Artificial intelligence (AI) Books
Springer Nature Switzerland AG Policing in the Era of AI and Smart Societies
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.
£94.95
Springer Nature Switzerland AG Artificial Intelligence and Bioethics
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.
£54.99
Springer Nature Switzerland AG Monitoring Multimode Continuous Processes: A Data-Driven Approach
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£80.99
Springer Nature Switzerland AG Life and Its Future
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.
£56.99
Springer Nature Switzerland AG Software Engineering Perspectives in Intelligent Systems: Proceedings of 4th Computational Methods in Systems and Software 2020, Vol.1
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.
£123.49
Springer Nature Switzerland AG An Intuitive Exploration of Artificial
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.
£49.49
Springer Nature Switzerland AG Concepts in Action: Representation, Learning, and
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).
£34.99
Springer Nature Switzerland AG Handbook of Artificial Intelligence for Music:
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
£237.49
Springer Nature Switzerland AG Decision-Making Analyses with Thermodynamic Parameters and Hesitant Fuzzy Linguistic Preference Relations
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£80.99
Springer Nature Switzerland AG Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers
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.
£49.49
Springer Nature Switzerland AG Human-Computer Interaction. Design and User
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.
£42.74
Springer Nature Switzerland AG Ethics, Governance, and Policies in Artificial Intelligence
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).
£113.99
Springer Nature Switzerland AG Applying Predictive Analytics: Finding Value in
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
£56.99
Springer Nature Switzerland AG Federated Learning for IoT Applications
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.
£94.99
Springer Nature Switzerland AG Manage Your Own Learning Analytics: Implement a
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.
£123.49
Springer Nature Switzerland AG Artificial Intelligence: 19th Russian Conference, RCAI 2021, Taganrog, Russia, October 11–16, 2021, Proceedings
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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Springer Nature Switzerland AG Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
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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Springer Nature Switzerland AG Modern Problems of Robotics: Second International Conference, MPoR 2020, Moscow, Russia, March 25–26, 2020, Revised Selected Papers
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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Springer Nature Switzerland AG Discovery Science: 24th International Conference, DS 2021, Halifax, NS, Canada, October 11–13, 2021, Proceedings
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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Springer Nature Switzerland AG Monte Carlo Search: First Workshop, MCS 2020, Held in Conjunction with IJCAI 2020, Virtual Event, January 7, 2021, Proceedings
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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Springer Nature Switzerland AG Dependable Software Engineering. Theories, Tools, and Applications: 7th International Symposium, SETTA 2021, Beijing, China, November 25–27, 2021, Proceedings
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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Springer Nature Switzerland AG Frontiers in Software Engineering: First International Conference, ICFSE 2021, Innopolis, Russia, June 17–18, 2021, Revised Selected Papers
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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Springer Nature Switzerland AG Deep Generative Modeling
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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Springer Nature Switzerland AG Gravity Compensation in Robotics
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.
£134.99
Springer Nature Switzerland AG Emerging Technology Trends in Internet of Things and Computing: First International Conference, TIOTC 2021, Erbil, Iraq, June 6–8, 2021, Revised Selected Papers
Book SynopsisThis volume constitutes selected papers presented at the First International Conference on Emerging Technology Trends in IoT and Computing, TIOTC 2021, held in Erbil, Iraq, in June 2021. The 26 full papers were thoroughly reviewed and selected from 182 submissions. The papers are organized in the following topical sections: Internet of Things (IOT): services and applications; Internet of Things (IOT) in healthcare industry; IOT in networks, communications and distributed computing; real world application fields in information science and technology.Table of ContentsInternet of Things (IOT): Services and Applications.- Internet of Things (IOT) in Healthcare Industry.- IOT in Networks, Communications and Distributed Computing.- Real World Application Fields in information Science and Technology.
£62.99
Springer Nature Switzerland AG Engineering Multi-Agent Systems: 9th International Workshop, EMAS 2021, Virtual Event, May 3–4, 2021, Revised Selected Papers
Book SynopsisThis book constitutes revised selected papers from the 9th International Workshop on Engineering Multi-Agent Systems, EMAS 2021, which was held during May 3-4, 2021. The conference was initially planned to take place in London, UK, but changed to an online event due to the COVID-19 pandemic. The 20 full papers and 1 short paper included in this volume were carefully reviewed and selected from a total of 27 submissions. The contributions deal with agent-oriented software engineering, programming multi-agent systems, declarative agent languages and technologies, artificial intelligence, and machine learning. Table of ContentsPanSim + Sim-2APL: A Framework for Large-Scale Distributed Simulation with Complex Agents.- Implementing Ethical Governors in BDI.- A Unifying Framework for Agency in Hypermedia Environments.- Multiagent Foundations for Distributed Systems: A Vision.- An Epistemic Logic for Modular Development of Multi-Agent Systems.- Attention Guidance Agents with Eye-tracking: A Use-case Based on the MATBII Cockpit Task.- StreamB: A Declarative Language for Automatically Processing Data Streams in Abstract Environments for Agent Platforms.- BDI for Autonomous Mobile Robot Navigation.- An Appraisal Transition System for Event-driven Emotions in Agent-based Player Experience Testing.- Developer Operations and Engineering Multi-Agent Systems.- Smart Cyber-physical System-of-Systems using Intelligent Agents and MAS.- Formal Verification of a Map Merging Protocol in the Multi-Agent Programming Contest.- Analysis of the Execution Time of the Jason BDI Reasoning Cycle.- Autonomous Economic Agent Framework.- Seamless Integration and Testing for MAS Engineering.- Engineering Explainable Agents: An Argumentation-Based Approach.- TPO: A Type System for the Architecture of Agent Societies.- A Practical Framework for General Dialogue-based Bilateral Interactions.- Implementing Durative Actions with Failure Detection in Gwendolen.- Concept Description and Definition Extraction for the ANEMONE System.- GenGPT: a Systematic Way to Generate Synthetic Goal-Plan Trees.
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Springer Nature Switzerland AG Security and Artificial Intelligence: A Crossdisciplinary Approach
Book SynopsisAI has become an emerging technology to assess security and privacy, with many challenges and potential solutions at the algorithm, architecture, and implementation levels. So far, research on AI and security has looked at subproblems in isolation but future solutions will require sharing of experience and best practice in these domains.The editors of this State-of-the-Art Survey invited a cross-disciplinary team of researchers to a Lorentz workshop in 2019 to improve collaboration in these areas. Some contributions were initiated at the event, others were developed since through further invitations, editing, and cross-reviewing. This contributed book contains 14 invited chapters that address side-channel attacks and fault injection, cryptographic primitives, adversarial machine learning, and intrusion detection. The chapters were evaluated based on their significance, technical quality, and relevance to the topics of security and AI, and each submission was reviewed in single-blind mode and revised. Table of ContentsAI for Cryptography.- Artificial Intelligence for the Design of Symmetric Cryptographic Primitives.- Traditional Machine Learning Methods for Side-Channel Analysis.- Deep Learning on Side-Channel Analysis.- Artificial Neural Networks and Fault Injection Attacks.- Physically Unclonable Functions and AI: Two Decades of Marriage.- AI for Authentication and Privacy.- Privacy-Preserving Machine Learning using Cryptography.- Machine Learning Meets Data Modification: the Potential of Pre-processing for Privacy Enhancement.- AI for Biometric Authentication Systems.- Machine Learning and Deep Learning for Hardware Fingerprinting. - AI for Intrusion Detection.- Intelligent Malware Defenses.- Open-World Network Intrusion Detection.- Security of AI.- Adversarial Machine Learning.- Deep Learning Backdoors. - On Implementation-level Security of Edge-based Machine Learning Models.
£61.74
Springer International Publishing AG Introduction to Semi-Supervised Learning
Book SynopsisSemi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and OutlookTable of ContentsIntroduction to Statistical Machine Learning.- Overview of Semi-Supervised Learning.- Mixture Models and EM.- Co-Training.- Graph-Based Semi-Supervised Learning.- Semi-Supervised Support Vector Machines.- Human Semi-Supervised Learning.- Theory and Outlook.
£26.59
Springer International Publishing AG Answer Set Solving in Practice
Book SynopsisAnswer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacities has sparked interest in many other areas even beyond KRR. This book presents a practical introduction to ASP, aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while illustrating the overall solving process by practical examples. Table of Contents: List of Figures / List of Tables / Motivation / Introduction / Basic modeling / Grounding / Characterizations / Solving / Systems / Advanced modeling / ConclusionsTable of ContentsList of Figures.- List of Tables.- Motivation.- Introduction.- Basic modeling.- Grounding.- Characterizations.- Solving.- Systems.- Advanced modeling.- Conclusions.
£37.85
Springer International Publishing AG Metric Learning
Book SynopsisSimilarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' BiographiesTable of ContentsIntroduction.- Metrics.- Properties of Metric Learning Algorithms.- Linear Metric Learning.- Nonlinear and Local Metric Learning.- Metric Learning for Special Settings.- Metric Learning for Structured Data.- Generalization Guarantees for Metric Learning.- Applications.- Conclusion.- Bibliography.- Authors' Biographies .
£42.74
Springer International Publishing AG Recent Trends in Image Processing and Pattern Recognition: 4th International Conference, RTIP2R 2021, Msida, Malta, December 8-10, 2021, Revised Selected Papers
Book SynopsisThis volume constitutes the refereed proceedings of the 4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021, held in Msida, Malta, in December 2021. Due to the COVID-19 pandemic the conference was held online. The 19 full papers and 14 short papers presented were carefully reviewed and selected from 84 submissions. The papers are organized in the following topical sections: healthcare: medical imaging and informatics; computer vision and pattern recognition; document analysis and recognition; signal processing and machine learning; satellite imaging and remote sensing. Table of ContentsHealthcare: medical imaging and informatics.- Computer Vision and Pattern Recognition.- Document analysis and recognition.- Signal processing and machine learning.- Satellite imaging and remote sensing.
£62.99
Springer International Publishing AG Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.Table of ContentsA Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times.- A SAT Encoding to compute Aperiodic Tiling Rhythmic Canons.- Transferring Information across Restarts in MIP.- Towards Copeland Optimization in Combinatorial Problems.- Coupling Different Integer Encodings for SAT.- Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions.- Shattering Inequalities for Learning Optimal Decision Trees.- Learning Pseudo-Backdoors for Mixed Integer Programs.- Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.- Solving the Job Shop Scheduling Problem extended with AGVs – Classical and Quantum Approaches.- Stochastic Decision Diagrams.- Improving the robustness of EPS to solve the TSP.- Efficient operations between MDDs and constraints.- Deep Policy Dynamic Programming for Vehicle Routing Problems.- Learning a Propagation Complete Formula.- A FastMap-Based Algorithm for Block Modeling.- Packing by Scheduling: Using Constraint Programming to Solve a Complex 2D Cutting Stock Problem.- Dealing with the product constraint.- Multiple-choice knapsack constraint in graphical models.- A Learning Large Neighborhood Search for the Staff Rerostering Problem.- Practically Uniform Solution Sampling in Constraint Programming.- Training Thinner and Deeper Neural Networks: Jumpstart Regularization.- Hybrid Offline/Online Optimization for Energy Management via Reinforcement Learning.- Enumerated Types and Type Extensions for MiniZinc.- A parallel algorithm for generalized arc-consistent filtering for the Alldifferent constraint.- Analyzing the Reachability Problem in Choice Networks.- Model-based Approaches to Multi-Attribute Diverse Matching.
£62.99
Springer International Publishing AG Computational Neuroscience: Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers
Book SynopsisThis book constitutes the refereed proceedings of the Third Latin American Workshop, LAWCN 2021, held in Sao Luis do Maranhao, Brazil, during December 8–10, 2021.The 13 full papers and 3 short papers included in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections as follows: Interdisciplinary applications of Artificial Intelligence (AI) and Machine Learning (ML); AI and ML applied to robotics; AI and ML applied to biomedical sciences; Health issues and computational neuroscience; Software and hardware implementations in neuroscience; and Neuroengineering – science and technology.Table of ContentsInterdisciplinary applications of Artificial Intelligence (AI) and Machine Learning (ML).- Semantic segmentation of the cultivated area of plantations with U-net.- Use and Interpretation of Item Response Theory applied to Machine Learning.- AI and ML applied to robotics.- Towards Loop Closure Detection for SLAM Applications using Bag of Visual Features: Experiments and Simulation.- Loss Function Regularisation on the Iterated Racing Procedure for Automatic Tuning of RatSLAM Parameters.- Controlling the UR3 robotic arm using a leap motion: a comparative study.- AI and ML applied to biomedical sciences.- Web service based epileptic seizure detection by applying machine learning techniques.- Health issues and computational neuroscience.- Machine Learning Search of Novel Selective NaV1.2 and NaV1.6 Inhibitors as Potential Treatment against Dravet Syndrome.- Implementation of intra and extracellular nonperiodic scale-free stimulation in silico for the NEURON simulator.- In silico investigation of the effects of distinct temporal patterns of electrical stimulation to the amygdala using a network of Izhikevich neurons.- Software and hardware implementations in neuroscience.- Brain connectivity measures in EEG-based biometry for epilepsy patients.- A multiplatform output stage for the development of current-fixed electrical stimulators applied to neural electrophysiology.- Neuroengineering – science and technology.- Physiological self-regulation using biofeedback training: from concept to clinical applicability.- Movement-Related Electroencephalography in Stroke Patients across a Brain-Computer Interface-based Intervention.- Electrophysiological Correlates of Freeze of Gait in Parkinson Disease: Increased STN-LFP Alpha Power and the Possible Role of Attentional Circuits.- Effect of hand dominance when decoding motor imagery grasping tasks within the same hand.- Kinematic responses as a control strategy to visual occlusion.
£58.49
Springer International Publishing AG Computing Science, Communication and Security: Third International Conference, COMS2 2022, Gujarat, India, February 6–7, 2022, Revised Selected Papers
Book SynopsisThis book constitutes revised selected papers of the Third International Conference on Computing Science, Communication and Security, COMS2 2022, held in Gandhinagar, India, in February 2022. Due to the COVID-19 pandemic the conference was held virtually. The 22 full papers were thoroughly reveiwed and selected from 143 submissions. The papers present ideas, and research results on the aspects of computing science, network communication, and security.Table of ContentsDevelopment of Smart Sensor for IoT Based Environmental Data Analysis Through Edge Computing.- Application of Forensic Audio-Video Steganography Technique to Improve Security, Robustness, and Authentication of Secret Data.- An Efficient Cluster Based Energy Routing Protocol (E-CBERP) for Wireless Body Area Networks Using Soft Computing Technique.- Ortho Image Mosaicing and Object Detection of UAV Data.- The Novel Approach of Down-Link Spectral Efficiency Enhancement using Massive MIMO in Correlated Rician Fading Scenario.- Blocking Estimation using Optimal Guard Channel Policy in GSM 900 System.- Systematic Review on Various Techniques of Android Malware Detection.- Adaptive Rider Grey Wolf Optimization enabled Pilot-design for Channel Estimation in Cognitive Radio.- To Identify Malwares using Machine Learning Algorithms.- Motor Imagery EEG Signal Classification using Deep Neural Networks.- Extending WSN life-time using Energy Efficient based on K-means clustering method.- One-to-One Matching for Cooperative Resource Sharing and Communication in CRNs.- Sandbox Environment for Real Time Malware Analysis of IoT Devices.- Machine Learning Based DDOS Attack Detection in Design of 5G Smart Healthcare Networks.- Traffic Flow Prediction using Deep Learning Techniques.- Meta Heuristic Backtracking Algorithm for Virtual Machine Placement in Cloud Computing Migration.- Achieving Energy Efficiency in Life-logging Applications of Internet of Things using Data Compression through incorporation of Machine Learning & Edge-Cloud Architecture.- Cab Fare Prediction Using Machine Learning.- Device to Device Communication Over 5G.- Energy efficient Allocation of Resources in NOMA Based (MU-HCRN) with Perfect Spectrum Sensing.- CV based person detection system for smart transportation.- A Performance of Low-Cost NVIDIA Jetson Nano Embedded System in the Real-Time Siamese Single Object Tracking.
£62.99
Springer International Publishing AG Artificial Intelligence and Machine Learning for
Book SynopsisIn line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on “Artificial Intelligence and Machine Learning for Healthcare”. The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments and healthcare services are presented. The selected studies in this book provide readers a glimpse on current progresses in AI and ML for undertaking a variety of healthcare-related tasks. The advances in AI and ML technologies for future healthcare are also discussed, shedding light on the potential of AI and ML to realize the next-generation medical treatments and healthcare services for the betterment of our global society. Table of ContentsArtificial Intelligence for the future of medicine.- A Survival Analysis Guide in Oncology.- Social Media Sentiment Analysis related to COVID-19 Vaccinations.- Healthcare support using data mining: A case study on stroke prediction.
£116.99
Springer International Publishing AG Advances in Computer Games: 17th International
Book SynopsisThis book constitutes the refereed post-conference proceedings of the 17th International Conference on Advances in Computer Games, ACG 2021, which was held as a virtual event during November 23–25, 2021. The 22 full papers included in this book were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: learning in games; search in games; solving games; chess patterns; player modelling; and game systems.Table of ContentsLearning in Games.- Improving Counterfactual Regret Minimization Agents Training in the Card Game Cheat.- Deep Reinforcement Learning for Morpion Solitaire.- Expert Iteration for Risk.- Search in Games.- Sequential Halving Using Scores.- Cosine Annealing, Mixnet and Swish Activation for Computer Go.- A Heuristic Approach to the Game of Sylver Coinage.- Evaluating Interpretability Methods for DNNs in Game-Playing Agents.- Solving Games.- Quixo is Solved.- Solving Bicoloring-Graph Games on Rectangular Boards – Part 1: Partisan Col and Snort.- Solving Bicoloring-Graph Games on Rectangular Boards – Part 2: Impartial Col and Snort.- BoxOff is NP-Complete.
£52.24
Springer International Publishing AG Data Analytics and Management in Data Intensive Domains: 23rd International Conference, DAMDID/RCDL 2021, Moscow, Russia, October 26–29, 2021, Revised Selected Papers
Book SynopsisThis book constitutes the post-conference proceedings of the 23rd International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2021, held in Moscow, Russia, in October 2021*.The 16 revised full papers were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: problem solving infrastructures, experiment organization, and machine learning applications; data analysis in astronomy; data analysis in material and earth sciences; information extraction from text* The conference was held virtually due to the COVID-19 pandemic.Table of ContentsProblem Solving Infrastructures, Experiment Organization, and Machine Learning Applications.- MLDev: Data Science Experiment Automation and Reproducibility Software.- Response to Cybersecurity Threats of Informational Infrastructure Based on Conceptual Models.- Social Network Analysis of the Professional Community Interaction - Movie Industry Case.- Data Analysis in Astronomy.- Cross-Matching of Large Sky Surveys and Study of Astronomical Objects Apparent in Ultraviolet Band Only.- The Diversity of Light Curves of Supernovae Associated with Gamma-Ray Bursts.- Application of Machine Learning Methods for Cross-Matching Astronomical Catalogs.- Pipeline for Detection of Transient Objects in Optical Surveys.- VALD in Astrophysics.- Data Analysis in Material and Earth Sciences.- Machine Learning Application to Predict New Inorganic Compounds – Results and Perspectives.- Interoperability and Architecture Requirements Analysis and Metadata Standardization for a Research Data Infrastructure in Catalysis.- Fast Predictions of Lattice Energies by Continuous Isometry Invariants of Crystal Structures.- Image Recognition for Large Soil Maps Archive Overview: Metadata Extraction and Georeferencing Tool Development.- Information Extraction from Text.- Cross-lingual Plagiarism Detection Method.- Methods for Automatic Argumentation Structure Prediction.- A System for Information Extraction from Scientific Texts in Russian.- Improving Neural Abstractive Summarization with Reliable Sentence Sampling.
£58.49
Springer International Publishing AG Mobile Web and Intelligent Information Systems: 18th International Conference, MobiWIS 2022, Rome, Italy, August 22–24, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 18th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2022, held in Rome, Italy, in August 2022.The 18 full papers and 1 short paper presented in this book were carefully reviewed and selected from 51 submissions. The papers of MobiWIS 2022 deal focus on topics such as security and privacy; web and mobile applications; networking and communication; intelligent information systems; and IoT and ubiquitous computing.Table of ContentsSession 1: Mobile Applications and Technologies.- Knowledge Behavior Gap Model: An Application for Technology Acceptance.- UI-Re-Engineering of a Mobile Documentation Software in the Care Sector.- GUI Element Detection from Mobile UI Images Using YOLOv5.- Session 2: Mobile Devices and Automous Vehicles.- Active Federated YOLOR Model for Enhancing Autonomous Vehicles Safety.- Neural Network for Public Transport Mode Inference on Mobile Devices.- Data-Driven Federated Autonomous Driving.- Session 3: Security in Healthcare and Smart Cities Environment.- Case Study on a Session Hijacking Attack: The 2021 CVS Health Data Breach.- Blockchain for Cybersecure Healthcare.- Design of a Method for Setting IoT Security Standards in Smart Cities.- Session 4: Software-Defined Networks.- Mathematical Models for Minimizing Latency in Software-Defined Networks.- Analyzing the Impact of DNN Hardware Accelerators-Oriented Compression Techniques on General-Purpose Low-End Boards.- Spatial Dependency in Software-Defined Networking In-Band Monitoring: Challenges and Future Perspective.- Session 5: Smart Systems and Applications.- What is a Smart Service?.- SSSB: An Approach to Insurance for Cross-Border Exchange by using Smart Contracts.- A Review: Sensors Used in Tool Wear Monitoring and Prediction.- Session 6: Advanced Information Systems.- Towards the use of IT technologies for health literacy and health information competences – a case study.- A Systematic Literature Review on Relationship between Internet Usage Behavior and Internet QoS in Campus.- Model Checking Intelligent Information Systems with 3-Valued Timed Commitments.
£52.24
Springer International Publishing AG Computer Algebra in Scientific Computing: 24th International Workshop, CASC 2022, Gebze, Turkey, August 22–26, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 24th International Workshop on Computer Algebra in Scientific Computing, CASC 2022, which took place in Gebze, Turkey, in August 2022. The 20 full papers included in this book were carefully reviewed and selected from 32 submissions. They focus on the theory of symbolic computation and its implementation in computer algebra systems as well as all other areas of scientific computing with regard to their benefit from or use of computer algebra methods and software. Table of ContentsSurvey on Generalizations of the Intermediate Value Theorem and Applications (Invited Talk).- On Truncated Series Involved in Exponential-Logarithmic Solutions of Truncated LODEs.- Subresultant Chains Using B´ezout Matrices.- Application of Symbolic-Numerical Modeling Tools for Analysis of Gyroscopic Stabilization of Gyrostat Equilibria.- Computer Science for Continuous Data: Vision, Theory, and Practice of a Computer (Algebra) ANALYSIS System.- Computational Aspects of Equivariant Hilbert Series of Canonical Rings for Algebraic Curves.- Symbolic-Numeric Algorithm for Calculations in Geometric Collective Model of Atomic Nuclei.- Analyses and Implementations of Chordality-Preserving Top-Down Algorithms for Triangular Decomposition.- Accelerated Subdivision for Clustering Roots of Polynomials Given by Evaluation Oracles.- On Equilibrium Positions in the Problem of the Motion of a System of Two Bodies in a Uniform Gravity Field.- An Interpolation Algorithm for Computing Dixon Resultants.- Distance Evaluation to the Set of Matrices with Multiple Eigenvalues.- On Boundary Conditions Parametrized by Analytic Functions.- Computing the Integer Hull of Convex Polyhedral Sets.- A Comparison of Algorithms for Proving Positivity of Linearly Recurrent Sequences.- Stability Analysis of Periodic Motion of the Swinging Atwood Machine.- New Heuristic to Choose a Cylindrical Algebraic Decomposition Variable Ordering Motivated by Complexity Analysis.- An Implementation of Parallel Number-Theoretic Transform Using Intel AVX-512 Instructions.- Locating the Closest Singularity in a Polynomial Homotopy.- A General Method of Finding New Symplectic Schemes for Hamiltonian Mechanics.- A Mechanical Method for Isolating Locally Optimal Points of Certain Radical Functions.
£58.49
Springer International Publishing AG Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 30th International Conference on Case-Based Reasoning, ICCBR 2022, which took place in Nancy, France, during September 12-15, 2022.The theme of ICCBR 2022 was Global Challenges for CBR aiming to consider how CBR can and might contribute to challenges in sustainability, climate change, and global health. The 26 papers presented in this volume were carefully reviewed and selected from 68 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI).Table of ContentsExplainability in CBR Using Case-based Reasoning for Capturing Expert Knowledge on Explanation Methods.- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations.- How close is too close? The Role of Feature Attributions in Discovering Counterfactual Explanations.- Algorithmic Bias and Fairness in Case-Based Reasoning.- ”Better” Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI).- Representation and Similarity Extracting Case Indices from Convolutional Neural Networks: A Comparative Study.- Exploring the Effect of Recipe Representation on Critique-based Conversational Recommendation.- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study.- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud.- Adaptation and Analogical Reasoning Case Adaptation with Neural Networks: Capabilities and Limitations.- A Deep Learning Approach to Solving Morphological Analogies.- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer.- Graphs and Optimisation Case-Based Learning and Reasoning Using Layered Boundary Multigraphs.- swarm optimization in small case bases for software effort estimation.- MicroCBR: Case-based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting.- GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning.- Never judge a case by its (unreliable) neighbors: Estimating Case Reliability for CBR.- CBR and Neural Networks Improving Automated Hyperparameter Optimization with Case-Based Reasoning.- A factorial study of neural network learning from differences for regression.- ase-Based Inverse Reinforcement Learning Using Temporal Coherence.- Analogy-based post-treatment of CNN image segmentations.- Case-Based Applications An Extended Case-Based Reasoning Approach to Race-Time Prediction in Recreational Marathon Runners.- Forecasting for Sustainable Dairy Produce: Enhanced Long-Term, Milk-Supply Forecasting Using k-NN for Data Augmentation, with Prefactual Explanations for XAI.- A Case-Based Approach for Content Planning in Data-to-Text Generation.- The use of computer-assisted Case-Based Reasoning to support clinical decision-making – a scoping review.
£58.49
Springer International Publishing AG Brain Informatics: 15th International Conference, BI 2022, Padua, Italy, July 15–17, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 15th International Conference on Brain Informatics, BI 2022, held as hybrid event in Padua, Italy (in person) and Queensland, Australia (online) in July 2022. The 29 papers were selected from 65 submissions and the main theme of BI 2022 is Brain Science meets Artificial Intelligence with respect to the five tracks: Cognitive and computational foundations of brain science; human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain inspired computing.Table of ContentsCognitive and Computational Foundations of Brain Science.- Estimating the Temporal Evolution of Synaptic Weights from Dynamic Functional Connectivity.- From concrete to abstract rules: A computational sketch.- Detection of Healthy and Unhealthy Brain States from Local Field Potentials using Machine Learning.- COSLETS: Recognition of Emotion Based on EEG signals.- Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach.- Investigations of Human Information Processing Systems.- Analysis of Semantic Processes as an Indication for Focal Point Selection by Decomposing Alpha Frequency Band.- Toward the study of the neural-underpinnings of dyslexia during Final-phoneme Elision: A machine learning approach.- Root-Cause Analysis of Activation Cascade Differences in Brain Networks.- Unstructured Categorization with Probabilistic Feedback: Learning Accuracy versus Response Time.- Brain Big Data Analytics, Curation and Management.- Optimizing measures of information encoding in astrocytic calcium signals.- Introducing the Rank-Biased Overlap as Similarity measure for Feature Importance in Explainable Machine Learning: a case study on Parkinson’s disease.- Prediction of neuropsychological scores from functional connectivity matrices using deep autoencoders.- Feature Fusion-Based Capsule Network for Cross-Subject Mental Workload Classification.- Brain Source Reconstruction Solution Quality Assessment with Spatial Graph Frequency Features.- Enhancing the MR Neuroimaging by Using the Deep Super-Resolution Reconstruction.- Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy based on Arousal State Detection from Multimodal Data.- Convex Hull in Brain Tumor Segmentation.- Informatics Paradigms for Brain and Mental Health Research.- Computer Added Diagnosis Framework for ADHD Detection using Quantitative EEG.- A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh.- Epilepsy Detection from EEG Data using a Hybrid CNN-LSTM Model.- Classifying Brain Tumor from MRI Images Using Parallel CNN Model.- Triplet-loss based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease.- Understanding syntax structure of language after a head injury.- A Belief Rule Based Expert System To Diagnose Alzheimer’s disease Using Whole Blood Gene Expression Data.- Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification.- Brain-Machine Intelligence and Brain-Inspired Computing.- Biologically Inspired Neural Path Finding.- A Second-Order Adaptive Social-Behavioural Model for Individual and Duo Motor Learning.- EEG signal classification using Shallow FBCSP ConvNet with a new cropping strategy.- Becoming Attuned To Each Other Over Time: A Computational Neural Agent Model for the Role of Time Lags in Subjective Synchrony Detection and Related Behavioural Adaptivity.
£56.99
Springer International Publishing AG Advances in Computational Intelligence: 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Monterrey, Mexico, October 24–29, 2022, Proceedings, Part I
Book SynopsisThe two-volume set LNAI 13612 and 13613 constitutes the proceedings of the 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, held in Monterrey, Mexico, in October 2022.The total of 63 papers presented in these two volumes was carefully reviewed and selected from 137 submissions.The first volume, Advances in Computational Intelligence, contains 34 papers structured into three sections: Machine and Deep Learning Image Processing and Pattern Recognition Evolutionary and Metaheuristic Algorithms The second volume contains 29 papers structured into two sections: Natural Language Processing Intelligent Applications and Robotics Table of ContentsNatural Language Processing.- Urdu Named Entity Recognition with Attention Bi-LSTM-CRF Model.- Impact Evaluation of Multimodal Information on Sentiment Analysis.- Improving Neural Machine Translation for Low Resource Languages using Mixed Training: The Case of Ethiopian Languages.- Machine translation of texts from languages with low digital resources: a systematic review.- Comparison between SVM and DistilBERT for multi-label text classification of scientific papers aligned with sustainable development goals.- A hybrid methodology based on CRISP-DM and TDSP for the execution of preprocessing tasks in Mexican environmental laws.- News Intention Study and Automatic Estimation of its Impact.- Evaluation of a New Representation for Noise Reduction in Distant Supervision.- Automatic identification of suicidal ideation in texts using cascade classifiers.- Web crawler and classifier for news articles.- Sentiment Analysis in the Rest-Mex challenge.- A bibliometric review of methods and algorithms for generating corpora for learning vector word embeddings.- Evaluating The Impact of OCR Quality on Short Text Classification Task.- Techniques for generating language learning resources a system for generating exercises for the differentiation of literal and metaphorical context.- Exploratory Data Analysis for the Automatic Detection of Question Paraphrasing in Collaborative Environments.- Diachronic Neural Network Predictor of Word Animacy.- Sequential Models for Sentiment Analysis: A Comparative Study.- Intelligent Applications and Robotics.- Analysis of Procedural Generated Textures for Video Games using a CycleGAN.- Vibration Analysis of an Industrial Motor with Autoencoder for Predictive Maintenance.- Modeling and simulation of swarm of foraging robots for collecting resources using RAOI behavior policies.- Data-driven adaptive force control for a novel soft-robot based on ultrasonic atomization.- Data-driven-modelling and control for a class of discrete-time robotic system using an adaptive tuning for Pseudo Jacobian Matrix algorithm.- Retrieval-based Statistical Chatbot in a Scientometric domain.- Red Light/Green Light: a lightweight algorithm for, possibly fraudulent, online behavior change detection.- Machine Learning Model of Digital Transformation Index for Mexican Households.- Credit Risk Models in the Mexican Context Using Machine Learning.- Ventilator Pressure Prediction Using a Regularized Regression Model.- An Intelligent Human Activity Recognizer for Visually Impaired People using Deep Learning Technique.- Takagi-Sugeno Type Neuro Fuzzy System Model based Fault Diagnostic in Photovoltaic System.
£58.49
Springer International Publishing AG Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles: 11th International Symposium, ISoLA 2022, Rhodes, Greece, October 22–30, 2022, Proceedings, Part I
Book SynopsisThis four-volume set LNCS 13701-13704 constitutes contributions of the associated events held at the 11th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2022, which took place in Rhodes, Greece, in October/November 2022. The contributions in the four-volume set are organized according to the following topical sections: specify this - bridging gaps between program specification paradigms; x-by-construction meets runtime verification; verification and validation of concurrent and distributed heterogeneous systems; programming - what is next: the role of documentation; automated software re-engineering; DIME day; rigorous engineering of collective adaptive systems; formal methods meet machine learning; digital twin engineering; digital thread in smart manufacturing; formal methods for distributed computing in future railway systems; industrial day.Table of ContentsSpecify This - Bridging gaps between program specification paradigms.- X-by-Construction Meets Runtime Verification.- Verification and Validation of Concurrent and Distributed Heterogeneous Systems.
£67.49
Springer International Publishing AG Artificial General Intelligence: 15th International Conference, AGI 2022, Seattle, WA, USA, August 19–22, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 15th International Conference on Artificial General Intelligence, AGI 2022, held as a hybrid event in Seattle, WA, USA, in August 2022.The 31 full papers presented in this book were carefully reviewed and selected from 61 submissions. The papers cover topics from foundations of AGI, to AGI approaches and AGI ethics, to the roles of systems biology, goal generation, and learning systems, and so much more. Additionally, this volume contains 13 posters. Table of ContentsAccepted for poster presentation.- A General Purpose Machine Reasoning Engine.- COMFO: Multilingual Corpus for Opinion Mining.- Information as Entanglement—A Framework for Artificial General Intelligence.- Causal Analysis of Generic Time Series Data Applied for Market Prediction.- Dynamic and Evolving Neural Network for event discrimination.- Hierarchical temporal DNN and Associative knowledge representation.- MARTI: new model of human brain, considering neocortex and basal ganglia – learns to play Atari game by reinforcement learning on a single CPU.- General-Purpose Minecraft Agents and Hybrid AGI.- Graph Strategy for Interpretable Visual Question Answering.- Analogical Problem Solving in the Causal Cognitive Architecture.- A Biologically Plausible Graph Structure for AGI.- The Delta Normal AGI.- Purely Symbolic Induction of Structure.- Accepted for full oral presentation.- Extended subdomains: a solution to a problem of Hernández-Orallo and Dowe.- Versatility-Efficiency Index (VEI): Towards a Comprehensive Definition of Intelligence Quotient (IQ) for Artificial General Intelligence (AGI) Agents.- Moral Space for Paraconsistent AGI.- PERI.2 Goes to PreSchool and Beyond, in Search of AGI.- Reinforcement Learning with Information-Theoretic Actuation.- Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems.- Core and Periphery as Closed-System Precepts for Engineering General Intelligence.- Toward Generating Natural-Language Explanations of Modal Logic Proofs.- ONA for autonomous ROS-based robots.- Generalized Identity Matching in NARS.- Adaptive Multi-Strategy Market-Making Agent For Volatile Markets.- Toward a Comprehensive List of Necessary Abilities for Human Intelligence, Part 2: Using Knowledge.- What can nonhuman animals, children, and g tell us about human-level artificial general intelligence (AGI)?.- Toward a Comprehensive List of Necessary Abilities for Human Intelligence, Part 1: Constructing Knowledge.- Cognitive Architecture for Co-Evolutionary Hybrid Intelligence.- An approach to generation triggers for parrying backdoor in neural networks.- The Learning Agent Triangle: Towards a unified disambiguation of the AGI challenge..- Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture.- Market Prediction as a Task for AGI Agents.- Monte Carlo Bias Correction in Q-learning.- Free Will Belief as a Consequence of Model-based Reinforcement Learning.- Thoughts on Architecture.- On the possibility of regulation of human emotions via multimodal social interaction with an embodied agent controlled by eBICA-based emotional interaction model.- QKSA: Quantum Knowledge Seeking Agent.- Elements of Active Continuous Learning and Uncertainty Self-Awareness: a Narrow Implementation for Face and Facial Expression Recognition.- Thrill-K Architecture: Towards a Solution to the Problem of Knowledge Based Understanding.- Grammar Induction - Experimental Results.- Brain Principles Programming.- A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems.- Artificial Open World for Evaluating AGI: a Conceptual Design.- Ownability of AGI.
£61.74
Springer International Publishing AG Technologies and Innovation: 8th International Conference, CITI 2022, Guayaquil, Ecuador, November 14–17, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 8th International Conference, CITI 2022, held in Guayaquil, Ecuador, during November 14–17, 2022.The 19 full papers included in this book were carefully reviewed and selected from 48 submissions. They were organized in topical sections as follows: machine Learning; knowledge based systems; computer vision and image analysis; networks, monitoring and collaborative systems; apps and user interfaces.Table of ContentsMachine Learning.- A machine learning study about the vulnerability level of poverty in Perú.- Predicting academic performance in mathematics using machine learning algorithms.- Analysis of classification algorithms for the prediction of purchase intention in electronic commerce.- Knowledge based systems.- Alignment techniques in Domain-specific models.- IVRMaker, an Interactive and Customizable telephone Chatbot services platform.- Digital Transformation of Health Care Services: Médikal Case Study.- Computer vision and Image analysis.- Texture and Color-Based Analysis to Determine the Quality of the Manila Mango using Digital Image Processing Techniques.- Detection of motorcyclists without a safety helmet through YOLO: support for road safety.- Computer Vision-Based Ovitrap for Dengue Control.- Networks, monitoring and collaborative systems.- Performance analysis of Multipath TCP congestion control variants.- Data stream processing method for clustering of trajectories.- Low-Cost Energy Consumption Monitoring System using NodeMCU.- Trend of the use and investment of Blockchain technology in the banking sector in Ecuador.- Effectiveness of monitoring indicators in the architecture of a collaborative system.- IoT monitoring for real-time control of industrial processes.- Metric identification evaluating security information: A Systematic Literature Review.- Apps and user interfaces.- Evaluation of User Interface (UI) and User Experience (UX) for web services of a weather data monitoring platform.- Comparison of Free Android Mobile 3D Modeling Tools for AR Apps.- A Web App for Teaching Specialized English Vocabulary – Case of Study: Computer Sciences.
£56.99
Springer International Publishing AG Smart Grid and Internet of Things: 5th EAI
Book SynopsisThis book constitutes the refereed proceedings of the 5th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2021, held in TaiChung, Taiwan, in December 2021.The 9 regular papers and 4 short papers presented were carefully reviewed and selected from 57 submissions. The papers cover a broad range of topics in wireless sensor, vehicular ad hoc networks, security, deep learning and big data. The papers are organized in two subject areas: applications on internet of things, and communication security, big data, neural networks and machine learning.Table of ContentsApplications on Internet of Things.- Intellectual Property Protection of Zhuang Nationality Funeral Culture on Internet of Things.- China IoT UBI Car Insurance Regulatory Development Trend.- Constructing a violence recognition technique for elderly patients with lower limb disability.- Aquaculture Monitoring Systems based on Lightweight Kubernetes and Rancher.- Design and Implementation of Water Monitoring Using LoRa and NB-IoT.- InterWorking Function for Mission Critical Push To Talk Services.- Communication Security, Big Data, Neural Networks and Machine Learning.- Lightweight Privacy-Preserving Data Aggregation Scheme Based on Elliptic Curve Cryptography for Smart Grid Communications.- Design and Implementation of Distributed Image Recognition App with Federal Learning Techniques.- Improving Vision Clarity and Object Detection Accuracy in Heavy Rain Base on Neural Network.- An Enhanced Location-data Differential Privacy Protection Method Based on Filter.- Big data grave register information management system outside cemeteries under Internet of Things.- Machine Learning-based Models for Monofacial and Bifacial PV Module and Critical Model Feature Selection.- Study on the discovery of personal information on the network of people diagnosed with COVID-19.
£49.49
Springer International Publishing AG Multi-Agent Systems: 19th European Conference, EUMAS 2022, Düsseldorf, Germany, September 14–16, 2022, Proceedings
Book SynopsisThis book constitutes thoroughly refereed and revised selected papers from the proceedings of 19th European Conference on Multi-Agent Systems, EUMAS 2022, held in Düsseldorf, Germany, during September 14–16, 2022.The 23 full papers included in this book were carefully reviewed and selected from 36 submissions. The book also contains 6 short summaries of talks from PhD students at the PhD day. The papers deal with current topics in the research and development of multi-agent systems.Table of ContentsEUMAS 2022 Papers.- Iterative Goal-Based Approval Voting.- Mind the Gap! Runtime Verification of Partially Observable MASs with Probabilistic Trace Expressions.- Advising Agent for Service-Providing Live-Chat Operators.- Initial Conditions Sensitivity Analysis of a Two-Species Butterfly-Effect Agent-Based Model.- Proxy Manipulation for Better Outcomes.- The Spread of Opinions via Boolean Networks.- Robustness of Greedy Approval Rules.- Using Multiwinner Voting to Search for Movies.- Allocating Teams to Tasks: An Anytime Heuristic Competence-Based Approach.- Collaborative Decision Making for Lane-Free Autonomous Driving in the Presence of Uncertainty.- Maximin Shares under Cardinality Constraints.- Welfare Effects of Strategic Voting under Scoring Rules.- Preserving Consistency for Liquid Knapsack Voting.- Strategic Nominee Selection in Tournament Solutions.- Sybil-Resilient Social Choice with Low Voter Turnout.- A Survey of Ad Hoc Teamwork Research.- Combining Theory of Mind and Abduction for Cooperation under Imperfect Information.- A Modular Architecture for Integrating Normative Advisors in MAS.- Participatory Budgeting with Multiple Resources.- A Methodology for Formalizing Different Types of Norms.- Explainability in Mechanism Design: Recent Advances and the Road Ahead.- Integrating Quantitative and Qualitative Reasoning for Value Alignment.- Resource Allocation to Agents with Restrictions: Maximizing Likelihood with Minimum Compromise.- PhD Day Short Papers.- Proactivity in Intelligent Personal Assistants: A Simulation-based Approach.- Stability, Fairness, and Altruism in Coalition Formation.- Pro-Social Autonomous Agents.- Axiomatic and Algorithmic Study on Different Areas of Collective Decision Making.- Participatory Budgeting: Fairness and Welfare Maximization.- Human Consideration in Analysis and Algorithms for Mechanism Design.
£58.49
Springer International Publishing AG Interpretability in Deep Learning
Book SynopsisThis book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition. Table of Contents1 INTRODUCTION 1.1 Deep Learning Glossary 1.2 Evolution of Deep Learning 1.2.1 Neural Learning 1.2.2 Fuzzy Learning 1.2.3 Convergence of Fuzzy Logic and Neural Learning 1.2.4 Synergy of Neuroscience and Deep Learning 1.3 Awakening of Interpretability 1.3.1 Relevance 1.3.2 Necessity 1.3.3 The Taxonomy of Interpretability 1.4 The Question of Interpretability 1.4.1 Interpretability - Metaverse 1.4.2 Interpretability - The Right Tool 1.4.3 Interpretability - The Wrong Tool 2 NEURAL NETWORKS FOR DEEP LEARNING 2.1 Neural Network Architectures 2.1.1 Perceptron 2.1.2 Artificial Neural Network 2.1.3 Recurrent Neural Network 2.1.4 Convolutional Neural Network 2.1.5 Autoencoder Neural Network 2.1.6 Generative Adversarial Network 2.1.7 Graph Neural Network 2.2 Learning Mechanisms 2.2.1 Activation function 2.2.2 Forward Propagation 2.2.3 Backpropagation 2.2.4 Gradient Descent 2.2.5 Learning Rate 2.2.6 Optimization 2.2.7 Initialization 2.2.8 Regularization 2.3 Challenges and Limitations of Traditional Techniques 2.3.1 Resource-Demanding Checks 2.3.2 Uncertainty Measure 2.3.3 Network Learning Sanity Check 2.3.4 Gradient Checks 2.3.5 Decision Transparency 3 KNOWLEDGE ENCODING AND INTERPRETATION 3.1 What is Knowledge? 3.1.1 Image Representation 3.1.2 Word Representation 3.1.3 Graph Representation 3.2 Knowledge Encoding and Architectural Understanding 3.2.1 Role of Neurons 3.2.2 Role of Layers 3.2.3 Role of Explanation 3.2.4 Semantic Understanding 3.2.5 Network Understanding 3.3 Design and Analysis of Interpretability 3.3.1 Divide and Conquer 3.3.2 Greedy 3.3.3 Back-tracking 3.3.4 Dynamic 3.3.5 Branch and Bound 3.3.6 Brute-force 3.4 Knowledge Propagation in Deep Network Optimizers 3.4.1 Knowledge versus Performance 3.4.2 Deep versus Shallow Encoding 4 INTERPRETATION IN SPECIFIC DEEP ARCHITECTURES 4.1 Interpretation in Convolution Networks 4.1.1 Case Study: Image Representation by Unmasking Clever Hans 4.1.2 Variants of CNNs 4.1.3 Interpretation of CNNs 4.1.4 Review: CNN Visualization Techniques 4.1.5 Review: CNN Adversarial Techniques 4.1.6 Inverse Image Representation 4.1.7 Case study: Superpixels Algorithm 4.1.8 Activation Grid and Activation Map 4.1.9 Convolution Trace 4.2 Interpretation in Autoencoder Networks 4.2.1 Visualization of Latent Space 4.2.2 Sparsity and Interpretation 4.2.3 Case Study: Microscopy Structure to Structure Learning 4.3 Interpretation in Adversarial Networks 4.3.1 Interpretation in Generative Network 4.3.2 Interpretation in Latent Spaces 4.3.3 Evaluation Metrics 4.3.4 Case study: Digital Staining of Microscopy Images 4.4 Interpretation in Graph Networks 4.4.1 Neural Structured Learning 4.4.2 Graph Embedding and Interpretability 4.4.3 Evaluation Metrics for Interpretation 4.4.4 Disentangled Representation Learning on Graphs 4.4.5 Future Direction 4.5 Self-Interpretable Models 4.6 Pitfalls of Interpretability Methods 5 FUZZY DEEP LEARNING 5.1 Fuzzy Theory 5.1.1 Fuzzy Sets and Fuzzy Membership 5.1.2 Fuzzification and Defuzzification 5.1.3 Fuzzy Rules and Inference Systems 5.2 Neuro-Fuzzy Inference Systems 5.2.1 Architecture of a Neuro-Fuzzy Inference System 5.2.2 Other Design Elements of Neuro-Fuzzy Inference Systems 5.2.3 Learning mechanisms for Neuro-Fuzzy Inference Systems 5.2.4 Online Learning with Dynamic Streaming Data 5.3 Case studies 5.3.1 POPFNN Family of NFS − evolution towards sophisticated brain-like learning 5.3.2 Combining Conventional Deep Learning and Fuzzy Learning A Mathematical models and theories A.1 Choquet Integral A.1.1 Restricting the Scope of FM/ChI A.1.2 ChI Understanding from NN A.2 Deformation Invariance Property A.3 Distance Metrics A.4 Grad Weighted Class Activation Mapping A.5 Guided Saliency A.6 Jensen-Shanon Divergence A.7 Kullback-Leibler Divergence A.8 Projected Gradient Descent A.9 Pythagorean Fuzzy Number A.10 Targeted Adversarial Attack A.11 Translation Invariance Property A.12 Universal Approximation Theorem A List of digital resources and examples References .
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