7. Applications to Organizations ; Chapter 8. Introducing Dynamic States ; Chapter 9. Outcomes of Generative Emergence ; Chapter 10. Introducing the Five-Phase Process Model Of Generative Emergence ; Chapter 11. Phase 2 - Stress & Experiments ; Chapter 12. Phase 3 - Amplification and Critical Events ; Chapter 13. Phase 4 - New Order through Recombination ; Chapter 14. Phase 5 - Stabilizing Feedback ; Chapter 15. Cycles of Emergence ; Chapter 16. Cycles of Re-Emergence ; Chapter 17. Boundaries of Emergence, and Beyond the Boundaries ; Chapter 18. Enacting Emergence

Read more less

Book SynopsisComputational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics.Trade ReviewFrom the reviews:“This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. … The writing in the book is quite clear and the author has done a good job providing the essence of each topic. … Overall, I think this is an excellent book. … This book will give a graduate student a good overview of the field. There are exercises provided for each chapter together with some solutions.” (Michael J. Evans, Mathematical Reviews, Issue 2011 b)“This book is a superior treatment of the important subject of statistical computing. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well. … All terms in this work including computing terms are clearly defined.” (Melvin Hinich, Technometrics, Vol. 53 (1), February, 2011)“I greatly appreciated the author’s command of both numerical and statistical computing … . The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. The target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. … I strongly recommend it for all scientific libraries.” (Soubhik Chakraborty, ACM Computing Reviews, October, 2010)“This book has a very large scope in that … it covers the dual fields of computational statistics and of statistical computing. … must-read for all students and researchers engaging into any kind of serious statistical programming. … is well-written, in a lively and personal style. … a reference book that should appear in the shortlist of any computational statistics/statistical computing graduate course as well as on the shelves of any researchers supporting his or her statistical practice with a significant dose of computing backup.”­­­ (Christian P. Robert, Statistical and Computation, Vol. 21, 2011)Table of ContentsPreliminaries.- Mathematical and Statistical Preliminaries.- Statistical Computing.- Computer Storage and Arithmetic.- Algorithms and Programming.- Approximation of Functions and Numerical Quadrature.- Numerical Linear Algebra.- Solution of Nonlinear Equations and Optimization.- Generation of Random Numbers.- Methods of Computational Statistics.- Graphical Methods in Computational Statistics.- Tools for Identification of Structure in Data.- Estimation of Functions.- Monte Carlo Methods for Statistical Inference.- Data Randomization, Partitioning, and Augmentation.- Bootstrap Methods.- Exploring Data Density and Relationships.- Estimation of Probability Density Functions Using Parametric Models.- Nonparametric Estimation of Probability Density Functions.- Statistical Learning and Data Mining.- Statistical Models of Dependencies.

Read more less

Book Synopsis Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Read more less

Book SynopsisManaging and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy.Trade ReviewFrom the reviews:“This book provides a survey of some recent advances in graph mining. It contains chapters on graph languages, indexing, clustering, pattern mining, keyword search, and pattern matching. … The book is targeted at advanced undergraduate or graduate students, faculty members, and researchers from both industry and academia. … I highly recommend this book to someone who is starting to explore the field of graph mining or wants to delve deeper into this exciting field.” (Dimitrios Katsaros, ACM Computing Reviews, December, 2010)Table of ContentsAn Introduction to Graph Data.- Graph Data Management and Mining: A Survey of Algorithms and Applications.- Graph Mining: Laws and Generators.- Query Language and Access Methods for Graph Databases.- Graph Indexing.- Graph Reachability Queries: A Survey.- Exact and Inexact Graph Matching: Methodology and Applications.- A Survey of Algorithms for Keyword Search on Graph Data.- A Survey of Clustering Algorithms for Graph Data.- A Survey of Algorithms for Dense Subgraph Discovery.- Graph Classification.- Mining Graph Patterns.- A Survey on Streaming Algorithms for Massive Graphs.- A Survey of Privacy-Preservation of Graphs and Social Networks.- A Survey of Graph Mining for Web Applications.- Graph Mining Applications to Social Network Analysis.- Software-Bug Localization with Graph Mining.- A Survey of Graph Mining Techniques for Biological Datasets.- Trends in Chemical Graph Data Mining.

Read more less

Book SynopsisThis work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space.Table of ContentsVector Quantisation and Topology-Based Graph RepresentationGraph-Based Clustering AlgorithmsGraph-Based Visualisation of High-Dimensional Data

Read more less

Book SynopsisIn recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems (RTDBS), where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity.Table of ContentsList of Figures. List of Tables. Acknowledgments. Preface. Contributing Authors. I: Overview, Misconceptions and Issues. 1. Real-Time Database Systems: An Overview of System Characteristics and Issues; Tei-Wei Kuo, Kam-Yiu Lam. 2. Misconceptions About Real-Time Databases; J.A. Stankovic, et al. 3. Applications and System Characteristics; D. Locke. II: Real-Time Concurrency Control. 4. Conservative and Optimistic Protocols; Tei-Wei Kuo, Kam-Yiu Lam. 5. Semantics-Based Concurrency Control; Tei-Wei Kuo. 6. Real-Time Index Concurrency Control; J.R. Haritsa, S. Seshadri. III: Run-Time System Management. 7. Buffer Management in Real-Time Active Database Systems; A. Datta, S. Mukherjee. 8. Disk Scheduling; Ben Kao, R. Cheng. 9. System Failure and Recovery; R.M. Sivasankaran, et al. 10. Overload Management in RTDBs; J. Hansson, S.H. Son. 11. Secure Real-Time Transaction Processing; J.R. Haritsa, B. George. IV: Active Issues and Triggering. 12. System Framework of ARTDBs; J. Hansson, S.F. Andler. 13. Reactive Mechanisms; J. Mellin, et al. 14. Updates and View Maintenance; Ben Kao, et al. V: Distributed Real-Time Database Systems. 15. Distributed Concurrency Control; Ö. Ulusoy. 16. Data Replication and Availability; Ö. Ulusoy. 17. Real-Time Commit Processing; J.R. Haritsa, et al. 18. Mobile Distributed Real-Time Database Systems; Kam-Yiu Liam, Tei-Wei Kuo.VI: Prototypes and Future Directions. 19. Prototypes: Programmed Stock Trading; B. Adelberg, Ben Kao. 20. Future Directions; Tei-Wei Kuo, Kam-Yiu Lam. Index.

Read more less