Artificial intelligence (AI) Books

Book SynopsisThis book constitutes the proceedings of the 18th International Conference on Unconventional Computation and Natural Computation, UCNC 2019, held in Tokyo, Japan, in June 2019.The 19 full papers presented were carefully reviewed and selected from 32 submissions. The papers cover topics such as hypercomputation; chaos and dynamical systems based computing; granular, fuzzy and rough computing; mechanical computing; cellular, evolutionary, molecular, neural, and quantum computing; membrane computing; amorphous computing, swarm intelligence; artificial immune systems; physics of computation; chemical computation; evolving hardware; the computational nature of self-assembly, developmental processes, bacterial communication, and brain processes.Table of ContentsInvited Paper.- Co-designing the computational model and the computing substrate.- Contributed Papers.- Generalized Membrane Systems with Dynamical Structure, Petri Nets, and Multiset Approximation Spaces.- Quantum Dual Adversary for Hidden Subgroups and Beyond.- Further Properties of Self-assembly by Hairpin Formation.- The Role of Structure and Complexity on Reservoir Computing Quality.- Lindenmayer Systems and Global Transformations.- Swarm-based multiset rewriting computing models.- DNA Origami Words and Rewriting Systems.- Computational Limitations of Affine Automata.- An Exponentially Growing Nubot System Without State Changes.- Impossibility of Sufficiently Simple Chemical Reaction Network Implementations in DNA Strand Displacement.- Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGs.- Viewing rate-based neurons as biophysical conductance outputting models.- The Lyapunov Exponents of Reversible Cellular Automata Are Uncomputable.- Geometric Tiles and Powers and Limitations of Geometric Hindrance in Self-Assembly.- DNA Computing Units Based on Fractional Coding.- The role of the representational entity in physical computing.- OIM: Oscillator-based Ising Machines for Solving Combinatorial Optimisation Problems.- Relativizations of Nonuniform Quantum Finite Automata Families.- Self-stabilizing Gellular Automata.

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Book SynopsisThis book constitutes the refereed proceedings of the 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019, held in Rome, Italy, in June 2019. The 41 full papers presented in this volume were carefully reviewed and selected from 206 submissions. The book also contains one invited talk in full paper length. The papers were organized in topical sections named: information system engineering; requirements and modeling; data modeling and analysis; business process modeling and engineering; information system security; and learning and mining in information systems. Abstracts on the CAiSE 2019 tutorials can be found in the back matter of the volume. Table of ContentsInvited Talk.- Direct and reverse rewriting in data interoperability.- Information System Engineering.- Efficient Engineering Data Exchange in Multi-Disciplinary Systems Engineering Enterprises.- Bing-CF-IDF+: A Semantics-Driven News Recommender System.- Methodological Framework to Guide the Development of Continual Evolution Methods.- Inter-organizational integration in the AEC/FM industry: Exploring the "addressed" and "unaddressed" information exchange needs between stakeholders.- A Lightweight Framework for Multi-Device Integration and Multi-Sensor Fusion to Explore Driver Distraction.- Exhaustive Simulation and Test Generation Using fUML Activity Diagrams.- A Block-Free Distributed Ledger for P2P Energy Trading: Case with IOTA.- Profile Reconciliation through Dynamic Activities across Social Networks.- Requirements and Modeling.- Towards an Ontology-based Approach for Eliciting Possible Solutions to Non-Functional Requirements.- Using a Modelling Language to Describe the Quality of Life Goals of People Living with Dementia.- Multi-Platform Chatbot Modeling and Deployment with the Jarvis Framework.- Information Systems Modeling: Language, Verification, and Tool Support.- Expert2Vec: Experts Representation in Community Question Answering for Question Routing.- A Pattern Language for Value Modeling in ArchiMate.- Paving Ontological Foundation for Social Engineering Analysis.- Improving Traceability Links Recovery in Process Models through an Ontological Expansion of Requirements.- Requirements Engineering for Cyber Physical Production Systems.- Data modeling and Analysis.- A Fourth Normal Form for Uncertain Data.- Revealing the Conceptual Schemas of RDF Datasets.- Modeling and In-Database Management of Relational, Data-Aware Processes.- D2IA: Stream Analytics on User-Defined Event Intervals.- Business Process Modeling and Engineering.- Extracting Declarative Process Models from Natural Language.- From Process Models to Chatbots.- Dynamic Role Binding in Blockchain-Based Collaborative Business Processes.- 3D virtual world BPM training systems: process gateway experimental results.- Deriving and Combining Mixed Graphs from Regulatory Documents Based on Constraint Relations.- A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle.- Generation and Transformation of Compliant Process Collaboration Models to BPMN.- GameOfFlows: Process Instance Adaptation in Complex, Dynamic and Potentially Adversarial Domains.- Information System Security.- Security Vulnerability Information Service with Natural Language Query Support.- Automated Interpretation and Integration of Security Tools Using Semantic Knowledge.- An Assessment Model for Continuous Security Compliance in Large Scale Agile Environments.- Learning and Mining in Information Systems.- Proactive Process Adaptation using Deep Learning Ensembles.- Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models.- Efficient Discovery of Compact Maximal Behavioral Patterns from Event Logs.- Discovering Responsibilities with Dynamic Condition Response Graphs.- Fifty Shades of Green: How Informative is a Compliant Process Trace.- Solution Patterns for Machine Learning.- Managing and Simplifying Cognitive Business Operations using Process Architecture Models.- A Constraint Mining Approach to Support Monitoring Cyber-Physical Systems.- Behavior-Derived Variability Analysis: Mining Views for Comparison and Evaluation.

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Book SynopsisThis book discusses the computational geometry, topology and physics of digital images and video frame sequences. This trio of computational approaches encompasses the study of shape complexes, optical vortex nerves and proximities embedded in triangulated video frames and single images, while computational geometry focuses on the geometric structures that infuse triangulated visual scenes. The book first addresses the topology of cellular complexes to provide a basis for an introductory study of the computational topology of visual scenes, exploring the fabric, shapes and structures typically found in visual scenes. The book then examines the inherent geometry and topology of visual scenes, and the fine structure of light and light caustics of visual scenes, which bring into play catastrophe theory and the appearance of light caustic folds and cusps. Following on from this, the book introduces optical vortex nerves in triangulated digital images. In this context, computational physics is synonymous with the study of the fine structure of light choreographed in video frames. This choreography appears as a sequence of snapshots of light reflected and refracted from surface shapes, providing a solid foundation for detecting, analyzing and classifying visual scene shapes.Trade Review“The book is presented in a very accessible fashion. … The book is suitable for graduate students and researchers interested in computational geometry and computer vision. Moreover, it can be used as a professional reference.” (Krzysztof Gdawiec, zbMATH 1437.68005, 2020)Table of ContentsComputational Geometry, Topology and Physics of Visual Scenes.- Cell Complexes, Filaments, Vortexes and Shapes Within a Shape.- Shape Fingerprints, Geodesic Trails and Free Abelian Groups on Skeletal Vortexes.- What Nerve Complexes Tell Us About Image Shapes.

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Book SynopsisArtificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. This book illustrates the importance of AI in an era of digitalization. It introduces the foundations of AI and explains its benefits and challenges for companies and entire industries. In this regard, AI is approached not just as yet another technology, but as a fundamental innovation, which will spread into all areas of the economy and life, and will disrupt business processes and business models in the years to come. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI. In a series of best-practice cases, the book subsequently highlights a broad range of sectors and industries, from production to services; from customer service to marketing and sales; and in industries like retail, health care, energy, transportation and many more. In closing, a dedicated chapter outlines a roadmap for a specific corporate AI journey.No one can ignore intensive work with AI today - neither as a private person, let alone as a top performer in companies. This book offers a thorough, carefully crafted, and easy to understand entry into the field of AI. The central terms used in the AI ​​context are given a very good explanation. In addition, a number of cases show what AI can do today and where the journey is heading. An important book that you should not miss!Professor Dr. Harley KrohmerUniversity of Bern"Inspiring, thought provoking and comprehensive, this book is wittingly designed to be a catalyst for your individual and corporate AI journey.”Avo Schönbohm, Professor at the Berlin School of Economics and Law, Enterprise Game Designer at LUDEO and Business PunkTable of ContentsWhat is Artificial Intelligence and how to exploit it?.- Basics and drivers of Artificial Intelligence.- Fields of application of Artificial Intelligence – production area.- Fields of application of Artificial Intelligence – customer service, marketing and sales.- Fields of application of Artificial Intelligence – retail, service and maintenance sector.- Fields of application of Artificial Intelligence – health care, education and human resource management.- Fields of application of Artificial Intelligence – energy sector, smart home, mobility and transport.- Fields of application of Artificial Intelligence – financial services and creative sector.- Fields of application of Artificial Intelligence – security sector and military sector.- AI challenge – how Artificial Intelligence can be anchored in a company.- Outlook.

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Book SynopsisThe two-volume set LNCS 11751 and 11752 constitutes the refereed proceedings of the 20th International Conference on Image Analysis and Processing, ICIAP 2019, held in Trento, Italy, in September 2019. The 117 papers presented were carefully reviewed and selected from 207 submissions. The papers cover both classic and the most recent trends in image processing, computer vision, and pattern recognition, addressing both theoretical and applicative aspects. They are organized in the following topical sections: Video Analysis and Understanding; Pattern Recognition and Machine Learning; Deep Learning; Multiview Geometry and 3D Computer Vision; Image Analysis, Detection and Recognition; Multimedia; Biomedical and Assistive Technology; Digital Forensics; Image processing for Cultural Heritage.Table of ContentsVideo Analysis and Understanding.- Pattern Recognition and Machine Learning.- Deep Learning.- Multiview Geometry and 3D Computer Vision.- Image Analysis, Detection and Recognition.- Multimedia.- Biomedical and Assistive Technology.- Digital Forensics.- Image processing for Cultural Heritage.

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Book SynopsisThis book constitutes the proceedings of the 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, held in Olslo, Norway, in September 2019. The 16 revised full papers,12 short papers and 18 poster papers presented were carefully reviewed and selected from 75 submissions. The general theme of TPDL 2019 was Connecting with Communities and so the papers attempt to facilitate establishing connections and convergences between diverse research communities such as Digital Humanities, Information Sciences and others that could benefit from ecosystems offered by digital libraries and repositories. To become especially useful to the diverse research and practitioner communities digital libraries need to consider special needs and requirements for effective data utilization, management and exploitation.Table of ContentsConer: A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications.- An unsupervised method for concept association analysis in text collections.- Linking Semantic Fingerprints of Literature.- Learning to Rank Claim-Evidence Pairs to Assist Scientific-Based Argumentation.- The OpenAIRE Research Community Dashboard: on Blending Scientific Workows and Scientific Publishing.- A Framework for Citing Nanopublications.- Analysis of Transaction Logs from National Museums Liverpool.- Knowledge Graph Implementation of Archival Descriptions through CIDOC-CRM.- Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data.- Who is Mona L.? Identifying Mentions of Artworks in Historical Archives.- Gatekeeper: Quantifying the Impacts of Service to the Scientific Community.- A Study on the Readability of Scientific Publications.- Interdisciplinary Collaborations in the Brazilian Scientific Community.- Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space.- The Memento Tracer Framework: Balancing Quality and Scalability for Web Archiving.- The immigration dilemma; Legal, ethical and practical issues in creating a living, growing archive.- Segmenting User Sessions in Search Engine Query Logs Leveraging Word Embeddings.- A Human-friendly Query Generation Frontend for a Scientific Events Knowledge Graph.- User Interface for Interactive Scientific Publications: A Design Case Study.- Stable Word-clouds for Visualising Text-changes over Time.- A Hierarchical Label Network for Multi-Label EuroVoc Classification of Legislative Contents.- Can Language Inference Support Metadata Generation?.- Information Governance Maturity Assessment using Enterprise Architecture Model Analysis and Description Logics.- Finding Documents Related to Taiwan in the Veritable Records of Qing Using Relevance Feedback.- Fake News Detection with the New German Dataset "GermanFakeNC".- The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles.- Non-parametric Subject Prediction.- Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction.- Towards Serendipitous Research Paper Recommender using Tweets and Diversification.- Enriching the Cultural Heritage Metadata Using Historical Events: a Graph-Based Representation.- Open Research Knowledge Graph: A System Walkthrough.- The Biodiversity Heritage Library: Unveiling a World of Knowledge About Life on Earth.- Clipping the Page { Automatic Article Detection and Marking Software in Production of Newspaper Clippings in a Digitized Historical Journalistic Collection.- Document recommendations in Slovenian academic digital libraries.- An Evaluation of the Effect of Reference Strings and Segmentation on Citation Matching.- A la Carte: Turning Historical Menu into Menu Network.- Semantic Representation of Scientific Publications.- Determining How Citations Are Used in Citation Contexts.- Dendro: a FAIR, open-source data sharing platform.- User's Behavior in Digital Libraries: Process Mining Exploration.- SciTo Trends: Visualising Scientific Topic Trends.- The-Shelf Semantic Author Name Disambiguation for Bibliographic Data Bases.- Rending Behaviour in Educational Search.- Qatar Digital Library as a Platform for Digital Repatriation of Qatar's Cultural Heritage.- Correcting and redesigning metadata for the excavation of an archaeological site.- Topic Modelling vs Distant Supervision: A Comparative Evaluation based on the Classification of Parliamentary Enquiries.

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Book SynopsisThe two-volume set of LNCS 11778 and 11779 constitutes the refereed proceedings of the 18th International Semantic Web Conference, ISWC 2019, held in Auckland, New Zealand, in October 2019. The ISWC conference is the premier international forum for the Semantic Web / Linked Data Community.The total of 74 full papers included in this volume was selected from 283 submissions. The conference is organized in three tracks: for the Research Track 42 full papers were selected from 194 submissions; the Resource Track contains 21 full papers, selected from 64 submissions; and the In-Use Track features 11 full papers which were selected from 25 submissions to this track.The chapter "The SEPSES knowledge graph: An integrated resource for cybersecurity" is open access under a CC BY 4.0 license at link.springer.com.Table of ContentsResources Track.- The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability.- VLog: A Rule Engine for Knowledge Graphs.- ArCo: the Italian Cultural Heritage Knowledge Graph.- Making Study Populations Visible through Knowledge Graphs.- LC-QuAD 2.0: A large dataset for complex question answering over Wikidata and DBpedia.- SEO: A Scientific Events Data Model.- DBpedia FlexiFusion - The Best of Wikipedia > Wikidata > Your Data.- The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data.- The RealEstateCore Ontology.- FoodKG: Semantics-Driven Knowledge Graph for Food Recommendation.- BTC-2019: The 2019 Billion Triple Challenge Dataset.- Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge.- The SEPSES knowledge graph: An integrated resource for cybersecurity.- SemanGit: A Linked Dataset from git.- Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources.- List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists.- A Scalable Framework for Quality Assessment of RDF Datasets.- QaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge Graphs.- Sparklify: A Scalable Software Component for Efficient evaluation of SPARQL queries over distributed RDF datasets.- ClaimsKG: A Knowledge Graph of Fact-Checked Claims.- CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison.- In-Use Track.- Semantically-enabled Optimization of Digital Marketing Campaigns.- An End-to-end Semantic Platform For Nutritional Diseases Management.- VLX-Stories: building an online Event Knowledge Base with Emerging Entity detection.- Personalized Knowledge Graphs for the Pharmaceutical Domain.- Use of OWL and Semantic Web Technologies at Pinterest.- An Assessment of Adoption and Quality of Linked Data in European Open Government Data.- Easy Web API Development with SPARQL Transformer.- Benefit graph extraction from healthcare policies.- Knowledge Graph Embedding for Ecotoxicological Effect Prediction.- Improving Editorial Workflow and Metadata Quality at Springer Nature.- A Pay-as-you-go Methodology to Design and Build Enterprise Knowledge Graphs from Relational Databases.

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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 addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies. Table of ContentsIntroduction.- Related Work.- Overview of Artificial Neural Networks (ANNs).- On the Exploration of Promising Analog IC Designs via ANNs.- ANNs as an Alternative for Automatic Analog IC Placement.- Conclusions.

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Book SynopsisThe two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

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Book SynopsisThis book constitutes the proceedings of the 16th International Conference on Distributed Computing and Internet Technology, ICDCIT 2020, held in Bhubaneswar, India, in January 2020. The 20 full and 3 short papers presented in this volume were carefully reviewed and selected from 110 submissions. In addition, the book included 6 invited papers. The contributions were organized in topical sections named: invited talks; concurrent and distributed systems modelling and verification; cloud and grid computing; social networks, machine learning and mobile networks; data processing and blockchain technology; and short papers. Table of ContentsInvited Talks.- Graph Analytics.- A distributed and trusted web of formal proofs.- Prosperos Books: A Distributed Architecture for AI.- Trust: Anthropomorphic Algorithmic.- A Very Gentle Introduction to Multiparty Session Types.- Constructing Knowledge Graph from Data Catalogues.- Concurrent and Distributed Systems Modelling and Verification.- Round-Message Trade-off in Distributed Steiner Tree Construction in the CON GEST model.- An Efficient Message Transmission and Verification Scheme for VANETs.- Generalised Dining Philosophers as Feedback Control.- Verifying Implicitly Quantified Modal Logic over dynamic networks of processes.- Cloud and Grid Computing.- Secure Content-based Image Retrieval using Combined Features in Cloud.- Design of a Scheduling Approach for Budget-deadline Constrained Applications in Heterogeneous Clouds.- Resource Scheduling for Tasks of a Workflow in Cloud Environment.- Bearing Fault Classification using Wavelet Energy and Autoencoder.- Social Networks, Machine Learning and Mobile Networks.- Community Detection in Social Networks using Deep Learning.- Multi-Winner Heterogeneous Spectrum Auction Mechanism for Channel Allocation in Cognitive Radio Networks.- A Hybrid Approach for Fake News Detection in Twitter based on User Features and Graph Embedding.- Online Context-Adaptive Energy-Aware Security Allocation in Mobile Devices: A Tale of Two Algorithms.- A Framework Towards Generalized Mid-term Energy Forecasting Model for Industrial Sector in Smart Grid.- An Online Low-Cost System for Air Quality Monitoring, Prediction, and Warning.-Word2vecs Distributed Word Representation for Hindi Word Sense Disambiguation.- Text Document Clustering using Community Discovery Approach.- Data Processing and Blockchain Technology.- An Efficient and Novel Buyer and Sellers Distributed Ledger Based Protocol Using Smart Contracts.- Distributed and Lazy Auditing of Outsourced Data HealthChain: A Secure Scalable Health Care Data Management System Using Blockchain.- HealthChain: A Secure Scalable Health Care Data Management System Using Blockchain.- Transcript Management Using Blockchain enabled Smart Contracts.- Short Papers.- Identifying Reduced Features based on IG-Threshold for DoS Attack Detection using PART.- Uniform Circle Formation by Swarm Robots Under Limited Visibility.- Histopathological Image Classification by Optimized Neural Network using IGSA.

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Book SynopsisThis book constitutes the thoroughly refereed proceedings of the 16th Italian Research Conference on Digital Libraries, IRCDL 2020, held in Bari, Italy, in January 2020.The 12 full papers and 6 short papers presented were carefully selected from 26 submissions. The papers are organized in topical sections on information retrieval, bid data and data science in DL; cultural heritage; open science. Table of ContentsInformation Retrieval.- Bid Data and Data Science in DL.- Cultural Heritage.- Open Science.

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Book SynopsisThis concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments. This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource adds value to this book.Table of ContentsFrom Adhoc to Agility: a General Framework.- Socio-cultural Aspects of Agility.- Agile Knowledge Management.- Agility at Scale.- Mastering Agility.- Developing and Fostering Smart Ecosystems.

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Book SynopsisThis book presents work on healthcare management and engineering using optimization and simulation methods and techniques. Specific topics covered in the contributed chapters include discrete-event simulation, patient admission scheduling, simulation-based emergency department control systems, patient transportation, cost function networks, hospital bed management, and operating theater scheduling. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.Table of ContentsA Two-Dimensional Categorization Scheme for Simulation-/Optimization-Based Decision Support in Hospitals Applied to Overall Bed Management in Interdependent Wards Under Flexibility.- Heuristics Based on the Hungarian Method for the Patient Admission Scheduling Problem.- A Bi-objective Algorithm for Robust Operating Theatre Scheduling.- Cost Function Networks to Solve Large Computational Protein Design Problems.- Modeling and Simulation in a Dialysis Center of the Hedi Chaker Hospital- Toward a Proactive and Reactive Simulation-Based Emergency Department Control System to Cope with Strain Situations.- A Decentralized Approach to the Home Healthcare Problem.- Wounded Transportation and Assignment to Hospital During Crises.- Carbon Footprints in Emergency Departments: A Simulation-Optimization Analysis.- The Effect of Risks on Discrete Event Simulation in Healthcare Systems.

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Book SynopsisThis book presents recent work that analyzes general issues of green logistics and smart cities. The contributed chapters consider operating models with important ecological, economic, and social objectives.The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.Table of ContentsThe Green-Vehicle Routing Problem: A Survey.- An Integrated Location-Inventory Routing Problem for ATMs in Banking Industry: A Green Approach.- Modelling a Future Routing Concept for Urban Air Mobility.- Putting the SC in SCORE: Solar Car Optimized Route Estimation and Smart Cities.- Evaluation and Prioritisation of Green Logistics and Transportation Practices Used in the Freight Transport Industry.- A Novel Hybrid Multi-objective Optimization Approach for Sustainable Delivery Systems with A Case Study in Izmir.- When Green Technology Meets Optimization Modelling: The Case of Routing Drones in Logistics, Agriculture and Healthcare.- Routing Electric Vehicles with Remote Servicing.

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Book SynopsisIn this book the author's theoretical framework builds on linguistic and psychological research, arguing that similar image-schematic notions should be grouped together into interconnected family hierarchies, with complexity increasing with regard to the addition of spatial and conceptual primitives. She introduces an image schema logic as a language to model image schemas, and she shows how the semantic content of image schemas can be used to improve computational concept invention. The book will be of value to researchers in artificial intelligence, cognitive science, psychology, and creativity.Table of ContentsCreating Concepts: Considerations from Psychology and Artificial Intelligence.- Image Schemas: Spatiotemporal Relationships Used as Conceptual Building Blocks.- Formal Structure: Image Schemas as Families of Theories.- Introducing ISLFOL: A Logical Language for Image Schemas.- Modelling Conceptualisations: Combining Image Schemas to Model Event Conceptualisations.- Generating Concepts: How Image Schemas Can Help Guide Computational.- Conceptual Blending.- Defining Concepts: Experiment on the Role of Image Schemas in Object Conceptualisation.- Identifying Image Schemas: Experiment Towards Automatic Image Schema Extraction.- Discussion and Conclusions.

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Book SynopsisChapter “Predictive Policing in 2025: A Scenario” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.Table of ContentsForeword; Lord Alex Carlile of Berriew CBE QC.- Rethinking Criminal Justice in Cyberspace: The EU E-evidence framework as a new model of cross-border cooperation in criminal matters; O. Sallavaci.- Policing in the era of AI and Smart Societies: austerity; legitimacy and blurring the line of consent; M. Manning, S.Agnew.- Behavioral Analytics; A Preventative Means for the Future of Policing; A. Daneshkhah et al.- Securing Transparency and Governance of Organ Supply Chain Through Blockchain; N. Chavez et al.- IoT and cloud forensic investigation guidelines; I. Mitchell et al.- Algorithms can predict domestic abuse, but should we let them?; M. Bland.- Tackling teen sexting - policing challenges when society and technology outpace legislation; E. Bond, A. Phippen.- Image Recognition in Child Sexual Exploitation Material - Capabilities, Ethics and Rights; A. Phippen, E. Bond.- Predictive policing in 2025: A scenario; K. Macnish et al.- Patterns in Policing; P. Cochrane, M.P. Pfeiffer.- Proposed Forensic Guidelines for the Investigation of Fake News; N. Omezi, H. Jahankhani.- Current Challenges of Modern-Day Domestic Abuse; J. Mayhew, H. Jahankhani.

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Book SynopsisThis book explores major bioethical issues emerging from the development and use of artificial intelligence in medical settings. The authors start by defining the past, present and future of artificial intelligence in medical settings and then proceed to address the resulting common and specific bioethical inquiries. The book discusses bioethical inquiries in two separate sets. The first set is comprised of ontological discussions mainly focusing on personhood and being an ethical agent of an artefact. The second set discusses bioethical issues resulting from the use of artificial intelligence. It focuses particularly on the area of artificial intelligence use in medicine and health services. It addresses the main challenges by considering fundamental principles of medical ethics, including confidentiality, privacy, compassion, veracity and fidelity. Finally, the authors discuss the ethical implications of involvement of artificial intelligence agents in patient care by expanding on communication skills in a case-based approach. The book is of great interest to ethicists, medical professionals, academicians, engineers and scientists working with artificial intelligence.Table of Contents1. Introduction.- 2. What is Artificial Intelligence?.- 2.1. Definitions.- 2.2. History.- 2.3. State of play and future prospects.- 3. Bioethical inquiries about artificial intelligence.- 3.1. Bioethical issues common to weak and strong artificial intelligence.- 3.2. Bioethical issues resulting from strong artificial intelligence.- 3.2.1. Ontological discussions.- 3.2.2. Consequential discussions.- 4. Medicine and artificial intelligence.- 4.1. Use of artificial in health services.- 4.2. Main challenges in medical ethics.- 4.2.1. Confidentiality and privacy.- 4.2.2. Compassion, veracity and fidelity.- 4.2.3. Communication skills and case based approach.- 5. Conclusion.

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Book SynopsisThis book is aimed at those who wish to understand more about the molecular basis of life and how life on earth may change in coming centuries. Readers of this book will gain knowledge of how life began on Earth, the natural processes that have led to the great diversity of biological organisms that exist today, recent research into the possibility of life on other planets, and how the future of life on earth faces unprecedented pressures from human-made activities. Readers will obtain a perspective on the potential risks of chemical or nuclear warfare, and the ever-increasing risks from human activities that are causing pollution and climate change with global heating. Readers will also learn about ongoing research efforts to generate “designer lifeforms” through synthetic biology and applications of artificial intelligence. The book makes an integrated, up-to-date, overview of topics often considered as separate fields. It should be valuable to students, teachers, and people who are concerned about the future of life.Table of ContentsIntroduction.- Early Ideas About the Origin of Life.- The Scientific View of the Origin of Life.- Biological Evolution.- Manipulated Evolution and Artificial Life.- Natural Risks to Life.- Human-made Risks from Nuclear and Chemical Warfare.- Human-made Risks and Climate Change with Global Heating.- Artificial Intelligence: Opportunity or Risk?- Life on Other Planets.- Outlook.

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Book SynopsisThis book constitutes the refereed proceedings of the 4th Computational Methods in Systems and Software 2020 (CoMeSySo 2020) proceedings. Software engineering, computer science and artificial intelligence are crucial topics for the research within an intelligent systems problem domain. The CoMeSySo 2020 conference is breaking the barriers, being held online. CoMeSySo 2020 intends to provide an international forum for the discussion of the latest high-quality research results.

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Book SynopsisThis book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.Table of ContentsPart I, Foundations.- AI Sculpture.- Make Me Learn.- Images and Sequences.- Why AI Works.- Learning to Sculpt.- Unleashing the Power of Generation.- The Road Most Rewarded.- The Classical World.- Part II, Applications.- To See is to Believe.- Read, Read, Read.- Lend Me Your Ear.- Create Your Shire and Rivendell.- Math to Code to Petaflops.- AI and Business.- Part III, Road Ahead.- Keep Marching on.- Benevolent AI for All.- Am I Looking at Myself?.- App. A, Solutions.- Further Reading.- Acronyms.- Glossary.- References.- Index.

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Book SynopsisThis open access book is a timely contribution in presenting recent issues, approaches, and results that are not only central to the highly interdisciplinary field of concept research but also particularly important to newly emergent paradigms and challenges. The contributors present a unique, holistic picture for the understanding and use of concepts from a wide range of fields including cognitive science, linguistics, philosophy, psychology, artificial intelligence, and computer science. The chapters focus on three distinct points of view that lie at the core of concept research: representation, learning, and application. The contributions present a combination of theoretical, experimental, computational, and applied methods that appeal to students and researchers working in these fields.Table of ContentsChapter 1. Introduction (Lucas Bechberger).- Chapter 2. The Geometric Structure of Word (Peter Gärdenfors).- Chapter 3. Aligning between Conceptual Systems Using Internal and External Information (Robert Goldstone).- Chapter 4. Lexical and Structural Divergences between WordNets of Different Languages (Christiane D. Fellbaum).- Chapter 5. Emergence of Grounded Communication and Concepts using Deep Reinforcement Learning (Michael Spranger).- Chapter 6. Prototypes, Theory, Trust: A Multi-Dimensional Model of Concepts And a Computational Approximation (David Schlangen).- Chapter 7. Machine Learning in Conceptual Spaces: Two Learning Processes (Lucas Bechberger).- Chapter 8. On the Evaluation of Conceptual Spaces: Qualitative and Quantitative Approaches (Hadi Banaee).- Chapter 9. Kind formation by Similarity (Helmar Gust).- Chapter 10. Theories about World Representations for the Internet of Things (Michael Färber).- Chapter 11. Effects of semantic specificity in action verb processing (Margit Scheibel).- Chapter 12. Evaluating Semantic CoCreation in Cognitive Representation Models (Stefan Schneider).- Chapter 13. Grounding Abstract Concepts in Action (Paola Vernillo).- Chapter 14. (José V. Hernández-Conde).- Chapter 15. Does the Activation of Motor Information Affect Semantic Processing? (Elisa Scerrati).

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Book SynopsisThis book presents comprehensive coverage of the latest advances in research into enabling machines to listen to and compose new music. It includes chapters introducing what we know about human musical intelligence and on how this knowledge can be simulated with AI. The development of interactive musical robots and emerging new approaches to AI-based musical creativity are also introduced, including brain–computer music interfaces, bio-processors and quantum computing.Artificial Intelligence (AI) technology permeates the music industry, from management systems for recording studios to recommendation systems for online commercialization of music through the Internet. Yet whereas AI for online music distribution is well advanced, this book focuses on a largely unexplored application: AI for creating the actual musical content.Table of ContentsPart I: Understanding Musical IntelligenceCognitive Neuroscience of Music Stefan Koelsh The Musical Brain Aniruddh Patel The Neuroscience of Musical Improvisation Psyche Loui Part II: Machine Perception and Analysis Machine Listening of Music Juan Pablo Bello Convolutional Neural Networks for Audio Spectrogram Representation Lonce Wyse Robot Musicianship Gil Weinberg Robot Understanding of Conductor Gestures Atsuo Takahishi Human-Robot Emotional Musical Interaction Massimiliano Zecca Machine Recognition of Musical Emotion Yi-Hsuan Yang and Homer H. Chen Optical Recognition of Music Notation Ana Bebelo Music Transcription: from Audio to Music Notation Emmanouil Benetos Machine Learning System for Harmonic Analysis of Music Tijl De Bie Machine Learning of Jazz Jon Gillick Artificial Intelligence Data Mining for Music Tao Li Machine Learning of Body Movement in Instrumental Music Performance Federico Visi Machine Learning of Orchestral Conductor’s Baton Movements Donald G. Dansereau Machine Recognition of Music Emotion Yi-Hsuan Yang and Homer H. Chen Part III: Machine Composing and Performance Flow Machines Francois Pachet Machine Improvisation Shlomo Dubnov Artificial Agents for Collaborative Free Improvisation Adam Linson Symbolic Computational Creativity David Cope Tracing the Compositional Process Hanns Holger Rutz Composing with Intelligent Interactive Musical Agents Marcelo Gimenes Creating Music Autonomously with Evolutionary Algorithms Francisco J. Vico Constraint-Solving System for Generating Music Scores Orjan Sandred Constraint Modeling of Music Theories Torsten Anders Machine Learning Algorithm for Musical Composition Rebecca Fierbink An Artificial Intelligence Approach to Concatenative Sound Synthesis Noris Modh Norowi Sound Synthesis with Deep Neural Networks Jesse Engel On Computer-Aided Orchestration Marcelo Caetano Gesture Data in Musical Composition Marlon Schumacher and Marcelo Wanderley Surveying Systems for Expressive Musical Performance by Computer Alexis Kirke Computer-Assisted Analysis of Musical Interpretation Gerhard Widmer Gesture Recognition in Interactive Music Performance Dan Overholt Designing Constraints for Composition and Performance with Computers Thor Magnusson Part IV: Emerging Developments in Musical AI Machine Learning for Brain-Computer Music Interfacing Eduardo R. Miranda and Satvik Venkatesh Biological Neural Networks Synthesiser Guy Ben-Ary Sound and Music Biocomputing Eduardo R. Miranda and Edward Braund Musical Machine Learning with Biomemristors Eduardo R. Miranda and Edward Braund The Dawn of Quantum Computer Music Eduardo R. Miranda

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Book SynopsisThis book contains a selection of the best papers of the 32nd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2020, held in Leiden, The Netherlands, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 12 papers presented in this volume were carefully reviewed and selected from 41 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.The chapter 11 is published open access under a CC BY license (Creative Commons Attribution 4.0 International License) Chapter “Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.. Table of ContentsEvaluating the Robustness of Question-Answering Models to Paraphrased Questions.- FlipOut: Uncovering Redundant Weights via Sign Flipping.- Evolving Virtual Embodied Agents using External Artifact Evaluations.- Continuous Surrogate-based Optimization Algorithms are Well-suitedfor Expensive Discrete Problems.- Comparing Correction Methods to Reduce Misclassification Bias.- A Spiking Neuron Implementation of Genetic Algorithms for Optimization.- Solving Hofstadter's Analogies using Structural Information Theory.- A Semantic Tableau Method for Argument Construction.- `Thy algorithm shalt not bear false witness': An Evaluation of Multiclass Debiasing Methods on Word Embeddings.- An Intelligent Tree Planning Approach using Location-based Social Networks Data.- Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning.- Swarm Construction Coordinated through the Building Material.

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Book SynopsisThe three-volume set LNCS 12762, 12763, and 12764 constitutes the refereed proceedings of the Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021, which took place virtually in July 2021.The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The 139 papers included in this HCI 2021 proceedings were organized in topical sections as follows: Part I, Theory, Methods and Tools: HCI theory, education and practice; UX evaluation methods, techniques and tools; emotional and persuasive design; and emotions and cognition in HCI Part II, Interaction Techniques and Novel Applications: Novel interaction techniques; human-robot interaction; digital wellbeing; and HCI in surgery Part III, Design and User Experience Case Studies: Design case studies; user experience and technology acceptance studies; and HCI, social distancing, information, communication and workTable of ContentsDesign Case Studies.- Graphic Representations of Spoken Interactions from Journalistic Data: Persuasion and Negotiations.- A Study on Universal Design of Musical Performance System.- Developing a Knowledge-based System for Lean Communications between Designers and Clients.- Learn & Share to Control Your Household Pests: Designing a Communication Based App to Bridge the Gap between Local Guides & The New Users Looking for a Reliable & Affordable Pest Control Solutions.- Developing User Interface Design Strategy to Improve Media Credibility of Mobile Portal News.- Elderly-Centered Design: A New Numeric Typeface for Increased Legibility.- Research on Interactive Experience Design of Peripheral Visual Interface of Autonomous Vehicle.- Human-Centered Design Reflections on Providing Feedback to Primary Care Physicians.- Interaction with Objects and Humans based on Visualized Flow using a Background-oriented Schlieren Method.- Research on Aging Design of News APP Interface Layout Based on Perceptual Features.- Research on Modular Design of Children's Furniture based on Scene Theory.- A Design Method of Children Playground Based on Bionic Algorithm.- Bias in, Bias out – The Similarity-Attraction Effect between Chatbot Designers and Users.- Research on Immersive Virtual Reality Display Design Mode of Cantonese Porcelain based on Embodied Interaction.- Design and Research of Children’s Robot Based on Kansei Engineering.- User Experience and Technology Acceptance Studies.- Exploring Citizens' Attitudes towards Voice-Based Government Services in Switzerland.- Too Hot to Enter: Investigating Users' Attitudes toward Thermoscanners in COVID times.- Teens’ Conceptual Understanding of Web Search Engines: The Case of Google Search Engine Result Pages (SERPs).- What Futuristic Technology Means for First Responders: Voices from the Field.- Blinking LEDs: Usability and User Experience of Domestic Modem Routers Indicator Lights.- The Smaller the Better? A Study on Acceptance of 3D Display of Exhibits of Museum's Mobile Media.- Research on Information Visualization Design for Public Health Security Emergencies.- Comparative Study of the Interaction of Digital Natives with Mainstream Web Mapping Services.- Success is not Final; Failure is not Fatal – Task Success and User Experience in Interactions with Alexa, Google Assistant and Siri.- Research on the Usability Design of HUD Interactive Interface.- Current Problems, Future Needs: Voices of First Responders about Communication Technology.- Exploring the Antecedents of Verificator Adoption.- Are Professional Kitchens Ready for Dummies? A Comparative Usability Evaluation between Expert and non-Expert Users.- Verification of the Appropriate Number of Communications between Drivers of Bicycles and Vehicles.- User Assessment of Webpage Usefulness.- How Workarounds Occur in Relation to Automatic Speech Recognition at Danish Hospitals.- Secondary Task Behavioral Analysis Based on Depth Image During Driving.- Research on the Relationship between the Partition Position of the Central Control Display Interface and the Interaction Efficiency.- HCI, Social Distancing, Information, Communication and Work.- Attention-based Design and Selective Exposure Amid COVID-19 Misinformation Sharing.- Digital Communication to Compensate for Social Distancing? - Results of a Survey on the Local Communication App DorfFunk.- An Evaluation of Remote Workers' Preferences for the Design of a Mobile App on Workspace Search.- Feasibility of Estimating Concentration Level for Not Disturbing Remote Office Workers Based on Kana-Kanji Conversion Confirmation Time.- A Smart City Stakeholder Online Meeting Interface.- Fostering Empathy and Privacy: The Effect of Using Expressive Avatars for Remote Communication.- PerformEyebrow: Design and Implementation of an Artificial Eyebrow Device Enabling Augmented Facial Expression.- Improving Satisfaction in Group Dialogue: A Comparative Study of Face-to-Face and Online Meetings.- EmojiCam: Emoji-Assisted Video Communication System Leveraging Facial Expressions.- Pokerepo Join: Construction of a Virtual Companion Experience System.- Visual Information in Computer-Mediated Interaction Matters: Investigating the Association Between the Availability of Gesture and Turn Transition Timing in Conversation.

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Book SynopsisThis book offers a synthesis of investigations on the ethics, governance and policies affecting the design, development and deployment of artificial intelligence (AI). Each chapter can be read independently, but the overall structure of the book provides a complementary and detailed understanding of some of the most pressing issues brought about by AI and digital innovation. Given its modular nature, it is a text suitable for readers who wish to gain a reliable orientation about the ethics of AI and for experts who wish to know more about specific areas of the current debate.Table of ContentsAcknowledgement.- Chapter 1. Introduction – The Importance of an Ethics First Approach to the Development of AI (Luciano Floridi).- Chapter 2. A unified framework of Five Principles for AI in Society (Luciano Floridi and Josh Cowls).- Chapter 3. An Ethical framework for a Good AI Society: Opportunities, Risks, Principles and Recommendations (Luciano Floridi, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, Burkhard Schafer, Peggy Valcke and Effy Vayena).- Chapter 4. Establishing the Rules for Building Trustworthy AI (Luciano Floridi).- Chapter 5. The Chinese Approach to AI: An Analysis of Policy, Ethics, and Regulation (Huw Roberts, Josh Cowls, Jessica Morley, Mariarosaria Taddeo, Vincent Wang and Luciano Floridi).- Chapter 6. Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical (Luciano Floridi).- Chapter 7. How AI can be a force for good (Mariarosaria Taddeo and Luciano Floridi).- Chapter 8. The Ethics of Algorithms (Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo and Luciano Floridi).- Chapter 9. How to Design AI for Social Good: Seven Essential Factors (Luciano Floridi, Josh Cowls, Thomas C King and Mariarosaria Taddeo).- Chapter 10. From What to How: An initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (Jessica Morley, Luciano Floridi, Libby Kinsey and Anat Elhalal).- Chapter 11. The Explanation Game: A Formal Framework for Interpretable Machine Learning (David Watson and Luciano Floridi).- Chapter 12. Artificial Agents and Their Moral Nature (Luciano Floridi).- Chapter 13. Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions (Thomas C King, Nikita Aggarwal, Mariarosaria Taddeo and Luciano Floridi).- Chapter 14. Regulate Artificial Intelligence to Avert Cyber Arms Race (Mariarosaria Taddeo and Luciano Floridi).- Chapter 15. Trusting Artificial Intelligence in Cybersecurity is a Double-edged Sword (Mariarosaria Taddeo, Tom McCutcheon and Luciano Floridi).- Chapter 16. Prayer-bots and Religious Worship on Twitter: A Call for a Wider Research Agenda (Carl Öhman, Robert Gorwa and Luciano Floridi).- Chapter 17. Artificial Intelligence, Deepfakes and a Future of Ectypes (Luciano Floridi).- Chapter 18. The Ethics of AI in Healthcare: A Mapping Review (Jessica Morley, Caio Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo and Luciano Floridi).- Chapter 19. Autonomous Vehicles: from Whether and When to Where and How (Luciano Floridi).- Chapter 20. Innovating with Confidence: Embedding AI Governance and Fairness in a Financial Services Risk Management Framework (Michelle Lee, Luciano Floridi and Alexander Denev).- Chapter 21. Robots, Jobs, Taxes and Responsibilities (Luciano Floridi).- Chapter 22. What the Near Future of Artificial Intelligence Could Be (Luciano Floridi).

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Book SynopsisThe new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.Table of ContentsChapter 1 Introduction to Predictive Analytics1 1.1 Predictive Analytics in Action2 1.2 Analytics Landscape8 1.3 Analytics 1.3.2 Predictive Analytics 1.4 Regression Analysis 1.5 Machine Learning Techniques 1.6 Predictive Analytics Model 1.7 Opportunities in Analytics 1.8 Introduction to the Automobile Insurance Claim Fraud Example 1.9 Chapter Summary References Chapter 239 Know Your Data – Data Preparation39 2.1 Classification of Data40 2.1.1 Qualitative versus Quantitative 2.1.2 Scales of Measurement 2.2. Data Preparation Methods. 2.2.1 Inconsistent Formats 2.2.2 Missing Data 2.2.3 Outliers 2.2.4 Other Data Cleansing Considerations 2.3 Data Sets and Data Partitioning 2.4 SAS Enterprise Miner™ Model Components 2.4.1 Step 1. Create Three of the Model Components 2.4.2 Step 2. Import an Excel File and Save as a SAS File 2.4.3 Step 3. Create the Data Source 2.4.4 Step 4. Partition the Data Source 2.4.5 Step 5 Data Exploration 2.4.6 Step 6 Missing Data 2.4.7 Step 7. Handling Outliers 2.4.8 Step 8. Categorical Variables with Too Many Levels 2.5 Chapter Summary References Chapter 35 What do Descriptive Statistics Tell Us 3.1 Descriptive Analytics 3.2 The Role of the Mean, Median and Mode 3.3 Variance and Distribution 3.4 The Shape of the Distribution 3.4.2 Kurtosis 3.5 Covariance and Correlation 3.6 Variable Reduction 3.6.1 Variable Clustering 3.6.2 Principal Component Analysis 3.7 Hypothesis Testing2 3.8 Analysis of Variance (ANOVA)5 3.9 Chi Square6 3. Fit Statistics8 3. Stochastic Models9 3.12 Chapter Summary1 References2 Chapter 4 Predictive Models Using Regression5 4.1 Regression6 4.1.1 Classical assumptions7 4.2 Ordinary Least Squares8 4.3 Simple Linear Regression8 4.3.1 Determining Relationship Between Two Variables9 4.3.2 Line of Best Fit and Simple Linear Regression Equation9 4.4 Multiple Linear Regression1 4.4.1 Metrics to Evaluate the Strength of the Regression Line2 4.3.2 Best-fit model3 4.3.3 Selection of Variables in Regression3 4.5 Principal Component Regression5 4.5.1 Principal Component Analysis Revisited5 4.5.2 Principal Component Regression6 4.6 Partial Least Squares6 4.7 Logistic Regression7 4.7.1 Binary Logistic Regression8 4.7.2 Examination of Coefficients1 4.7.3 Multinomial Logistic Regression3 4.7.4 Ordinal Logistic Regression3 4.8 Implementation of Regression in SAS Enterprise Miner™3 4.8.1 Regression Node Train Properties: Class Targets4 4.8.2 Regression Node Train Properties: Model Options5 4.8.3 Regression Node Train Properties: Model Selection6 4.9 Implementation of Two-Factor Interaction and Polynomial Terms8 4.9.1 Regression Node Train Properties: Equation8 4. DMINE Regression in SAS Enterprise Miner™0 4..1 DMINE Properties0 4..2 DMINE Results2 4. Partial Least Squares Regression in SAS Enterprise Miner™4 4..1 Partial Least Squares Properties4 4..2 Partial Least Squares Results7 4. Least Angles Regression in SAS Enterprise Miner™9 4..1 Least Angle Regression Properties0 4..2 Least Angles Regression Results1 4. Other Forms of Regression4 4. Chapter Summary6 References9 Chapter 5 The Second of the Big Three – Decision Trees1 5.1 What is a Decision Tree?2 5.2 Creating a Decision Tree4 5.3 Data Partitions and Decision Trees6 5.4 Creating a Decision Tree Using SAS Enterprise Miner™9 The key properties include:5 Subtree Properties5 5.4.1 Overfitting1 5.5 Creating an Interactive Decision Tree using SAS Enterprise Miner ™1 5.6 Creating a Maximal Decision Tree using SAS Enterprise Miner ™6 5.7 Chapter Summary9 References1 Chapter 6 The Third of the Big Three - Neural Networks3 6.1 What is a Neural Network?4 6.2 History of Neural Networks6 6.3 Components of a Neural Network8 6.4 Neural Network Architectures2 6.5 Training a Neural Network5 6.6 Radial Basis Function Neural Networks6 6.7 Creating a Neural Network using SAS Enterprise MinerÔ7 6.8 Using SAS Enterprise MinerÔ to Automatically Generate a Neural Network0 6.9 Explaining a Neural Network6 6. Chapter Summary0 References3 Chapter 7 Model Comparisons and Scoring5 7.1 Beyond the Big 7.2 Gradient Boosting6 7.3 Ensemble Models0 7.4 Random Forests2 7.6 Two-Stage Model8 7.7 Comparing Predictive Models0 7.7.1 Evaluating Fit Statistics – Which Model Do We Use?2 7.8 Using Historical Data to Predict the Future – Scoring5 7.8.1 Analyzing and Reporting Results8 7.8.2 Save Data Node9 7.8.3 Reporter Node0 7.9 The Importance of Predictive Analytics2 7.9.1 What Should We Expect for Predictive Analytics in the Future?3 7. Chapter Summary4 References6 Chapter 8 finding Associations in Data through Cluster Analysis9 8.1 Applications and Uses of Cluster Analysis9 8.2 Types of Clustering Techniques0 8.3 Hierarchical Clustering1 8.3.1 Agglomerative Clustering1 8.3.2 Divisive Clustering1 8.3.3 Agglomerative vs Divisive Clustering6 8.4 Non-hierarchical clustering7 8.4.1 K-means Clustering7 8.4.2 Initial Centroid Selection1 8.4.3 Determining the Number of Clusters2 8.4.4 Evaluating your clusters5 8.5 Hierarchical vs Nonhierarchical6 8.6 Cluster Analysis using SAS Enterprise Miner™6 8.6.1 Cluster Node7 8.6.2 Additional Key Properties of the Cluster Node8 8.7 Applying Cluster Analysis to the Insurance Claim Fraud Data Set9 8.8 Chapter Summary8 References9 9.1 What is Text Analytics?1 9.2 Information Retrieval2 9.3 Text Parsing5 9.4 Zipf’s Law8 9.5 Text Filter9 9.6 Text Cluster1 9.7 Text Topic4 9.8 Text Rule Builder7 9.9 Text Profile8 9. Chapter Summary9 Discussion Questions0 References1 Appendix A3 Data Dictionary for the Automobile Insurance Claim Fraud Data Example3 Appendix B5 Can you Predict the Money Laundering Cases?5 B.1 Introduction5 B.2. Business Problem8 B.3. Analyze Data9 B.4. Development and Optimization of a Best Fit Model2 B.5. Final Report3 References4

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Book SynopsisThis book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering. Table of ContentsChapter 1. Introduction to Federated Learning.- Chapter 2. Federated Learning for IoT Devices.- Chapter 3. Personalized Federated Learning.- Chapter 4. Federated Learning for an IoT Application.- Chapter 5. Some observations on the behaviour of Federated Learning.- Chapter 6. Federated Learning with Cooperating Devices: A Consensus Approach.- Chapter 7. A prospective study of federated machine learning in medical image fusion.- Chapter 8. Communication-Efficient Federated Learning in Wireless-Edge Architecture.- Chapter 9. Towards Ubiquitous AI in 6G with Federated Learning.- Chapter 10. Federated Learning using Tensor Flow.- Chapter 11. Cyber Security and privacy of Connected and Automated Vehicles (CAVs) based Federated Learning: Challenges, Opportunities and Open Issues.- Chapter 12. Security Issues & Solutions for Healthcare Informatics.- Chapter 13. Federated Learning: Challenges, Methods, and Future Directions.- Chapter 14. Quantum Federated Learning for Wireless Communications.- Chapter 15. Federated machine learning with data mining in health care.- Chapter 16. Federated Learning for data mining in Healthcare.

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Book SynopsisThis book sheds light on the practice of learning analytics, illuminating how others approach their data analysis. At the beginning of the book, a ‘prescriptive learning analytics planning model’ gives straightforward instructions for people to follow. This book is organized into ten chapters, falling into four topical sections: Managing Learning Analytics (overview, instructional systems design (ISD), instructional design, and planning data analysis); Cognitive Performance Measurement Practices (classical test theory (CTT), Rasch measurement theory (RMT), Item response theory(IRT), Rasch Modeling Tools (research design, setting methodology); and Case Studies (corporate training settings, healthcare industry, and educational courseware design). This book is an important reference for: educational research community and instructional systems designers; corporate training developers; postgraduate course developers; and doctoral students.Table of ContentsPrivacy-Driven Learning Analytics.- Introductory Analysis of the Rasch Model.- Social Media Analytics, Learning Analytics and Healthcare Industry: Risky Drinking.

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Book SynopsisThis book constitutes the proceedings of the 19th Russian Conference on Artificial Intelligence, RCAI 2021, held in Moscow, Russia, in October 2021. The 19 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 80 submissions. The conference deals with a wide range of topics, categorized into the following topical headings: cognitive research; data mining, machine learning, classification; knowledge engineering; multi-agent systems and robotics; natural language processing; fuzzy models and soft computer; intelligent systems; and tools for designing intelligent systems. Table of ContentsCognitive Research.- Heterogeneous Formal Neurons and Modeling of Multi-Transmitter Neural Ensembles.- Methods for Recognition of Frustration-Derived Reactions in Social Media.- Identification of the Network State Based on the ART-2 Neural Network with a Hierarchical Memory Structure in Parallel Mode.- Data Mining, Machine Learning, Classification.- Ranking Weibull Survival Model: Boosting Concordance Index of Weibull Time-to-event Prediction Model with Ranking Losses.- Predicting Different Health and Lifestyle Behaviors of Social Media Users.- Methods for Finding Consequences with Specified Properties.- Data Mining Methods for Analysis and Forecast of Emerging Technology Trend: A Systematic Mapping Study from SCOPUS Papers.- Machine Learning for Assessment of Cardiometabolic Risk Factors Predictive Potential and Prediction of Obstructive Coronary Arteries Lesions.- Knowledge Engineering.- Application of FCA for Domain Model Theory Investigation.- The Metagraph Model for Complex Networks: Definition, Calculus and Granulation Issues.- Subjective Expert Evaluations in the Model-Theoretic Representation of Object Domain Knowledge.- Multiagent Systems and Robotics.- Q-Mixing Network for Multi-Agent Path Finding in Partially Observable Grid Environments.- Subdefinite Computations for Reducing the Search Space in Mobile Robot Localization Task.- Enhancing Exploration Algorithms for Navigation with Visual SLAM.- Natural Language Processing.- Relying on Discourse Trees to Extract Medical Ontologies from Text.- TITANIS: A Tool for Intelligent Text Analysis in Social Media.- Approach to the Automated Development of Scientific Subject Domain Ontologies Based on Heterogeneous Ontology Design Patterns.- Fuzzy Models and Soft Computing.- PC-algorithm of Algebraic Bayesian Network Secondary Structure Training.- Logistic-based Design of Fuzzy Interpretable Classifiers.- Intelligent Systems.- Knowledge-Based Diagnostic System with a Precedent Library.- Semiotic Models in Monitoring and Decision Support Systems.- Cognitive Patterns for Semantic Presentation of Natural-language Descriptions of Well-formalizable Problems.- Detecting Anomalous Behavior of Users of Data Centers based on the Application of Artificial Neural Networks.- Tools for Designing Intelligent Systems.- Study of the Feasibility of Creating of a Real-time Neuronetwork Infrared Ground Objects Recognition System.- The Implementation of the Ontological Approach to Control of the Processes of Designing Integrated Expert Systems Based on the Problem-oriented Methodology.- A Module for Industrial Safety Inspection Planning Based on Self-organization.-

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Book SynopsisThis book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems. Table of ContentsDGM4MICCAI 2021 - Image-to-Image Translation, Synthesis.- Frequency-Supervised MRI-to-CT Image Synthesis.- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain.- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images.- Bridging the gap between paired and unpaired medical image translation.- Conditional generation of medical images via disentangled adversarial inference. -CT-SGAN: Computed Tomography Synthesis GAN.- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference.- CaCL: class-aware codebook learning for weakly supervised segmentation on diffuse image patterns.- BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification.- Evaluating GANs in medical imaging.- DGM4MICCAI 2021 - AdaptOR challenge.- Improved Heatmap-based Landmark Detection.- Cross-domain Landmarks Detection in Mitral Regurgitation.- DALI 2021.- Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph.- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency.- One-shot Learning for Landmarks Detection.- Compound Figure Separation of Biomedical Images with Side Loss.- Data Augmentation with Variational Autoencoders and Manifold Sampling.- Medical image segmentation with imperfect 3D bounding boxes.- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images.- How Few Annotations are Needed for Segmentation using a Multi-planar U-Net?.- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation.- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning.- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions.- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation.- Label Noise in Segmentation Networks : Mitigation Must Deal with Bias.- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization.- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation.

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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 constitutes the proceedings of the 24th International Conference on Discovery Science, DS 2021, which took place virtually during October 11-13, 2021.The 36 papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: applications; classification; data streams; graph and network mining; machine learning for COVID-19; neural networks and deep learning; preferences and recommender systems; representation learning and feature selection; responsible artificial intelligence; and spatial, temporal and spatiotemporal data. Table of ContentsApplications.- Automated Grading of Exam Responses: An Extensive Classification Benchmark.- Automatic human-like detection of code smells.- HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM.- Predicting reach to find persuadable customers: improving uplift models for churn prevention.- Classification.- A Semi-Supervised Framework for Misinformation Detection.- An Analysis of Performance Metrics for Imbalanced Classification.- Combining Predictions under Uncertainty: The Case of Random Decision Trees.- Shapley-Value Data Valuation for Semi-Supervised Learning.- Data streams.- A Network Intrusion Detection System for Concept Drifting Network Traffic Data.- Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams.- Statistical Analysis of Pairwise Connectivity.- Graph and Network Mining.- FHA: Fast Heuristic Attack against Graph Convolutional Networks.- Ranking Structured Objects with Graph Neural Networks.- Machine Learning for COVID-19.- Knowledge discovery of the delays experienced in reporting covid19 confirmed positive cases using time to event models.- Multi-Scale Sentiment Analysis of Location-Enriched COVID-19 Arabic Social Data.- Prioritization of COVID-19 literature via unsupervised keyphrase extraction and document representation learning.- Sentiment Nowcasting during the COVID-19 Pandemic.- Neural Networks and Deep Learning.- A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data.- Calibrated Resampling for Imbalance and Long-Tails in Deep learning.- Consensus Based Vertically Partitioned Multi-Layer Perceptrons for Edge Computing.- Controlling BigGAN Image Generation with a Segmentation Network.- GANs for tabular healthcare data generation: a review on utility and privacy.- Preferences and Recommender Systems.- An Ensemble Hypergraph Learning framework for Recommendation.- KATRec: Knowledge Aware aTtentive Sequential Recommendations.- Representation Learning and Feature Selection.- Elliptical Ordinal Embedding.- Unsupervised Feature Ranking via Attribute Networks.- Responsible Artificial Intelligence.- Deriving a Single Interpretable Model by Merging Tree-based Classifiers.- Ensemble of Counterfactual Explainers. Riccardo Guidotti and Salvatore Ruggieri.- Learning Time Series Counterfactuals via Latent Space Representations.- Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems.- Local Interpretable Classifier Explanations with Self-generated Semantic Features.- Privacy risk assessment of individual psychometric profiles.- The Case for Latent Variable vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19.- Spatial, Temporal and Spatiotemporal Data.- Local Exceptionality Detection in Time Series Using Subgroup Discovery.- Neural Additive Vector Autoregression Models for Causal Discovery in Time Series.- Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data.

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Book SynopsisThis book constitutes the refereed proceedings of the First Workshop on Monte Carlo Search, MCS 2020, organized in conjunction with IJCAI 2020. The event was supposed to take place in Yokohama, Japan, in July 2020, but due to the Covid-19 pandemic was held virtually on January 7, 2021. The 9 full papers of the specialized project were carefully reviewed and selected from 15 submissions. The following topics are covered in the contributions: discrete mathematics in computer science, games, optimization, search algorithms, Monte Carlo methods, neural networks, reinforcement learning, machine learning.Table of ContentsThe αµ Search Algorithm for the Game of Bridge.- Stabilized Nested Rollout Policy Adaptation.- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models.- Ordinal Monte Carlo Tree Search.- Monte Carlo Game Solver.- Generalized Nested Rollout Policy Adaptation.- Monte Carlo Inverse Folding.- Monte Carlo Graph Coloring.- Enhancing Playout Policy Adaptation for General Game Playing.

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Book SynopsisThis book constitutes the proceedings of the 7th International Symposium on Dependable Software Engineering, SETTA 2021, held in Beijing, China, in November 2021. The 16 full papers in this volume were carefully reviewed and selected from 39 submissions, and are presented with 3 abstracts of keynote speeches. They deal with latest research results and ideas on bridging the gap between formal methods and software engineering.Table of ContentsSystems Development.- Translating a Large Subset of State ow to Hybrid CSP with Code Optimization.- DeepGlobal: a Global Robustness Verifiable FNN Framework.- Leveraging Event-B Theories for handling domain knowledge in design models.- Program Analysis and Verification.- Reasoning about Iteration and Recursion Uniformly based on Big-step Semantics.- Trace Semantics and Algebraic Laws for MCA ARMv8 Architecture based on UTP.- Formal Analysis of 5G AKMA.- Verifying the Correctness of Distributed Systems via Mergeable Parallelism.- Testing and Fault Detection.- Mutation Testing of Reinforcement Learning Systems.- AIdetectorX: A Vulnerability Detector based on TCN and Self-attention Mechanism.- MC/DC Test Cases Generation based on BDDs.- Software Quality.- Predicting and Monitoring Bug-proneness at The Feature Level.- CSFL: Fault Localization on Real Software Bugs Based on the Combination of Context and Spectrum.- A Distributed Simplex Architecture for Multi-Agent Systems.- Satisfiability, Reachability and Model Checking.- OURS: Over- and Under-Approximating Reachable Sets for Analytic Time-invariant Differential Equations.- ESampler: Efficient Sampling of Satisfying Assignments for Boolean Formulas.- API Usage Pattern Search Based on Model Checking.

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Book SynopsisThis volume constitutes selected papers presented at the First International Conference on Frontiers in Software Engineering, ICFSE 2021, hekd in Innopolis, Russia, in June 2021. The 13 presented full papers were thoroughly reviewed and selected from 37 submissions. The papers present discussion on such topics as software engineering tools and environments; empirical software engineering; model-driven and domain-specific engineering, human factors and social aspects of software engineering, cooperative, distributed, and global software engineering, component-based software engineering, software metrics, and software engineering for green and sustainable technologies.Table of ContentsInstitutional Commitment and Leadership as Prerequisites for Successful Comprehensive Internationalization.- Software Engineering as an Alchemical Process: Establishing a philosophy of the discipline.- AI Empowered DevSecOps Security for Next Generation Development.- A Case Study on Combining Agile and User Centered Design.- An Analysis of the Sensitivity of Software Reliability Growth Models using Bootstrap and Monte Carlo Simulations.- A study: Design patterns detection approaches and Impact on software quality.- Skills development through agile capstone projects.- Impact of the Communication Issues: A Case Study of IT Start-Up.- Evolution of Information System Design Methodologies: the IFIP Conference Management Problem Revisited.- Development of a Method and a Software for Decision-Making, System Modeling and Planning of Business Processes.- “Extreme development” as a means for learning agile.- A Meta-Analytical Comparison of Energy Consumed by Two Different Programming Languages.- Toward Inclusion of Children as Software Engineering Stakeholders.

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Book SynopsisThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.Table of ContentsWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.

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Book SynopsisThis book presents new research results in the field of gravity compensation in robotic systems. It explores topics such as gravity compensation of planar articulated robotic manipulators; the stiffness modeling of manipulators with gravity compensators; the multi-degree-of-freedom counter-balancing; the design of actuators with partial gravity compensation; a cable-driven robotic suit with gravity compensation for load carriage; various compensation systems for medical cobots and assistive devices; gravity balancing of parallel robots. The volume demonstrates that gravity compensation methods continue to develop, and new approaches and solutions are constantly being reported. These solutions apply both to new structural solutions and to their new applications. Cobots, exoskeletons and robotic suits, assistive devices, as well as biomechanical systems are among the most promising applications and most pressing areas for further innovation.Table of ContentsA Modularization Approach for Gravity Compensation of Planar Articulated Robotic Manipulators.- Stiffness modeling for gravity compensators.- Multi-DOF Counterbalancing and Applications to Robots.- Parallel Elastic Actuator: Variable recruitment of parallel springs for partial gravity compensation.- Optimization and Control of a Cable-driven Robotic Suit for Load Carriage.- Tool Compensation for a Medical Cobot-Assistant.- Design of Statically Balanced Assistive Devices.- Design of Multifunctional Assistive Devices with Various Arrangements of Gravity Compenstion.- Gravity Balancing of Parallel Robots by Constant-Force Generators.

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