Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources-one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

A comprehensive examination of blockchain architecture and its key characteristics Blockchain architecture is a way of recording data such that it cannot be altered or falsified. Data is recorded in a kind of digital ledger called a blockchain, copies of which are distributed and stored across a network of participating computer systems. With the advent of cryptocurrencies and NFTs, which are entirely predicated on blockchain technology, and the integration of blockchain architecture into online and high-security networked spaces more broadly, there has never been a greater need for software, network, and financial professionals to be familiar with this technology. Blockchain for Real World Applications provides a practical discussion of this subject and the key characteristics of blockchain architecture. It describes how blockchain technology gains its essential irreversibility and persistency and discusses how this technology can be applied to the information and security needs of different kinds of businesses. It offers a comprehensive overview of the ever-growing blockchain ecosystem and its burgeoning role in a connected world. Blockchain for Real World Applications readers will also find: Treatment of real-world applications such as ID management, encryption, network security, and more Discussion of the UID (Unique Identifier) and its benefits and drawbacks Detailed analysis of privacy issues such as unauthorized access and their possible blockchain-based solutions Blockchain for Real World Applications is a must for professionals in high-security industries, as well as for researchers in blockchain technologies and related areas.

The emergence of highly promising and potent technologies has enabled the transition of ordinary objects into smart artifacts-providing wider connectivity of digitized entities that can facilitate the building of connected cities. This book provides readers with a solid foundation on the latest technologies and tools required to develop and enhance smart cities around the world. The book begins by examining the rise of the cloud as the fundamental technology for establishing and sustaining smart cities and enterprises. Explaining the principal technologies and platform solutions for implementing intelligent cities, the book details the role of various technologies, standards, protocols, and tools in establishing flexible homes and the buildings of the future. Examines IT platforms and tools from various product vendors Considers service-oriented architecture and event-driven architecture for smart city applications Explains how to leverage big data analytics for smart city enhancement and improved decision making Includes case studies of intelligent cities, smart homes, buildings, transports, healthcare systems, and airports The authors explore the convergence of cloud computing and enterprise architecture and present valuable information on next-generation cloud computing. They also cover the various architectural types, including enterprise-scale integration, security, management, and governance. The book concludes by explaining the various security requirements of intelligent cities as well as the threats and vulnerabilities of the various components that form the basis of the intelligent city framework, including cloud, big data, Internet of Things, and mobile technologies.

This book aims to answer two questions that are fundamental to the study of agent-based economic models: what is agent-based computational economics and why do we need agent-based economic modelling of economy? This book provides a review of the development of agent-based computational economics (ACE) from a perspective on how artificial economic agents are designed under the influences of complex sciences, experimental economics, artificial intelligence, evolutionary biology, psychology, anthropology and neuroscience. This book begins with a historical review of ACE by tracing its origins. From a modelling viewpoint, ACE brings truly decentralized procedures into market analysis, from a single market to the whole economy. This book also reviews how experimental economics and artificial intelligence have shaped the development of ACE. For the former, the book discusses how ACE models can be used to analyse the economic consequences of cognitive capacity, personality and cultural inheritance. For the latter, the book covers the various tools used to construct artificial adaptive agents, including reinforcement learning, fuzzy decision rules, neural networks, and evolutionary computation. This book will be of interest to graduate students researching computational economics, experimental economics, behavioural economics, and research methodology.

The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.

For the past 50 years, the advancements of technology have equipped architects with unique tools that have enabled the development of new computer-mediated design methods, fabrication techniques, and architectural expressions. Simultaneously, in contemporary architecture new frameworks emerged that have radically redefined the traditional conceptions of design, of the built environment, and of the role of architects. Cybernetic Architectures argues that such frameworks have been constructed in direct reference to cybernetic thinking, a thought model that emerged concurrently with the origins of informatics and that embodies the main assumptions, values, and ideals underlying the development of computer science. The book explains how the evolution of the computational perspective in architecture has been parallel to the construction of design issues in reference to the central ideas fostered by the cybernetic model. It unpacks and explains this crucial relationship, in the work of digital architects, between the use of information technology in design and the conception of architectural problems around an informational ontology. This book will appeal to architecture students and scholars interested in understanding the recent transformations in the architectural landscape related to the advent of computer-based design paradigms.

This book explores the making of robots in labs at the Massachusetts Institute of Technology (MIT). It examines the cultural ideas that go into the making of robots, and the role of fiction in co-constructing the technological practices of the robotic scientists. The book engages with debates in anthropological theorizing regarding the way that robots are reimagined as intelligent, autonomous and social and weaved into lived social realities. Richardson charts the move away from the "worker" robot of the 1920s to the "social" one of the 2000s, as robots are reimagined as companions, friends and therapeutic agents.

The Routledge Handbook of FinTech offers comprehensive coverage of the opportunities, challenges and future trends of financial technology. This handbook is a unique and in-depth reference work. It is organised in six thematic parts. The first part outlines the development, funding, and the future trends. The second focuses on blockchain technology applications and various aspects of cryptocurrencies. The next covers FinTech in banking. A significant element of FinTech, mobile payments and online lending, is included in the fourth part. The fifth continues with several chapters covering other financial services, while the last discusses ethics and regulatory issues. These six parts represent the most significant and overarching themes of FinTech innovations. This handbook will appeal to students, established researchers seeking a single repository on the subject, as well as policy makers and market professionals seeking convenient access to a one-stop guide.

The fourth edition of this best-selling guide to Prolog and Artificial Intelligence has been updated to include key developments in the field while retaining its lucid approach to these topics. New and extended topics include Constraint Logic Programming, abductive reasoning and partial order planning. Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. This textbook is meant to teach Prolog as a practical programming tool and so it concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The fourth edition has been fully revised and extended to provide an even greater range of applications, making it a self-contained guide to Prolog, AI or AI Programming for students and professional programmers.

Marketing in the tourism and hospitality industry has transformed with the development of digital marketing tools and the evolution of social culture. Recently, the advent of new technologies such as smartphones, artificial intelligence, virtual reality, robots, and new GIS systems has created more possibilities for marketing innovations. Advancements in information technology are leading to changes in business processes, service standards, and management mindsets. Meanwhile, consumers are also adapting to the new marketing paradigm. Researchers are interested in studying this newly-emerging and unpredictable business environment, customer decision making, new management tactics, and business analytic strategies. Future of Tourism Marketing aims to assess the role of modern technologies in marketing tourism destinations and their effects on potential visitors. This book will provide an update on research into the new marketing paradigm that is developing as a result of new technologies in a post-modern era. The chapters in this book were originally published in Journal of Travel & Tourism Marketing.

This book contains a collection of high-quality papers describing the results of relevant investigations and cutting-edge technologies, aimed at improving key aspects of real life, including major challenges such as the development of smart cities, smart buildings, smart grids, and the reduction of the impact of human activities on the environment. Sustainability requires the use of green technologies and techniques and good practices. Artificial intelligence seems to be an appropriate approach to optimize the use of resources. The main focus of this book is the dissemination of novel and innovative technologies, techniques and applications of artificial intelligence, computing and information and communications technologies, and new digital services such as digital marketing, smart tourism, smart agriculture, green and renewable energy sources. Besides, this book focuses on nurturing energy trends including renewable energies, smart grids, human activity impact, communication, behaviour, and social development, and quality of life improvement fields based on the innovative use of sensors, big data and the Internet of things (IoT), telecommunications and machine learning.

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

- Accessible introduction to the application of Artificial Intelligence in schools - Provides a practical framework for teachers and headteachers to develop an AI strategy - Examines evidence informed interventions across all school age groups - Written by a world-leading expert

Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements. Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. Leaders in the field address a wide range of challenges, including camera jitter and background subtraction. The book presents the top methods and algorithms for detecting moving objects in video surveillance. It covers statistical models, clustering models, neural networks, and fuzzy models. It also addresses sensors, hardware, and implementation issues and discusses the resources and datasets required for evaluating and comparing background subtraction algorithms. The datasets and codes used in the text, along with links to software demonstrations, are available on the book's website. A one-stop resource on up-to-date models, algorithms, implementations, and benchmarking techniques, this book helps researchers and industry developers understand how to apply background models and foreground detection methods to video surveillance and related areas, such as optical motion capture, multimedia applications, teleconferencing, video editing, and human-computer interfaces. It can also be used in graduate courses on computer vision, image processing, real-time architecture, machine learning, or data mining.

This book examines the growing importance of algorithms and automation-including emerging forms of artificial intelligence-in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on 'reporting algorithms', such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner. The chapters in this book were originally published as a special issue of Digital Journalism.

This book aims to teach the core concepts that make Self-driving vehicles (SDVs) possible. It is aimed at people who want to get their teeth into self-driving vehicle technology, by providing genuine technical insights where other books just skim the surface. The book tackles everything from sensors and perception to functional safety and cybersecurity. It also passes on some practical know-how and discusses concrete SDV applications, along with a discussion of where this technology is heading. It will serve as a good starting point for software developers or professional engineers who are eager to pursue a career in this exciting field and want to learn more about the basics of SDV algorithms. Likewise, academic researchers, technology enthusiasts, and journalists will also find the book useful. Key Features: Offers a comprehensive technological walk-through of what really matters in SDV development: from hardware, software, to functional safety and cybersecurity Written by an active practitioner with extensive experience in series development and research in the fields of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Covers theoretical fundamentals of state-of-the-art SLAM, multi-sensor data fusion, and other SDV algorithms. Includes practical information and hands-on material with Robot Operating System (ROS) and Open Source Car Control (OSCC). Provides an overview of the strategies, trends, and applications which companies are pursuing in this field at present as well as other technical insights from the industry.

'Someday we expect that computers will be able to keep us informed about the news. People have imagined being able to ask their home computers questions such as "What's going on in the world?"...'. Originally published in 1984, this book is a fascinating look at the world of memory and computers before the internet became the mainstream phenomenon it is today. It looks at the early development of a computer system that could keep us informed in a way that we now take for granted. Presenting a theory of remembering, based on human information processing, it begins to address many of the hard problems implicated in the quest to make computers remember. The book had two purposes in presenting this theory of remembering. First, to be used in implementing intelligent computer systems, including fact retrieval systems and intelligent systems in general. Any intelligent program needs to use and store and use a great deal of knowledge. The strategies and structures in the book were designed to be used for that purpose. Second, the theory attempts to explain how people's memories work and makes predictions about the organization of human memory.

To understand the dynamic patterns of behaviours and interactions between athletes that characterize successful performance in different sports is an important challenge for all sport practitioners. This book guides the reader in understanding how an ecological dynamics framework for use of artificial intelligence (AI) can be implemented to interpret sport performance and the design of practice contexts. By examining how AI methodologies are utilized in team games, such as football, as well as in individual sports, such as golf and climbing, this book provides a better understanding of the kinematic and physiological indicators that might better capture athletic performance by looking at the current state-of-the-art AI approaches. Artificial Intelligence in Sport Performance Analysis provides an all-encompassing perspective in an innovative approach that signals practical applications for both academics and practitioners in the fields of coaching, sports analysis, and sport science, as well as related subjects such as engineering, computer and data science, and statistics.

Virtual Humans provides a much-needed definition of what constitutes a 'virtual human' and places virtual humans within the wider context of Artificial Intelligence development. It explores the technical approaches to creating a virtual human, as well as emergent issues such as embodiment, identity, agency and digital immortality, and the resulting ethical challenges. The book presents an overview of current research and practice in this area, and outlines the major challenges faced by today's developers and researchers. The book examines the possibility for using virtual humans in a variety of roles, from personal assistants to teaching, coaching and knowledge management, and the book situates these discussions around familiar applications (e.g. Siri, Cortana, Alexa) and the portrayal of virtual humans within Science Fiction. Features Presents a comprehensive overview of this rapidly developing field Provides an array of relevant, real-life examples from expert practitioners and researchers from around the globe in how to create the avatar body, mind, senses and ability to communicate Intends to be broad in scope yet practical in approach, so that it can serve the needs of several different audiences, including researchers, teachers, developers and anyone with an interest in where these technologies might take us Covers a wide variety of issues which have been neglected in other research texts; for example, definitions and taxonomies, the ethical challenges of virtual humans and issues around digital immortality Includes numerous examples and extensive references

Automata and Computability is a class-tested textbook which provides a comprehensive and accessible introduction to the theory of automata and computation. The author uses illustrations, engaging examples, and historical remarks to make the material interesting and relevant for students. It incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus. The book also shows how to sculpt automata by making the regular language conversion pipeline available through a simple command interface. A Jupyter notebook will accompany the book to feature code, YouTube videos, and other supplements to assist instructors and students Features Uses illustrations, engaging examples, and historical remarks to make the material accessible Incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus Shows how to "sculpt" automata by making the regular language conversion pipeline available through simple command interface Uses a mini functional programming (FP) notation consisting of lambdas, maps, filters, and set comprehension (supported in Python) to convey math through PL constructs that are succinct and resemble math Provides all concepts are encoded in a compact Functional Programming code that will tesselate with Latex markup and Jupyter widgets in a document that will accompany the books. Students can run code effortlessly. All the code can be accessed here.

Master the application of artificial intelligence in your enterprise with the book series trusted by millions In Enterprise AI For Dummies, author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI. Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business. The book includes discussions of AI applications, including: Streamlining business operations Improving decision making Increasing automation Maximizing revenue The For Dummies series makes topics understandable, and as such, this book is written in an easily understood style that's perfect for anyone who seeks an introduction to a usually unforgiving topic.

accessible for readers unfamiliar with both SDGs and ESG concepts; it will enable leaders and businesses to assess deployment and development of AI more easily

Intelligent Autonomy of UAVs: Advanced Missions and Future Use provides an approach to the formulation of the fundamental task typical to any mission and provides guidelines of how this task can be solved by different generic robotic problems. As such, this book aims to provide a systems engineering approach to UAV projects, discovering the real problems that need to be resolved independently of the application. After an introduction to the rapidly evolving field of aerial robotics, the book presents topics such as autonomy, mission analysis, human-UAV teams, homogeneous and heterogeneous UAV teams, and finally, UAV-UGV teams. It then covers generic robotic problems such as orienteering and coverage. The book next introduces deployment, patrolling, and foraging, while the last part of the book tackles an important application: aerial search, tracking, and surveillance. This book is meant for both scientists and practitioners. For practitioners, it presents existing solutions that are categorized according to various missions: surveillance and reconnaissance, 3D mapping, urban monitoring, precision agriculture, forestry, disaster assessment and monitoring, security, industrial plant inspection, etc. For scientists, it provides an overview of generic robotic problems such as coverage and orienteering; deployment, patrolling and foraging; search, tracking, and surveillance. The design and analysis of algorithms raise a unique combination of questions from many fields, including robotics, operational research, control theory, and computer science.