Artificial intelligence (AI) Books

Book SynopsisIs competition law able to deal with algorithmic collusion? This evaluative book provides an insight into tackling this important question for competition law, with contrasting critical perspectives, including theoretical, empirical, and doctrinal – the latter frequently from a comparative perspective.Bringing together scholarly discussion on algorithmic collusion, the book questions whether competition law is adeptly equipped to deal with its various facets. With a comprehensive overview of the recent literature on algorithmic collusion, chapters offer a critical appraisal of the effectiveness of competition law to deal with algorithmic collusion. Covering a unique collection of legal, theoretical, and experimental case studies, it initiates debate among legal scholars for a better understanding of the data upon which algorithms decide prices.With a comparative identification of both the potentialities and limitations of competition law in relation to algorithmic collusion, this book will be of key value to students and scholars of competition law, economics and finance. It will also be an invaluable resource for legal practitioners and policy makers in the field.Trade Review‘This book is essential reading for those with an interest in algorithmic collusion, or competition and technology more generally. I would also recommend it to those who have limited knowledge of this area of competition scholarship and feel overwhelmed by the sheer volume of existing literature available. This collection is an excellent starting point, as the early chapters are written in a very clear and accessible style. They do an excellent job of explaining the main issues and critically summarising and discussing the previous literature, setting the scene for the original contributions that follow.’ -- Andreas Stephan, Competition Policy Blog‘This unique book offers a window into the fascinating world of algorithmic collusion. Several contributions assess how this new phenomenon is dealt with under the laws of various jurisdictions (Australia, China, India, Japan and the EU). That alone would make the book worth the read, but there is more. Another chapter dives deep into the algorithms used by Uber and Amazon and draws implications on the likelihood of competition law infringements. Yet another chapter shares the results of a screening exercise for algorithmic collusion in Singapore. Together, the chapters in this book reflect the great diversity and originality of research into this topic, and take the reader on a wonderful journey through this novel area of competition law.’ -- Simon Vande Walle, The University of Tokyo, Japan‘In many online markets, prices are set in an automated manner by algorithms, which raises significant competitive concerns and numerous competition law issues, in particular the danger of collusive behaviour of algorithms. This collection of essays provides an excellent overview of the key economic and legal aspects of algorithmic collusion, as well as the approaches taken in different jurisdictions to address this problem. It is a valuable volume that should be consulted by all interested in algorithmic collusion and its economic and competition law aspects.’ -- Ulrich Schwalbe, University of Hohenheim, Germany.‘This collection of essays helps to improve our understanding across AI collusion.’ -- D. Daniel Sokol, USC Gould School of Law and Marshall School of Business, USTable of ContentsContents: Preface x Salil K. Mehra Acknowledgements xii 1 The algorithmic collusion debate: a focus on (autonomous) tacit collusion 1 Steven Van Uytsel 2 Algorithms and the limits of antitrust 39 Thomas Weck 3 Artificially intelligent collusion caught under EU competition law 48 Jan Blockx 4 Can the reformed Australian competition law stop algorithmic collusion? 67 Baskaran Balasingham 5 Tackling algorithmic collusion: the scope of the Indian Competition Act 92 Nikita Koradia, Kiran Manokaran and Zara Saeed 6 Challenges brought by and in response to algorithms: the perspective of China’s Anti-Monopoly Law 142 Wei Han, Yajie Gao and Ai Deng 7 Algorithmic collusion and the Japanese antimonopoly law 165 Steven Van Uytsel and Yoshiteru Uemura 8 Price-monitoring algorithms and resale price maintenance: an analysis of recent cases in Europe 189 Yoshiteru Uemura 9 Pricing in online grocery markets: challenges in monitoring competition 203 Cassey Lee and Gloria Lin 10 Algorithms unravelled: observations on the audit of Uber and Amazon marketplace algorithms 237 Steven Van Uytsel Index 260

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Book SynopsisThis provocative and timely book identifies and disrupts the conventional regulation and governance discourses concerning AI and big data. It suggests that, instead of being used as tools for exclusionist commercial markets, AI and big data can be employed in governing digital transformation for social good. Analysing the ways in which global technology companies have colonised data access, the book reveals how trust, ethics, and digital self-determination can be reconsidered and engaged to promote the interests of marginalised stakeholders in data arrangement. Chapters examine the regulation of labour engagement in digital economies, the landscape of AI ethics, and a multitude of questions regarding participation, costs, and sustainability. Presenting several informative case studies, the book challenges some of the accepted qualifiers of frontier tech and data use and proposes innovative ways of actioning the more conventional regulatory components of big data. Scholars and students in information and media law, regulation and governance, and law and politics will find this book to be critical reading. It will also be of interest to policymakers and the AI and data science community.Trade Review‘Based on wisely selected case studies, the authors offer a compelling reframing of the orthodox tech-and-regulation relationship. They build a strong case that AI is more than a regulatory target: “Distruptive Regulation” uses technology to protect and advance the interests of vulnerable stakeholders instead of serving those in power.’ -- Urs Gasser, Technical University of Munich, Germany‘If you're looking for a thought-provoking read on governing AI and big data, then I highly recommend checking out this book. Using real-life examples, the authors offer a new approach to regulation that empowers people and promotes trust and data responsibility. The authors also provide practical pathways to advance digital self-determination and to promote fairness, and non-discrimination in how we use AI. Overall, the book challenges conventional thinking and is a must-read for anyone interested in technology and its impact on our society.’ -- Stefaan G. Verhulst, New York University, USTable of ContentsContents: 1. Disruptive regulation 2. Trust as regulation 3. Disrupting data – digital self-determination 4. Modern AI ethics is a field in the making 5. Modelling disruptive regulation Index

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Book SynopsisThis forward-looking Research Handbook makes an insightful contribution to the emerging field of studies on communication of, by and with AI. Bringing together state-of-the-art research from over 50 leading international scholars across various fields, it provides a comprehensive overview of the complex intersections between AI and communication. The team of expert contributors explore key conceptual, theoretical and methodological approaches and examine a variety of ethical considerations, legal issues and policy implications of AI across diverse contexts. The Handbook spans a wide range of topics related to AI-empowered, immersed, mediated and integrated communications. These range from the role of news media and digital communication platforms in constructing, representing and framing AI across different countries and cultures, to the public understanding of, attitude towards and interaction with AI and its related technologies. Offering foundational guidance on AI and communication, the Research Handbook will stimulate further intellectual inquiry for future scholarship in this rapidly evolving area. Cross-disciplinary in scope, this dynamic Research Handbook will prove an essential reference for students and scholars in multiple fields, including communication, computer science, data and information science, sociology, business, and education. Policymakers and practitioners will also find it a valuable resource to help inform AI-related regulations and policies.Trade Review‘This is an essential and refreshing collection of work that examines some of the most crucial questions facing our communication and media systems. It is sure to help guide research over the next decade.’ -- Siva Vaidhyanathan, University of Virginia, USTable of ContentsContents: Preface xvi Introduction to the Research Handbook on Artificial Intelligence and Communication xvii Seungahn Nah PART I MAPPING RESEARCH ON ARTIFICIAL INTELLIGENCE AND COMMUNICATION 1 A systematic review of scholarship in AI and communication research (1990–2022) 2 Sumita Louis and Seungahn Nah 2 AI-integrated communication: conceptualization and a critical review 29 Donghee Yvette Wohn and Mashael Almoqbel 3 Toward a sociology of machines: an interviewing methodology for human–machine communication 44 Cait Lackey 4 Discovering developmental trajectories and trends of conversational agent research using dynamic topic modeling 58 Hüseyin Özçinar and Aylin Sabanci Bayramoğlu 5 A systematic review of scholarship on metaverse 79 Jun Luo, Sumita Louis, and Seungahn Nah PART II FRAMING ARTIFICIAL INTELLIGENCE 6 AI in schools and universities: mapping central debates through enthusiasms and concerns 94 Kristjan Kikerpill and Andra Siibak 7 How news organizations and journalists understand artificial intelligence: application of news language database to AI-related news stories 108 Jeongsub Lim 8 AI in Portugal: news framing, tone, and sources 125 Paulo Nuno Vicente 9 AI bias, news framing, and mixed-methods approach 145 Jun Luo, Seungahn Nah, and Jungseock Joo PART III PUBLIC UNDERSTANDING OF ARTIFICIAL INTELLIGNECE 10 Risk perceptions and trust mechanisms related to everyday AI 163 Hichang Cho and Rosalie Hooi 11 Fearing the future: examining the conditional indirect correlation of attention to artificial intelligence news on artificial intelligence attitudes 176 Alex Kirkpatrick, Jay D. Hmielowski, and Amanda Boyd 12 A machine-learning approach to assessing public trust in AI-powered technologies 193 Poong Oh and Younbo Jung 13 Machine learning and deep learning for social science: a bibliometric approach 214 Jang Hyun Kim and Dongyan Nan 14 AI and data-driven political communication (re)shaping citizen–government interactions 231 Jérôme Duberry 15 AI folk tales: how nontechnical publics make sense of artificial intelligence 246 Barbara Pohl and Lauri Goldkind PART IV INTERACTING WITH ARTIFICIAL INTELLIGENCE 16 Facilitating stakeholder communication around AI-enabled systems and business processes 268 Matthew Bundas, Chasity Nadeau, Thanh H. Nguyen, Jeannine Shantz, Marcello Balduccini, Edward Griffor, and Tran Cao Son 17 The levels of automation and autonomy in the AI-augmented newsroom: toward a multi-level typology of computational journalism 284 Hannes Cools, Baldwin Van Gorp, and Michaël Opgenhaffen 18 AI as communicative other: critical relationality in human–AI communication 300 Marco Dehnert 19 Needs and practices for AI-mediated messaging in uncertain circumstances 315 Adam M. Rainear, Patric R. Spence, and Kenneth A. Lachlan 20 Why wasn’t I ready for that? Suggestions and research directions for the use of machine agents in organizational life 325 Patric R. Spence 21 The Media Are Social Actors paradigm and beyond: theory, evidence, and future research 337 Kun Xu, Fanjue Liu, Xiaobei Chen, and Matthew Lombard PART V POLICING ARTIFICIAL INTELLIGENCE 22 Evaluating the self-disclosure of personal information to AI-enabled technology 355 Jessica K. Barfield 23 To reimagine more deeply: understanding what AI communicates 376 John S. Seberger, Hyesun Choung, and Prabu David 24 Automated inequalities: examining the social implications of artificial intelligence in China 391 Bibo Lin and Joanne Kuai 25 Design + power: policy for the ecology of influence 405 Jasmine McNealy Index 418

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Book SynopsisThis engaging Handbook identifies and critically examines the moral opportunities and challenges typically attributed to artificial intelligence. It provides a comprehensive overview and examination of the most pressing and urgent problems with this technology by drawing on a wide range of analytical methods, traditions, and approaches.

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Book SynopsisThis timely Companion provides a comprehensive overview of the relationship between applied ethics and the development and use of Artificial Intelligence (AI). Adopting a holistic approach, an array of global experts identify the norms at stake, map the legal landscape, and contextualize normative expectations in relevant use cases of AI.

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Book Synopsis

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Book SynopsisAs artificial intelligence (AI) continues to seep into more areas of society and culture, critical social perspectives on its technologies are more urgent than ever before. Bringing together state-of-the-art research from experienced scholars across disciplines, this Handbook provides a comprehensive overview of the current state of critical AI studies. Moving beyond narrow technological definitions of AI, the Handbook provides readers with an in-depth understanding of its social, ethical and political implications. Chapters cover a broad range of timely issues related to AI, including the risk of bias and discrimination in its systems, its impact on democracy and governance, concerns surrounding privacy and surveillance, and the use of its technologies in decision-making processes. Underscoring the urgent need for deeper critical analyses of AI, the Handbook constitutes a major contribution to the ongoing discussion about what critical studies of AI can entail, what questions they may pose, and what concepts they can offer to address them. Rich in theoretical and empirical analysis, this cutting-edge Handbook will prove an invaluable resource for students and scholars of digital sociology and science and technology studies. Its extensive coverage of this emerging field will also appeal to practitioners, developers and policymakers seeking orientation in the complex social and political dynamics of AI.Trade Review‘AI is not only technology; it also means power. In times when AI ethics is often closely aligned with big tech and when AI teams are expelled or undervalued, a critical view of AI is much needed. Addressing a diversity of aspects from political economy to sociotechnological imaginaries and activism, this Handbook offers a range of critical scholarship on AI that shows how AI is entangled with the social structures and power relations in society. A welcome antidote to the ideologies of technological optimism, technodeterminism, and technosolutionism, and great support for the critical and interdisciplinary project of developing technology that contributes to, rather than undermines, conviviality and the common good.’ -- Mark Coeckelbergh, University of Vienna, Austria‘AI has proliferated in everyday life. Virtual assistants such as Alexa and Siri are present on our phones and in our homes. More and more people use robotic lawnmowers and robot hoovers. There are bots on the Internet that post, comment, and like. Robots and AI have changed the world of work. ChatGPT has given us an impression of how online search could look like in the future. The world’s largest military forces are investing heavily into the development of AI. We need to better understand what impacts AI has on society. For doing so, we need critical theories and analysis of AI. The Handbook of Critical Studies of Artificial Intelligence provides 75 chapters that help us to better understand what it means to critically study AI in society. This book is excellent reading for everyone interested in AI & society.’ -- Christian Fuchs, Paderborn University, GermanyTable of ContentsContents: 1 Introducing critical studies of artificial intelligence 1 Simon Lindgren PART I AI AND CRITICAL THEORY: CONCEPTUAL DISCUSSIONS 2 Recursive power: AI governmentality and technofutures 21 Fenwick McKelvey and Jonathan Roberge 3 The danger of smart ideologies: counter-hegemonic intelligence and antagonistic machines 33 Peter Bloom 4 The becoming of AI: a critical perspective on the contingent formation of AI 43 Anna Jobin and Christian Katzenbach 5 Artificial intelligence and the problem of radical uncertainty 56 Robert Holton 6 Trading human autonomy for technological automation 67 Simona Chiodo 7 Automation anxiety: a critical history – the apparently odd recurrence of debates about computation, AI and labour 79 Caroline Bassett and Ben Roberts 8 AI, critical knowledge and subjectivity 94 Eran Fisher 9 Habits and habitus in algorithmic culture 108 Stefka Hristova 10 Algorithms and emerging forms of intimacy 117 Tanja Wiehn 11 It’s incomprehensible: on machine learning and decoloniality 128 Abeba Birhane and Zeerak Talat 12 Pragmatism and AI: a critical approach 141 Johnathan Flowers 13 Digital humanism and AI 152 Wolfgang Hofkirchner and Hans-Jörg Kreowski 14 Beyond AI solutionism: toward a multi-disciplinary approach to artificial intelligence in society 163 Simon Lindgren and Virginia Dignum 15 Artificial intelligence and social memory: towards the cyborgian remembrance of an advancing mnemo-technic 173 Samuel Merrill 16 Making sense of AI-influenced geopolitics using STS theories 187 Arun Teja Polcumpally PART II AI IMAGINARIES AND DISCOURSES 17 Bothering the binaries: unruly AI futures of hauntings and hope at the limit 199 Amanda Lagerkvist and Bo Reimer 18 Imaginaries of artificial intelligence 209 Vanessa Richter, Christian Katzenbach and Mike Schäfer 19 Language of algorithms: agency, metaphors and deliberations in AI discourses 224 Kaisla Kajava and Nitin Sawhney 20 Technological failures, controversies and the myth of AI 237 Andrea Ballatore and Simone Natale 21 Marking the lines of artificial intelligence 245 Mario Verdicchio 22 The critical potential of science fiction 254 Miroslav Kotásek 23 A critical review of news framing of artificial intelligence 266 Ching-Hua Chuan 24 Media representations of artificial intelligence: surveying the field 277 Saba Rebecca Brause, Jing Zeng, Mike S. Schäfer and Christian Katzenbach 25 Educational imaginaries of AI 289 Lina Rahm PART III THE POLITICAL ECONOMY OF AI: DATAFICATION AND SURVEILLANCE 26 Critical AI studies meets critical political economy 302 Pieter Verdegem 27 The industry of automating automation: the political economy of the AI industry 312 James Steinhoff 28 AI, class societies and the social life of reason 323 Scott Timcke 29 Re-imagining democracy: AI’s challenge to political theory 333 Guy Paltieli 30 AI as automated inequality: statistics, surveillance and discrimination 343 Mike Zajko 31 Digital tracking and infrastructural power 354 Stine Lomborg, Rasmus Helles and Signe Sophus Lai 32 AI and the everyday political economy of global health 367 Michael Strange and Jason Tucker 33 Addressing global inequity in AI development 378 Chinasa T. Okolo PART IV AI TRANSPARENCY, ETHICS AND REGULATION 34 A critical approach to AI ethics 391 Rosalie A. Waelen 35 Power and inequalities: lifting the veil of ignorance in AI ethics 402 Anais Resseguier 36 Barriers to regulating AI: critical observations from a fractured field 413 Ashlin Lee, Will Orr, Walter G. Johnson, Jenna Imad Harb and Kathryn Henne 37 Why artificial intelligence is not transparent: a critical analysis of its three opacity layers 424 Manuel Carabantes 38 How to critique the GDPR: when data protection is turned against the working class 435 Carl Öhman 39 Four facets of AI transparency 445 Stefan Larsson, Kashyap Haresamudram, Charlotte Högberg, Yucong Lao, Axel Nyström, Kasia Söderlund and Fredrik Heintz 40 An inclusive approach to ascribing responsibility in robot ethics 456 Janina Loh 41 Machines and morals: moral reasoning ability might indicate how close AI is to attaining true equivalence with human intelligence 470 Sukanto Bhattacharya 42 A women’s rights perspective on safe artificial intelligence inside the United Nations 481 Eleonore Fournier-Tombs 43 From ethics to politics: changing approaches to AI education 493 Randy Connolly 44 The transparency of reason: ethical issues of AI art 504 Dejan Grba PART V AI BIAS, NORMATIVITY AND DISCRIMINATION 45 Learning about human behavior? The transcendental status of grammars of action in the processing of HCI data 516 Andreas Beinsteiner 46 Algorithmic moderation: contexts, perceptions, and misconceptions 528 João Gonçalves and Ina Weber 47 Algorithmic exclusion 538 Kendra Albert and Maggie Delano 48 Prospective but disconnected partners: AI-informed criminal risk prediction 549 Kelly Hannah-Moffat and Fernando Avila 49 Power asymmetries, epistemic imbalances and barriers to knowledge: the (im)possibility of knowing algorithms 563 Ana Pop Stefanija 50 Gender, race and the invisible labor of artificial intelligence 573 Laila Brown 51 Machine learning normativity as performativity 584 Tyler Reigeluth 52 Queer eye on AI: binary systems versus fluid identities 595 Karin Danielsson, Andrea Aler Tubella, Evelina Liliequist and Coppélie Cocq 53 Representational silence and racial biases in commercial image recognition services in the context of religion 607 Anton Berg and Katja Valaskivi 54 Social media as classification systems: procedural normative choices in user profiling 619 Severin Engelmann and Orestis Papakyriakopoulos 55 From hate speech recognition to happiness indexing: critical issues in datafication of emotion in text mining 631 Salla-Maaria Laaksonen, Juho Pääkkönen and Emily Öhman PART VI POLITICS AND ACTIVISM IN AI 56 Democratic friction in speech governance by AI 643 Niva Elkin-Koren and Maayan Perel 57 Automating empathy: overview, technologies, criticism 656 Andrew McStay and Vian Bakir 58 Ideational tensions in the Swedish automation debate: initial findings 670 Kalle Eriksson 59 En-countering AI as algorhythmic practice 682 Shintaro Miyazaki 60 Introducing political ecology of Creative-Ai 691 Andre Holzapfel PART VII AI AND AUTOMATION IN SOCIETY 61 Automated decision-making in the public sector 705 Vanja Carlsson, Malin Rönnblom and Andreas Öjehag-Pettersson 62 The landscape of social bot research: a critical appraisal 716 Harry Yaojun Yan and Kai-Cheng Yang 63 Introducing robots and AI in human service organizations: what are the implications for employees and service users? 726 Susanne Tafvelin, Jan Hjelte, Robyn Schimmer, Maria Forsgren, Vicenc Torra and Andreas Stenling 64 Critically analyzing autonomous materialities 737 Mikael Wiberg 65 Exploring critical dichotomies of AI and the Rule of Law 749 Markus Naarttijärvi 66 The use of AI in domestic security practices 763 Jens Hälterlein 67 Methodological reflections on researching the sociotechnical imaginaries of AI in policing 773 Carrie B. Sanders and Janet Chan 68 Emergence of artificial intelligence in health care: a critical review 783 Annika M. Svensson and Fabrice Jotterand 69 The politics of imaginary technologies: innovation ecosystems as political choreographies for promoting care robotics in health care 793 Jaana Parviainen 70 AI in education: landscape, vision and critical ethical challenges in the 21st century 804 Daniel S. Schiff and Rinat Rosenberg-Kima 71 Critically assessing AI/ML for cultural heritage: potentials and challenges 815 Anna Foka, Lina Eklund, Anders Sundnes Løvlie and Gabriele Griffin 72 AI ethnography 826 Anne Dippel and Andreas Sudmann 73 Automating social theory 845 Ralph Schroeder 74 Artificial intelligence and scientific problem choice at the nexus of industry and academia 859 Steve G. Hoffman 75 Myths, techno solutionism and artificial intelligence: reclaiming AI materiality and its massive environmental costs 869 Benedetta Brevini 76 AI governance and civil society: the need for critical engagement 878 Megan LePere-Schloop and Sandy Zook Index 891

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Book SynopsisWith AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.The Handbook on Artificial Intelligence and Transport provides a full investigation of the most recent AI transport developments, authored by an international collective of renowned contributors. Chapters examine several often challenging topics such as autonomous driving and cyber security ethics. They conclude that AI technology is likely to offer resolutions to persistent transport issues that have been almost impossible to solve using conventional approaches.This timely Handbook will be an important resource for students of transport planning and engineering, innovation and regional law. It will also benefit practitioners within the sectors of urban planning and engineering seeking updated evidence on the role of AI in transport improvement.Trade Review‘Under the astute editorship of Hussein Dia, the Handbook on Artificial Intelligence and Transport deftly elucidates a panoply of AI advancements across a myriad of transportation spheres. An indispensable tome for both academia and industry, it propels the transportation field towards a future replete with innovation and sagacity.’ -- Der-Horng Lee, Zhejiang University-University of Illinois Urbana-Champaign InstituteTable of ContentsContents: Introduction to the Handbook on Artificial Intelligence and Transport 1 Hussein Dia PART I SHORT-TERM TRAFFIC FORECASTING AND CONGESTION PREDICTION 1 A comparative evaluation of established and contemporary deep learning traffic prediction methods 14 Ta Jiun Ting, Scott Sanner, and Baher Abdulhai 2 Fault tolerance and transferability of short-term traffic forecasting hybrid AI models 47 Rusul Abduljabbar, Hussein Dia, and Pei-Wei Tsai 3 A review of deep learning-based approaches and use cases for traffic prediction 80 Rezaur Rahman, Jiechao Zhang, and Samiul Hasan 4 The ensemble learning process for short-term prediction of traffic state on rural roads 102 Arash Rasaizadi, Fateme Hafizi, and Seyedehsan Seyedabrishami 5 Using machine learning and deep learning for traffic congestion prediction: a review 124 Adriana-Simona Mihaita, Zhulin Li, Harshpreet Singh, Nabin Sharma, Mao Tuo, and Yuming Ou PART II PUBLIC TRANSPORT PLANNING AND OPERATIONS 6 The potential of explainable deep learning for public transport planning 155 Wenzhe Sun, Jan-Dirk Schmöcker, Youxi Lai, and Koji Fukuda 7 Neural network approaches for forecasting short-term on-road public transport passenger demands 176 Sohani Liyanage, Hussein Dia, Rusul Abduljabbar, and Pei-Wei Tsai PART III RAILWAYS 8 Artificial intelligence in railway traffic planning and management Taxonomy, a systematic review of the state-of-the-art of AI, and transferability analysis 222 Ruifan Tang, Zhiyuan Lin, Ronghui Liu, Rob M.P. Goverde, and Nikola Bešinović 9 Artificial intelligence in railways: current applications, challenges, and ongoing research 249 Lorenzo De Donato, Ruifan Tang, Nikola Bes̆inović, Francesco Flammini, Rob M.P. Goverde, Zhiyuan Lin, Ronghui Liu, Stefano Marrone, Elena Napoletano, Roberto Nardone, Stefania Santini, Valeria Vittorini PART IV FREIGHT AND AVIATION 10 Artificial intelligence and machine learning applications in freight transport 285 Yijie Su, Hadi Ghaderi, and Hussein Dia 11 A paradigm shift in the aviation industry with digital twin, blockchain, and AI technologies 323 Tommy Cheung, Bo Li, and Zheng Lei PART V VIDEO ANALYTICS AND MACHINE VISION APPLICATIONS 12 A deep learning approach to real-time video analytics for people and passenger counting 348 Chris McCarthy, Hadi Ghaderi, Prem Prakash Jayaraman, and Hussein Dia 13 AI machine vision for safety and mobility: an autonomous vehicle perspective 380 Sagar Dasgupta, Xishi Zhu, Muhammad Sami Irfan, Mizanur Rahman, Jiaqi Gong, and Steven Jones PART VI DATA ANALYTICS AND PATTERN ANALYSIS 14 A review of AI-enabled and model-based methodologies for travel demand estimation in urban transport networks 411 Sajjad Shafiei and Hussein Dia 15 Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis 434 Yuchen Lu, Ying Jin, and Xi Chen 16 An intelligent machine learning alerting system for distracted pedestrians 465 M.L. Cummings, Lixiao Huang, and Michael Clamann PART VII PREDICTIVE TRAFFIC SIGNAL CONTROL 17 A critical review of traffic signal control and a novel unified view of reinforcement learning and model predictive control approaches for adaptive traffic signal control 482 Xiaoyu Wang, Baher Abdulhai, and Scott Sanner PART VIII AI ETHICS AND CYBERSECURITY CHALLENGES 18 A review of AI ethical and moral considerations in road transport and vehicle automation 534 Dorsa Alipour and Hussein Dia 19 Cybersecurity challenges in AI-enabled smart transportation systems 567 Lyuyi Zhu, Ao Qu, and Wei Ma 20 Autonomous driving: present and emerging trends of technology, ethics, and law 596 Gustav Lindberg, Ikeya Carrero, Fermín Mallor, Julián Estévez, Manuela Battaglini, and Ricardo Vinuesa Index 617

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Book SynopsisDevelopment in any country is impossible if reliable and affordable energy, safe water and sanitation, as well as telecommunication facilities, are not easily accessible. Artificial intelligence and machine learning techniques are now widely used in all branches of engineering to build and optimize systems. The combination of AI and engineering can indeed act as a real catalyst to achieve the UN SDGs. The volume editors present an analysis of different concepts and case studies in engineering disciplines such as chemical, civil, electrical, telecommunications and mechanical engineering, demonstrating how engineering systems and processes can leverage the power of AI to drive and achieve the UN SDGs. Topics covered include sustainable crop production and consumption, AI based clean water and sanitation monitoring, intelligent transport systems and achieving affordable and clean energy through AI and 5G powered internet of energy. Such a study is of paramount importance and is a valuable source of information for researchers, engineers, and policy makers to be able to better design and adopt AI enabled techniques in different engineering areas, with a view to catalyze the achievement of the UN SDGs.Table of ContentsChapter 1. Advances of Artificial Intelligence in Engineering; Tulsi Pawan Fowdur, Satyadev Rosunee, Robert T. F. Ah King, Pratima Jeetah and Mahendra Gooroochurn Part 1: Impact of AI Enabled Chemical and Environmental Engineering Systems on UN SDGs Chapter 2. Adoption of machine learning for sustainable solid waste management; P D Jeetah, G D Somaroo, D Surroop, A K Ragen, and N S Amode Chapter 3. Smart fertilizer application in agricultural land for sustainable crop production and consumption; Robert T. F. Ah King, Bhimsen Rajkumarsingh, Pratima Jeetah, Geeta Somaroo, and Deejaysing Jogee Chapter 4. Predicting household plastic level consumption using machine learning and AI; Jeetah P D , Chuttur, Hurry N , Tahalooa K , and Seebun D Chapter 5. Ant colony, bee colony and elephant herd optimizations for estimating aqueous-phase adsorption model parameters; Ackmez Mudhoo, Gaurav Sharma, Khim Hoong Chu, and Mika Sillanpää Part 2: Impact of AI Enabled Civil Engineering Systems on UN SDGs Chapter 6. Artificial Intelligence based clean water and sanitation monitoring; Deejaysing Jogee, Manta Devi Nowbuth, Virendra Proag, and Jean-Luc Probst Chapter 7. Achieving SDG targets in the land transport sector using Intelligent Transportation Systems; Zaheer Doomah, Asish Seeboo, and Tulsi Pawan Fowdur Part 3: Impact of AI Enabled Electrical Electronic and Telecommunications Engineering Systems on UN SDGs Chapter 8. Achieving affordable and clean Energy through AI and 5G Powered Internet of Energy (IoE); Tulsi Pawan Fowdur and Ashven Sanghan Chapter 9. Leveraging the power of Blockchain in Industry 4.0 and Intelligent Real-time Systems for achieving the SDGs; Tulsi Pawan Fowdur, Visham Hurbungs, and, and Lavesh Babooram Chapter 10. A Reliability-based Two Stage PMU Placement Optimisation Model using Mathematical and Nature-based Evolutionary Algorithms; Robert T. F. Ah King and Samiah Mohangee Chapter 11. Quantitative Assessment of Models and Indices for Interior Thermal Comfort taking into account the Effects of Solar Radiation and Wind; Bhimsen Rajkumarsingh, Robert T. F. Ah King, and Khalid Adam Joomun Chapter 12. The role of the Internet of Things for a more Sustainable Future; Anshu Prakash Murdan, and Vishwamitra Oree Part 4: Impact of AI Enabled Mechanical and Mechatronics Engineering Systems on UN SDGs Chapter 13. Mechatronics implementation of passive building elements to improve thermal comfort and promote energy efficiency in buildings; Mahendra Gooroochurn Chapter 14. Demystifying climate change and climate action through the circular homes concept - an educational tool for community engagement; Mahendra Gooroochurn Chapter 15. Robotics and automated systems for enabling an Industry 4.0 transformation in Mauritius; Mahendra Gooroochurn and Riaan Stopforth Chapter 16. Potential beneficial impact of AI-driven atmospheric corrosion prediction on the UN SDGs; Yashwantraj Seechurn Chapter 17. In-situ Durability Assessment of Natural Composite Structures by Considering ANN Modelling; Ramful Raviduth Part 5: Impact of AI Enabled Sustainability and Enterprise Development on UN SDGs Chapter 18. The Manufacturing Sector in Mauritius: Building Supply Chain Resilience & Business Value with AI; Satyadev Rosunee and Roshan Unmar Chapter 19. AI for Social Good: Opportunities for Inclusive & Sustainable Development; Satyadev Rosunee and Roshan Unmar Chapter 20. The Applications of Artificial Intelligence in the Textile Industry; Naraindra Kistamah

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Book SynopsisArtificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence.Providing a cutting edge analysis of the impact of AI in business and society, the editors offer an opportunity to assess what is known about managing other forms of information systems, strategy, and marketing, and to re-examine this knowledge in situations involving AI. This comprehensive book explores how human- centric AI systems create value inside organizations, distinguishing three main components: ethical value, societal value, and business value.Using a multidisciplinary perspective, this discerning book addresses the interests of a wide spectrum of practitioners, students, and researchers alike who are interested in identifying the value generated by AI systems in management.Trade Review‘This book is current and to the point. It makes a cutting-edge contribution to the field and is an extremely valuable asset for practitioners, students, and researchers alike who are interested in identifying value generated by AI systems in management and discovering opportunities and challenges.’ -- Leon Wang, International Journal of Data Science‘The analyses put forward, while being characterized by scholarly rigor, are accessible to anyone interested in understanding how AI creates value for management practitioners, be they the practitioners themselves, students, or scholars. The editors boldly choose to promote a holistic view of value creation which does not solely focus on economic performance. The Covid-19 pandemic has made companies more digitally agile, and practitioners must be informed about AI to remain competitive now more than ever. Furthermore, the book does not bow to the general hype around AI but aims at providing a nuanced view of what AI systems are today, based on rigorous empirical investigation rather than speculation. The book is also the perfect springboard for scholars and Phd students who wish to conduct further research related to AI and management, which is a hot topic in academia as well.’ -- Nathan Sorin, Micro & Macro Marketing‘What Pagani and Champion have achieved with Artificial Intelligence for Sustainable Value Creation is a seminal pragmatic roadmap prioritizing people and the planet via systems level design. AI technologies empower the metrics of success that humans provide and this book shows why values-driven sustainability is the only path forward for businesses to achieve a purpose driven future for their organizations and the world.’ -- John C. Havens, author of Heartificial Intelligence: Embracing Our Humanity to Maximize Machines‘The editors and the authors are to be congratulated for this important book. They show that AI can play a key, ethical role in the creation of societal, environmental, and business value by helping businesses gain better information, optimise operations, improve products and services, and become more competitive and sustainable.’ -- Luciano Floridi, University of Oxford, UK‘Artificial Intelligence by itself is just the latest in a long series of techno buzzwords. However, when you pair AI with the essential idea of Value Creation, as does this book, now you have something of critical importance to anyone who wants to know about the future of business!’ -- Charles Hofacker, Florida State University, USTable of ContentsContents: Foreword by Luc Julia xiv Acknowledgements xvi Introduction to Artificial Intelligence for Sustainable Value Creation 1 Margherita Pagani and Renaud Champion PART I HUMAN-CENTRIC AI 1 Creating business value through human-centric AI 9 Margherita Pagani and Renaud Champion 2 Value-driven design of AI enabled experiences 32 Yihyun Lim PART II BUSINESS VALUE 3 Digital platform ecosystems: the coming context for AI 55 Omar El Sawy, Milan Miric and Margherita Pagani 4 Unlocking value from AI in financial services: strategic and organizational tradeoffs vs. media narratives 70 Gianvito Lanzolla, Simone Santoni and Christopher Tucci PART III ETHICAL AND SOCIETAL VALUE 5 The challenge of responsible AI 99 Christine Balagué 6 A model of fair and explainable artificial intelligence 122 Amy Wenxuan Ding 7 Ethical maintenance of artificial intelligence systems 151 Boris Düdder, Florian Möslein, Norman Stürtz, Magnus Westerlund and Roberto V. Zicari Afterword 172 Margherita Pagani and Renaud Champion Index

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Book SynopsisAutonomous systems are on the frontiers of Artificial Intelligence (AI) research, and they are slowly finding their business applications. Driven mostly by Reinforcement Learning (RL) methods (one of the most difficult, but also the most promising modern AI algorithms), autonomous systems help create self-learning and self-optimising systems, ranging from simple game-playing agents to robots able to efficiently act in completely new environments. Based on in-depth study of more than 100 projects, Andrzej Wodecki explores RL as a key component of modern digital technologies, its real-life applications to activities in a value chain and the ways in which it impacts different industries. Artificial Intelligence in Management will help project leaders, decision makers and investors evaluate new autonomous projects and will serve as an inspiring guide for future research.Trade Review‘This publication is a very useful guide for organization administrators who hope to optimize operation management with proper applications of AI. For readers who have already known AI and management science, and is trying to integrate AI into management for new strategies and modes, or who want to extend their knowledge of AI in management, this book must be an ideally enlightening resource and will serve as an inspiring guide for future studies.’ -- Ren Yuan, International Journal of Knowledge-Based OrganizationsTable of ContentsContents: Introduction: From Intelligent Machines To Self-Driven Organizations 1. Artificial Intelligence And Machine Learning Landscape 2. The Impact Of Autonomous Systems On Technologies, Processes And Industry Sectors 3. Autonomous Systems In Value Generation 4. Prospects For The Future Conclusions Index

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Book SynopsisThis cutting-edge Handbook offers a comprehensive introduction to the emerging research field of artificial intelligence (AI) in human resource management (HRM). Broadly mapping AI fields relevant for HR, it not only considers the more well-known areas of machine learning and natural language processing, but also lesser-known fields such as affective computing and robotic process automation.Expert contributors analyze the applications of machine learning in human resources, including machine learning on text data, audio and video data, social media data, and in recruiting and staffing. They also explore a range of innovative topics such as knowledge representation and reasoning, and evolutionary computing. Discussing the explainability, fairness, accountability, and legitimacy of AI in HR, chapters bring normative issues to the fore. Approaches to researching AI in HR and to employing AI in HR research are also tackled. Offering an insight into existing research on artificial intelligence in human resources, the Handbook introduces core issues and considers implications for future research.This Handbook will be critical reading for scholars and students of human resource management, knowledge management, organizational innovation, computer science, and information systems. It will also be beneficial for practitioners in these fields.Trade Review‘This Handbook is a must-have whether you know a little or a lot about AI and human resource management. Topics range from the highly technical for specialists to the more foundational for novices. Readers can dive in to get answers to specific questions or read the whole volume to gain a thorough grounding. AI is here to stay in human resource management. It poses many challenges for scholars and practitioners. This Handbook is a great guide for addressing those challenges.' -- Mark Lengnick-Hall, University of Texas at San Antonio, US‘The potential for better decisions in managing people and also the conflicts between AI principles and those that have governed human resources are profound. This Handbook offers the most detailed and wide-ranging account available as to what AI solutions look like in this realm, not just those available now but most importantly those in the works.’ -- Peter Cappelli, University of Pennsylvania, US‘This Handbook provides a comprehensive overview of how AI might be deployed in the field of human resources and includes incisive analysis of some of the key challenges: explaining AIs’ decisions, fairness and legal regulation. It is a really excellent resource.’ -- Andy Charlwood, University of Leeds, UKTable of ContentsContents: Preface xii 1 Artificial intelligence in human resources – an introduction 1 Stefan Strohmeier PART I APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES PART I.1 APPLICATIONS OF MACHINE LEARNING IN HUMAN RESOURCES 2 HR machine learning – an introduction 25 Stefan Strohmeier 3 HR machine learning on text data 46 Felix Gross 4 HR machine learning on audio and video data 68 Carmen Fernández-Martinez and Alberto Fernández 5 HR machine learning on social media data 89 Jake T. Harrison and Christopher J. Hartwell 6 HR machine learning in recruiting 105 Sven Laumer, Christian Maier, and Tim Weitzel 7 Machine learning in HR staffing 127 Florian J. Meier and Sven Laumer 8 Machine learning in personnel selection 149 Cornelius J. König and Markus Langer PART I.2 FURTHER APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 9 HR knowledge representation and reasoning 169 Jorge Martinez-Gil 10 HR robotic process automation 187 Peter Fettke and Stefan Strohmeier 11 HR evolutionary computing 207 Lena Wolbeck and Charlotte Köhler 12 HR natural language processing – conceptual overview and state of the art on conversational agents in human resources management 226 Sven Laumer and Stefan Morana 13 HR affective computing 243 William J. Becker, Sarah E. Tuskey, and Constant D. Beugré PART II CONSEQUENCES OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 14 Consequences of artificial intelligence in human resource management 261 Maarten Renkema PART III NORMATIVE ISSUES OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 15 Explainability of artificial intelligence in human resources 285 Markus Langer and Cornelius J. König 16 Fairness of artificial intelligence in human resources – held to a higher standard? 303 Sandra L. Fisher and Garret N. Howardson 17 Accountability of artificial intelligence in human resources 323 Katharina A. Zweig and Franziska Raudonat 18 Legitimacy of artificial intelligence in human resources – the legal framework for using artificial intelligence in human resource management 337 Kai von Lewinski and Raphael de Barros Fritz PART IV RESEARCH ISSUES OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 19 Design considerations for conducting artificial intelligence research in human resource management 353 Richard D. Johnson and Dianna L. Stone 20 Employing artificial intelligence in human resources research 371 Chulin Chen and Richard Landers Index 392

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Book SynopsisThis timely book presents a detailed analysis of the role of law and regulation in the utilisation of Artificial Intelligence (AI) in the media sector. As well as contributing to the wider discussion on law and AI, the book also digs deeper by exploring pressing issues at the intersections of AI, media, and the law. Chapters critically re-examine various rights and responsibilities from the perspectives of incentives for accountable utilisation of AI in the industry.Featuring chapters from leading scholars in the field, Artificial Intelligence and the Media provides a timely and in-depth research-based contribution to complex themes - especially at the interface of new technology (including AI) with media and regulation. Analysing both legislative and ethical solutions, chapters explore what “AI” and “accountability” mean in terms of media practices, principles, and power relations, as well as how to address the AI revolution with informed law and policy in order to incentivise accountable utilisation of AI and to reduce negative societal impacts.Offering ideas for further research in the area, this book is key reading for academics and researchers in the fields of information and media law, regulation, and technology law. It may also interest media law practitioners, with research-based guidance for everyday practices and tools to prepare for future developments in the area.Trade Review‘Artificial Intelligence and the Media is an urgently needed contribution to the research on AI and its impacts. While much of the scholarship so far has been field-specific, what makes this volume especially poignant is its multidisciplinary approach to the questions about the roles AI can play for media industries but also for media consumers and users as citizens, and to democracy as a whole.’ -- Minna Aslama Horowitz, University of Helsinki, Finland and St. John's University, USTable of ContentsContents List of contributors vii Introduction to Artificial Intelligence and the Media 1 Taina Pihlajarinne and Anette Alén-Savikko PART I JOURNALISTIC PRINCIPLES AND ARTIFICIAL INTELLIGENCE 1 Bias, journalistic endeavours, and the risks of artificial intelligence 8 M.R. Leiser 2 Transparency in algorithmic journalism: from ethics to law and back 33 Anette Alén-Savikko 3 The journalistic exemption in personal data processing 61 Päivi Korpisaari PART II TRUST, DISINFORMATION AND PLATFORMS 4 Social media platforms as public trustees: an approach to the disinformation problem 93 Philip M. Napoli and Fabienne Graf 5 Artificial intelligence is not a panacea: policing content on social media platforms, three dilemmas and their ethical and legal implications 123 Jingrong Tong 6 The commercial unfairness of recommender systems on social media 148 Catalina Goanta and Gerasimos Spanakis PART III REMITS AND LIMITS OF EXCLUSIVE RIGHTS 7 Creations caused by humans (or robots)? Artificial intelligence and causation requirements for copyright protection in EU law 172 Ole-Andreas Rognstad 8 Artificial intelligence and intellectual property rights: the quest or plea for artificial intelligence as a legal subject 192 Rosa Maria Ballardini and Robert van den Hoven van Genderen 9 The European copyright system as a suitable incentive for AI-based journalism? 215 Taina Pihlajarinne, Alexander Thesleff, Leo Leppänen and Sini Valmari 10 Press publishers’ right and artificial intelligence 240 Juha Vesala 11 Access to data for training algorithms in machine learning: copyright law and ‘right-stacking’ 272 Inger B. Ørstavik Conclusions on Artificial Intelligence and the Media 296 Taina Pihlajarinne and Anette Alén-Savikko Index 300

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Book SynopsisIt is almost universally agreed that consciousness and possession of a conscience are essential characteristics of human intelligence. While some believe it to be impossible to create artificial beings possessing these traits, and conclude that ultimate major goal of Artificial Intelligence is hopeless, this book demonstrates that not only is it possible to create entities with capabilities in both areas, but that they demonstrate them in ways different from our own, thereby showing a new kind of consciousness. This latter characteristic affords such entities performance beyond the reach of humans, not for lack of intelligence, but because human intelligence depends on networks of neurons which impose processing restrictions which do not apply to computers. At the beginning of the investigation of the creation of an artificial being, the main goal was not to study the possibility of whether a conscious machine would possess a conscience. However, experimental data indicate that many characteristics implemented to improve efficiency in such systems are linked to these capacities. This implies that when they are present it is because they are essential to the desired performance improvement. Moreover, since the goal is not to imitate human behavior, some of these structural characteristics are different from those displayed by the neurons of the human brain - suggesting that we are at the threshold of a new scientific field, artificial cognition, which formalizes methods for giving cognitive capabilities to artificial entities through the full use of the computational power of machines.Table of ContentsAcknowledgements ix Note on the Terminology xi Chapter 1. Presenting the Actors 1 1.1. The book 1 1.2. Human and artificial beings 4 1.3. The computer 7 1.4. The author 9 1.5. CAIA, an artificial AI scientist 11 1.6. The research domains of CAIA 15 1.7. Further reading 19 Chapter 2. Consciousness and Conscience 21 2.1. Several meanings of “consciousness” 22 2.2. Extending the meaning of “conscience” for artificial beings 25 2.3. Why is it useful to build conscious artificial beings with a conscience? 29 2.4. Towards an artificial cognition 31 2.4.1. A new kind of consciousness 32 2.4.2. A new kind of conscience 33 Chapter 3. What Does “Itself” Mean for an Artificial Being? 35 3.1. Various versions of an individual 36 3.1.1. The concept of an individual for human beings 36 3.1.2. The boundaries of an artificial being 39 3.1.3. Passive and active versions of an individual 41 3.1.4. Reflexivity 47 3.2. Variants of an individual 49 3.2.1. An individual changes with time 50 3.2.2. Learning by comparing two variants 50 3.2.3. Genetic algorithms 52 3.2.4. The bootstrap 54 3.3. Cloning artificial beings 57 3.3.1. Cloning an artificial being is easy 57 3.3.2. Cloning artificial beings is useful 58 3.4. Dr. Jekyll and Mr. Hyde 61 3.5. The Society of Mind 63 3.6. More on the subject 65 Chapter 4. Some Aspects of Consciousness 67 4.1. Six aspects of consciousness 68 4.1.1. One is in an active state 68 4.1.2. One knows what one is doing 72 4.1.3. One examines his/its internal state 80 4.1.4. One knows what one knows 84 4.1.5. One has a model of oneself 87 4.1.6. One knows that one is different from the other individuals 90 4.2. Some limits of consciousness 92 4.2.1. Some limits of consciousness for man 93 4.2.2. Some limits of consciousness for artificial beings 100 Chapter 5. Why is Auto-observation Useful? 105 5.1. Auto-observation while carrying out a task 105 5.1.1. To guide toward the solution 106 5.1.2. To avoid dangerous situations 111 5.1.3. To detect mistakes 121 5.1.4. To find where one has been clumsy 125 5.1.5. To generate a trace 126 5.2. Auto-observation after the completion of a task 129 5.2.1. Creation of an explanation 130 5.2.2. Using an explanation 133 5.2.3. Finding anomalies 138 Chapter 6. How to Observe Oneself 143 6.1. Interpreting 146 6.2. Adding supplementary orders 150 6.3. Using timed interruptions 154 6.4. Using the interruptions made by the operating system 158 6.5. Knowing its own state 159 6.6. Examining its own knowledge 160 6.7. The agents of the Society of Mind. 165 6.8. The attention 166 6.9. What is “I” 169 Chapter 7. The Conscience 173 7.1. The conscience of human beings 174 7.2. The conscience of an artificial being 179 7.3. Laws for artificial beings 183 7.3.1. Asimov’s laws of robotics 183 7.3.1. How can moral laws be implemented? 184 7.3.3. The present situation 191 Chapter 8. Implementing a Conscience 195 8.1. Why is a conscience helpful? 197 8.1.1. The conscience helps to solve problems 197 8.1.2. The conscience helps to manage its life 198 8.1.3. Two ways to define moral knowledge 199 8.1.4. Who benefits from the conscience of an artificial being? 200 8.2. The conscience of CAIA. 201 8.3. Implicit principles 202 8.4. Explicit principles 206 8.5. The consciences in a society of individuals 215 8.5.1. The Society of Mind. 216 8.5.2. Genetic algorithms 217 Chapter 9. Around the Conscience 219 9.1. Emotions 220 9.2. Changing its conscience 223 9.3. A new human conscience for our relationships with artificial beings 228 Chapter 10. What is the Future for CAIA? 237 Appendices 239 1. Constraint Satisfaction Problems 239 2. How to implement some aspects of consciousness 253 Bibliography 263 Index 269

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Book SynopsisThe energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.Table of ContentsPreface ix Introduction xi Chapter 1. Overview of Building Energy Analysis 1 1.1. Introduction 1 1.2. Physical models 3 1.3. Gray models 6 1.4. Statistical models 6 1.5. Artificial intelligence models 8 1.5.1. Neural networks 8 1.5.2. Support vector machines 13 1.6. Comparison of existing models 14 1.7. Concluding remarks . 16 Chapter 2. Data Acquisition for Building Energy Analysis 17 2.1. Introduction 17 2.2. Surveys or questionnaires 18 2.3. Measurements 21 2.4. Simulation 25 2.4.1. Simulation software 26 2.4.2. Simulation process 28 2.5. Data uncertainty 34 2.6. Calibration 35 2.7. Concluding remarks 37 Chapter 3. Artificial Intelligence Models 39 3.1. Introduction 39 3.2. Artificial neural networks 40 3.2.1. Single-layer perceptron 41 3.2.2. Feed forward neural network 43 3.2.3. Radial basis functions network 44 3.2.4. Recurrent neural network 47 3.2.5. Recursive deterministic perceptron 49 3.2.6. Applications of neural networks 51 3.3. Support vector machines 53 3.3.1. Support vector classification 54 3.3.2. ε-support vector regression 59 3.3.3. One-class support vector machines 62 3.3.4. Multiclass support vector machines 63 3.3.5. v-support vector machines 64 3.3.6. Transductive support vector machines 65 3.3.7. Quadratic problem solvers . 67 3.3.8. Applications of support vector machines 75 3.4. Concluding remarks 76 Chapter 4. Artificial Intelligence for Building Energy Analysis 79 4.1. Introduction 79 4.2. Support vector machines for building energy prediction 80 4.2.1. Energy prediction definition 80 4.2.2. Practical issues 81 4.2.3. Support vector machines for prediction 85 4.3. Neural networks for fault detection and diagnosis 91 4.3.1. Description of faults 94 4.3.2. RDP in fault detection 95 4.3.3. RDP in fault diagnosis 100 4.4. Concluding remarks 102 Chapter 5. Model Reduction for Support Vector Machines 103 5.1. Introduction 103 5.2. Overview of model reduction 104 5.2.1. Wrapper methods 105 5.2.2. Filter methods 106 5.2.3. Embedded methods 107 5.3. Model reduction for energy consumption 108 5.3.1. Introduction 108 5.3.2. Algorithm 109 5.3.3. Feature set description 111 5.4. Model reduction for single building energy 112 5.4.1. Feature set selection 112 5.4.2. Evaluation in experiments 114 5.5. Model reduction for multiple buildings energy 116 5.6. Concluding remarks 119 Chapter 6. Parallel Computing for Support Vector Machines 121 6.1. Introduction 121 6.2. Overview of parallel support vector machines 122 6.3. Parallel quadratic problem solver 123 6.4. MPI-based parallel support vector machines 127 6.4.1. Message passing interface programming model 127 6.4.2. Pisvm 129 6.4.3. Psvm 130 6.5. MapReduce-based parallel support vector machines 130 6.5.1. MapReduce programming model 131 6.5.2. Caching technique 133 6.5.3. Sparse data representation 133 6.5.4. Comparison of MRPsvm with Pisvm 134 6.6. MapReduce-based parallel ε-support vector regression 138 6.6.1. Implementation aspects 138 6.6.2. Energy consumption datasets 139 6.6.3. Evaluation for building energy prediction 140 6.7. Concluding remarks 142 Summary and Future of Building Energy Analysis 145 Bibliography 149 Index 163

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Book SynopsisInformation and communication technologies have increased their share of services in contemporary economic exchanges. We are witnessing a transformation of modern economies characterized by a predominant role of information and knowledge in the production of wealth. In order to make this intangible resource bear fruit, organizations are looking for ways, methods, procedures, processes and technical solutions to efficiently manage knowledge Within a framework of research into synergies and resource interdependence, organizations also rely on strategic alliances (joint venture), mergers or other legal forms of association that have an impact on knowledge management. This book explores the range of knowledge management techniques.Table of ContentsIntroduction ix Part 1 A Systemic Approach to the Organization Based on Knowledge Management and its Tools 1 Chapter 1 Theoretical Anchoring of Knowledge 3 1.1 Individual knowledge and skills 4 1.2 From individual learning to organizational learning 9 1.3 Knowledge management 16 1.4 Knowledge management systems 23 1.5 Communities, teams and knowledge management 26 1.6 Knowledge management and cultures 31 Chapter 2 The Design of the Learning Organization 37 2.1 From the systemic approach to the organizational design 37 2.2 Proposal of an organizational design for knowledge management: "learning organization design" 44 Part 2 Emergence of a New Design: that of the Learning Organization 61 Chapter 3 Real-World Access Methodology 63 3.1 Methodological choices 64 3.2 The field of research 67 3.3 Data collection 85 3.4 Processing of the collected data 92 Chapter 4 Case Study 99 4.1 Design of the learning organization SCCC (before the merger) 99 4.2 Design of the learning organization SCCC (period of merger with N) 108 4.3 Design of the learning organization NSN (post-merger) 114 4.4 Overview of the three phases 122 Chapter 5 Emergence of a New Organizational Design 127 5.1 Emergence of a design of the learning organization 127 5.2 Emergence of a new organizational design in view of the impact of culture 130 5.3 Emergence of a new organizational design when faced with knowledge boundaries 131 Conclusion 139 Bibliography 147 Index 171

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Book SynopsisThe idea of autonomous systems that are able to make choices according to properties which allow them to experience, apprehend and assess their environment is becoming a reality. These systems are capable of auto-configuration and self-organization. This book presents a model for the creation of autonomous systems based on a complex substratum, made up of multiple electronic components that deploy a variety of specific features. This substratum consists of multi-agent systems which act continuously and autonomously to collect information from the environment which they then feed into the global system, allowing it to generate discerning and concrete representations of its surroundings. These systems are able to construct a so-called artificial corporeity which allows them to have a sense of self, to then behave autonomously, in a way reminiscent of living organisms.Table of ContentsIntroduction ix List of Algorithms xi Chapter 1 Systems and their Design 1 1.1 Modeling systems 1 1.1.1 Conventional systems 2 1.1.2 Complex systems 3 1.1.3 System of systems 3 1.2 Autonomous systems 5 1.3 Agents and multi-agent systems 6 1.3.1 The weak notion of agent 7 1.3.2 The strong notion of agent 7 1.3.3 Cognitive agents and reactive agents 8 1.3.4 Multi-agent systems 9 1.3.5 Reactive agent-based MAS 10 1.3.6 Cognitive agent-based MAS 11 1.4 Systems and organisms 13 1.5 The issue of modeling an autonomous system 13 Chapter 2 The Global Architecture of an Autonomous System 17 2.1 Introduction 17 2.2 Reactivity of a system 17 2.3 The basic structure of an autonomous system: the substratum 18 2.3.1 A detailed example: smoothing the flow or urban traffic 20 2.4 The membrane of autonomous systems 22 2.4.1 Membrane and information 25 2.5 Two types of proactivity and the notion of artificial organ 26 2.5.1 Weak proactivity 26 2.5.2 Strong proactivity 27 2.5.3 Measuring proactivity with dynamic graphs 30 2.6 Autonomy and current representation 31 2.6.1 Current representation in an autonomous system 32 2.7 The unifying system that generates representations 33 Chapter 3 Designing a Multi-agent Autonomous System 41 3.1 Introduction 41 3.2 The object layer on the substratum 41 3.3 The agent representation of the substratum: interface agents, organs and the notion of sensitivity 44 3.3.1 Artificial organs 46 3.3.2 Sensitivity of the corporeity 47 3.4 The interpretation system and the conception agents 47 3.4.1 The properties of a conception agent in the interpretation system 49 3.4.2 An example 52 3.4.3 Creating a conception agent 57 3.5 Aggregates of conception agents 58 3.6 The intent and the activity of conception agents 60 3.7 Agentifying conception agents 63 3.8 Activity of a conception agent 65 3.9 The three layers of conceptual agentification and the role of control 70 3.9.1 First guiding principle for the architecture of an autonomous system 74 3.10 Semantic lattices and the emergence of representations in the interpretation system 77 3.11 The general architecture of the interpretation system 84 3.12 Agentification of knowledge and organizational memory 86 3.13 Setting up the membrane network of an autonomous system 94 3.14 Behavioral learning of the autonomous system 96 Chapter 4 Generation of Current Representation and Tendencies 105 4.1 Introduction 105 4.2 Generation of current representation and semantic lattices 105 4.2.1 Openness and deployment: major properties of autonomous systems 106 4.2.2 Incentive-based control and evaluation agents 107 4.2.3 Evaluation agents’ access to organizational memory 110 4.2.4 The role of evaluation agents in the extracted lattice 110 4.2.5 The notion of dynamic lattices 110 4.2.6 Algorithms for generating representations 111 4.2.7 Mathematical interpretation 115 4.3 The cause leading the system to choose a concrete intent 116 4.3.1 Determination of intent 118 4.3.2 Intent and tendencies 120 4.4 Presentation of artificial tendencies 123 4.5 Algorithm for the generation of a stream of representations under tendencies 134 Chapter 5 The Notions of Point of View, Intent and Organizational Memory 137 5.1 Introduction 137 5.2 The notion of point of view in the generation of representations 137 5.3 Three organizational principles of the interpretation system for leading the intent 144 5.3.1 Principle of continuity engagement 145 5.3.2 The bifurcation principle 146 5.3.3 The principle of necessary reason and reliability 147 5.4 Algorithms for intent decisions 147 5.6 Organizational memory and the representation of artificial life experiences 151 5.7 Effective autonomy and the role of the modulation component 156 5.8 Degree of organizational freedom 159 Chapter 6 Towards the Minimal Self of an Autonomous System 161 6.1 Introduction 161 6.2 The need for tendencies when leading the system 161 6.3 Needs and desires of the autonomous system 164 6.4 A scaled-down autonomous system: the artificial proto-self 168 6.5 The internal choice of expressed tendencies and the minimal self 171 6.6 The incentive to produce representations 176 6.7 Minimal self affectivity: emotions and sensations 179 6.8 Algorithms for tendency activation 182 6.9 The feeling of generating representations 188 Chapter 7 Global Autonomy of Distributed Autonomous Systems 197 7.1 Introduction 197 7.2 Enhancement of an autonomous system by itself 197 7.3 Communication among autonomous systems in view of their union 201 7.4 The autonomous meta-system composed of autonomous systems 204 7.5 The system generating autonomous systems: the meta-level of artificial living 207 Conclusion 211 Bibliography 213 Index 215

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Book SynopsisThis important book presents new and original work at the frontiers of economics - namely the interface between artificial intelligence (AI) and neoclassical economics.Artificial Intelligence and Economic Analysis focuses on three quite distinct lines of AI orientated research in economics: applications intended to extend neoclassical theory, applications intended to undermine neoclassical theory and applications which ignore neoclassical theory in the quest for new modelling techniques and fields of analysis. The contributors - all of whom are well established in the field - seek to identify those areas where the science of artificial intelligence could enrich standard economic analysis. It includes material from mainstream economists who are willing to express their own views about the limits of mainstream economic modelling and AI based economic modelling.The book makes an important contribution to a new and exciting area of economics which holds much hope for the future.Trade Review'It provides interesting reading and a source of speculation for those who hope to find uses for AI techniques in economic research.' -- Kent D. Wall, Journal of Economic Behavior & OrganizationTable of ContentsContents: 1. Introduction (S. Moss) 2. Economics and Intelligence (R. Marris) 3. Artificial Intelligence Models of Complex Economic Systems (S. Moss) 4. AI Modelling Techniques: The Emergence of a Supportive Framework for Modelling Complex Behaviour in Economics (J. Rae and M.L. Reynolds) 5. Strategic Decision Making: Orthodox Theory Versus Artificial Intelligence Approaches (A. and S. Moss) 6. Experiments, Games and Economics (J.D. Hey and M.L. Reynolds) 7. Artificial Intelligence and the Economics of Technological Change (P. Stoneman) 8. Some Thoughts on Economic Theory and Artificial Intelligence (H. Dixon) 9. Church’s Thesis and Game Theory: An Overview of Some Results (L. Anderlini) 10. The Development of Intelligent Macroeconometric Models and Modelling Procedures (M. Artis, S. Moss and P. Ormerod) Bibliography Index

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Book SynopsisMarshall Goldsmith wrote in his book, What Got You Here, Won’t Get You There, that people rely on their past experience to address new challenges. The limitation with this approach is that these new challenges often arise from different contexts and may not be susceptible to traditional approaches.In the coming era of artificial intelligence (AI), expanded use of robots, and increased trans-national commerce, humanity will face monumental challenges that will differ from those we have faced in the past, including how to avoid mass unemployment due to rapid growth of automation. In order to survive and thrive in this new era, we will have to think and act differently, so that new ideas can solve not only the problems of the present but also of the near and distant future.Economic Renaissance in the Age of Artificial Intelligence explores a wide range of new approaches to the economic, social, legal, scientific, technological, financial, architectural, environmental, and humanistic challenges that humanity will face due to increased automation. The new methods and approaches outlined by the various experts in this book will help inform and inspire humanity to create a more balanced world in which science, economics, and the environment can thrive for years to come.

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Book SynopsisIn today’s high-pressured world, digital transformation is everywhere on the agendas of corporate boards and has risen to the top of CEOs’ strategic plans. Artificial intelligence, blockchain, 3D printing, the Internet of Things, and drones are some of the emerging technologies that are already transforming our world. In this fast changing domain— predicted by few and now reality for all how can companies transform today’s challenges into tomorrow’s opportunities?This book is targeted to help a broad audience such as students, professionals, business, and technology managers to transform an old-world brick and mortar organization to a new-world digital leader. The author addresses various questions including: what essential components does digital transformation include, and how does it impact the enterprise? How does convergence of emerging technologies benefit your organization? How can you start transformation and technology planning projects?

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Book SynopsisThis world first summary of the evolution of 2D chatbots in websites, backends of portals and social media apps, and conversationally advanced 3D mixed reality cognitive interfaces, serves several purposes.It dissects some of the best-known case studies to emerge from the past two decades of tech giants launching the best chatbot, or supposedly the smartest, intelligent virtual assistant. From Microsoft’s Tay.ai to London’s Eugene Goostman claim to turing test fame, from the market dominating Amazon Alexa to Gatebox’s IoT innovation with its multi-cloned Japanese hologram girlfriend, this is the first ever history of bots.This book also touches on the Trump vs Clinton chatbot wars as well as the UK Labour Party’s dating site stunt, including references made to Facebook Messenger bots and the impact of the Cambridge Analytica scandal. Included in the book is a hands-on checklist and guidelines in for people wanting to buy or license bots for their companies and organizations. The author also outlines the possible use cases and key issues to consider when sourcing and commissioning your first botification project, with the fi nal chapters predicting where the future development – and development traps – might lie.In this easy-to-read overview, Tania Peitzker cites leading business intelligence and analyst firms’ research, and takes a deeper dive into the practical challenges of chatbots, including the obstacles and triumphs experienced by business chatbots.

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Book SynopsisThe field of multimedia is unique in offering a rich and dynamic forum for researchers from “traditional” fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking.In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment.Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia.At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.Table of Contents Preface PART I: MULTIMEDIA CONTENT ANALYSIS 1. Deep Learning for Video Classification and Captioning 2. Audition for Multimedia Computing 3. Multimodal Analysis of Free-standing Conversational Groups 4. Encrypted Domain Multimedia Content Analysis 5. Efficient Similarity Search PART II: HUMAN-CENTERED MULTIMEDIA COMPUTING 6. Social-Sensed Multimedia Computing 7. Situation Recognition Using Multimodal Data 8. Hawkes Processes for Events in Social Media 9. Utilizing Implicit User Cues for Multimedia Analytics PART III: MULTIMEDIA COMMUNICATION AND SYSTEMS 10. Multimedia Fog Computing: Minions in the Cloud and Crowd 11. Cloud Gaming Bibliography Index Editor Biography

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Book SynopsisThe field of multimedia is unique in offering a rich and dynamic forum for researchers from “traditional” fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking.In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment.Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia.At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.Table of Contents Preface PART I: MULTIMEDIA CONTENT ANALYSIS 1. Deep Learning for Video Classification and Captioning 2. Audition for Multimedia Computing 3. Multimodal Analysis of Free-standing Conversational Groups 4. Encrypted Domain Multimedia Content Analysis 5. Efficient Similarity Search PART II: HUMAN-CENTERED MULTIMEDIA COMPUTING 6. Social-Sensed Multimedia Computing 7. Situation Recognition Using Multimodal Data 8. Hawkes Processes for Events in Social Media 9. Utilizing Implicit User Cues for Multimedia Analytics PART III: MULTIMEDIA COMMUNICATION AND SYSTEMS 10. Multimedia Fog Computing: Minions in the Cloud and Crowd 11. Cloud Gaming Bibliography Index Editor Biography

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Book SynopsisCet ouvrage collectif s’inscrit dans le cadre des travaux de la Chaire justice sociale et intelligence artificielle Abeona-ENS-OBVIA. Il propose une réflexion multidisciplinaire sur les enjeux des usages de l’intelligence artificielle, mais surtout à partir d’une perspective de justice sociale. Le concept de justice sociale permet d’inclure des dimensions, principalement saisies par les sciences sociales et humaines, et qui ne sont pas traditionnellement associées aux technologies d’intelligence artificielle. Cela permet alors d’appréhender des dimensions telles que la justice et l’équité, mais aussi la solidarité ou encore la dignité ; ces dimensions constituent de puissants outils de changement social lorsqu’ils sont mobilisés par différents acteurs. Les contributions de cet ouvrage mettent en évidence des réflexions quant à la mise en place de conditions sociétales et de pistes d’action pour un déploiement des technologies d’intelligence artificielle en respect des sociétés humaines.This collective book is part of the work of the Abeona-ENS-OBVIA Chair in Social Justice and Artificial Intelligence. It proposes a multidisciplinary reflection on the challenges posed by the uses of artificial intelligence, above all from a social justice perspective.The concept of social justice allows to include dimensions that are not traditionally associated with artificial intelligence technologies, and which are primarily addressed by the social sciences and humanities. This makes it possible to comprehend dimensions such as justice and equity, but also solidarity and dignity; these dimensions constitute powerful tools for social change when mobilized by different actors.The contributions in this book highlight reflections on the definition of societal conditions and avenues of action for artificial intelligence technologies deployment in respect for human societies.

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Book SynopsisAs voice interfaces and virtual assistants have moved out of the industry research labs and into the pockets, desktops and living rooms of the general public, a demand for a new kind of user experience (UX) design is emerging. Although the people are becoming familiar with Siri, Alexa, Cortana and others, their user experience is still characterized by short, command- or query-oriented exchanges, rather than longer, conversational ones. Limitations of the microphone and natural language processing technologies are only part of the problem. Current conventions of UX design apply mostly to visual user interfaces, such as web or mobile; they are less useful for deciding how to organize utterances, by the user and the virtual agent, into sequences that work like those of natural human conversation. This edited book explores the intersection of UX design, of both text- or voice-based virtual agents, and the analysis of naturally occurring human conversation (e.g., the Conversation Analysis, Discourse Analysis and Interactional Sociolinguistics literatures). It contains contributions from researchers, from academia and industry, with varied backgrounds working in the area of human-computer interaction. Each chapter explores some aspect of conversational UX design. Some describe the design challenges faced in creating a particular virtual agent. Others discuss how the findings from the literatures of the social sciences can inform a new kind of UX design that starts with conversation.Table of ContentsConversational UX Design: An Introduction.- Adapting to Customer Initiative: Insights from Human Service Encounters.- Safety First: Conversational agents for Health Care.- Conversational Agents for Physical World Navigation.- Helping Users Reflect on Their Own Heath-related Behaviors.- Teaching Agents When they Fail: End User Development in Goal-oriented Conversational Agents.- Recovering from Dialogue Failures Using Multiple Agents in Wealth Management Advice.- Conversational Style: Beyond the nuts and bolts of conversation.- A natural Conversation Framework for Conventional UX Design.

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Book SynopsisThis book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to be able to secure our cyberfuture. The approaches and findings described in this book are of interest to businesses and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

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Book SynopsisIs it possible to energise the reuse of urban abandoned spaces with low financial capital investment? Addressing this question requires a normative and cultural change, where the rules are less focused on the material processes of producing space and more aimed at fostering the construction of relationships. The reality of several European cities shows how traditional forms of stimulating urban renewal – with respect to the financing of operations, how to design and build, and urban planning legislation – no longer work. This book examines an alternative culture of design and regulation, drawing on the richness of the various approaches to the subject to present an integrated study of the phenomenon of reuse across its economic, architectural and urban dimensions. From this theoretical base, it empirically analyses six Italian case studies in terms of the broadness of geography and in their governance models, and of the important role of the unity of cultural destination for their reuse proposal. The book is intended for all those involved in the cultural challenge of reusing urban abandoned spaces, including public administrators, entrepreneurs, architects, planners and academics. Table of ContentsVacant Buildings. Distinguishing Heterogeneous Cases: Public Items Vs. Private Items; Empty Properties Vs. Abandoned Properties.- Participation, Culture, Entrepreneurship: Using Public Real Estate Assets to Create New Urban Regeneration Models.- Intensity of Uses and Spatial Devices.- The Appraisal Challenge in Cultural Urban Regeneration: A Proposal of An Evaluation Procedure.- Theoretical Basis and Design of Analysis.- The Case Study Profiles.- Governance, Economic Sustainability and Socio-Spatial Relationships.- Shapes, Rules and Value.

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Book SynopsisAs Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.Table of ContentsIntroduction to Artificial Intelligence in Economics and Finance Theories.- The Growth Model.- Comparative Advantage.- The Dual-Sector Model.- Dynamic Inconsistency Theory.- The Philipps Curve.- The Laffer Curve.- Adverse Selection.- Moral Hazard.- Creative Destruction.- The Agency Theory.- The Legitimacy Theory and the Legitimacy Gap.- Synopsis: Artificial Intelligence in Finance and Economics Theories.- Index

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Book Synopsis​This book presents the outcomes of the symposium “NEW METROPOLITAN PERSPECTIVES,” held at Mediterranea University, Reggio Calabria, Italy on May 26–28, 2020.Addressing the challenge of Knowledge Dynamics and Innovation-driven Policies Towards Urban and Regional Transition, the book presents a multi-disciplinary debate on the new frontiers of strategic and spatial planning, economic programs and decision support tools in connection with urban–rural area networks and metropolitan centers. The respective papers focus on six major tracks: Innovation dynamics, smart cities and ICT; Urban regeneration, community-led practices and PPP; Local development, inland and urban areas in territorial cohesion strategies; Mobility, accessibility and infrastructures; Heritage, landscape and identity;and Risk management,environment and energy. The book also includes a Special Section on Rhegion United Nations 2020-2030. Given its scope, the book will benefit all researchers, practitioners and policymakers interested in issues concerning metropolitan and marginal areas.Table of ContentsDisposal of Bergamot By-products by Animal Productions.- Sustainable Attitudes of Local People on the Purchase of Local Food. An empirical investigation on Italian products.- Transhumance Routes in the Perspective of Tourist Use: Case Studies in Calabria, Italy.- Italy Testing the Place-based Approach: River Agreements and National Strategy for Inner Areas.- The "blue vision" of Ionian Coastal Rural Area.

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Book SynopsisSince its inception, fuzzy logic has attracted an incredible amount of interest, and this interest continues to grow at an exponential rate. As such, scientists, researchers, educators and practitioners of fuzzy logic continue to expand on the applicability of what and how fuzzy can be utilised in the real-world. In this book, the authors present key application areas where fuzzy has had significant success. The chapters cover a plethora of application domains, proving credence to the versatility and robustness of a fuzzy approach. A better understanding of fuzzy will ultimately allow for a better appreciation of fuzzy. This book provides the reader with a varied range of examples to illustrate what fuzzy logic can be capable of and how it can be applied. The text will be ideal for individuals new to the notion of fuzzy, as well as for early career academics who wish to further expand on their knowledge of fuzzy applications. The book is also suitable as a supporting text for advanced undergraduate and graduate-level modules on fuzzy logic, soft computing, and applications of AI.Table of ContentsRecognising Handwritten Digits Using a Fuzzy Neural Network Joshua Reynolds and Tianhua Chen Fuzzy Assessment of Student Academic Performances Shangen Yang and Tianhua Chen A Hybrid Fuzzy Neural Network for Image Recognition Samaresh Nayak and Tianhua Chen A Fuzzy Diagnostic System for Heart Disease Siyue Song, Tianhua Chen, and Grigoris Antoniou Analysing Medical Notes using Fuzzy Logic Siyue Song, Tianhua Chen, and Grigoris Antoniou Fostering Positive Personalisation through Fuzzy Clustering Raymond Moodley Fuzzy Logic in Modern Information Retrieval Steve Wade Fuzzy Applied to Sentiment Analysis Orestes Appel Fuzzy Logic, a Logicians Perspective Patrick Fogarty Applications of Fuzzy Logic in an Automated Warehouse Patrick Fogarty Can Fuzzy Systems Assist with Project Planning? Daniel Maia and Arjab Khuman Fuzzy Logic in Autonomous Vehicles David McDougall and Arjab Khuman AI Spawning Fuzzy Logic Fuzzy Inference System Reece Carey and Arjab Khuman The Application of Fuzzy Logic on Intelligent Transportation Systems Nath Lloyd and Arjab Khuman Fuzzy Logic Applied to Water Processes Will Chapman and Arjab Khuman Applications of Fuzzy Logic in Autonomous Vehicles Sam Asquith and Arjab Khuman Predicting Cyber Threats using Fuzzy Logic Jarrad Morden and Arjab Khuman Implementations of Fuzzy Logic in Camera Systems Sophie Hughes and Arjab Khuman Application of a Fuzzy Logic Control System for Stock Market Prediction Based on Technical Indicators and Fundamental Analysis Humza Nazir and Arjab Khuman The Application of Fuzzy Logic in Determining Outcomes of Sporting Events Spencer Deane and Arjab Khuman Using Fuzzy Logic to Educate People on Phishing Harry Taylor and Arjab Khuman

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Book SynopsisThis book presents the proceedings of the 8th International Ergonomics Conference (ERGONOMICS), held in Zagreb, Croatia on December 2-5, 2020. By highlighting the latest theories and models, as well as cutting-edge technologies and applications, and by combining findings from a range of disciplines including engineering, design, robotics, healthcare, management, computer science, human biology and behavioral science, it provides researchers and practitioners alike with a comprehensive, timely guide on human factors and ergonomics. It also offers an excellent source of innovative ideas to stimulate future discussions and developments aimed at applying knowledge and techniques to optimize system performance, while at the same time promoting the health, safety and wellbeing of individuals. The proceedings include papers from researchers and practitioners, scientists and physicians, institutional leaders, managers and policy makers that contribute to constructing the Human Factors and Ergonomics approach across a variety of methodologies, domains and productive sectors.Table of Contents

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Book SynopsisThis book brings together technical expertise, best practices, case studies and ground-level application of the ideas for empowering the rural population of the world to live economically prosperous, environmentally sustainable, and socially progressive lives, on par or comparable with the quality of life enjoyed by the global urban population. The idea of Smart Villages takes on greater urgency in light of the investments made in this millennium on “Smart Cities”, taking advantage of the technological advances, particularly in digital connectivity. These investments have and will continue to expand the urban-rural divide, unless similar investments are made in the villages as well. The book provides a much-needed guide for a holistic development of a Smart Village, by defining the need, developing the framework, and describing the delivery, complete with successful case studies. Contributors to the book, from Canada, USA, Africa and India bring years of academic, industry and governmental experience, including organization of several Smart Village conferences. The knowledge base in the book will be of great value to anyone interested in or active in rural planning, including governmental and non-governmental organizations, industrial solution providers, public healthcare professionals, public policy professionals and students, as well as rural communities around the world. Consolidates all the aspects of creating/developing a Smart Village; Delivers an effective tool-kit for practitioners in the area of Smart Villages; Provides a policy-based framework for the development of an ideal Smart Village; Illustrates, through case studies, the fulfillment of key requirements of a Smart Village; Brings together experts from around the world to share their vision of a Smart Village; Highlights the importance of balancing development with social/gender equity and cultural traditions. Table of ContentsIntroduction.- Part 1. Defining the Need.- Chapter 1. Setting the Scene.- Chapter 2. Smart Village-Concepts and Intended Benefits.- Chaper 3. Bridging the Urban-Rural Divide.- Chapter 4. An Ideal Smart Village – Methodology, Parameters and Metrics.- Chapter 5. Ensuring a Sustainable Development Ecosystem.- Chapter 6. Preserving Indigenous Traditions and Values.- Part II. Buidling the Framework.- Chapter 7.Governance Aspects of a Smart Village – Developed Economy.- Chapter 8. Decentralizing towards Good Governance at the Grassroots.- Chapter 9. Components of an Ontology for a Smart Village.- Chapter 10. A Blueprint for Rural Public Health.- Chapter 11. Socio-Economic Conceptualization of Smart Villages.- Chapter 12. Smart Villages – Indian Realities, Opportunities and Way Forward.- Chapter 13. Leveraging Physical, Digital and Knowledge Connectivity for Smart Villages.- Chapter 14. An Appropriate Technology for Value Addition in Rural Indian Villages.- Chapter 15. The Role of Skills Development in Smart Villages.- Chapter 16. Financing and Development of a Smart Village.- Part III. The Enablers-Delivery and Case Studies.- Chapter 17. Smart Village – The Canadian Experience.- Chapter 18. Strengthening Rural Economy through Farmer Producer Companies.- Chapter 19. Resources and Agriculture - Smart Village Enablers in African Smart Villages.- Chapter 20. Clean Water Supply in Rural Odisha – A Case Study.- Chapter 21.Sustainable Water for Smart Villages – A Case Study.- Chapter 22. Value Added Options in Agriculture in Smart Villages.- Chapter 23. A Healthcare Case Study from Botswana, Africa.- Chapter 24. Smart Health and Wellness Promoting Villages – A Case Study from India.- Chapter 25. Lessons from Distance Healthcare Delivery Case Study - India.- Chapter 26. A Rural Nurse-led Public Health Case Study in Tamil Nadu, India.- Chapter 27.Cloud-based Solutions for Education and Skill Development – Botswana.- Chapter 28. Pan-African E-Network – A Distance Education Case Study.- Chapter 29. Implementing Appropriate Technology for Empowerment of Women in Indian Villages – A Case Study.- Chapter 30. A Unique Smart Village for People with Different Abilities.- Chapter 31. Preserving Traditions in a Smart Village – A Pan-African Perspective.- Chapter 32. COVID-19 – Implications of the Pandemic in the 21st Century.- Chapter 33. Going Forward.

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Book SynopsisThis book addresses some of the challenges suffered by the well-known and robust sliding-mode control paradigm. The authors show how the fusion of fuzzy systems with sliding-mode controllers can alleviate some of these problems and promote applicability.Fuzzy systems used as soft switches eliminate high-frequency signal oscillations and can substantially lower the noise sensitivity of sliding-mode controllers. The amount of a priori knowledge required concerning the nominal structure and parameters of a nonlinear system is also shown to be much reduced by exploiting the general function-approximation property of fuzzy systems so as to use them as identifiers. The main features of this book include:• a review of various existing structures of sliding-mode fuzzy control;• a guide to the fundamental mathematics of sliding-mode fuzzy controllers and their stability analysis;• state-of-the-art procedures for the design of a sliding-mode fuzzy controller;• source codes including MATLAB® and Simulink® codes illustrating the simulation of these controllers, particularly the adaptive controllers;• a short bibliography for each chapter for readers interested in learning more on a particular subject; and• illustrative examples and simulation results to support the main claims made in the text.Academic researchers and graduate students interested in the control of nonlinear systems and particularly those working in sliding-mode controller design will find this book a valuable source of comparative information on existing controllers and ideas for the development of new ones.Table of ContentsMathematica Preliminaries.- Fuzzy and Fuzzy Neural-Network Systems: Type 1 and Type 2.- Sliding-Mode Control: Design, Advantages and Challenges.- Sliding-Mode Fuzzy-Logic Controllers.- Sliding-Mode Fuzzy Neural-Network Controllers.- Sliding-Mode Fuzzy Neural-Network Controllers to Control Systems over Networks.- Simulation Results.

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Book SynopsisThis book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms.The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities. Table of ContentsCross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms.- Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms.- Diagnosis of collateral effects in climate change through the identification of leaf damage using a novel heuristics and machine learning framework.- Feature engineering for Machine Learning and Deep Learning assisted Wireless Communication.- Genetic operators and their impact on the training of deep neural networks.- Implementation of metaheuristics with Extreme Learning Machines.- Architecture optimization of convolutional neural networks by micro genetic algorithms.- Optimising Connection Weights in Neural Networks using a Memetic Algorithm Incorporating Chaos Theory.- A review of metaheuristic optimization algorithms for wireless sensor networks.- A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics.- A Review of multi-level thresholding image segmentation using nature-inspired optimization algorithms.- Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering.- Variable Mesh Optimization for Continuous Optimization and Multimodal Problems.- Traffic control using image processing and deep learning techniques.- Drug Design and Discovery: Theory,Applications, Open Issues and Challenges.- Thresholding algorithm applied to Chest X-Ray images with Pneumonia.- Artificial neural networks for stock market prediction: a comprehensive review.- Image classification with Convolutional Neural Networks.- Applied Machine Learning Techniques to Find Patterns and Trends in the Use of Bicycle Sharing Systems Influenced by Traffic Accidents and Violent Events in Guadalajara, Mexico.- Machine Reading Comprehension (LSTM) Review (state of art).- A Survey of Metaheuristic Algorithms for Solving Optimization Problems.- Integrating metaheuristic algorithms and minimum cross entropy for image segmentation in mist conditions.- A Machine Learning application for Particle Physics: Mexico’s involvement in the Hyper- Kamiokande observatory.- A novel metaheuristic approach for Image Contrast Enhancement based on gray-scale mapping.- Geospatial Data Mining Techniques Survey.- Integration of Internet of Things and cloud computing for Cardiac health recognition.- Combinatorial Optimization for Artificial Intelligence Enabled Mobile Network Automation.- Performance Optimization of PID Controller based on Parameters Estimation using Meta-Heuristic Techniques : A Comparative Study.- Solar Irradiation Changes Detection for Photovoltaic Systems through ANN trained with a Metaheuristic Algorithm.- Genetic Algorithm based Global and Local Feature Selection Approach for Handwritten Numeral Recognition.

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Book SynopsisThis book includes constructive approximation theory; it presents ordinary and fractional approximations by positive sublinear operators, and high order approximation by multivariate generalized Picard, Gauss–Weierstrass, Poisson–Cauchy and trigonometric singular integrals. Constructive and Computational Fractional Analysis recently is more and more in the center of mathematics because of their great applications in the real world. In this book, all presented is original work by the author given at a very general level to cover a maximum number of cases in various applications. The author applies generalized fractional differentiation techniques of Riemann–Liouville, Caputo and Canavati types and of fractional variable order to various kinds of inequalities such as of Opial, Hardy, Hilbert–Pachpatte and on the spherical shell. He continues with E. R. Love left- and right-side fractional integral inequalities. They follow fractional Landau inequalities, of left and right sides, univariate and multivariate, including ones for Semigroups. These are developed to all possible directions, and right-side multivariate fractional Taylor formulae are proven for the purpose. It continues with several Gronwall fractional inequalities of variable order. This book results are expected to find applications in many areas of pure and applied mathematics. As such this book is suitable for researchers, graduate students and seminars of the above disciplines, also to be in all science and engineering libraries.Table of ContentsVariable order general fractional integral inequalitie.- Variable order fractional integral inequalities for spherical shell.- Left fractional integral inequalities of E.R. Love type.- Right side fractional integral inequalities of E.R. Love type.- General fractional Landau inequalities.- Abstract fractional Landau inequalities.- Fractional Landau inequalities of Riemann-Liouville type.- Generalized Canavati fractional Landau inequalities.- Sequential left abstract fractional Landau inequalities.- Iterated left abstract generalized fractional Landau inequalities.

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Book SynopsisThis book contains expanded versions of research papers presented at the international sessions of Annual Conference of the Japanese Society for Artificial Intelligence (JSAI), which was held online in June 2020. The JSAI annual conferences are considered key events for our organization, and the international sessions held at these conferences play a key role for the society in its efforts to share Japan’s research on artificial intelligence with other countries. In recent years, AI research has proved of great interest to business people. The event draws both more and more presenters and attendees every year, including people of diverse backgrounds such as law and the social sciences, in additional to artificial intelligence. We are extremely pleased to publish this collection of papers as the research results of our international sessions.Table of ContentsA Node Classification Approach for Dynamically Extracting the Structures of Online Discussions.- Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier Assessment.- On the Legal Revision in PROLEG program.- Viewpoint Planning based on Uncertainty Maps Created from the Generative Query Network.- Active Learning-based Data Collection in Crowd Replication.- Mining in Discharge Summaries.- Identifying Snowfall Clouds at Syowa Station, Antarctica via a Convolutional Neural Network.- Transfer Learning based Data Collection Method for Dialogue Response Generation concerning Causality.- Impact of Domain Knowledge's Quality on Inverse Reinforcement Learning.- Intrinsically Motivated Lifelong Exploration in Reinforcement Learning.- A Preliminary Analysis of Offensive Language Detection Transferability from Social Media to Video Live Streaming Platforms.- BERT-based Dialogue Evaluation Methods with RUBER Framework.- The Morandi Room Entering the World of Morandi’s Paintings through Machine Learning.

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Book SynopsisThis book explores the dynamic landscape in contemporary newsrooms across three continents by investigating the impact that the processes of searching, processing, and distributing data and information and the use of big data, with secure, automatic, and agile retrieval of information all have in this context. Journalistic organizations have undergone digital transformations, and only those implementing accurate transformations survive. In so doing, the book addresses the fields of e-Communication, Computer Science, and Information Science and other areas of the authors’ expertise. The first five chapters focus on technical visits to investigate newsrooms’ productive routines and flows in major dailies from Brazil, Costa Rica, and England. The remaining chapters consider that the news production routines are cooperative and distributed and at the same time need to be managed from different perspectives to support the convergence of digital media. Last but not least, the book also identifies an increase in ICT-based tools, with an increasing connection from new media combined with the growing trend of digital economy practices as important factors in the new landscape of digital journalism.Table of ContentsJournalistic newsrooms: convergence and Innovation on three continents. A case study on five media organization.- Technological Convergence, the Integration of Media in the Offers, and the Information Workflow of the daily O Globo (Brazil).- Big Data and the Studies of Communication in Brazil — Notes on reconfiguring a knowledge field.- Perspectives of Journalists’ content production from print newspaper to virtual newsroom 4.0.- Appendix: The MDM Project.

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Book SynopsisThis book is a collection of cutting-edge and cross-disciplinary studies on natural, human-made, and coupled human-natural systems, addressing the challenge of developing integrated knowledge from multiple disciplines. The authors explore the structure, function, and dynamic mechanisms of various systems, both natural and human-made, as well as analyze their reciprocal interactions under the concept of “coupled human-natural systems.” These interactions are used to understand feedback, nonlinearities, thresholds, time lags, legacy effects, and path dependencies, emerging across multiple spatial, temporal, and organizational scales. In other words, this book is a collection of advanced research on unique properties of natural and human-made systems, as well as human-environment dynamics, reciprocal relationships, and cross-scale interactions.The authors outline prospects on building a holistic view of social development and coherent sustainability. Among the topics covered are the following: human networks research; adaptation of local people to social and environmental challenges; coupled dynamics of socioeconomic and environmental systems; critical issues in social science climate change research; education for greater sustainability; peace, justice, and strong institutions; advances in cultural traditions and strategies for social stability; innovative development and barriers to sustainable development; economic systems in the age of digital changes and unstable external environments. The scholars analyze how more effective technologies can enhance resilience, reduce vulnerability, and minimize human impacts on natural systems, taking into consideration critical thresholds to prevent harmful feedback to human systems.The authors grasp the complexity of systems by integrating knowledge of constituent subsystems and their interactions. The framework developed by the authors is used to integrate human and natural systems for achieving greater sustainability, covering critical threats, challenges, and best governance approaches and practices. The research results obtained from studies on coupled human-natural systems are stronger, the authors argue, if compared with traditional (discipline) approaches. Table of ContentsPublic Opinion on Park Transformation Projects: The Case of the Oktyabrsky District in Barnaul, Russia.- Application of Polygraph in the Environmental Crimes Investigation.- Reproductive Attitudes of Young Women as a Potential Threat to Social Safety.- International Tourism Before and After the Pandemic.- A Novel Natural Strain of Bacillus Pumilus as a Biological Resource for the Microbial Preparations Development.

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Book SynopsisThis book is the result of a multi-year research project led and sponsored by the University of Chieti-Pescara, National Chengchi University, University of Salamanca, and Osaka University. It is the fifth volume to emerge from that international project, held under the aegis of the United Nations Academic Impact in 2020. All the essays in this volume were (virtually) discussed at the University of L’Aquila―as the venue of the 2nd International Conference on Decision Economics, a three-day global gathering of approximately one hundred scholars and practitioners—and were subjected to thorough peer review by leading experts in the field. The essays reflect the extent, diversity, and richness of several research areas, both normative and descriptive, and are an invaluable resource for graduate-level and PhD students, academics, researchers, policymakers and other professionals, especially in the social and cognitive sciences. Given its interdisciplinary scope, the book subsequently delivers new approaches on how to contribute to the future of economics, providing alternative explanations for various socio-economic issues such as computable humanities; cognitive, behavioural, and experimental perspectives in economics; data analysis and machine learning as well as research areas at the intersection of computer science, artificial intelligence, mathematics, and statistics; agent-based modelling and the related. The editors are grateful to the scientific committee for its continuous support throughout the research project as well as to the many participants for their insightful comments and always probing questions. In any case, the collaboration involved in the project extends far beyond the group of authors published in this volume and is reflected in the quality of the essays published over the years.

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Book SynopsisThis book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.Table of ContentsStudy on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.

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Book SynopsisThis book lists current and potential biomedical uses of computational intelligence methods. These methods are used in diagnostics and treatment of such diseases as cancer, cardiac diseases, pneumonia, stroke, and COVID-19. Many biomedical problems are difficult; so, often, the current methods are not sufficient, new methods need to be developed. To confidently apply the new methods to critical life-and-death medical situations, it is important to first test these methods on less critical applications. The book describes several such promising new methods that have been tested on problems from agriculture, computer networks, economics and business, pavement engineering, politics, quantum computing, robotics, etc. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.Table of ContentsPart I: Biomedical Applications of Computational Intelligence Techniques.- Bilattice CADIAG-II: Theory and Experimental Results.- A Combination Model of Robust Principal Component Analysis and Multiple Kernel Learning for Cancer Patient Stratification.- Attention U-Net with Active Contour based Hybrid Loss for Brain Tumor Segmentation.- Refining Skip Connections by Fusing Multi-scaled Context in Neural Network for Cardiac MR Image Segmentation.- End-to-end Hand Rehabilitation System with Single-shot Gesture Classification for Stroke Patients.- Feature Selection based on Shapley Additive Explanations on Metagenomic Data for Colorectal Cancer Diagnosis.- Clinical Decision Support Systems for Pneumonia Diagnosis using Gradient-weighted Class Activation Mapping and Convolutional Neural Networks.- Improving 3D Hand Pose Estimation with Synthetic RGB Image Enhancement using RetinexNet and Dehazing.- Imbalance in Learning Chest X-ray Images for COVID-19 Detection.- Deep Learning based COVID-19 Diagnosis by Joint Classification and Segmentation.- Part II: General Computational Intelligence Techniques and Their Applications.- Why It Is Sufficient to Have Real-Valued Amplitudes in Quantum Computing.

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Book SynopsisThis book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination. The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter – providing the details that enable the business analysts and the data scientists to implement these fundamentals. AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it. Table of ContentsIntroduction.- Fairness and proxy features.- Bias in data.- Explainability.- Remove bias from ML model.- Remove bias from ML output.- Accountability in AI.- Data & Model privacy.- Conclusion.

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Book SynopsisThis book outlines the recent advancements in the field of artificial intelligence (AI) and addresses how useful it is in achieving truly sustainable solutions. The book also serves as a useful reference literature in developing sustainable engineering solutions to various social and techno-commercial issues of global significance. This book is organized into two sections: section 1 is focused on fundamentals and principles of AI to lay the groundwork for the second section. Section 2 explores the sustainable engineering solutions development using AI, which addresses challenges in various computing techniques and opportunities in engineering design for sustainable development using IoT/AI and smart cities. Applications include waste minimization, re-manufacturing, reuse and recycling technologies using IoT/AI, Industry 4.0, intelligent and smart grid systems, energy conservation using technology, and robotic process automation (RPA). The book is ideal for the engineers, researchers and students interested in how AI can aid in sustainable development applications.Table of ContentsIntroduction.- Artificial Intelligence in Healthcare.- IoT for Modern Life.- Artificial Intelligence in Multimedia Technology.- Artificial Intelligence in Security and Surveillance.- Artificial Intelligence in Big Data Analytics.- Communication Technologies.- Machine Learning and Computing.- Optimizations using Deep learning.- Engineering design for sustainable development using IOT/AI.- Intelligent and Smart Grid Systems.- Energy conservation using AI Technology.- Smart Cities.- Industry 4.0.- Robotic Process Automation.- Conclusion.

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Book SynopsisThis book constitutes the refereed proceedings of the artificial intelligence in intelligent systems section of the 10th Computer Science Online Conference 2021 (CSOC 2021), held online in April 2021. Artificial intelligence in intelligent systems topics are presented in this book. Modern hybrid and bio-inspired algorithms and their application are discussed in selected papers.Table of ContentsOnline teaching as an important means of technogenic society development during the pandemic.- Application of Principal Component Analysis based Support Vector Machine for long-run Economic Growth Forecasting.- Experimental Study of Speed Parameters and Resource Intensity of Programming Languages for Embedded Systems.- Classification of Cardiotocography data for fetal health using Feature Selection Techniques.- Intersection as an Event- and Agent-Based System.- Comparison of UAV Landing Site Classifications with Deep Neural Networks.- An Approach to Optimize Multi-objective Problems using Hybrid Genetic Algorithms Supported by Initial Centroid Selection Optimization Enhanced K-Means Based Selection Operator.- Numerical Modelling of the Impact of Dredging on Stability of Oum Rabiâ estuary (Morocco) using SWAN Model.- New method of verifying cryptographic protocols, based on the process model.- A Method of Search Space Adaptation in the False Pseudonyms Detection Procedure.- Analytical Studies of Behavior of Users of the Moscow Electronic School Service.- Information and Control Systems with Distributed Ledger Usage: a Reliability Issue.- A Modified Ontology-Based Method of Workload Relocation Problem Solving for Monitoring and Forecasting Systems.- Intelligent data analysis using a classification method for data mining knowledge discovery process.- Multimodal Transportation Overview andOptimization Ontology for a Greener Future.- A Complex Cognitive-Based Technique for Social Tension Detection in the Internet Communities.- Method of crystallization of alternatives for solving the problem of placing VLSI elements.- Resilient Rerouting in IoT Systems with Evolutionary Computing.- Urban Monthly Water Consumption Forecasting Based on Signal Decomposition and Optimized Extreme Learning Machine.- Comparison of Control Systems Based on PID Controllers and Based on Fuzzy Logic Using MatLab and Simulink.

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Book SynopsisThis book constitutes the refereed proceedings of the informatics and cybernetics in intelligent systems section of the 10th Computer Science Online Conference 2021 (CSOC 2021), held online in April 2021. Modern cybernetics and computer engineering papers in the scope of intelligent systems are an essential part of actual research topics. In this book, a discussion of modern algorithms approaches techniques is held. Table of ContentsDesign of multi loop control systems with decision makers under incomplete information.- Forecasting and Assessing the Risk of Negative Consequences of Strategic Decisions in Organisation Systems.- Application of Computer Methods for acoustic analysis of the West-Middle German Dialect.- The Concept of Intellectualized Control in Precision Farming.- Allocation of Organizational and Financial Resources of the Information Protection Side Using a Genetic Algorithm.- Application of information security technologies for improving the imitation resistance of low-orbital satellite communication systems.- Time series forecasting based on neural analysis.- Alternative designs of high load queuing systems with small queue.- Verification of HotStuff BFT Consensus Protocol With TLA+/TLC in an Industrial Setting.- Architecture of InnoChain, a Formally-Veried Distributed Ledger System.- A New Approach to Modelling and Verification of Functional Programs.- Measuring Random Pulses with Unknown Time- Frequency and Power Parameters.- Efficiency of Detection of Ultra-Wideband Signals against Additive and Pulse Random Distortions.- Development of a risk assessment tool for manufacturing processes.- Determining the future profitability of the main types of cryptocurrencies.- THE PECULIARITIES OF COMPUTER TECHNOLOGIES APPLICATION IN INFORMATICS TRAINING IN CONDITIONS OF THE ECONOMY AND EDUCATION DIGITALIZATION.- LSTM forecasting: time series forecasting to predict the concentration of air pollutants (CO, SO2, NO and NO2) in Krasnoyarsk, Russia.- Local weather station for decision making in civil engineering.- Algorithmic approach to the design of e-learning courses.- New failures occurrence software testing based on predicting the time organization.

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Book SynopsisThis book provides practical knowledge on different aspects of information and knowledge management in businesses. In contemporary unstable time, enterprises/businesses deal with various challenges—such as large-scale competitions, high levels of uncertainty and risk, rush technological advancements, while increasing customer requirements. Thus, businesses work continually on improving efficiency of their operations and resources towards enabling sustainable solutions based on the knowledge and information accumulated previously. Consequently, this third volume of our subline persists to highlight different approaches of handling enterprise knowledge/information management directing to the importance of unceasing progress of structural management for the steady growth. We look forward that the works of this volume can encourage and initiate further research on this topic.Table of ContentsCreating a system based on CRM solutions that will manage the supplier base.- Voucher 4.0 - Digitisation potential in voucher sales from the works council's point of view.- Use of E-service Analytics in Slovakia.- Managing Quality of Human-Based Electronic Services.- Sustainability Drives of the Sharing Economy.- Sentiment analysis for diagnostic purposes.- SZZ Unleashed-RA-C: An improved implementation of the SZZ algorithm and empirical comparison with existing open source solutions.- Which static code metrics can help to predict test case effectiveness? New metrics and their empirical evaluation on projects assessed for industrial relevance.- Intelligent Freight Forwarder with tabu search Algorithm.- Comparison the genetic algorithm and selected heuristics for the vehicle routing problem with capacity limitation.

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Book SynopsisThis book provides empirical evidence and statistical analyses to uncover answers to some of the most debated questions in the NBA. The sports world lives and breathes off of debates on who deserves an MVP award, and which athletes should be considered all-stars. This book provides some statistics-backed perspectives to some of these debates that are specific to the NBA. Was LeBron snubbed of an MVP in the 2010-2011 season? Why has the G.O.A.T. debate turned into LeBron vs. Jordan….Did Kobe get overlooked? How come Klay Thompson didn’t get All-NBA honors in the 2018-2019 season? This book explores these questions and many more with empirical evidence. This book is invaluable for any undergraduate or masters level course in sport analytics, sports marketing, or sports management. It will also be incredibly useful for scouts, recruiters, and general managers in the NBA who would like to use analytics in their work.Table of ContentsIntroduction.- 1. Da Real MVP.- 2. A Tribe of Goats.- 3. The Myth of the Superteam.- 4. Hey Now, You're an All-Star...But Are You All-NBA?- 5. Small Ball in a Big Man's Game.- 6.Is the Clutch Gene Real.- 7. Offense Wins Games, But Does Defense Win Championships? - 8. Strategic Implications of the Findings in This Book.- 9. Debates the Future Work Should Consider.

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Book SynopsisThis book constitutes the thoroughly refereed proceedings of the CAiSE Forum 2021 which was held as part of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021, in June 2021. The conference was held virtually due to the COVID-19 pandemic.The CAiSE Forum is a place within the CAiSE conference for presenting and discussing new ideas and tools related to information systems engineering. Intended to serve as an interactive platform, the Forum aims at the presentation of emerging new topics and controversial positions, as well as demonstration of innovative systems, tools and applications. This year’s theme was “Intelligent Information Systems”.The 18 full papers presented in this volume were carefully reviewed and selected for inclusion in this book.Table of ContentsVisionary papers.- Evolution of an Adaptive Information System for Precision Medicine.- Security Risk Estimation and Management in Autonomous Driving Vehicles.- BPMN Extensions for Modeling Continuous Processes.- Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes.- Requirements Elicitation for Applications Running on a Blockchain: Preliminary Results.- ISGE: A conceptual Model-based Method to correctly manage genome data.- Case Level Counterfactual Explanation in Process Mining.- Evaluating Fidelity of Explainable Methods for Predictive Process Analytics.- Data-Driven Process Performance Measurement and Prediction: A Process-Tree-Based Approach.- Detecting Privacy, Data and Controlflow Deviations in Business Processes.- Dynamic Strategic Modeling for Alliance-Driven Data Platforms: The Case of Smart Farming.- Modelling Cyber-physical Security in Healthcare Systems.- Declarative Process Discovery: Linking Process and Textual Views.- A Tool for Computing Probabilistic Trace Alignments.- Innovative tools and prototypes.- Applied Predictive Process Monitoring and Hyper Parameter Optimization in Camunds.- SmartRPA: A Tool to Reactively Synthesize Software Robots from User Interface Logs.- PatternLens: Inferring evolutive patterns from web API usage logs.- Designing a Self-Service Analytics System for Supply Base Optimization.

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