Expert systems / knowledge-based systems Books

Book SynopsisEnterprise Systems have been used for many years to integrate technology with the management of an organization but rapid technological disruptions are now creating new challenges and opportunities that require urgent consideration. This book reappraises the implementation and management of Enterprise Systems in the digital age and investigates the vital link between business processes, information technology and the Internet for an organization’s competitive advantage and success.This book primarily focuses on the implementation, operation, management and integration of Enterprise Systems with fastemerging disruptive technologies such as blockchains, big data, cryptocurrencies, artificial intelligence, cloud computing, data mining and data analytics. These disruptive technologies are now becoming mainstream and the book proposes several innovations that organizations need to adopt to remain competitive within this rapidly changing landscape. In addition, it examines Enterprise Systems, their components, architecture, and applications and enlightens readers on the benefits and shortcomings of implementing them. This book contains primary research on organizations, case studies, and benchmarks ERP implementation against international best practice.

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Book SynopsisEnterprise Systems have been used for many years to integrate technology with the management of an organization but rapid technological disruptions are now creating new challenges and opportunities that require urgent consideration. This book reappraises the implementation and management of Enterprise Systems in the digital age and investigates the vital link between business processes, information technology and the Internet for an organization’s competitive advantage and success.This book primarily focuses on the implementation, operation, management and integration of Enterprise Systems with fastemerging disruptive technologies such as blockchains, big data, cryptocurrencies, artificial intelligence, cloud computing, data mining and data analytics. These disruptive technologies are now becoming mainstream and the book proposes several innovations that organizations need to adopt to remain competitive within this rapidly changing landscape. In addition, it examines Enterprise Systems, their components, architecture, and applications and enlightens readers on the benefits and shortcomings of implementing them. This book contains primary research on organizations, case studies, and benchmarks ERP implementation against international best practice.

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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.

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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.

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Book SynopsisThe life cycle of companies and enterprises, at present, is short-lived due to rapid social and technological changes. Despite the growing awareness on the importance of knowledge management (KM) among academic researchers, it is still not widely practiced in industry. Why is this? Most KM programs emphasize the importance of capturing, retaining, and sharing organisational knowledge amongst their stakeholders. The beneficial effect of these programs is rarely felt immediately, which often results in senior management avoiding prioritising KM initiatives. To overcome this hurdle in implementing KM an approach that includes the assessment of knowledge risk factors and the disastrous effect on the daily operation of the company is explored. This book is the first attempt of its kind to provide a pragmatic view to launch knowledge risk management at the grassroot level, with steps by steps on what should be the mission and practical skills needed for a KM practitioner. Another surprise of this book is the numerous cases, examples and data that are brough about from the real business world. For business practitioners, KM researchers and those in HR, risk management, management accounting and Leadership this work is a must for expanding their understanding of Knowledge Management and knowledge risks.Table of ContentsChapter 1. IntroductionChapter 2. Assessment of Knowledge Risks Chapter 3. Intellectual Capital Charting, Accounting and Risks Chapter 4. Knowledge Audit Chapter 5. Knowledge Elicitation for Unstructured Business Process Chapter 6. Building a Learning Organization Chapter 7. KM Implementation Chapter 8. Measuring Corporate Performance

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Book SynopsisAcclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.Table of ContentsPREFACE xi Gérald KEMBELLEC, Ghislaine CHARTRON and Imad SALEH CHAPTER 1. GENERAL INTRODUCTION TO RECOMMENDER SYSTEMS 1 Ghislaine CHARTRON and Gérald KEMBELLEC 1.1. Putting it into perspective 1 1.2. An interdisciplinary subject 2 1.3. The fundamentals of algorithms 4 1.3.1. Collaborative filtering 4 1.3.2. Content filtering 7 1.3.3. Hybrid methods 9 1.3.4. Conclusion on historical recommendation models 11 1.4. Content offers and recommender systems 11 1.4.1. Culture and recommender systems 11 1.4.2. Recommender systems and the e-commerce of content 16 1.4.3. The behavior of users 18 1.5. Current issues 19 1.6. Bibliography 19 CHAPTER 2. UNDERSTANDING USERS’ EXPECTATIONS FOR RECOMMENDER SYSTEMS: THE CASE OF SOCIAL MEDIA 25 Jean-Claude DOMENGET and Alexandre COUTANT 2.1. Introduction: the omnipresence of recommender systems 25 2.2. The social approach to prescription 27 2.2.1. The theory of the prescription and online interactions 27 2.2.2. Conditions for recognition of the prescription 29 2.2.3. The specificities of social media 30 2.3. Users who do not focus on the prescriptions of platforms 31 2.3.1. Facebook: the link, the type of activity and the context 32 2.3.2. Twitter: prescription between peers and explanation of prescription 38 2.3.3. Conditions for the recognition of a prescription: announcement and enunciation 44 2.4. A guide for considering recommender systems adapted to different forms of social media 45 2.5. Conclusion 48 2.6. Bibliography 49 CHAPTER 3. RECOMMENDER SYSTEMS AND SOCIAL NETWORKS: WHAT ARE THE IMPLICATIONS FOR DIGITAL MARKETING? 53 Maria MERCANTI-GUÉRIN 3.1. Social recommendations: an ancient practice revived by the digital age 54 3.1.1. Recommendations: a difficult management for brands 55 3.1.2. Internet recommendations: social presence and personalized recommendations 55 3.2. Social recommendations: how are they used for e-commerce? 58 3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites 58 3.2.2. Recommender systems used by social networks: from e-commerce to social commerce 59 3.3. Conclusion 66 3.4. Bibliography 68 CHAPTER 4. RECOMMENDER SYSTEMS AND DIVERSITY: TAKING ADVANTAGE OF THE LONG TAIL AND THE DIVERSITY OF RECOMMENDATION LISTS 71 Muriel FOULONNEAU, Valentin GROUÈS, Yannick NAUDET and Max CHEVALIER 4.1. The stakes associated with diversity within recommender systems 72 4.1.1. Individual diversity or the individual perception of diversity 73 4.1.2. The stakes and impacts of aggregate diversity 74 4.2. Recommendation algorithms and diversity: trends, evaluation and optimization 77 4.2.1. The tendency for recommendation algorithms to focus on the head 78 4.2.2. The evaluation of diversity in recommender systems 80 4.2.3. Recommendation algorithms which favor individual diversity 81 4.2.4. Recommendation algorithms which favor aggregate diversity 81 4.2.5. The shift toward user-centered diversity approaches 82 4.3. Conclusion and new directions 85 4.4. Bibliography 87 CHAPTER 5. ISONTRE: INTELLIGENT TRANSFORMER OF SOCIAL NETWORKS INTO A RECOMMENDATION ENGINE ENVIRONMENT 93 Rana CHAMSI ABU QUBA, Salima HASSAS, Usama FAYYAD, Hammam CHAMSI and Christine GERTOSIO 5.1. Summary 93 5.2. Introduction 94 5.3. Latest developments, definition and history 97 5.3.1. Collaborative filtering techniques 97 5.3.2. General use social networks: what do they contain? 97 5.3.3. Social recommendation 99 5.3.4. The recommendation of concepts 100 5.4. iSoNTRE 101 5.4.1. iSoNTRE: transformer of social networks 102 5.4.2. iSoNTRE: the core of recommendation 107 5.5. Experiments 110 5.5.1. The preparation of data 110 5.5.2. Testing methodology 110 5.5.3. The creation of avatars 111 5.5.4. Results 112 5.5.5. Discussion 113 5.6. Conclusion 114 5.7. Bibliography 115 CHAPTER 6. A TWO-LEVEL RECOMMENDATION APPROACH FOR DOCUMENT SEARCH 119 Manel HMIMIDA and Rushed KANAWATI 6.1. Introduction 119 6.2. Tag recommendation: a brief state of the art 120 6.3. The hypertagging system 122 6.3.1. Metadata 122 6.3.2. Architecture 123 6.4. Recommendation approach 124 6.4.1. Presentation 124 6.4.2. Recommendation algorithm 126 6.5. Evaluation 127 6.5.1. Generation of facets 127 6.5.2. Generation of association rules 129 6.5.3. Evaluation of recommendation rules 130 6.6. Conclusion 131 6.7. Bibliography 132 CHAPTER 7. COMBINING CONFIGURATION AND RECOMMENDATION TO ENABLE AN INTERACTIVE GUIDANCE OF PRODUCT LINE CONFIGURATION 135 Raouia TRIKI , Raúl MAZO and Camille SALINESI 7.1. Introduction 135 7.2. Context 137 7.2.1. Configuration 137 7.2.2. Recommendation 139 7.2.3. Obstacles and challenges of interactive PL configuration 141 7.3. Overview of the proposed approach 142 7.4. Preliminary evaluation 148 7.5. Discussion and related work 148 7.5.1. Recommendation techniques 148 7.6. Conclusion and future work 151 7.7. Bibliography 151 CHAPTER 8. SEMIO-COGNITIVE SPACES: THE FRONTIER OF RECOMMENDER SYSTEMS 157 Hakim HACHOUR, Samuel SZONIECKY and Safia ABOUAD 8.1. Introduction 157 8.2. Latest developments: finalized activities, recommender systems and the relevance of information 159 8.2.1. Cognitive dynamics of finalized activities 159 8.2.2. The foundations of recommender systems 161 8.2.3. What information relevance? 166 8.3. Observable interests for decision theory: a combination of content-based, collaboration based and knowledge-based recommendations 169 8.3.1. Methodology: meta-analysis and modeling of the process 169 8.3.2. Analysis and modeling of a macro-process for responding to a call for R&D projects 171 8.3.3. Analysis and model of a socio-organizational tool for the management of customer complaints 173 8.4. Discussion and conclusions 177 8.4.1. Discussion: the performance of the filtering methods and semio-cognitive criteria for relevance 177 8.5. Conclusions: recommender systems linked to finalized activities 181 8.5.1. The localization of activities and geographical information systems: a new kind of data 182 8.5.2. Transparency of the use of personal data, data protection and ownership 183 8.6. Acknowledgments 185 8.7. Bibliography 185 CHAPTER 9. THE FRENCH-SPEAKING LITERARY PRESCRIPTION MARKET IN NETWORKS 191 Louis WIART 9.1. Introduction 191 9.2. The economy of prescription 193 9.2.1. The notion of prescription 193 9.2.2. From the advisors market to the prescription market 194 9.3. Methodology 196 9.4. The competitive structure of the market of online social networks of readers 197 9.4.1. Pure player networks and the audience strategy 199 9.4.2. Amateur networks and the survival strategy 201 9.4.3. Backed networks and the hybridization strategy 202 9.5. The organization of prescription 204 9.5.1. Social prescription 205 9.5.2. Editorial prescription 206 9.5.3. Algorithmic prescription 207 9.6. Conclusion: what legitimacy for literary prescription? 208 9.7. Appendix: list of interviews undertaken 210 9.8. Bibliography 210 CHAPTER 10. PRESENTATION OF OFFERED SERVICES: BABELIO, A RECOMMENDATION ENGINE DEDICATED TO BOOKS 213 Vassil STEFANOV, Guillaume TEISSEIRE and Pierre FRÉMAUX 10.1. Introduction 213 10.2. The problem of qualitative pertinence 216 10.3. The problem of quantitative pertinence 217 10.4. Balancing recall and precision 217 10.5. The issue of sparse data 218 10.6. Performance and scalability 218 10.7. A few issues specific to books 219 CHAPTER 11. PRESENTATION OF THE OFFER OF SERVICES: NOMAO, RECOMMENDER SYSTEMS AND INFORMATION SEARCH 221 Estelle DELPECH, Laurent CANDILLIER and Étienne CHAI 11.1. Introduction: the actors of Internet recommendation 221 11.2. Approaches to recommendation 222 11.3. Nomao: a local outlets search and recommendation engine 223 11.3.1. Popularity score 223 11.3.2. Affinity score 224 11.3.3. Social recommendation 225 11.4. Prospects: the move toward interactive recommender systems 225 11.5. Appendix 226 LIST OF AUTHORS 227 INDEX 231

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Book SynopsisThis book contains the thoroughly refereed technical papers presented in eight workshops collocated with the International Conference on Software Technologies: Applications and Foundations, STAF 2018, held in Toulouse, France, in June 2018. The 65 full papers presented were carefully reviewed and selected from 120 submissions. The events whose papers are included in this volume are: CoSim-CPS 2018: 2nd International Workshop on Formal Co-Simulation of Cyber-Physical Systems DataMod 2018: 7th International Symposium From Data to Models and Back FMIS 2018: 7th International Workshop on Formal Methods for Interactive Systems FOCLASA 2018: 16th International Workshop on Foundations of Coordination Languages and Self-adaptative Systems GCM 2018: 9th International Workshop on Graph Computation Models MDE@DeRun 2018: 1st International Workshop on Model-Driven Engineering for Design-Runtime Interaction in Complex Systems MSE 2018: 3rd International Workshop on Microservices: Science and Engineering SecureMDE 2018: 1st International Workshop on Security for and by Model-Driven Engineering Table of ContentsFormal Co-Simulation of Cyber-Physical Systems (CoSim-CPS).- From Data to Models and Back (DataMod).- Formal Methods for Interactive Systems (FMIS).- Foundations of Coordination Languages and Self-adaptative Systems (FOCLASA).- Graph Computation Models (GCM).- Model-Driven Engineering for Design-Runtime Interaction in Complex Systems (MDE@DeRun).- Microservices: Science and Engineering (MSE).- Security for and by Model-Driven Engineering (MDE).

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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

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Book SynopsisThis open access book is about public open spaces, about people, and about the relationship between them and the role of technology in this relationship. It is about different approaches, methods, empirical studies, and concerns about a phenomenon that is increasingly being in the centre of sciences and strategies – the penetration of digital technologies in the urban space. As the main outcome of the CyberParks Project, this book aims at fostering the understanding about the current and future interactions of the nexus people, public spaces and technology. It addresses a wide range of challenges and multidisciplinary perspectives on emerging phenomena related to the penetration of technology in people’s lifestyles - affecting therefore the whole society, and with this, the production and use of public spaces. Cyberparks coined the term cyberpark to describe the mediated public space, that emerging type of urban spaces where nature and cybertechnologies blend together to generate hybrid experiences and enhance quality of life.Table of ContentsThe Unveiling Potential of Cyberparks.- Socio-Spatial Practices.- Programming and Activating Cyberparks.- Digital Hybrids - Between Tool and Methods.

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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.

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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.

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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.

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Book SynopsisThis book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources.Chapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema.org to specific domains and tasks.To illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction to knowledge graphs and their implementation. Table of ContentsIntroduction: What is a Knowledge Graph?.- How to build a Knowledge Graph.- How to use a Knowledge Graph.- Why we need Knowledge Graphs: Applications.- Conclusions.- References.- Appendix.- Index.

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Book SynopsisThis open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.Table of ContentsMultivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder.- Dual Sequential Variational Autoencoders for Fraud Detection.- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks.- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams.- GraphMDL: Graph Pattern Selection Based on Minimum Description Length.- Towards Content Sensitivity Analysis.- Gibbs Sampling Subjectively Interesting Tiles.- Even Faster Exact k-Means Clustering.- Ising-Based Consensus Clustering on Special Purpose Hardware.- Transfer Learning by Learning Projections from Target to Source.- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs.- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces.- Vouw: Geometric Pattern Mining Using the MDL Principle.- A Consensus Approach to Improve NMF Document Clustering.- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.- Widening for MDL-Based Retail Signature Discovery.- Addressing the Resolution Limit and the Field of View Limit in Community Mining.- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics.- Adversarial Attacks Hidden in Plain Sight.- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code.- Overlapping Hierarchical Clustering (OHC).- Digital Footprints of International Migration on Twitter.- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks.- A Late-Fusion Approach to Community Detection in Attributed Networks.- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction.- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.- Actionable Subgroup Discovery and Urban Farm Optimization.- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model.- Detection of Derivative Discontinuities in Observational Data.- Improving Prediction with Causal Probabilistic Variables.- DO-U-Net for Segmentation and Counting.- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media.- Event Recognition Based on Classification of Generated Image Captions.- Human-to-AI Coach: Improving Human Inputs to AI Systems.- Aleatoric and Epistemic Uncertainty with Random Forests.- Master your Metrics with Calibration.- Supervised Phrase-Boundary Embeddings.- Predicting Remaining Useful Life with Similarity-Based Priors.- Orometric Methods in Bounded Metric Data.- Interpretable Neuron Structuring with Graph Spectral Regularization.- Comparing the Preservation of Network Properties by Graph Embeddings.- Making Learners (More) Monotone.- Combining Machine Learning and Simulation to a Hybrid Modelling Approach.- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.- Angle-Based Crowding Degree Estimation for Many-Objective Optimization.

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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

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Book SynopsisThis open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions.Table of ContentsSupporting cross-domain system-level environmental and earth science.- ICT infrastructure for environmental and earth sciences.- Common challenges and requirements.- ENVRI reference model.- Reference model guided engineering.- Semantic and knowledge engineering using ENVRI RM.- Data curation and preservation.- Data cataloguing.- Data identification and citation.- Data processing.- Virtual infrastructure optimization.- Data provenance.- Metadata, semantic linking.- Authentication, Authorization, and Accounting.- Virtual research environment.- Case study: e.g., data subscriptions using elastic Cloud service.- Case study: e.g., D4Science: a VRE solution for RI.- Case study: LifeWatch.- Sustainability.- Future challenges.

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Book SynopsisThis textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail.The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified.The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.Table of ContentsPart I: Introduction to Visual Analytics in Data Science.- 1. Introduction to Visual Analytics by an Example.- 2. General Concepts.- 3. Principles of Interactive Visualisation.- 4. Computational Techniques in Visual Analytics.- Part II: Visual Analytics along the Data Science Workflow.- 5. Visual Analytics for Investigating and Processing Data.- 6. Visual Analytics for Understanding Multiple Attributes.- 7. Visual Analytics for Understanding Relationships between Entities.- 8. Visual Analytics for Understanding Temporal Distributions and Variations.- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation.- 10. Visual Analytics for Understanding Phenomena in Space and Time.- 11. Visual Analytics for Understanding Texts.- 12. Visual Analytics for Understanding Images and Video.- 13. Computational Modelling with Visual Analytics.- 14. Conclusion.

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Book SynopsisThis open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic.Table of ContentsThe New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows.- DBpedia Archivo - A Web-Scale Interface for Ontology Archiving under Consumer-oriented Aspects,. A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge.- Semantic Annotation, Representation and Linking of Survey Data.- QueDI: from Knowledge Graph Querying to Data Visualization.- EcoDaLo: Federating advertisement targeting with Linked Data.- MINDS: a translator to embed mathematical expressions inside SPARQL queries.- Integrating Historical Person Registers as Linked Open Data in the WarSampo Knowledge Graph.

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Book SynopsisThis open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. Table of Contents

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Book SynopsisThis book constitutes revised selected papers from the International Workshops held at the Second International Conference on Process Mining, ICPM 2020, which took place during October 4-9, 2020. The conference was planned to take place in Padua, Italy, but had to be held online due to the COVID-19 pandemic.The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 29 papers included in this volume were carefully reviewed and selected from 59 submissions. They stem from the following workshops: 1st International Workshop on Event Data and Behavioral Analytics (EDBA) 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20) 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 3rd International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 1st International Workshop on Trust and Privacy in Process Analytics (TPPA) Table of Contents1st International Workshop on Event Data and Behavioral Analytics (EDBA).- Visually Representing History Dependencies in Event Logs.- Analysis of Business Process Batching using Causal Event Models.- Process Procespecting to Improve Renewable Energy Interconnection Queues: A Case Study.- Automated Discovery of Process Models with True Concurrency and Inclusive Choices.- A Novel Approach to Discover Switch Behaviours in Process Mining.- Process Model Discovery from Sensor Event Data.- Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study.- 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM).- Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-based Approach.- Time Matters:Time-Aware LSTMs for Predictive Business Process Monitoring.- A preliminary study on the application of Reinforcement Learning for Predictive Process Monitoring.- An Alignment Cost-Based Classi cation of Log Traces Using Machine-Learning.- Improving the Extraction of Process Annotations from Text with Inter-Sentence Analysis.- Case2vec: Advances in Representation Learning for Business Processes.- Supervised Conformance Checking using Recurrent Neural Network Classifiers.- 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20).- Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events.- Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation.- OTOSO: Online Trace Ordering for Structural Overviews.- Performance Skyline: Inferring Process Performance Models from Interval Events.- 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2020).- Alignment Approximation for Process Trees.- Stochastic Process Discovery By Weight Estimation.- Graph-based Process Mining.- Third International Workshop on Process-Oriented Data Science for Healthcare (PODS4H).- A Process Mining approach to statistical analysis: application to a real-world advanced melanoma dataset.- Process Mining of Disease Trajectories in MIMIC-III: A Case Study.- The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study.- Process mining on the extended event log to analyse the system usage during healthcare processes (Case study: the GP Tab usage during chemotherapy treatments).- Process Mining on FHIR - An Open Standards-Based Process Analytics Suite for Healthcare.- Deriving a sophisticated clinical pathway based on patient conditions from electronic health record data.- Exploration on How Global Warming Affects Emergency Services.- 1st Workshop on Trust and Privacy in Process Analytics (TPPA).- Towards Quantifying Privacy in Process Mining.

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Book SynopsisThis open access State-of-the-Art Survey describes and documents the developments and results of the Once-Only Principle Project (TOOP). The Once-Only Principle (OOP) is part of the seven underlying principles of the eGovernment Action Plan 2016-2020. It aims to make the government more effective and to reduce administrative burdens by asking citizens and companies to provide certain standard information to the public authorities only once.The project was horizontal and policy-driven with the aim of showing that the implementation of OOP in a cross-border and cross-sector setting is feasible. The book summarizes the results of the project from policy, organizational, architectural, and technical points of view. Table of ContentsThe Once-Only Principle: A Matter of Trust.- Implementation of the 'once-only' principle in Europe – national approaches.- Drivers for and Barriers to the Cross-Border Implementation of the Once-Only Principle - Once-Only Principle Good Practices in Europe.- The Single Digital Gateway Regulation as an Enabler and Constraint of Once-Only in Europe.- Legal Basis and Regulatory Applications of the Once-Only Principle: the Italian Case.- TOOP Trust Architecture.- The Technical challenges in OOP application across the European Union and the TOOP OOP architecture.- Testing methodology for the TOOP pilots.- TOOP pilot experiences: challenges and achievements in implementing once-only in different domains and Member States.- Measuring the Impact of the Once Only Principle for Businesses Across Borders.- The Future of the Once-Only Principle in Europe.

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Book SynopsisThis book focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories. The book contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. In addition, both a website and a Github account are also maintained for the book. They contain the code, data, and notebooks for the case studies; a summary of all the stories shared by the librarians/faculty; and hyperlinks to open an interactive virtual RStudio/Jupyter Notebook environment. The interactive virtual environment runs case studies based on the R programming language for hands-on practice in the cloud without installing any software. From understanding different types and forms of data to case studies showing the application of each text mining approaches on data retrieved from various resources, this book is a must-read for all library professionals interested in text mining and its application in libraries. Additionally, this book will also be helpful to archivists, digital curators, or any other humanities and social science professionals who want to understand the basic theory behind text data, text mining, and various tools and techniques available to solve and visualize their research problems. Table of Contents1. The Computational Library.- 2. Text Data and Where to Find Them?.- 3. Text Pre-Processing.- 4. Topic Modeling.- 5. Network Text Analysis.- 6. Burst Detection.- 7. Sentiment Analysis.- 8. Predictive Modeling.- 9. Information Visualization.- 10. Tools and Techniques for Text Mining and Visualization.- 11. Text Data and Mining Ethics.

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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.

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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.

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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.

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Book SynopsisThis book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. To enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. In addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme.This book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. Researchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book. Table of ContentsOverview of Internet Access of Vehicular Networks.- Internet Access Modeling of Vehicular Internet Access.- V2X Interworking via Vehicular Internet Access.- Intelligent Link Management for Vehicular Internet Access.- Intelligent Networking enabled Vehicular Distributed Learning.- Conclusion and Future Works.

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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.

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Book SynopsisThis text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.Table of ContentsChapter 1. Introduction to Analytics and Data Science. Chapter 2. Data Types Structure & Data Preparation Process. Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance. Chapter 5. Decision Trees & Ensemble. Chapter 6. Regression Models. Chapter 7. Neural Networks. Chapter 8. Model Deployment.

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Book SynopsisThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. Table of ContentsOral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.

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Book SynopsisThis book bridges the gap between knowledge management and technology. It embraces the complete lifecycle of knowledge, information, and data from how knowledge flows through an organization to how end users want to handle it and experience it. Whether your intent is to design and implement a single technology or a complete collection of KM systems, this book provides the foundations necessary for success. It will help you understand your organization’s needs and opportunities, strategize and prioritize features and functions, design with the end user in mind, and finally build a system that your users will embrace and which will realize meaningful business value for your organization. The book is the culmination of the authors’ collective careers, a combined sixty years of experience doing exactly what is detailed in this book. Their guidance has been honed by their own successes and failures as well as many others they have researched in order to provide a comprehensive study on KM transformations and the technologies that help to enable them. They have successfully applied this knowledge as the founders and leaders of the world’s largest dedicated knowledge management consultancy, which runs these projects for many of the world’s most complex organizations. They are writing as practitioners directly to other practitioners with the intent to enable them to apply and benefit from their knowledge and experience.“Compelling reading for KM practitioners looking to ensure their technology decisions support their business and organizational objectives.” - Margot Brown, Director of Knowledge Management, World Bank Group "We are two years into our KM Transformation and if I’d had this book beforehand, it would have made the journey smoother and faster! This is a great playbook for how to plan, organize, and execute a KM transformation." - Stephanie Hill, Senior Director, Global Customer Services, PayPalTrade Review“This book … spans the crevasse between KM and IT and does so with considerable flair. … this is a very good overview of the importance of integrating KM and IT and should be on the desktop of all KM managers, especially in larger organisations with complex IT infrastructures. The experience of the authors is evident throughout and they write in an engaging style which makes for a very readable book.” (Martin White, intranetfocus.com, June 30, 2022)Table of Contents1. Knowledge Management Primer.- Part I: Knowledge Management Transformation Strategy and Planning.- 2. Assessing Your Organization’s KM Strengths and Weaknesses (Current State).- 3. Understanding Your Organization’s Future KM Needs (Target State).- 4. Creating the Target State Vision.- 5. Getting from Here to There (KM Transformation Roadmap).- Part II: Understanding KM Systems.- 6. Content Management Solutions.- 7. Collaboration Suites.- 8. Learning Management Systems.- 9. Enterprise Search.- 10. Taxonomy Management.- 11. Data Catalogs and Governance Tools.- 12. Text Analytics Tools.- 13. Graph Databases.- 14. KM as a Foundation for Enterprise Artificial Intelligence.- 15. Integration Patterns for KM Systems.- Part III: Running a KM Systems Project.- 16. Project Phases.- 17. Common KMS Project Challenges and Mistakes.- 18. Foundational Design Elements.- 19. Content.- 20. Operations and Iterative Improvements.- 21. Envisioning Success: Putting KM Solutions and Outcomes Together.

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Book SynopsisThis open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty.Table of ContentsPart I: Foundations and Context.- 1. The Evolution of Data Spaces.- 2. How to Build, Run, and Govern Data Spaces.- 3. International Data Spaces in a Nutshell.- 4. Role of Gaia-X in the European Data Space Ecosystem.- 5. Legal Aspects of IDS: Data Sovereignty—What Does It Imply?.- 6. Tokenomics: Decentralized Incentivization in the Context of Data Spaces.- Part II: Data Space Technologies.- 7. The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange.- 8. Data Usage Control.- 9. Building Trust in Data Spaces.- 10. Blockchain Technology and International Data Spaces.- 11. Federated Data Integration in Data Spaces.- 12. Semantic Integration and Interoperability.- 13. Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning.- 14. IDS as a Foundation for Open Data Ecosystems.- 15. Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure.- Part III: Use Cases and Data Ecosystems.- 16. Silicon Economy: Logistics as the Natural Data Ecosystem.- 17. Agricultural Data Space.- 18. Medical Data Spaces in Healthcare Data Ecosystems.- 19. Industrial Data Spaces.- 20. Energy Data Space.- 21. Mobility Data Space.- Part IV: Solutions and Applications.- 22. Data Sharing Spaces: The BDVA Perspective.- 23. Data Platform Solutions.- 24. FIWARE for Data Spaces.- 25. Sovereign Cloud Technologies for Scalable Data Spaces.- 26. Data Space Based on Mass Customization Model.- 27. Huawei and International Data Spaces.- International Collaboration Between Data Spaces and Carrier Networks.- 29. From Linear Supply Chains to Open Supply Ecosystems.- 30. Data Spaces: First Applications in Mobility and Industry.- 31. Competition, Security, and Transparency: Data in Connected Vehicles.- Data Space Functionality.- The Energy Data Space: The Path to a European Approach for Energy.

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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.

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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.

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Book SynopsisThis book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments.To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution of Ordinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most. Table of ContentsPart I: Numerical Techniques.- 1. Introduction.- 2. Numerical Algorithms.- 3. Statistics.- 4. Linear Algebra.- 5. N-Dimensional Arrays.- 6. Ordinary Differential Equations.- 7. Signal Processing.- Part II: Advanced Data Analysis Techniques.- 8. Algorithmic Differentiation.- 9. Optimisation.- 10. Regression.- 11. Neural Network.- 12. Vector Space Modelling.- Part III: Use Cases.- 13. Case Study: Image Recognition.- 14. Case Study: Instance Segmentation.- 15. Case Study: Neural Style Transfer.- 16. Case Study: Recommender System.

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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.

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Book SynopsisFeedback control systems is an important course in aerospace engineering, chemical engineering, electrical engineering, mechanical engineering, and mechatronics engineering, to name just a few. Feedback control systems improve the system's behavior so the desired response can be acheived. The first course on control engineering deals with Continuous Time (CT) Linear Time Invariant (LTI) systems. Plenty of good textbooks on the subject are available on the market, so there is no need to add one more. This book does not focus on the control engineering theories as it is assumed that the reader is familiar with them, i.e., took/takes a course on control engineering, and now wants to learn the applications of MATLAB® in control engineering. The focus of this book is control engineering applications of MATLAB® for a first course on control engineering.Table of ContentsPreface.- Acknowledgments.- Introduction to MATLAB®.- Commonly Used Commands in Analysis of Control Systems.- Introduction to Simulink®.- Controller Design in MATLAB®.- Introduction to System Identification Toolbox™.- References.- Authors' Biographies.

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Book SynopsisA lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people worldwide.A core challenge presents itself: How can one efficiently and effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining applications? In this book, we investigated one promising paradigm for representing unstructured text, that is, through automatically identifying high-quality phrases from innumerable documents. In contrast to a list of frequent n-grams without proper filtering, users are often more interested in results based on variable-length phrases with certain semantics such as scientific concepts, organizations, slogans, and so on. We propose new principles and powerful methodologies to achieve this goal, from the scenario where a user can provide meaningful guidance to a fully automated setting through distant learning. This book also introduces applications enabled by the mined phrases and points out some promising research directions.Table of ContentsAcknowledgments.- Introduction.- Quality Phrase Mining with User Guidance.- Automated Quality Phrase Mining.- Phrase Mining Applications.- Bibliography.- Authors' Biographies .

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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

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