Artificial intelligence (AI) Books

Book SynopsisFew scientific topics since the theory of biological evolution have inspired as much controversy as artificial intelligence has. Even now, fifty years after the term first made its appearance in academic journals, many philosophers and more than a few prominent scientists and software programmers dismiss the pursuit of thinking machines as the modern-day equivalent of medieval alchemists’ hunt for the philosopher’s stone-a pursuit based more on faith than on skeptical inquiry.In Arguing A.I., journalist Sam Williams charts both the history of artificial intelligence from its scientific and philosophical roots and the history of the A.I. debate. He examines how and why the tenor of the debate has changed over the last half-decade in particular, as scientists are struggling to take into account the latest breakthroughs in computer science, information technology, and human biology. For every voice predicting machines like 2001’s HAL within the next twenty

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Book SynopsisAddresses emerging biotechnologies with prodigious potential to benefit humankind but that are also fraught with ethical consequences. James Bradley guides discussions of the thorny issues resulting from the development of new biotechnologies. He also highlights the responsibilities of scientists to conduct research in an ethical manner.Trade Review“Building further on his remarkable scholarly work, James Bradley once again observes and dissects modern science and modern life in ways that challenge any kind of reader: student, scholar, research scientist, and most especially political decision makers. His interdisciplinary approach to studying the implications of biotechnology is the most accessible and useful, yet profound, of any academic work in this vast field. With characteristic good humor and patience, he confronts the fundamental issues within not only life sciences but moral and political philosophy as well. This is a necessary, although uncomfortable, wake-up call for humankind generally." - Timothy P. Terrel, Emory University School of Law and author of The Dimensions of Legal Reasoning: Developing Analytical Acuity from Law School to Law PracticeTable of Contents Abbreviations and Acronyms Preface Acknowledgments Chapter 1: Cells, Molecules, Genes, and Nature Chapter 2: Embryos, Stem Cells, Genetic Enhancement, Genomics, and Synthetic Biology Chapter 3: Genetically Engineered Organisms Chapter 4: CRISPR and Life's Future Chapter 5: Nanotechnology, Life, and Nanoethics Chapter 6: Brains, Minds, and Neuroethics Chapter 7: Robots and Roboethics Chapter 8: Responsibilities and Living Well with Modern Biotechnologies Chapter 9: The Urgency of Now Appendix 1: The Central Dogma of Biology, CRISPR, and Gene Drive Appendix 2: Tools for Neuroscience and Clinical Neurology Appendix 3: Sources of Scientific Information for Non-Scientists References Index

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Book SynopsisArgues that the near future of library work will be enormously impacted and perhaps forever changed as a result of artificial intelligence (AI) and machine learning systems becoming commonplace. Essays cover both essays theory and predictions of the future of these systems in libraries.Table of Contents Chapter 1 Introduction Chapter 2 HAMLET: Neural-Net-Powered Prototypes for Library Discovery Chapter 3 AI and Creating the First Multidisciplinary AI Lab Chapter 4 An Exploration of Machine Learning in Libraries Chapter 5 Conclusion Chapter 6 Sources Consulted

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Book SynopsisWith artificial intelligence on the rise, the way we run our organisations will changeand drastically. But what exactly will that future look like? And who will take the leading role: machines or people?In this compelling new book, leading management guru David De Cremer identifies the key areas where algorithms will collide with human skills, and assesses the likely outcomes.Will your next boss be a robot? Can an AI boss display the human qualities that define a good leader: compassion, empathy, imagination, ethics, and strategic awareness?Drawing on his own research findings, and those from thought leaders around the world, the author presents fascinating insights into the challenges that an automated work environment poses for organisations of the future.Leadership by Algorithm offers some startling conclusions that make clear the true nature of the power struggle between man and machine. It also identifies the leadership qualities needed to deal with this struggle most effectively.

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Book SynopsisAI is changing the world at frightening speed. A bestselling author decides to find out more…Trade Review‘There’s no book like it. Scholarly, childish, fascinating and hilarious – one of our funniest writers dissects what it takes to build a story and what that tells us about being human. It’ll really make you think, if you can stop laughing.’ —Chris Addison, co-creator of Breeders'Entertaining and alarmingly relevant, provocative and philosophically satisfying, it’s ultimately a profoundly human text.'—Observer‘A magnificent experiment by a perfect fool – deep and shallow and stupid and clever – the perfect use of AI (Andy Intelligence).’ —Robin Ince, author of The Importance of Being Interested‘In detailing his hysterical efforts to get ChatGPT to write a masterpiece, Stanton offers real insight into how it works or, well, doesn’t.’ —New Scientist'It's sometimes hard not to feel sorry for the priggish chatbot as Stanton deploys all his impish (some might say puerile) irreverence to goad the programme... Ultimately, however it is there, in Stanton's footnotes that the real genius of the book is found. For all the hilarity and absurdity, it asks profound questions about the relationship between humans and machines... you'll be hooked on the conundrum that is AI. There really is no turning back.' —Perspective'Benny the Blue Whale is many things. It’s a fascinating discourse on the nature of language and storytelling. It’s a philosophical treatise on the possibilities of artificial intelligence. It’s a receptacle for obscenely hilarious jokes, and the abstruse and arcane learning that fills Stanton’s brain... A brilliant and beautiful cyborg: part human brain, part computational muscle. It’s a post-post-modern work of genius.' —Anthony McGowan, Carnegie Medal-winning author of Lark‘A funny and surprising creative battle between man and machine.’ —The Bookseller‘The real draw, though, is Stanton’s breakdown of ChatGPT’s craft… The irreverent tone buoys a surprisingly thoughtful exploration of how AI might aid artists, and the ways in which it comes up short against its human competitors. This fascinates.’ —Publishers Weekly

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Book SynopsisTable of Contents1. Introduction; 2. The basic theory of memory structures; 3. Equivalence relations; 4. A plethora of simple examples; 5. The effectiveness theorems; 6. Fragments of lisp; 7. Derivations and transformations; 8. The Robson marking algorithm and applications; 9. Programs as data and the eval function; 10. Editing data efficiently; 11. Conclusions; Bibliography.

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Book SynopsisThere is an urgent need for responsible governance of Artificial Intelligence systems. This Handbook maps important features of responsible AI governance and demonstrates how to achieve and implement them at the regional, national and international level.Trade Review'… an indispensable and thought-provoking resource for shaping the future of AI and its societal impact.' Matija Franklin, PrometheusTable of ContentsIntroduction; Part I. Foundations of Responsible AI: 1. Artificial Intelligence – Key Technologies and Opportunities Wolfram Burgard; 2. Automating Supervision of AI Delegates Jaan Tallinn and Richard Ngo; 3. Artificial Moral Agents – Conceptual Issues and Ethical Controversy Catrin Misselhorn;4. Risk Imposition by Artificial Agents – The Moral Proxy Problem Johanna Thoma; 5. Artificial Intelligence and its Integration into the Human Lifeworld Christoph Durt; Part II. Current and Future Approaches to AI Governance: 6. Artificial Intelligence and the Past, Present and Future of Democracy Mathias Risse; 7. The New Regulation of the European Union on Artificial Intelligence – Fuzzy Ethics Diffuse into Domestic Law and Sideline International Law Thomas Burri; 8. Fostering the Common Good – An Adaptive Approach Regulating High-Risk AI-Driven Products and Services Thorsten Schmidt and Silja Voeneky; 9. China's Normative Systems for Responsible AI – From Soft Law to Hard Law Weixing Shen and Yun Liu; 10. Towards a Global Artificial Intelligence Charter Thomas Metzinger; 11. Intellectual Debt – With Great Power Comes Great Ignorance Jonathan Zittrain; Part III. Responsible AI Liability Schemes: 12. Liability for Artificial Intelligence – The Need to Address both Safety Risks and Fundamental Rights Risks Christiane Wendehorst; 13. Forward to the Past – A Critical Evaluation of the European Approach to Artificial Intelligence in Private International Law Jan von Hein; Part IV. Fairness and Non-Discrimination in AI Systems: 14. Differences that Make a Difference – Computational Profiling and Fairness to Individuals Wilfried Hinsch; 15. Discriminatory AI and the Law – Legal Standards for Algorithmic Profiling Antje von Ungern-Sternberg; Part V. Responsible Data Governance: 16. Artificial Intelligence and the Right to Data Protection Ralf Poscher; 17. Artificial Intelligence as a Challenge for Data Protection Law – And Vice Versa Boris Paal; 18. Data Governance and Trust – Lessons from South Korean Experiences Coping with COVID-19 Haksoo Ko, Sangchul Park and Yong Lim; Part VI. Responsible Corporate Governance of AI Systems: 19. From Corporate Governance to Algorithm Governance – Artificial Intelligence as a Challenge for Corporations and their Executives Jan Lieder; 20. Autonomization and Antitrust – On the Construal of the Cartel Prohibition in the Light of Algorithmic Collusion Stefan Thomas; 21. Artificial Intelligence in Financial Services – New Risks and the Need for More Regulation? Matthias Paul; Part VII. Responsible AI Healthcare and Neurotechnology Governance: 22. Medical AI – Key Elements at the International Level Fruzsina Molnár-Gábor and Johanne Giesecke; 23. 'Hey Siri, How Am I Doing?' – Legal Challenges for Artificial Intelligence Alter Egos in Healthcare Christoph Kroenke; 24. Neurorights – A Human-Rights Based Approach for Governing Neurotechnologies Philipp Kellmeyer; 25. AI-Supported Brain-Computer Interfaces and the Emergence of 'Cyberbilities' Boris Essmann and Oliver Mueller; Part VIII. Responsible AI for Security Applications and in Armed Conflict: 26. Artificial Intelligence, Law and National Security Ebrahim Afsah; 27. Morally Repugnant Weaponry? Ethical Responses to the Prospect of Autonomous Weapons Alex Leveringhaus; 28. On 'Responsible AI' in War – Exploring Preconditions for Respecting International Law in Armed Conflict Dustin A. Lewis.

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Book SynopsisIn the era of far-reaching technological innovation, from artificial intelligence to Big Data, it is possible that there will be conscious machines in our lifetime. This book establishes a foundation for the philosophy of technology and investigates how the digital age might alter our most basic political practices and ideas.Trade Review'Human beings are unprepared for the consequences of artificial intelligence and big data analysis - consequences for human rights, democratic government, public spheres, distributive justice, individual dignity, pursuit of meaningful human lives, and the moral status of potentially self-guided artificial intelligence itself. Drawing on a range of political theories and welcome common sense, Mathias Risse briskly frames the agenda to prepare for a world of 'deepfakes,' surveillance capitalism, and machines that alter human lives while operating without human supervision. Especially helpful in challenging assumptions such as that those who gather our data also own it, this book points the way toward preserving both individual and social lives of meaning and deliberate choices.' Martha Minow, Harvard Law School'In this outstanding book, Harvard's Mathias Risse explores hitherto uncharted terrain of political theory. The liberal-egalitarian tradition, with Rawls as its central figure, has by and large ignored the political nature of technology, thus condemning itself to irrelevance with regard to one of the most pressing political questions of our time: How will digital technology affect human life in the decades to come? [Without appropriate political steps, it is imaginable that humanity will not survive the further development of artificial intelligence. So we, ordinary citizens of the world, had better pay close attention.] Starting from the Rawlsian idea of public reason, Risse's explorative study draws on intellectual traditions ranging from Marxism to science and technology studies in order to toughen up liberal-egalitarian philosophy for the challenge. I hope this well-written book will find a broad readership and inspire much- needed work on this topic of topics.' Michael Schefczyk, Karlsruhe Institute of Technology'Risse could not be more right that we need to do political theory for a digital age, to come to grips with the political dimensions of our social lives as the very notion of social life is transformed by advances in AI in both the near and far terms. Weaving together insights from the philosophy of technology, rights theory, science and technology studies, and traditional political theory and political philosophy, Risse reveals both the depth of challenges we face across a spectrum of issues in the immediate and distant future, and the promise of a reconfigured political theory for resolving them. The breadth and care that Risse takes with both the technological possibilities and the philosophical and political foundations is remarkable. This is a must-read!' John Basl, Northeastern UniversityTable of Contents1. Introduction: digital lifeworlds in human history; 2. Learning from the Amish: political philosophy as philosophy of technology in the digital century; 3. Artificial intelligence and the past, present, and future of democracy; 4. Truth will not set you free: is there a right to it anyway? Elaborating on the work public reason does in life 2.0; 5. Knowing and being known: investigating epistemic entitlements in digital lifeworlds; 6. Beyond porn and discreditation: epistemic promises and perils of deepfake technology; 7. The fourth generation of human rights: epistemic rights in life 2.0 and life 3.0; 8. On surveillance capitalism, instrumentarian power, and social physics: securing the enlightenment for digital lifeworlds; 9. Data as social facts: distributive justice meets big data; 10. God, Golem, and gadget worshippers: meaning of life in the digital age; 11. Moral status and political membership: toward a political theory for life 3.0; Epilogue.

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Book SynopsisThis book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in computer science and artificial intelligence, it covers a wide spectrum of search methods. Readers will be able to begin with simple approaches and gradually progress to more complex algorithms applied to a variety of problems. It demonstrates that search is all pervasive in artificial intelligence and equips the reader with the relevant skills. The text starts with an introduction to intelligent agents and search spaces. Basic search algorithms like depth first search and breadth first search are the starting points. Then, it proceeds to discuss heuristic search algorithms, stochastic local search, algorithm A*, and problem decomposition. It also examines how search is used in playing board games, deduction in logic and automated planning. The book concludes with a coverage on constraint satisfaction.

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Book SynopsisThis book gives legal practitioners, academics, and law students a comprehensive look at the main impacts of artificial intelligence use in legal practice. Contributors identify the main challenges surrounding a legally compliant and ethical development of AI and craft a framework for analyzing the costs and benefits of new technology.Table of Contents1. Lawyering in the Digital Age Pietro Ortolani and Larry A. DiMatteo; Part I. Effects of Technology on Legal Practice: 2. Disruptive effects of legal tech Larry A. DiMatteo, Jiang Christine Jiaying and Robert Thomas; 3. The effects of technology on legal practice: from punch card to artificial intelligence? Andrė Janssen and Tom J. Vennmanns; 4. Legal drafting and automation Benjamin Werthmann; 5. Emerging rules on artificial intelligence: Trojan horses of ethics in the realm of law? Florian Möslein and Maximilian Horn; Part II. Legal Tech and ADR: 6. Legal tech in ADR Mateja Durovic and Franciszek Lech; 7. A blockchain-based smart dispute resolution method Alessandro Palombo, Raffaele Battaglini and Luigi Cantisani; 8. Digital dispute resolution: blurring the boundaries of ADR Pietro Ortolani; Part III. Legal Tech in Consumer Relations and Small Claims 9. Legal tech in consumer relations and small-value claims: a survey Francisco de Elizalde; 10. Regulation of legal services and access to justice in the digital age: a war report Jin-Ho Verdonschot and Max Houben; 11. Legal tech and EU consumer law Martin Ebers; 12. The two faces of legal tech in B2C relations Eric Tjong Tjin Tai; Part IV. Legal Tech and Public Law: 13. Blockchain's heterotopia: technological infrastructures and lawyering in the public sector Georgios Dimitropoulos; 14. Fundamental rights and the use of artificial intelligence in court Jean-Marc van Gyseghem; 15. Legal tech in public administration: prospects and challenges Antonios Kouroutakis; Part V. Legal Ethics and Societal Values Confront Technology: 16. Ethics guidelines for trustworthy AI Michel Cannarsa; 17. Ethical digital lawyering: technical and philosophical insights Mathieu Guillermin, Arnaud Billion, Carine Copain-Héritier and Emmanuel de Vaujany; 18. Law, disintermediation, and the future of trust Christoph Kletzer; Part VI. Fate of the Legal Professions: 19. Lawyering somewhere between computation and the will to act: a digital age reflection Jeffrey M. Lipshaw; 20. Surviving the digital transformation – a method for lawyers to approach legal tech Paw Fruerlund and Sebastian Peters; 21. Road forward: promise and danger Larry A. DiMatteo and Pietro Ortolani.

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Book SynopsisDesigned for upper undergraduate and graduate courses on adversarial learning and AI security, this textbook connects theory with practice using real-world examples, case studies, and hands-on student projects.Trade Review'This textbook is one of the first major efforts to systematically examine adversarial machine learning. It clearly outlines the most common types of attacks on machine learning/AI, and defenses, with rigorous yet practical discussions. I would highly recommend it to any instructor or machine learning student who seeks to understand how to make machine learning more robust and secure.' Carlee Joe-Wong, Carnegie Mellon University'This is a clear and timely introduction to the vital topic of adversarial learning. As leading international experts, the authors provide an accessible explanation of the foundational principles and then deliver a nuanced and extensive survey of recent attack and defense strategies. Multiple suggested projects allow the book to serve as the core of a graduate course.' Mark Coates, McGill University'Remarkably comprehensive, this book explores the realm of adversarial learning, revealing the vulnerabilities and defenses associated with deep learning. With a mix of theoretical insights and practical projects, the book challenges the misconceptions about the robustness of Deep Neural Networks, offering strategies to fortify them. It is well suited for students and professionals with basic calculus, linear algebra, and probability knowledge, and provides foundational background on deep learning and statistical modeling. A must-read for practitioners in the machine learning field, this book is a good guide to understanding adversarial learning, the evolving landscape of defenses, and attacks.' Ferdinando Fioretto, Syracuse University'In a field that is moving at break-neck speed, this book provides a strong foundation for anyone interested in joining the fray.' Amir Rahmati, Stony BrookTable of ContentsContents; Preface; Notation; 1. Overview of adversarial learning; 2. Deep learning background; 3. Basics of detection and mixture models; 4. Test-time evasion attacks (adversarial inputs); 5. Backdoors and before/during training defenses; 6. Post-training reverse-engineering defense (PT-RED) Against Imperceptible Backdoors; 7. Post-training reverse-engineering defense (PT-RED) against patch-incorporated backdoors; 8. Transfer post-training reverse-engineering defense (T-PT-RED) against backdoors; 9. Universal post-training backdoor defenses; 10. Test-time detection of backdoor triggers; 11. Backdoors for 3D point cloud (PC) classifiers; 12. Robust deep regression and active learning; 13. Error generic data poisoning defense; 14. Reverse-engineering attacks (REAs) on classifiers; Appendix. Support Vector Machines (SVMs); References; Index.

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Book SynopsisThis collection is the first to comprehensively examine the implications of AI technology on legal and regulatory systems. Featuring experts from Europe and the US, this book will appeal to scholars of law, economics, and public policy, as well as readers generally interested in emerging legal questions related to algorithms.Trade Review'There is a shift in the academic debate from the 'if' to the 'how' AI should and could be regulated. This volume covers a broad range of fields, from robotics to copyrights and financial services, all united in one question: what would a regulatory framework that allows us to de-mystify algorithms and get to grips with the commercialisation of data look like? The regulatability of AI is the key issue of our times. The ten contributions provide dense up-to-date information and enticing inspiration in the search for societally acceptable solutions.' Hans W. Micklitz, European University Institute'A timely book that finely addresses a crucial issue in the age of digitalization - the governance of algorithms - and helps to identify a new and necessary field of legal studies.' Ugo Pagallo, University of Turin'The ubiquity of algorithms in many areas of our lives has become one of the burning issues of our time, with legislators and policy-makers around the world grappling with the many challenges associated with Artificial Intelligence and Algorithms. This development is significant for many disciplines, including law. This collection of essays examines many of the legal issues of AI and algorithms and illustrates just how complex an area this has become. It will be welcomed by any reader interested in understanding the many legal and ethical questions which need to be resolved.' Christian Twigg-Flesner, University of Warwick'The book accomplishes a difficult task. It is an excellent source for those who dive for the first time into the legal challenges that AI poses to law … The book is written in such a clear manner that it allows an interdisciplinary understanding. The authors and editors should be applauded for the clarity with which they explore an extremely complex subject.' Francisco de Elizalde, PrometheusTable of ContentsPreface; 1. Robotics and Artificial Intelligence: The Present and Future Visions Sami Haddadin and Dennis Knobbe; 2. Regulating AI and Robotics: Ethical and Legal Challenges Martin Ebers; 3. Regulating Algorithms – How to De-Mystify the Alchemy of Code? Mario Martini; 4: Automated Decision-Making under Article 22 GDPR: Towards a More Substantial Regime for Solely Automated Decision-Making Diana Sancho; 5. Robot Machines and Civil Liability Susana Navas; 6. Extra-contractual Liability for Wrongs Committed by Autonomous Systems Ruth Janal; 7. Control of Algorithms in Financial Markets – the Example of High Frequency Trading Gerald Spindler; 8. Creativity of Algorithms and Copyright Susana Navas; 9. 'Wake Neutrality' of Artificial Intelligence Devices Brian Subirana, Renwick Bivings and Sanjay Sarma; 10. The (envisaged) Legal Framework of Commercialisation of Digital Data within the EU Björn Steinrötter.

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Book SynopsisAI can assist the linguist in doing research on the structure of language. This Element illustrates this possibility by showing how a conversational AI based on a Large Language Model can assist the Construction Grammarian, and especially the Frame Semanticist.Table of ContentsIntroduction; 1. Safety Instructions: Risks and Limitations of LLMs and Generative AI; 2. Constructions; 3. Using an AI to Help Study Constructions; 4. Limitations of LLMs for Constructional Analysis; 5. Cognitive Frames and FrameNet; 6. Prompt Engineering for Building FrameNet; 7. Final safety instructions: Risks and limitations revisited; 8. Imagining the Future of Copilots for Linguists.

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Book SynopsisThe Routledge Social Science Handbook of AI is a landmark volume providing students and teachers with a comprehensive and accessible guide to the major topics and trends of research in the social sciences of artificial intelligence (AI), as well as surveying how the digital revolution from supercomputers and social media to advanced automation and robotics is transforming society, culture, politics and economy. The Handbook provides representative coverage of the full range of social science engagements with the AI revolution, from employment and jobs to education and new digital skills to automated technologies of military warfare and the future of ethics. The reference work is introduced by editor Anthony Elliott, who addresses the question of relationship of  social sciences to artificial intelligence, and who surveys various convergences and divergences between contemporary social theory and the digital revolution.The Handbook is excepTrade Review"As expected from a handbook with the goal of summarizing current debates, questions are posed and controversies noted more often than answers are offered in this collection of 21 essays. However, surveying so many different angles on artificial intelligence (AI) allows some insight-inducing themes to emerge. AI and machine learning (ML) are everywhere, from a cellphone's virtual assistant to tech support chatbots, including in the machines that decipher handwritten addresses for the US Postal Service. Many AI systems are assisted by small armies of humans who fill in when the software fails. Such technology remains invisible to most people yet shapes their understandings of the world and themselves. People think and categorize, work, play, and govern themselves differently because of AI—they adopt algorithmic thinking, see new value in inferential reasoning because of big data, and treat anthropomorphic robots like persons. Sometimes these changes are obvious or can be articulated, but some seem to influence human experience and expectations of the world itself, as in the debatable but widespread idea that minds are computers, and computers are (so far fairly limited) minds. Many will use this book, though specialists are likely to be most interested. Summing Up: Highly recommended. Lower- and upper-division undergraduates. Graduate students and faculty. General readers."Matthew J. Moore, Professor of Political Science, California Polytechnic State University, San Luis Obispo, USATable of ContentsPart I: Social Science Approaches to Artificial Intelligence 1. The Complex Systems of AI: Recent Trajectories of Social Theory 2. Geographies of AI 3. Artificial Intelligence and Psychology 4. AI in the Age of Technoscience: On the Rise of Data-Driven AI and its Epistem-Ontological Foundations 5. Work, Employment and Unemployment After AI 6. Affects After AI: Sociological Perspectives on Artificial Companionship 7. Anthropology, AI and Robotics 8. Ethics of Artificial Intelligence 9. Human-Machine Interaction and Design Methods Part II: Fields of Artificial Intelligence in Social Science Research 10. Management and Organisation in the Age of AI 11. Ambivalent Places of Politics: The Social Construction of Certainties in Automated Mobilities and Artificial Intelligence 12. Smart Environments 13. Models of Law and Regulation for AI 14. Artificial Intelligence and Cyber-security 15. Lethal Autonomous Weapons Systems 16. AI and Worldviews in the Age of Computational Power 17. Technogenarians: Ageing and Robotic Care 18. Big Data and Data Analytics 19. AI, Culture Industries and Entertainment 20. AI, Robotics, Medicine and Health Sciences 21. AI, Smart Borders and Migration

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Book SynopsisThis book covers all the necessary topics that a professional game AI programmer needs to know, from math and steering behaviours to terrain analysis, pathfinding and decision-making. Written to be easily accessible, each topic is accompanied by an example game that allows the reader to add their own code to see the effects their changes have.Each chapter is split into two parts. The first part covers the necessary theory in a friendly, conversational manner, using visual examples and fictional game scenarios to give additional context. The second part is a coding tutorial in C# for the topic at hand. Each chapter has its own example game available to download, written in C# in the Unity Game Engine.This book will be suitable for students and aspiring games programmers looking to gain a grounding in game AI techniques.Table of ContentsChapter 1 IntroductionChapter 2 MathChapter 3 Steering BehavioursChapter 4 Terrain AnalysisChapter 5 PathfindingChapter 6 Decision-MakingChapter 7 Fuzzy LogicChapter 8 Chess AIChapter 9 Genetic AlgorithmsChapter 10 Neural Networks

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Book SynopsisWhat is artificial intelligence? Can I realistically use it in my school? Whatâs best done by human intelligence vs. artificial intelligence, and how do I bring these strengths together? What would it look like for me, and my school, to be AI Ready?AI for School Teachers will help teachers and headteachers understand enough about AI to build a strategy for how it can be used in their school. Examining the needs of schools to ensure they are ready to leverage the power of AI and drawing examples from early years to high school students, this book outlines the educational implications and benefits that AI brings to school education in practical ways. It develops an understanding of what AI is and isn't and how we define and measure what we value and provides a framework which supports a step-by-step approach to developing an AI mindset, focusing on ways to improve educational opportunities for students with evidence-informed interventions.Trade Review"This book is designed to engage members of the teaching profession directly and in an accessible way. It is starting with teachers not technology. It then places the importance of AI to teachers in their practice and follows by guiding them through how to make AI work for them. I am excited by the potential of this book to empower the teaching profession. Too many feel hard pressed by accountability systems and the pressures of the pandemic. New AI technology offers the possibility of relieving the pressure and liberating teachers to re-connect with their vocation and realise the potential of their pupils." -- Lord Jim Knight, Chair of E-ACT multi academy trust and CAST "Importantly, Rose and Karine assure us we don’t need to be experts or data scientists to be substantively engaged with AI. And with this book, they have provided teachers an all-access pass to one of the most important public conversations of our era. Jump right in, enjoy the ride and then take everyone on it with you!" --Tabitha Goldstaub, Co-Founder of Cognition X, Chair of the UK Government's AI Council, from the Foreword"The COVID-19 pandemic has further highlighted the importance of digital systems and data in education, but while there are many questions, credible answers are still few and far between. This book will help educationalists understand what AI is, what questions and dilemmas it raises, and how to respond." --Timo Hannay, Founder, SchoolDash LimitedTable of ContentsForeword. Introduction: Understanding the ingredients. 1 What is AI and why might AI be useful in education? 2 Educational challenges and AI. 3 Data, data everywhere. 4 Looking at data differently. 5 Applying AI to understand data. 6 Learning from AI. 7 Ethical questions and what is next? Index.

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Book SynopsisAI for Creativity provides a fascinating read of what is currently emerging in the very cutting edge area of artificial intelligence and the tools being developed to enable computational creativity that hold the propensity to dramatically change our lives.Table of ContentsAuthor. Introduction. 1 The Creative Process. 2 The Creative Enigma. 3 Perspectives on Creativity. 4 Computational Creativity. References. Index.

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Book SynopsisArtificial intelligence (AI) is increasingly impacting many aspects of peopleâs lives across the globe, from relatively mundane technology to more advanced digital systems that can make their own decisions. While AI has great potential, it also holds great peril depending on how it is designed and used. AI for Diversity questions how AI technology can lead to inclusion or exclusion for diverse groups in society. The way data is selected, trained, used, and embedded into societies can have unfortunate consequences unless we critically investigate the dangers of systems left unchecked, and can lead to misogynistic, homophobic, racist, ageist, transphobic, or ableist outcomes. This book encourages the reader to take a step back to see how AI is impacting diverse groups of people and how diversity-awareness strategies can impact AI.Trade Review"The book is written in a really approachable way for non-specialists and will engage introductory and interdisciplinary audiences. The sections on gender and queering AI are particularly strong, and the book is a highly worthy and important contribution for those chapters alone." --Ashley Shew, Associate Professor, Virginia TechTable of Contents1.Opening the Black Box of AI. 2. Gendered AI: performativity, expectations, and sexism. 3. Queering AI: gender expression, identity, and binaries. 4. AI and Race: recognition, bias, and systemic issues. 5. Bodies and AI: Health, ageing, and disabilities. 6. AI and Class: socioeconomic issues reproduced by technology. 7. Intersectionality and Responsible AI.

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Book SynopsisArtificial intelligence (AI) is increasingly impacting many aspects of people's lives across the globe, from relatively mundane technology to more advanced digital systems that can make their own decisions. While AI has great potential, it also holds great peril depending on how it is designed and used. AI for Diversity questions how AI technology can lead to inclusion or exclusion for diverse groups in society. The way data is selected, trained, used, and embedded into societies can have unfortunate consequences unless we critically investigate the dangers of systems left unchecked, and can lead to misogynistic, homophobic, racist, ageist, transphobic, or ableist outcomes. This book encourages the reader to take a step back to see how AI is impacting diverse groups of people and how diversity-awareness strategies can impact AI.Trade Review"The book is written in a really approachable way for non-specialists and will engage introductory and interdisciplinary audiences. The sections on gender and queering AI are particularly strong, and the book is a highly worthy and important contribution for those chapters alone." --Ashley Shew, Associate Professor, Virginia TechTable of Contents1.Opening the Black Box of AI. 2. Gendered AI: performativity, expectations, and sexism. 3. Queering AI: gender expression, identity, and binaries. 4. AI and Race: recognition, bias, and systemic issues. 5. Bodies and AI: Health, ageing, and disabilities. 6. AI and Class: socioeconomic issues reproduced by technology. 7. Intersectionality and Responsible AI.

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Book SynopsisAn international and interdisciplinary perspective on the adoption and governance of artificial intelligence (AI) and machine learning (ML) in defence and military innovation by major and middle powers. Advancements in AI and ML pose pressing questions related to evolving conceptions of military power, compliance with international humanitarian law, peace promotion, strategic stability, arms control, future operational environments, and technology races. To navigate the breadth of this AI and international security agenda, the contributors to this book include experts on AI, technology governance, and defence innovation to assess military AI strategic perspectives from major and middle AI powers alike. These include views of how the United States, China, Japan, South Korea, the European Union, and Russia see AI/ML as a technology with the potential to reshape military affairs and power structures in the broader international system. This diverse set of views aims to help elucTrade Review"This is a must-read volume for anybody interested in understanding both the general implications of AI in the world of defence and more specific issues, including a set of in-depth country-studies illustrating what exactly different countries are doing and trying to achieve."Andrea Gilli, Senior Researcher, NATO Defence College"This book is going to become the most important roadmap on how AI is likely to shape and influence the strategically consequential militaries of the world. No one can predict how AI is going to re-engineer wars and conflicts, but the authors offer a unique peek around the corner. And Raska and Bitzinger as co-editors remain ahead of the curve."Dr. Chung Min Lee, Senior Fellow, Carnegie Endowment for International Peace, University Professor, Korea Advanced Institute of Science and Technology (KAIST), and Chairman of the International Advisory Council, IISS "This book provides important perspectives on different national approaches to the development of artificial intelligence that offer critical insights for current debates on the military and strategic impacts of AI." Elsa Kania, Adjunct Senior Fellow, Technology and National Security Program, Center for a New American SecurityTable of ContentsList of figuresList of tablesList of boxesAcknowledgmentsAcronyms and abbreviationsIntroduction: The AI Wave in Defence InnovationMichael Raska and Richard A. Bitzinger1. Artificial Intelligence in Warfare: Military Uses of AI and Their International Security Implications Jean-Marc Rickli and Federico Mantellassi2. Artificial Intelligence and Technological Governance: Catalysts for Abounding National Security Risks in the Post-COVID-19 World Tate Nurkin3. AI and Governance in Defence Innovation: Implementing an AI Ethics Framework Cansu Canca4. European Military AI: Why Regional Approaches are Lagging BehindSimona R. Soare5. US Governance of Artificial Intelligence for National Security: Competitive Advantage from the Moral High Ground?Zoe Stanley-Lockman6. China’s Evolving AI Development: Emergent Process Transcending Instrumentalism and Morality Qi Haotian7. Assessing Russia’s National Strategy for AI Development Vadim Kozyulin8. Military AI Developments in Russia Samuel Bendett9. Comparing Military AI Strategic Perspectives: Japan and South Korea Ryo Hinata-Yamaguchi10. Australia’s Approach to AI Governance in Military and Defence Kate Devitt and Damian CopelandAbout the ContributorsIndex

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Book SynopsisThe chapters in this book explore the main domains that represent considerable risks for the respect of privacy, such as education, health, finance or social media.Through its place in the massive data production industry, the Internet of Things participates in the development of artificial intelligence and is increasingly attracting the attention of web giants, governments and especially all types of hackers. Thanks to this book, private and public organizations will have at their disposal a tool that highlights, on the one hand, the major challenges raised by privacy in the context of the Internet of Things and, on the other hand, recommendations for improving good practices.Digital identity is presented as a bulwark for the protection of privacy. It opens up new avenues for improving digital trust. Concretely, there are a set of challenges that are associated with the management of digital identity, mainly in relation to the compliance and governance of personnel daTable of ContentsSection I Digital Identity Era. 1. Demystifying the Digital Identity Challenges and the Blockchain Role. 2. Blockchain for Digital Identity. Section II Privacy Dilemma. 3. Security and Corporate Violation to Privacy in the Internet Of Things Age. 4. Security, Privacy and Blockchain in Financial Technology. Section III Sensitive Data Challenges. 5. Where Does the Novel Legal Framework for AI in Canada Stand against the Emerging Trend of Online Test Proctoring? 6. - Blockchain, AI and Data Protection in Healthcare: A Comparative Analysis of Two Blockchain Data Marketplaces in Relation to Fair Data Processing and the ‘Data Double-Spending’ Problem. 7 - Cyber Influence Stakes. Postface.

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Book SynopsisArtificial intelligence (AI), machine learning, and advanced electronic circuits involve learning from every data input and using those inputs to generate new rules for future business analytics. AI and machine learning are now giving us new opportunities to use big data that we already had, as well as unleash a whole lot of new use cases with new data types. With the increasing use of AI dealing with highly sensitive information such as healthcare, adequate security measures are required to securely store and transmit this information. This book provides a broader coverage of the basic aspects of advanced circuits design and applications.AI for Big Data-Based Engineering Applications from Security Perspectives is an integrated source that aims at understanding the basic concepts associated with the security of advanced circuits. The content includes theoretical frameworks and recent empirical findings in the field to understand the associated principles, key challenges, and recent real-time applications of advanced circuits, AI, and big data security. It illustrates the notions, models, and terminologies that are widely used in the area of Very Large Scale Integration (VLSI) circuits, security, identifies the existing security issues in the field, and evaluates the underlying factors that influence system security. This work emphasizes the idea of understanding the motivation behind advanced circuit design to establish the AI interface and to mitigate security attacks in a better way for big data. This book also outlines exciting areas of future research where already existing methodologies can be implemented. This material is suitable for students, researchers, and professionals with research interest in AI for big dataâbased engineering applications, faculty members across universities, and software developers.

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Book SynopsisArtificial intelligence is shaking up economies around the world as well as society at large and is predicted to be either the best or worst thing to happen to humanity. This book looks at what exactly artificial intelligence is, how it can be classified, how it differentiates from other concepts such as machine learning, big data, blockchain, or the Internet-of-Things, and how it has evolved and might evolve over time. Providing a clear and unbiased picture of artificial intelligence, the book provides critical analyses of the advantages and disadvantages, opportunities and threats of AI progress for business and civilisation. Solutions and possible directions of how humanity might deal with rapid development and evolutions will be given and discussed, and consider regulation, employment, ethics, education and international cooperation. Unlike existing literature, this book provides a comprehensive overview of AI based on detailed analysis and insight. Finally, several real-Table of Contents Commencements: AI, business, and civilization Clarifications: AI, big data, the internet-of-things, and robotics What is (and is not) artificial intelligence? Classifying, exemplifying, and envisioning AI’s past, present, and (future) perspective Concerns: AI’s double-edged sword in education, enterprises, and elections Did higher education dig its own grave by developing AI? Both blessing and curse? AI in the workplace Might AI become a threat to democracy…or is it already? Constructions: Preparing for the AI revolution Regulation and the role of the state AI literacy and ethical conduct International relations and cross-cultural cooperation Cases: From retailing to retelling to retaliation Walmart: The retAIl giant New York’s Metropolitan Museum of ARTificial intelligence China: The ultimate AI trAIning ground Conclusions: Our fate made in machines

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Book SynopsisHow can we give data physical form?And how might those creations change the ways we experience data and the stories it can tell?Making with Data: Physical Design and Craft in a Data-Driven World provides a snapshot of the diverse practices contemporary creators are using to produce objects, spaces, and experiences imbued with data. Across 25+ beautifully-illustrated chapters, international artists, designers, and scientists each explain the process of creating a specific data-driven pieceâillustrating their practice with candid sketches, photos, and design artifacts from their own studios.The author website, featuring updates and more information about the projects behind the book, can be found here: https://makingwithdata.org/.Featuring influential voices in computer science, data science, graphic design, art, craft, and architecture, Making with Data is accessible and inspiring for entTrade Review"A mind-blowing collection! With the rich visual process descriptions, the creators invite us into their workshops and let us look over their shoulders. You will discover both an exhibition of wonderful data-inspired works as well as the backstories of each of these pieces. Whether hand-made, machine-controlled, or through natural processes, all the chapters show fascinating and bespoke creations of data objects. A much needed collection highlighting what is happening at the frontiers of art and sciences in this new field of data design."-- Giorgia Lupi, partner at Pentagram and author of Dear Data"What a much-needed book! Till, Sam, Lora, and Wes show us that data communication can be so much more than just visualization. There is a whole exciting world of data physicalization waiting to be explored, and the authors open the door for us and lead us through it with intelligent commentary. The book takes us to visit different artists, who explain their approaches and tools – from copper pipes to paper, from wood to electronics. It's a hugely inspiring tour. Reading this book will make you want to experiment with data in the realm of the physical."-- Lisa Charlotte Muth, data vis designer and writer at Datawrapper "This book has fresh inspirations from innovative artist-inventors who open up new possibilities for anyone who has data that tells a story. The screen is no longer the goal or the limit; freeing designers to explore more dimensions and shape deeper experiences to reach people with important messages about their health, communities, and climate. Data physicalizations break free into new dimensions where playful imaginations can use water, plastic, wood, or stone to fabricate data stories for public installations and private reflections. This book makes me want to turn on the laser cutter and restart the 3D printer to fabricate something startling, informative, and eye opening."-- Ben Shneiderman, Professor, Computer science, University of Maryland, USA"A collection of recent and diverse data-driven physical artifacts and sensorial experiences. Projects are beautifully illustrated and described in jargon-free language packed with practical information elucidating the design process, from the tools used to the context of their conception. Making with Data is an invaluable resource for educators and practitioners alike. It broadens our perspective of representing data by engaging all our senses."-- Isabel Meirelles, Professor, Faculty of Design, OCAD University, Toronto, Canada"“Designing with Data” is one of today’s key mantras. What next? Perhaps “Making with Data”, as argued by professors Huron, Nagel, Oehlberg and Willett. This timely book explores new ways data is penetrating our living environment and is crossing the boundary between the physical and the digital. Innovative fabrication methods lend materiality to data, as designers experiment with the use of laser cutters and 3D printers to transform maps and charts into tactile models and artworks. A compelling read for any data enthusiast!"-- Carlo Ratti, Director, MIT Senseable City Lab, USATable of Contents1. Handcraft - Introduction by Sheelagh Carpendale and Lora Oehlberg. 1.1 Snow Water Equivalent by Adrien Segal. 1.2 Life in Clay by Alice Thudt. 1.3 V-Pleat Data Origami by Sarah Hayes. 1.4 Anthropocene Footprints by Mieka West. 1.5 Endings by Loren Madsen. 2. Participation - Introduction by Georgia Panagiotidou and Andrew Vande Moere. 2.1 Cairn by Pauline Gourlet and Thierry Dassé. 2.2 SeeBoat by Laura Perovich. 2.3 Let’s Play with Data by Jose Duarte and EasyDataViz. 2.4 100% [City] by Rimini Protokoll (Helgard Haug, Stefan Kaegi, and Daniel Wetzel). 2.5 Data Strings by Daniel Pearson, Pau Garcia, and Alexandra de Requesens. 3. Digital Production - Introduction by Yvonne Jansen. 3.1 Chemo Singing Bowl by Stephen Barrass. 3.2 Wage Islands by Ekene Ijeoma. 3.3 Data That Feels Gravity by Volker Schweisfurth. 3.4 Orbacles by MINN_LAB Design Collective (Daniel F. Keefe, Ross Altheimer, Andrea J. Johnson, Mahdieh Mahmoudi, Patrick Moe, Maura Rockcastle, Marc Swackhamer, and Aaron Wittkamper). 3.5 Dataseeds by Nick Dulake and Ian Gwilt. 4 Actuation - Introduction by Pierre Dragicevic. 4.1 Tenison Road Charts by David Sweeney, Alex Taylor, and Siân Lindley. 4.2 LOOP by Kim Sauvé and Steven Houben. 4.3 AirFIELD by Nik Hafermaas, Dan Goods, and Jamie Barlow. 4.4 EMERGE by Jason Alexander, Faisal Taher, John Hardy, and John Vidler. 4.5 Zooids by Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, and Pierre Dragicevic. 5. Environment - Introduction by Dietmar Offenhuber. 5.1 Perpetual Plastic by Liina Klauss, Moritz Stefaner and Skye Morét. 5.2 Dataponics: Human-Vegetal Play by Robert Cercós. 5.3 Solar Totems by Charles Sowers. 5.4 Staubmarke (Dustmark) by Dietmar Offenhuber.

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Book SynopsisArtificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures.FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.Table of ContentsIntroduction. Environmental Models for Sustainable Development. Role of Artificial Intelligence in Water Sector: Dependency on Automation Systems. Modeling and Prediction of Water Security Connected to Global Challenges. Simulation Models of Threatened Aquatic Ecosystems. Monitoring of Contaminants in Aquatic Ecosystems using Big Data. Mitigation of Water Shortage Issues: Water 4.0. Water Pollution Monitoring Using Artificial Intelligent: Basic Algorithm Design. Neural Networks in Wastewater Treatment Process. Circular Economy Models in Water and Wastewater. Integrated Water Resources Management: Perspectives and Challenges. Hydrological Modeling for Sustainable Groundwater Resources.

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Book SynopsisThe development of effective and usable software for spatial computing platforms like virtual reality (VR) requires an understanding of how these devices create new possibilities (and new perils) when it comes to interactions between humans and computers. Virtual Reality Usability Design provides readers with an understanding of the techniques and technologies required to design engaging and effective VR applications.The book covers both the mechanics of how human senses and the mind experience immersive virtual environments, as well as how to leverage these mechanics to create human-focused virtual experiences. Deeply rooted in principles of human perception and computational interaction, the current and future limitations of these replacements are also considered. Full of real-world examples, this book is an indispensable guide for any practising VR developer interested in making efficient and effective interfaces. Meanwhile, explorations of concrete theory in their practical application will be useful for VR students and researchers alike.Table of Contents1. What Makes Virtual Reality Remarkable? 2. Making the Virtual Seem Real 3. Sensation and Perception 4. Supporting Primary Senses 5. Supporting Peripheral Senses 6. Perceiving Space and Scale 7. Further Psychological Effects of Inhabiting a Virtual Environment 8. Experience Usability 9. Fictions of Physics 10. Locomotion and Navigation 11. Activities and Interactions 12. Information Display 13. Translating Traditional Interfaces for VR

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Book SynopsisSociety 5.0 points toward a human-centred approach by the use of modern, advanced technologies and artificial intelligence. This book explores and offers an overview of knowledge management embraced in the current scenario of Society 5.0, shedding light on its importance in a society that is increasingly digital and interconnected. The book enhances current managerial and economic research by offering the âœhumanâ side of knowledge management (KM) intertwined with the use of artificial intelligences (AIs). Each chapter explores KM from different perspectives, including entrepreneurship, innovation, marketing, and strategy, in a theoretical and practical way. They include insights from both practitioners and scholars, enriched by practical tools that can be used during laboratories, workshops and tutorials. The book presents evidence on how to manage KM and develop new knowledge in different subjects, with the aim of overcoming conventional KM strategy and show how business anTable of ContentsChapater 1 – Knowledge Management and AI for CreativityChapater 2 - Knowledge Management and and AI for InnovationChapater 3 - Knowledge Management and and AI for MarketingChapater 4 - Knowledge Management and and AI for StrategyConclusion

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Book SynopsisAuditory Interfaces explores how human-computer interactions can be significantly enhanced through the improved use of the audio channel. Providing historical, theoretical and practical perspectives, the book begins with an introductory overview, before presenting cutting-edge research with chapters on embodied music recognition, nonspeech audio, and user interfaces. This book will be of interest to advanced students, researchers and professionals working in a range of fields, from audio sound systems, to human-computer interaction and computer science.Table of ContentsList of Figures List of Tables Preface0.1 Introduction0.2 Overview0.3 The Authors1 Nonspeech audio: an introduction1.1 Introduction1.2 What About Noise?1.3 Figure and Ground in Audio1.4 Sound and the Visually Impaired1.5 Auditory Display Techniques1.6 Some Examples1.7 Sound in Collaborative Work1.8 Function and Signal Type1.8.1 Alarms and Warning Systems1.9 Audio Cues and Learning1.10 Perception and Psychoacoustics1.11 The Logistics of Sound1.12 Summary2 Acoustics and psychoacoustics2.1 Introduction2.2 Acoustics2.2.1 Waveforms2.2.2 Fourier analysis and spectral plots 2.3 More Complex waves 2.3.1 Sound, Obstacles, Bending and Shadows2.3.2 Phase: its Implication on Sound and Representations2.3.3 The Inverse Square Law2.3.4 Helmholtz Revisited2.3.5 Spectrograms2.3.6 Formants vs Partials2.4 Some digital signal processing concepts2.5 Spatial Hearing2.5.1 Head-related transfer functions (HRTF)2.5.2 3D sound distance and reverberation2.6 Psychoacoustics2.6.1 Just Noticeable Difference (JND)2.6.2 Critical Bands2.6.3 Pitch2.6.4 Pitches, Intervals, Scales and Ratios2.6.5 Loudness2.6.6 Duration, Attack Time and Rhythm.2.6.7 Microvariation and Spectral Fusion2.6.8 Timbre2.6.9 Masking2.6.10 Auditory Streaming2.6.11 Sounds with Variations2.6.12 Psychoacoustic Illusions2.7 Perception of 3D sound2.7.1 Precedence / Hass effect2.7.2 Binaural Rendering2.8 Hearing versus listening2.9 Annoying sounds2.10 Pleasant sounds2.11 Embodied sound and music cognition2.12 Conclusions3 Sonification3.1 Introduction3.2 History3.3 Model based sonification3.4 Case Studies3.4.1 Case Study 1: Presenting Information in Sound3.4.2 Case Study 2: Dynamic Representation of Multivariate Time Series Data3.4.3 Case Study 3: Stereophonic and Surface Sound Generation3.4.4 Case Study 4: Auditory Presentation of Experimental Data3.4.5 Case Study 5: Sonification of EEG data3.5 Discussion3.6 Issues3.7 Issues of Data3.7.1 Issues of Sound Parameters3.7.2 Issues of Evaluation3.8 Conclusions4 Earcons4.1 Introduction4.2 Case Studies4.2.1 Case Study 1: Alarms and Warning Systems4.2.2 Alarms as Applied Psychoacoustics4.2.3 Problems With Traditional Alarms and Convergences with Audio Interfaces4.2.4 Case Study 2: Concurrent earcons4.2.5 Case Study 3: Earcons for visually impaired users4.3 Conclusions5 Everyday listening5.1 Introduction5.2 Musical and Everyday Listening5.2.1 Musical and Everyday Listening are Experiences5.3 The Psychology of Everyday Listening5.3.1 Knowledge About Everyday Listening5.4 The Ecological Approach To Perception5.4.1 Developing An Ecological Account Of Listening5.5 What Do We Hear?5.6 The Physics of Sound-Producing Events 5.7 Vibrating Objects 5.7.1 Aerodynamic Sounds 5.7.2 Liquid Sounds 5.7.3 Temporally Complex Events 5.8 Asking People What They Hear 5.9 Attributes of Everyday Listening 5.10 Patterned, Compound, and Hybrid Complex Sounds 5.10.1 Problems and Potentials of the Framework 5.11 How Do We Hear It? 5.12 Analysis and Synthesis of Sounds and Events 5.12.1 Breaking and Bouncing Bottles 5.12.2 Impact Sounds 5.12.3 Material and Length 5.12.4 Internal Friction and Material 5.13 Sound synthesis by physical modelling 5.14 Conclusions 6. Auditory icons 6.1 Introduction 6.2 Advantages of Auditory Icons 6.3 Systems Which Use Auditory Icons 6.3.1 Case Study 1: The SonicFinder: Creating an Auditory Desktop 6.3.2 Case study 2: SoundShark: Sounds in a Large Collaborative Environment 6.3.3 Case study 3: ARKola: Studying the Use of Sound in a Complex System 6.3.4 Case study 4: ShareMon: Background Sounds for Awareness 6.3.5 Case study 5: EAR: Environmental Audio Reminders 6.3.6 Case study 6: Shoogle: Excitatory Multimodal Interaction on Mobile Devices 6.3.7 Summary 6.4 Issues for Auditory Icons 6.4.1 Mapping Sounds to Events 6.4.2 What is Being Mapped to What? 6.4.3 Types of Mapping6.5 The Vocabulary of Auditory Icons6.5.1 Beyond Literal Mappings: Metaphors, Sound-effects, Cliche´s, and Genre Sounds6.6 Annoyance6.7 The Psychoacoustics of Annoying Sounds6.7.1 The Principle of Optimal Complexity6.7.2 Semantic Effects6.7.3 The Tension Between Clarity and Obtrusiveness6.8 Conclusions6.9 What’s Next?7 Sonic Interaction Design7.1 Introduction7.2 Psychology of sonic interactions7.3 Sonic interactions in products7.4 Examples of objects with interesting sounds7.5 Methods in sonic interaction design7.6 Case studies7.6.1 Case study 1: Naturalness influences perceived usability and pleasantness7.6.2 Case study 2: The Ballancer: continuous sonic feedback from a rolling ball7.7 Challenges of evaluation7.8 Conclusions8 Multimodal Interactions8.1 Introduction8.2 Audio-visual Interactions8.3 Embodied interactions8.4 Audio-haptic Interactions8.5 Case study 1: Haptic Wave8.6 Conclusions9 Spatial auditory displays9.1 Introduction9.2 Hearables9.3 Case studies9.3.1 Case study 1: the LISTEN system9.3.2 Case study 2: Soundscape by Microsoft9.3.3 Case study 3: SWAN: a system for wearable audio navigation9.3.4 Case study 4: Superhuman hearing9.4 Conclusions 10 Synthesis and control of auditory icons 10.1 Introduction10.2 Generating and Controlling Sounds10.3 Parameterized Icons10.3.1 Creating Parameterized Auditory Icons10.3.2 Acoustic Information For Events10.3.3 Analysis and Synthesis of Events10.3.4 Impact Sounds10.3.5 Mapping Synthesis Parameters to Source Attributes10.3.6 An Efficient Algorithm for Synthesis10.3.7 Breaking, Bouncing, and Spilling10.3.8 From Impacts To Scraping10.3.9 Machine Sounds10.4 Physics based simulations10.5 Communicating with sound models10.6 Evaluation of sound synthesis methods10.7 Conclusions11 Summary and future research Bibliography Index

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Book SynopsisUrban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS).The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport.Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical ITable of ContentsPart I. Methods. 1. Introduction. 2. Data collection and analyses. 3. Geographic information systems and spatial analysis. 4. Optimisation. 5. Multi-agent simulation with machine learning. 6. Reliability and resilience. 7. Evaluation. Part II. Applications. 8. Autonomous Vehicles and Robots. 9. Access management and pricing. 10. Environmental sustainability. 11. Disruption of Networks. 12. Future directions.

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Book SynopsisWhere most introductory texts to the field of digital signal processing assume a degree of technical knowledge, this class-tested textbook provides a comprehensive introduction to the fundamentals of digital signal processing in a way that is accessible to all.Beginning from the first principles, readers will learn how signals are acquired, represented, analyzed and transformed by digital computers. Specific attention is given to digital sampling, discrete Fourier analysis and linear filtering in the time and frequency domains. All concepts are introduced practically and theoretically, combining intuitive illustrations, mathematical derivations and software implementations written in the Python programming language. Practical exercises are included at the end of each chapter to test reader knowledge.Written in a clear and accessible style, Digital Signals Theory is particularly aimed at students and general readers interested in audio and digital signal processiTable of ContentsSignals. Digital Sampling. Convolution. The Discrete Fourier Transform. Properties of the DFT. DFT Invertibility. Fast Fourier Transform. Time Frequency Representation. Frequency Domain Convolution. Infinite Impulse Response Filters. Analyzing IIR filters. Appendix.

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Book SynopsisWe live in a digital world, where we use digital tools and smart devices to communicate over the Internet. In turn, an enormous amount of data gets generated. The traditional computing architectures are inefficient in storing and managing this massive amount of data. Unfortunately, the data cannot be ignored as it helps businesses to make better decisions, solve problems, understand performance, improve processes, and understand customers. Therefore, we need modern systems capable of handling and managing data efficiently. In the past few decades, many distributed computing paradigms have emerged, and we have noticed a substantial growth in the applications based on such emerging paradigms. Some well-known emerging computing paradigms include cloud computing, fog computing, and edge computing, which have leveraged the increase in the volume of data being generated every second. However, the distributed computing paradigms face critical challenges, including network management and cyTable of Contents1 Edge computing-enabled secure information-centric networking: Privacy challenges, benefits, and future trends KAVISH TOMAR, SARISHMA DANGI, AND SACHIN SHARMA2 Weighted attack graphs and behavioral cyber game theory for cyber risk quantification FLORIAN K. KAISER, MARCUS WIENS, AND FRANK SCHULTMANN3 NetFlow-based botnet detection in IoT edge environment using ensemble gradient boosting machine learning framework D. SANTHADEVI AND B. JANET4 Exploring the possibility of blockchain and smart contract-based digital certificate P. RAVI KUMAR, P. HERBERT RAJ, AND SHARUL TAJUDDIN5 Senso Scale: A framework to preserve privacy over cloud using sensitivity range NIHARIKA SINGH, ISHU GUPTA, AND ASHUTOSH KUMAR SINGH6 Addressing the cybersecurity issues in cloud computing SHIVANSHU OLIYHAN AND CHANDRASHEKHAR AZAD7 Role of medical image encryption algorithms in cloud platform for teleradiology applications SIJU JOHN AND S. N. KUMAR8 Machine-learning approach for detecting cyberattacks in Medical Internet of Things THULASI M. SANTHI AND M. C. HELEN MARY9 Secure IoV-enabled systems at Fog Computing: Layout, security, and optimization algorithms and open issuesANSHU DEVI, RAMESH KAIT, AND VIRENDER RANGA10 A capability maturity model and value judgment systems for a distributed network of ethical and context aware digital twin agents MEZZOUR GHITA, BENHADOU SIHAM, MEDROMI HICHAM, AND GRIGUER HAFID11 A detailed cram on artificial intelligence industrial systems 4.0 P. DHARANYADEVI, R. SRI SAIPRIYA, T. C. ADITYAA, B. SENTHILNAYAKI, M. JULIE THERESE, A. DEVI, AND K. VENKATALAKSHMI12 Ensuring liveliness property in safety-critical systems ANKUR MAURYA, SHARAD NIGAM, AND DIVYA KUMAR13 Machine learning for intelligent analytics JYOTI POKHARIYA, PANKAJ KUMAR MISHRA, AND JYOTI KANDPAL14 Secure 3D route optimization of combat vehicles in war field using IoV ALOK NATH PANDEY, PIYUSH AGARWAL, AND SACHIN SHARMA15 Healthcare therapy for treating teenagers with internet addiction using behavioral patterns and neuro-feedback analysis B. DHANALAKSHMI, K. SELVAKUMAR, AND L. SAI RAMESH16 Containerization in cloud computing for OS-level virtualization MANOJ KUMAR PATRA, BIBHUDATTA SAHOO, AND ASHOK KUMAR TURUK17 An adaptive deep learning approach for stock price forecasting RESHMA MO, JITENDRA KUMAR, AND ABHISHEK VERMA

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Book SynopsisToday, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today's world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy.Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the proTable of Contents1. A Comprehensive Review of Machine Application in the Oil and Gas Industry 2. AI and ML Application in the Upstream Sector of the Oil and Gas Industry 3. One Step Further in Upstream Sector 4. Midstream Sector with ML Models and Techniques 5. Downstream Sector with Machine Learning 6. Safety and Maintenance with AI and ML 7. Finance with ML and AI 8. Market and Trading in Oil and Gas (Petroleum) Industry 9. Future of Oil and Gas (Petroleum) Industry with AI

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Book SynopsisThe fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.This book is written for researchers, practitioners, engineers, and AI consultants.Table of Contents1. Multiobjective Optimization: An Overview. 2. Inverse Problems. 3. Decision Tree for Classification and Forecasting. 4. A Review of Choice Topics in Quantum Computing and Some Connections with Machine Learning. 5. Sparse Models for Machine Learning. 6. Interpretability in Machine Learning. 7. Big Data: Concepts, Techniques, and Considerations. 8. A Machine of Many Faces: On the Issue of Interface in Artificial Intelligence and Tools from User Experience. 9. Artificial Intelligence Technologies and Platforms. 10. Artificial Neural Networks. 11. Multicriteria Optimization in Deep Learning. 12. Natural Language Processing: Current Methods and Challenges. 13. AI and Imaging in Remote Sensing. 14. AI in Agriculture. 15. AI and Cancer Imaging. 16. AI in Ecommerce: From Amazon and TikTok, GPT-3 and LaMDA, to the Metaverse and Beyond. 17. The Difficulties of Clinical NLP. 18. Inclusive Green Growth in OECD Countries: Insight from The Lasso Regularization and Inferential Techniques. 19. Quality Assessment of Medical Images. 20. Securing Machine Learning Models: Notions and Open Issues.

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Book SynopsisDigitalization is shaping our everyday lives, yet navigating the changes it entails can feel like trekking into the unknown, where both the possibilities and the consequences are unclear and difficult to grasp. Exploring how digitalization affects all aspects of our lives, from health to culture, this book aims to develop and strengthen the reader's ability to think critically about such developments.Written in a clear and concise manner with reference to science fiction and pop culture, this book presents potent theoretical perspectives for understanding digitalization processes as societal change. Various exercises are included throughout to encourage readers to critically explore digitalization in their own lives.Replete with illustrations and examples, this book is an accessible guide to digitalization in the modern societal context, appealing to students at the undergraduate level as well as general readership.Table of ContentsPrefaceSection 1Chapter 1: Getting lost in a the digital1.1 Limited or liberated by ubiquitous digital technology? 1.2 It Could Be Otherwise (ICBO) – the foundation of critical thinking1.3 Opening the black box1.4 A response to political and corporate solutionism1.5 Digitalization as a topic for Science and Technology Studies (STS) 1.6 A critical sociotechnical perspective1.7 The structure of the book1.8 ConclusionReferencesChapter 2: What is "digitalization," exactly? 2.1 Digitalization as technological fix2.2 Defining digitalization2.3 Defining digitalization as a political act in itself2.4 A digitalized world2.5 Digitalization as a sociotechnical process2.6 ConclusionReferencesSection 2Chapter 3: A sociotechnical perspective on digitalization3.1 What is a sociotechnical perspective on digitalization? 3.2 What do we mean by "technology"? 3.3 Technologies and their agency3.4 Why technological determinism is a dead end3.5 Technological reductionism3.6 How social determinism is equally problematic3.7 ConclusionReferencesChapter 4: Domestication: User perspectives on technology4.1 A user perspective on technology4.2 Domestication theory4.3 The dimensional model of domestication4.4 The history of domestication4.5 Strengths and weaknesses of domestication theory4.6 Re-domestication and dis-domestication4.7 What non-users can teach us about the use of technology4.8 Normativity and use4.9 ConclusionReferencesChapter 5: Script: Technology’s manual for use5.1 Script as technology’s manual5.2 The historical and theoretical position of script theory5.3 How do you do a script analysis? 5.4 Making scripts through technology development5.5 ConclusionReferencesChapter 6: Technologies as normality machines6.1 A thought experiment on a student app6.2 Technology as inclusion or exclusion? 6.3 Scripting the use and users to create differences6.4 The digital divide6.5 ConclusionReferencesChapter 7: Digital technologies in the past and present7.1 Becoming a communication society7.2 What comes after the communication society? 7.3 Digitalization and some sample diagnoses of the times7.4 ConclusionReferencesSection 3Chapter 8: Digitalization of health: Networks of care and technology8.1 In search of good health: Robots to the rescue? 8.2 Digital technology for better health? 8.3 Talking flowerpots: Welfare technology in the home8.4 Exergames: Gamifying health8.5 Support groups in social media: Communities for mental health8.6 Digitalization makes the actor network of health visible8.7 ConclusionReferencesChapter 9: Digitalization of work: Automation, responsibility, and reskilling9.1 Two visions of future work9.2 From animal laborans to homo faber9.3 Automating workers? 9.4 Who operates self-service checkouts? 9.5 The digital stopwatch and the attempt to automate care work9.6 Craftspeople at construction sites working with robots9.7 What will we do in the future—and how will we do it? 9.8 ConclusionReferencesChapter 10: Digitalization of control: Surveillance, automation, and algorithms10.1 Control through surveillance and digital tracking10.2 Control of animals using virtual fences10.3 Care, technology, and the desire for boundaries when surveilling children10.4 Predictive police algorithms: Surveillance of data sets and predictions of the future10.5 Life in a surveillance society: What digitalization does to surveillance10.6 ConclusionReferencesChapter 11: Digitalization of culture: Remix, community, and prosumers11.1 SKAM and transmedia storytelling11.2 Remix culture as the foundation of digital culture11.3 Understanding where remix culture comes from: Participatory culture and networked publics11.4 Memes: Collective creativity, both serious and humorous11.5 Fan fiction: When fans take ownership of the story11.6 Twitch.tv and livestreaming games: How innovative gamers made one of the world’s biggest platforms11.7 Discussion: Prosumers’ new cultural expressions11.8 ConclusionReferencesChapter 12: Digitalization of the self: Selfies, influencers and the quantified self12.1 Picture perfect? What "Instagram vs. reality" can teach us about being fakeness and authenticity online12.2 From anonymity to persistent identities on the internet12.3 Frontstage, backstage, and the cyborg’s theater12.4 Selfies: The cyborg’s self-portrait? 12.5 Influencers: The professionalized digital self12.6 The quantified self: Believing in a countable and optimized self12.7 Discussion: The cyborg’s expanded toolbox12.8 ConclusionReferencesSection 4Chapter 13: Digitalization summarized13.1 Part 1: A critical perspective on digitalization13.2 Part 2: Theoretical Tools13.3 Part 3: Empirical case studies13.4 Digitalization as social change13.5 A user perspective on digitalization13.6 Critical thinking about digitalizationChapter 14: Analytical cheat sheet: A guide for thinking critically about digitalization14.1 Interpretative flexibility14.2 Delegation14.3 Actor-network14.4 Script14.5 DomesticationChapter 15: Methods cheat sheet: How to study digitalization15.1 Research question: What are you going to find out? 15.2 Choosing method: How are you going to find it? 15.3 Tips for getting good data15.4 From data to analysis

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Book SynopsisArtificial Intelligence for Business Creativity provides an in-depth examination of the integration of Artificial Intelligence (AI) into the business sector to foster creativity. The book explores the interplay between micro-level individual creativity and macro-level organizational innovation through the lens of AI. It delves into three crucial areas where AI can stimulate business creativity: product and service design, optimized processes, and enhanced organizational collaboration. The authors also highlight the versatility and capability of generative AI systems in promoting creativity and innovation.Intended for business leaders, managers, entrepreneurs, and those interested in AI and creativity, the book offers practical guidance and insightful recommendations on how organizations can effectively utilize AI to enhance their creative process. By offering a comprehensive understanding of the role of AI in fostering creativity, the book equips its readers with theTrade Review"Artificial Intelligence for Business Creativity" provides inspiration and practical guidance for business leaders to explore the power of AI to achieve greater creative success and improve business outcomes. This book is a critical contribution at a time of great innovation in the field" - Jerry Dischler, VP/GM, Ads, Google"In this new age of generative AI, understanding how AI can enhance creativity in business is a critical emerging topic whether for marketing, new business ventures, or designing new business models. Pagani & Champion have produced a valuable resource that shows how to manage AI-augmented work for creative business tasks".- Omar A. El Sawy, Kenneth King Stonier Chair in Business Administration, Professor of Information Systems, USC Marshall School of Business, Los Angeles, California"This leaves no doubts about whether AI can help evolve your business. It illuminates how AI can serve as both a catalyst and muse to anyone seeking new approaches to business problem solving. An excellent roadmap for professionals charged with creating meaningful business transformation at a velocity never before imaginable."- Alan Schulman, Managing Partner, UpperRight Former Chief Creative Officer, Deloitte DigitalTable of ContentsForewordIgor JablokovINTRODUCTION Margherita Pagani and Renaud ChampionPART ONE - Artificial Intelligence and Creativity1. Creativity and Innovation in the Age of AIYoram (Jerry) Wind, Margherita Pagani, and Stacey Lynn Schulman2. Could Artificial Intelligence make us humans more creative?Margherita Pagani, Renaud ChampionPART TWO - From Individual Creativity Towards Business Creativity When Artificial Intelligence Systems help to inspire creative new venture ideas Margherita Pagani, Nathan Sorin How AI can foster Business Creativity Margherita Pagani, Renaud Champion Artificial Intelligence and Creativity in Marketing: a proposed typology and new directions for academia-industry collaborations Nisreen Ameen, Gagan Deep Sharma, Shlomo Tarba Towards AI-enabled support for creative thinking about business models Mark Dowsett, Neil Maiden, Charles Baden-Fuller Conclusions and Future Directions Margherita Pagani and Renaud ChampionAPPENDIX – Think ForwardLegal Issues of AI for Business CreativityOlivier LasmolesIndex

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Book SynopsisHandbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike. Features Includes cutting edge applications in machine learning, fintech, and more Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics Features chapters from many leading researchers and practitioners Trade Review"Alternative data has become a hot topic in finance. New kinds of data, new data sources, and of course new tools for processing such data offer the possibility of new and previously unsuspected signals. In short alternative data lead to the promise of enhanced predictive power. But such advance does not come without its challenges - in terms of the quality of the data, the length of its history, reliable data capture, the development of appropriate statistical, AI, machine learning, and data mining tools, and, of course, the ethical challenges in the face of increasingly tough data protection regimes. Gautam Mitra and his colleagues have put together a superb collection of chapters discussing these topics, and more, to show how alternative data, used with care and expertise, can reveal the bigger picture."– Professor David J. Hand, Emeritus Professor of Mathematics and Senior Research Investigator, Imperial College, London"Digital capital is now so important that it can rightly be viewed as a factor of production, especially in the financial sector. This handbook does for the field of alternative data what vendors of alternative data do for data itself; and that is to provide structure, filter noise, and bring clarity. It is an indispensable work which every financial professional can consult, be it for an overview of the field or for specific details about alternative data."– Professor Hersh Shefrin, Mario L. Belotti Professor of Finance, Santa Clara UniversityAn impressive and timely contribution to the fast developing discipline of data driven decisions in the trading and management of financial risk. Automated data collection, organization, and dissemination is part and parcel of Data Science and the Handbook covers the current breadth of these activities, their risks, rewards, and costs. A welcome addition to the landscape of quantitative finance.–Professor Dilip Madan, Professor of Finance, Robert H. Smith School of Business"The Handbook of Alternative Data in Finance is the most comprehensive guide to alternative data I have seen. It could be called the Encyclopaedia of Alternative Data. It belongs to the desktop, not the bookshelf, of every investor."– Ernest Chan, Respected Academic, Author, Practicing Fund Manager, Entrepreneur and Founder of PredictNow.AI "Professor Gautam Mitra and his team unpack the topic of alternative data in finance, an ambitious endeavor given the fast-expanding nature of this new and exciting space. Alternative data powered by Natural Language Processing and Machine Learning has emerged as a new source of insights that can help investors make more informed decisions, stay ahead of competition and mitigate emerging risks. This handbook provides a strong validation of the substantial added value that alternative data brings. It also helps promote the idea that data driven decisions are better and more sustainable – something we, at RavenPack, firmly believe."– Armando Gonzalez, CEO and Founder of RavenPack"As the 1st Duke of Marlborough, John Churchill, wrote in 1715: 'No war can be conducted successfully without early and good intelligence.' The same can be said for successful trading. In that light, the Handbook of Alternative Data in Finance contains vital insights about how to gather and use alternative data —in short, intelligence —to facilitate successful trading."– Professor Steve H. Hanke, Professor of Applied Economics, The Johns Hopkins University, Baltimore, USA"The Handbook of Alternative Data in Finance is cutting edge and it bridges a huge gap in the representative studies on emerging areas of finance where alternative data can be profitably utilised for better informed decisions. The practical insights in the book would come very handy to both investors and researchers who look for fresh ideas."– Ashok Banerjee, Director, Indian Institute of Management Udaipur, Formerly Dean, and Faculty-in-charge of the Finance Lab at Indian Institute of Management CalcuttaTable of Contents1. Alternative Data: Overview. Part I. Alternative Data: Processing and Impact. 2. Contemplation and Reflection on Using Alternative Data for Trading and Fund Management. 3. Global Economy and Markets Sentiment Model. Part II. Coupling Models with Alternative Data for Financial Analytics. 4. Enhanced Corporate Bond Yield Modelling Incorporating Macroeconomic News Sentiment. 5. AI, Machine Learning and Quantitative Models. Part III. Handling Different Alternative Datasets. 6. Asset Allocation Strategies: Enhanced by Micro-Blog. 7. Asset Allocation Strategies: Enhanced by News. 8. Extracting Structured Datasets from Textual Sources: Some Examples. 9. Comparative Analysis of NLP Approaches for Earnings Calls. 10. Sensors Data. Part IV. Alternative Data Use Cases in Finance. Part IV.A. Application in Trading and Fund Management (Finding New Alpha). 11. Media Sentiment Momentum. 12. Defining Market States with Media Sentiment. Part IV.B. Application in Risk Control. 13. A Quantitative Metric for Corporate Sustainability. 14. Hot off the Press: Predicting Intraday Risk and Liquidity with News Analytics. 15. Exogenous Risks Alternative Data Implications for Strategic Asset Allocation: Multi-Subordination Levy Processes Approach. Part IV.C. Case Studies on ESG. 16. ESG Controversies and Stock Returns. 17. Oil and Gas Drilling Waste: A Material Externality. 18: ESG Scores and Price Momentum Are Compatible: Revisited. Part V. Directory of Alternative Data Vendors.

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This comprehensive guide to agricultural robots is the ideal companion for any student or professional engineer looking to understand and develop autonomous vehicles to use on the modern farm.With world hunger one of the modern era's most pressing issues, autonomous agricultural vehicles are a key tool in tackling this problem. Smart farming can increase total factory productivity through designing autonomous vehicles based on specific needs, in addition to implementing smart systems into day-to-day operations. This book provides step-by-step guidance, from the theory behind autonomous vehicles, through to the design process and manufacture. Detailing all components of an autonomous agricultural vehicle, from sensors, controlling algorithms, communication and controlling units, the book covers topics such as artificial intelligence and machine learning. It also includes case studies, and a detailed guide to international policymaking in recent years.Suitable fo

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Book SynopsisThis book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimalTable of ContentsPreface. About the Editors. Chapter 1 Estimation of Uncertainties for Multiserver Queuing Systems with Bernoulli Feedback. Chapter 2 Optimality for Fuzzy Transportation Problem under Ranking Method. Chapter 3 Solution of Bilevel Linear Fractional Transportation Problem with Pythagorean Fuzzy Numbers. Chapter 4 Optimal Production Evaluation of Cotton in Different Soil and Water Conditions in Sundarban of West Bengal under Hesitant Interval Fuzzy Environment Using Projection Measures. Chapter 5 A Novel Approach for Feature Detection in Vector Graphics. Chapter 6 On Uncertain Matrix Games Involving Linguistic Pythagorean Fuzzy Sets. Chapter 7 Cyclic Surgery Scheduling using Variations of Cohort Intelligence. Chapter 8 Cone Method for Uncertain Multiobjective Optimization Problems with Minmax Robustness. Chapter 9 Solving Multi-Index Transportation Problem with Axial Constraints Having Impaired Flow. Chapter 10 STAR Heuristic Method: A Novel Approach and Its Comparative Analysis with CI Algorithm to Solve CBAP in Healthcare. Chapter 11 Development and Optimization of Quadratic Programming Problems with Intuitionistic Fuzzy Parameters. Index

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Book SynopsisThis book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems.Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain.Table of Contents1 Healthcare Fees-Centric to Value-Centric Transformation through Data, Analytics, and Artificial Intelligence2 AI-Based Healthcare: Top Businesses and Technologies 3 Insights into AI, Machine Learning, and Deep Learning 4 Deep Learning for Visual Perceptual Brain Decoding as Image Classification 5 Automatic Brain Tumor Segmentation in Multimodal MRI Images Using Deep Learning 6 Automated Prediction of Lung Cancer Using Deep Learning Algorithms 7 Cervical Cancer Screening Approach Using AI D. Santhi, M. Carmel Sobia, and M. Jayalakshmi 8 Progression Detection of Multiple Sclerosis in Brain MRI Images 9 Artificial Intelligence Clustering Techniques on Dermoscopic Skin Lesion Images 10 Automated Alzheimer’s Disease Detection with Optimized Fuzzy Neural Network 11 A Comprehensive Survey with Bibliometric Analysis on Recent Research Opportunities of Multimodal Medical Image Fusion in Various Applications 12 Big Data in IoT for Healthcare Application 13 Automatic Detection of Diabetic Retinopathy to Avoid Blindness 14 A Review on Wireless BAN to Measure the Respiration Rate Using SoC Architecture 15 Deep Feature Extraction for EEG Signal Classification in Motor Imagery Tasks 16 Effect of Age in Normal Women by Heart Rate Variability Analysis17 EEG Signal Analysis Using Machine Learning and Artificial Intelligence for Identification of Brain Dysfunction18 Cervical Cancer Screening Methods: Comprehensive Survey 19 Understanding Assessment Methods and Sensors for ADHD Hyperactive-Impulsive Type among Children 20 Security of Medical Image Information by Cryptography and Watermarking Using Python 21 Integration of Biosensors and Drug Delivery Systems for Biomedical Applications 22 Automatic Liver and Lesion Segmentation in CT Using 3-D Context Convolutional Neural Network: 3-D Context U-Net

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Book SynopsisArtificial Intelligence for Capital Market throws light on the application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to black box syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book.Features: Showcases artificial intelligence in finance service industry Explains credit and risk analysis Elaborates on cryptocurrencies and blockchain technology Focuses on the optimal choice of asset pricing model Introduces testing of market efficiency and forecasting in the Indian stock market This book serves as a reference book for academicians, industry professionals, traders, finance managers and stock brokers. It may also be used as textbook for graduate level courses in financial services and financial analytics.Table of Contents1. Artificial Intelligence in the Financial Service Industry. 2. Machine Learning and Big Data in Finance Services. 3. Artificial Intelligence in Financial Services: Advantages and Disadvantages. 4. Upscaling Profits in Financial Market. 5. Credit and Risk Analysis in the Financial and Banking Sectors: An Investigation. 6. Cryptocurrencies and Blockchain Technology Applications. 7. Machine Learning and the Optimal Choice of Asset Pricing Model. 8. Testing for Market Efficiency Using News-Driven Sentiment: Evidence from Select NYSE Stocks. 9. Comparing Statistical, Deep Learning, and Additive Models for Forecasting in the Indian Stock Market. 10. Applications and Impact of Artificial Intelligence in the Finance Sector.

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Book SynopsisThis handbook covers smart manufacturing development, processing, modifications, and applications. It provides a complete understanding of the recent advancements in smart manufacturing through its various enabling manufacturing technologies, and how industries and organizations can find the needed information on how to implement smart manufacturing towards sustainability of manufacturing practices.Handbook of Smart Manufacturing: Forecasting the Future of Industry 4.0 covers all related advances in manufacturing such as the integration of reverse engineering with smart manufacturing, industrial internet of things (IIoT), and artificial intelligence approaches, including Artificial Neural Network, Markov Decision Process, and Heuristics Methodology. It offers smart manufacturing methods like 4D printing, micro-manufacturing, and processing of smart materials to assist the biomedical industries in the fabrication of human prostheses and implants. The handbook goes on to discusTable of Contents1. Smart Manufacturing and Industry 4.0: State-of-the-Art Review. 2. Study And Analysis Of Iot (Industry 4.0): A Review. 3. Recent advances in Cybersecurity in Smart Manufacturing Systems in the Industry. 4. Integration of Circular Supply Chain and Industry 4.0 to Enhance Smart Manufacturing Adoption. 5. Artificial Intelligence with Additive Manufacturing. 6. Robotic additive manufacturing vision towards smart manufacturing and envisage the trend with patent landscape. 7. Smart Materials for Smart Manufacturing. 8. Smart Biomaterials in Industry and Healthcare. 9. Ferroelectric polymer composites and evaluation of their properties. 10. 4D print today and envisaging the trend with patent landscape for versatile applications. 11. Investigating the work generation potential of SMA wire actuator. 12. Troubleshooting on the sample preparation during Fused Deposition Modelling. 13. Hybrid Additive Manufacturing Technologies. 14. Smart Manufacturing Using 4d Printing. 15. Developments in 4D Printing and Associated Smart Materials. 16. Role of smart manufacturing systems in improving electric vehicle production. 17. Safety management with application of Internet of Things, Artificial Intelligence and Machine Learning for Industry 4.0 environment.

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Book SynopsisThis title covers computer vision and machine learning (ML) advances that facilitate automation in diagnostic, therapeutic, and preventative healthcare. The book shows the development of algorithms and architectures for healthcare. Table of Contents1. Human–AI Relationship in Healthcare. 2. Deep Learning in Medical Image Analysis: Recent Models and Explainability. 3. An Overview of Functional Near-Infrared Spectroscopy and Explainable Artificial Intelligence in fNIRS. 4. An Explainable Method for Image Registration with Applications in Medical Imaging. 5. State-of-the-Art Deep Learning Method and Its Explainability for Computerized Tomography Image Segmentation. 6. Interpretability of Segmentation and Overall Survival for Brain Tumors. 7. Identification of MR Image Biomarkers in Brain Tumor Patients Using Machine Learning and Radiomics Features. 8. Explainable Artificial Intelligence in Breast Cancer Identification. 9. Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis. 10. Predictive Analytics in Hospital Readmission for Diabetes Risk Patients. 11. Continuous Blood Glucose Monitoring Using Explainable AI Techniques. 12. Decision Support System for Facial Emotion-Based Progression Detection of Parkinson’s Patients. 13. Interpretable Machine Learning in Athletics for Injury Risk Prediction. 14. Federated Learning and Explainable AI in Healthcare.

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Book SynopsisThis book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.Trade Review"Data science is no longer the exclusive domain of computer scientists and engineers. The contributions of other stakeholders are required for taking a holistic approach to the problems that can be addressed by analysing a given dataset. Not only is this likely to lead to better solutions, but also a smoother journey to their implementation, validation and widespread adoption. However, in the same way that a computer scientist should at least gain an operational understanding of the tackled problem, the domain expert should also understand the foundations and correct use of the tools unveiling its solutions. In this context, How to Think about Data Science is an unusual book in that it provides an accessible introduction to this broad and booming discipline without sacrificing the understanding of key questions in data science. I can only recommend this book to those aspiring to acquire this knowledge and mindset."--Pedro J. Ballester, PhD, Senior Lecturer, Imperial College London; Wolfson Fellow, The Royal Society"What is the difference between a regular cook from a renowned chef? A regular cook may follow recipes and create edible dishes, but knowing which ingredients to use and how to combine them, how to cook each one and for how long, and how to finally present them is what makes all the difference. The tools and processes are important for sure, but what really provides value is being able to choose and integrate the right tools, ingredients and processes to create a terrific dish. In data science it is the same: anyone can execute a clustering or build a neural network with default parameters but what matters is to know, given a dataset, what questions can be answered, what algorithms we should use to answer each question and what ethical issues and privacy concerns should be considered; answering these questions would allow a data scientist not just to follow recipes, but to apply the right algorithms to answer the right questions while minimizing potentially discriminating outputs. This book focuses on these relevant questions. If you want to cook a terrific dish, this book will help you."--Jordi Conesa i Caralt, PhD, Associate Professor of Computer Science, Universitat Oberta de Catalunya"Today, big data influences nearly everything we do, and harnessing its enormous power remains a key driver of business analytics, research innovation, cultural revolution, and global politics. This book offers a great gateway to this broad and evolving subject by asking the right questions, introducing concepts clearly and succinctly, and making rational connections between computation and their wide ranging applications. The book also discusses important issues related to data bias, discrimination, data privacy, and security. The final chapter debates the limits of artificial intelligence and the computational, ethical, and philosophical conundrums it presents. Thought-provoking and refreshing – it is a must-read book!"-- Subhajyoti De, PhD, Associate Professor at the Center for Systems and Computational Biology, Rutgers, the State University of New JerseyTable of ContentsA bird’s-eye view and the art of asking questions. Descriptive Analytics. Predictive Analytics. How are predictive models trained and evaluated? Are our algorithms racist, sexist and discriminating? Personal data, privacy and cybersecurity. What are the limits of Artificial Intelligence?

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Book SynopsisThis book applies both industrial engineering and computational intelligence to demonstrate intelligent machines that solve real-world problems in various smart environments. It presents fundamental concepts and the latest advances in multi-criteria decision-making (MCDM) techniques and their application to smart environments. Though managers and engineers often use multi-criteria analysis in making complex decisions, many core problems are too difficult to model mathematically or have simply not yet been modeled.In response, as well as discussing AI-based approaches, Soft Computing for Smart Environments covers various optimization techniques, decision analytics, and data science in applying soft computing techniques to a defined set of smart environments, including smart and sustainable cities, disaster response systems, and smart campuses.This state-of-the-art book will be essential reading for both undergraduate and graduate students, researchers, practitioners, and decision-makers interested in advanced MCDM techniques for management and engineering in relation to smart environments.

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Book SynopsisThe demand for telehealth solutions has been growing exponentially after the Covid-19 pandemic. Hospitals remain understaffed, which leads to staff burnouts and unsatisfactory patient experience. They also find it difficult to use AI to reduce the workload for doctors and nurses. Doctors barely use data collected from wearables and home-use medical devices to make diagnosis. As generative AI advances, traditional medical device manufacturers are exploring with open innovation to transform into a software-based business model facing competition from large tech companies and startups. This book shares the perspectives from different stakeholders around the challenges of the use of AI in healthcare.Table of ContentsTelehealth Solution Market Demands in China and in Europe (Germany, Denmark and Italy). Developing the Innovation Ecosystem: A Case Study on the Chinese Digital Healthcare Industry. Use of AI in Combating COVID-19: Practices in Different Economies. COVID-19 and the Digitalization of the Healthcare System.

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