Artificial intelligence (AI) Books
Taylor & Francis The Algorithmic Unconscious How Psychoanalysis Helps in Understanding AI Antinomies
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£128.25
Taylor & Francis Ltd The Algorithmic Unconscious
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£39.99
Taylor & Francis Ltd How Things Work
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£43.69
Taylor & Francis Ltd DataDriven ModelFree Controllers
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£128.25
Taylor & Francis Ltd How Things Work
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£114.00
Taylor & Francis Ltd The Economics of Digital Transformation
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£128.25
Taylor & Francis Ltd Reinventing Manufacturing and Business Processes Through Artificial Intelligence
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£128.25
Taylor & Francis Ltd Smart Sensor Networks Using AI for Industry 4.0
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£104.50
Taylor & Francis Ltd Applied Edge AI
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£94.99
Taylor & Francis Machine Learning and AI in Finance
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£128.25
Taylor & Francis Ltd Machine Learning and AI in Finance
Book SynopsisThe significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stockTable of ContentsForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis
£39.99
Taylor & Francis The Future of Digital Data Heritage and Curation in a MorethanHuman World
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£128.25
Taylor & Francis Ltd Fieldbus and Networking in Process Automation
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£137.75
Taylor & Francis Ltd Introduction to Lattice Algebra
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£99.75
Taylor & Francis Expert Systems in Finance
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£39.99
Taylor & Francis Ltd AI vs Humans
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£128.25
Taylor & Francis Ltd Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
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£104.50
Taylor & Francis Ltd The Routledge Handbook of FinTech
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£39.99
Taylor & Francis Ltd Artificial IntelligenceAided Materials Design
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£104.50
Taylor & Francis Ltd Biodesign in the Age of Artificial Intelligence
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£128.25
Taylor & Francis Ltd Physics of Data Science and Machine Learning
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£80.74
Taylor & Francis Ltd Everyday Automation
Book SynopsisThis Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with actually existing' AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societalTable of ContentsIntroduction: Everyday Automation: setting a research agenda PART I: Challenging dominant narratives of automation 1. Imagining Mundane Automation: Historical Trajectories of Meaning Making around Technological Change 2. Trust, Ethics and Automation: Anticipatory Imaginaries in Everyday Life 3. The Quantified Pandemic: Digitised Surveillance, Containment and Care in Response to the COVID-19 Crisis 4. Less Work for Teacher? The Ironies of Automated Decision-making in Schools PART II Embedding automated systems in the everyday 5. Alexa’s Got a Hunch: The Human Decisions behind Programming Emotion-sensing and Caregiving into Digital Assistants 6. Framing Fashion: Human-Machine Learning and the Amazon Echo Look 7. Coffee with the Algorithm: Imaginaries, Maintenance and Care in the Everyday Life of a News-ranking Algorithm 8. Everyday AI at Work: Self-tracking and Automated Communication for Smart Work 9. Exploring ADM in Clinical Decision-Making: Healthcare Experts Encountering Digital Automation PART III Experimenting with Automation in Society 10. Hate it? Automate it!: Thinking and Doing Robotic Process Automation and Beyond 11. Smart Thermostats and the Algorithmic Control of Thermal Comfort 12. Prisoners Training AI: Ghosts, Humans and Values in Data Labour 13. Investigating ADM in Shared Mobility: A Design Ethnographic Approach 14. Ad Accountability Online: A Methodological Approach.
£36.99
Taylor & Francis Ltd Everyday Automation
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£128.25
Taylor & Francis Ltd Regulating Artificial Intelligence in Industry
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£128.25
Taylor & Francis Ltd Artificial Intelligence in Commercial Aviation
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£128.25
Taylor & Francis Ltd Cyber Arms
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£68.39
Taylor & Francis AgentBased Computational Economics
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£147.25
Taylor & Francis Handbook of Automated Essay Evaluation Current Applications and New Directions By Mark D Shermis published May 2013
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£114.00
Taylor & Francis Data Mining Methods for the Content Analyst An Introduction to the Computational Analysis of Content Routledge Communication Series
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£128.25
Taylor & Francis Artificial Psychology
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£156.75
Taylor & Francis Simulating Societies The Computer Simulation of Social Phenomena Routledge Library Editions Artificial Intelligence
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£114.00
Taylor & Francis Simulating Societies The Computer Simulation of Social Phenomena Routledge Library Editions Artificial Intelligence
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£33.99
Taylor & Francis The Industrialization of Intelligence Mind and Machine in the Modern Age Volume 7 Routledge Library Editions Artificial Intelligence
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£104.50
Taylor & Francis The Industrialization of Intelligence Mind and Machine in the Modern Age Routledge Library Editions Artificial Intelligence
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£32.99
Taylor & Francis Novice Programming Environments Explorations in HumanComputer Interaction and Artificial Intelligence Routledge Library Editions Artificial Intelligence
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£114.00
Taylor & Francis Novice Programming Environments Explorations in HumanComputer Interaction and Artificial Intelligence Routledge Library Editions Artificial Intelligence
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£32.99
Taylor & Francis Artificial Intelligence Its Philosophy and Neural Context Routledge Library Editions Artificial Intelligence
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£32.99
Taylor & Francis Artificial Intelligence
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£128.25
Taylor & Francis Ltd Isotope Effects In Chemistry and Biology
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£90.24
Taylor & Francis The Routledge Social Science Handbook of AI
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
£39.99
Taylor & Francis Ltd AI for Creativity
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.
£20.19
Taylor & Francis Ltd The AI Wave in Defence Innovation
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
£35.99
Taylor & Francis Ltd Blockchain and Artificial IntelligenceBased
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.
£80.74
Taylor & Francis Ltd Artificial Intelligence Business and Civilization
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
£19.99
Taylor & Francis Knowledge Management and AI in Society 5.0
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
£47.49
Taylor & Francis Ltd Auditory Interfaces
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
£43.99
Taylor & Francis Ltd Engineering Mathematics and Artificial
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.
£147.25
Taylor & Francis Ltd Handbook of Alternative Data in Finance Volume I
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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