Takes an interdisciplinary approach to contribute to the ongoing development of human-AI interaction. Current debate and development of AI is "algorithm-driven" or technical-oriented in lieu of human-centered. At present, there is no systematic interdisciplinary discussion to effectively deal with issues and challenges arising from AI. This book offers critical analysis of the logic and social implications of algorithmic processes. Reporting from the processes of scientific research, the results can be useful for understanding the relationship between algorithms and humans, allowing AI designers to assess the quality of the meaningful interactions with AI systems.

Offers fundamental theories and practical and more sophisticated applications of Evolutionary Computation in varied industries Provides insight into various platforms, paradigms, techniques, and tools used in Evolutionary Computation for diverse fields Presents an understanding related to optimization, performance tuning, virtualization, deployment models, and their applications Covers a variety of applications for social and essential models and is based on real life examples Useful for decision making based on optimized data through Evolutionary Computation in multi-dimensions

The text comprehensively discusses the essentials of the Internet of Things (IoT), machine learning algorithms, industrial and medical IoT, robotics, data analytics tools, and technologies for smart cities. It further covers fundamental concepts, advanced tools, and techniques, along with the concept of energy-efficient systems. It also highlights software and hardware interfacing into the IoT platforms and systems for better understanding. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. Features: Covers cognitive Internet of Things and emerging network, IoT in robotics, smart cities, and health care Discusses major issues in the field of the IoTsuch as scalable and secure issues, energy-efficient, and actuator devices Highlights the importance of industrial and medical IoT Illustrates applications of the IoT in robotics, smart grid, and smart cities Presents real-time examples for better understanding The text comprehensively discusses design principles, modernization techniques, advanced developments in artificial intelligence.This will be helpful for senior undergraduates, graduate students, and academic researchers in diverse engineering fields including electrical, electronics and communication, and computer science.

Systematically teaches key paradigmic algorithm design methods

Provides a deep insight into randomization

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design Details the similarities, differences, weaknesses, and strengths of each swarm optimization method Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB (R) package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Real-world information is imperfect and is usually described in natural language (NL). Moreover, this information is often partially reliable and a degree of reliability is also expressed in NL. In view of this, the concept of a Z-number is a more adequate concept for the description of real-world information. The main critical problem that naturally arises in processing Z-numbers-based information is the computation with Z-numbers. Nowadays, there is no arithmetic of Z-numbers suggested in existing literature.This book is the first to present a comprehensive and self-contained theory of Z-arithmetic and its applications. Many of the concepts and techniques described in the book, with carefully worked-out examples, are original and appear in the literature for the first time.The book will be helpful for professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics, and computational economics.

How do we understand the world around us? How do we solve problems? Often the answer to these questions follows a certain pattern, an algorithm if you wish. This is the case when our analytical left-brain side is at work. However, there are also elements in our behaviour where intelligence appears to follow a more elusive path, which cannot easily be characterised as a specific sequence of steps. Is Intelligence an Algorithm? offers an insight into intelligence as it functions in nature, like human or animal intelligence, but also sheds light on modern developments in the field of artificial intelligence, proposing further architectural solutions for the creation of a so-called global Webmind.

In recent years, popular media have inundated audiences with sensationalised headlines recounting data breaches, new forms of surveillance and other dangers of our digital age. Despite their regularity, such accounts treat each case as unprecedented and unique. This book proposes a radical rethinking of the history, present and future of our relations with the digital, spatial technologies that increasingly mediate our everyday lives. From smartphones to surveillance cameras, to navigational satellites, these new technologies offer visions of integrated, smooth and efficient societies, even as they directly conflict with the ways users experience them. Recognising the potential for both control and liberation, the authors argue against both acquiescence to and rejection of these technologies. Through intentional use of the very systems that monitor them, activists from Charlottesville to Hong Kong are subverting, resisting and repurposing geographic technologies. Using examples as varied as writings on the first telephones to the experiences of a feminist collective for migrant women in Spain, the authors present a revolution of everyday technologies. In the face of the seemingly inevitable dominance of corporate interests, these technologies allow us to create new spaces of affinity, and a new politics of change.

The heart of any system that simulates the physical interaction between objects is collision detection-the ability to detect when two objects have come into contact. This system is also one of the most difficult aspects of a physical simulation to implement correctly, and invariably it is the main consumer of CPU cycles. Practitioners, new to the field or otherwise, quickly discover that the attempt to build a fast, accurate, and robust collision detection system takes them down a long path fraught with perils and pitfalls unlike most they have ever encountered. Without in-depth knowledge and understanding of the issues associated with engineering a collision detection system, the end of that path is an abyss that has swallowed many a good programmer!
Gino van den Bergen's new book is the story of his successful journey down that path. The outcome is his well-known collision detection system, the SOftware Library for Interference Detection (SOLID). Along the way, he covers the topics of vector algebra and geometry, the various geometric primitives of interest in a collision system, the powerful method of separating axes for the purposes of intersection testing, and the equally powerful Gilbert-Johnson-Keerthi (GJK) algorithm for computing the distance between convex objects. But this book provides much more than a good compendium of the ideas that go into building a collision system. The curse of practical computational geometry is floating-point arithmetic. Algorithms with straightforward implementations when using exact arithmetic can have catastrophic failures in a floating-point system. Specifically, intersection and distance algorithms implemented in a floating-point systemtend to fail exactly in the most important case in a collision system-when two objects are just touching. Great care must be taken to properly handle floating-point round off errors. Gino's ultimate accomplishment in this book is his presentation on how to correctly implement the GJK distance algorithm in the presence of single-precision floating-point arithmetic. And what better way to illustrate this than with a case study, the final chapter on the design and implementation of SOLID.
About the CD-ROM
The companion CD-ROM includes the full C++ source code of SOLID 3.5 as well as API documentation in HTML and PDF formats. Both single (32bit) and double (64bit) precision versions of the SOLID SDK plus example programs can be compiled for Linux platforms using GNU g++ version 2.95 to 3.3 and for Win32 platforms using Microsoft Visual C++ version 6.0 to 7.1. Use of the SOLID source code is governed by the terms of either the GNU GPL or the Trolltech QPL (see CD-ROM documentation for details).
About the Author
Gino van den Bergen is a game developer living and working in The Netherlands. He is the creator of SOLID and holds a Ph.D. in computing science from Eindhoven University of Technology. Gino implemented collision detection and physics in NaN Technologies' Blender, a creation suite for interactive 3D content.
*Explains the fundamental geometric and numerical concepts that underlie the key algorithms of collision detection.
*CD-ROM includes the full C++ source code of SOLID, a well-known library for collision detection, plus binaries and example programs for Win32.
*Discusses algorithms for commonly used primitive types, such as spheres, boxes, cylinders, cones, triangles, rays, and convex polyhedra.
*Presents techniques for accelerating collision detection for complex models and scenes.

Ecosystems, the human brain, ant colonies, and economic networks are all complex systems displaying collective behaviour, or emergence, beyond the sum of their parts. Complexity science is the systematic investigation of these emergent phenomena, and stretches across disciplines, from physics and mathematics, to biological and social sciences. This introductory textbook provides detailed coverage of this rapidly growing field, accommodating readers from a variety of backgrounds, and with varying levels of mathematical skill. Part I presents the underlying principles of complexity science, to ensure students have a solid understanding of the conceptual framework. The second part introduces the key mathematical tools central to complexity science, gradually developing the mathematical formalism, with more advanced material provided in boxes. A broad range of end of chapter problems and extended projects offer opportunities for homework assignments and student research projects, with solutions available to instructors online. Key terms are highlighted in bold and listed in a glossary for easy reference, while annotated reading lists offer the option for extended reading and research.

This easy-to-follow textbook provides a student-friendly introduction to programming and algorithms. Emphasis is placed on the threshold concepts that present barriers to learning, including the questions that students are often too embarrassed to ask. The book promotes an active learning style in which a deeper understanding is gained from evaluating, questioning, and discussing the material, and practised in hands-on exercises. Although R is used as the language of choice for all programs, strict assumptions are avoided in the explanations in order for these to remain applicable to other programming languages. Features: provides exercises at the end of each chapter; includes three mini projects in the final chapter; presents a list of titles for further reading at the end of the book; discusses the key aspects of loops, recursions, program and algorithm efficiency and accuracy, sorting, linear systems of equations, and file processing; requires no prior background knowledge in this area.

This work is concerned with theoretical developments in the area of mathematical programming, development of new algorithms and software and their applications in science and industry. It aims to expose recent mathematical developments to a larger audience in science and industry.

Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.

Few books comprehensively cover the software and programming aspects of reversible computing. Filling this gap, Introduction to Reversible Computing offers an expanded view of the field that includes the traditional energy-motivated hardware viewpoint as well as the emerging application-motivated software approach.

Collecting scattered knowledge into one coherent account, the book provides a compendium of both classical and recently developed results on reversible computing. It explores up-and-coming theories, techniques, and tools for the application of reversible computing the logical next step in the evolution of computing systems.

The book covers theory, hardware and software aspects, fundamental limits, complexity analyses, practical algorithms, compilers, efficiency improvement techniques, and application areas. The topics span several areas of computer science, including high-performance computing, parallel/distributed systems, computational theory, compilers, power-aware computing, and supercomputing.

The book presents sufficient material for newcomers to easily get started. It provides citations to original articles on seminal results so that readers can consult the corresponding publications in the literature. Pointers to additional resources are included for more advanced topics. For those already familiar with a certain topic within reversible computing, the book can serve as a one-stop reference to other topics in the field.

Matrix algorithms are at the core of scientific computing and are indispensable tools in most applications in engineering. This book offers a comprehensive and up-to-date treatment of modern methods in matrix computation. It uses a unified approach to direct and iterative methods for linear systems, least squares and eigenvalue problems. A thorough analysis of the stability, accuracy and complexity of the treated methods is given.

"Numerical Methods in Matrix Computations" is suitable for use in courses on scientific computing and applied technical areas at advanced undergraduate and graduate level. A large bibliography is provided, which includes both historical and review papers as well as recent research papers. This makes the book useful also as a reference and guide to further study and research work.

For computer scientists, especially those in the security field, the use of chaos has been limited to the computation of a small collection of famous but unsuitable maps that offer no explanation of why chaos is relevant in the considered contexts. Discrete Dynamical Systems and Chaotic Machines: Theory and Applications shows how to make finite machines, such as computers, neural networks, and wireless sensor networks, work chaotically as defined in a rigorous mathematical framework. Taking into account that these machines must interact in the real world, the authors share their research results on the behaviors of discrete dynamical systems and their use in computer science. Covering both theoretical and practical aspects, the book presents: Key mathematical and physical ideas in chaos theory Computer science fundamentals, clearly establishing that chaos properties can be satisfied by finite state machines Concrete applications of chaotic machines in computer security, including pseudorandom number generators, hash functions, digital watermarking, and steganography Concrete applications of chaotic machines in wireless sensor networks, including secure data aggregation and video surveillance Until the authors' recent research, the practical implementation of the mathematical theory of chaos on finite machines raised several issues. This self-contained book illustrates how chaos theory enables the study of computer security problems, such as steganalysis, that otherwise could not be tackled. It also explains how the theory reinforces existing cryptographically secure tools and schemes.

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

Synthetic Aperture Radar Automatic Detection Algorithms (SARADA) for Oil Spills conveys the pivotal tool required to fully comprehend the advanced algorithms in radar monitoring and detection of oil spills, particularly quantum computing and algorithms as a keystone to comprehending theories and algorithms behind radar imaging and detection of marine pollution. Bridging the gap between modern quantum mechanics and computing detection algorithms of oil spills, this book contains precise theories and techniques for automatic identification of oil spills from SAR measurements. Based on modern quantum physics, the book also includes the novel theory on radar imaging mechanism of oil spills. With the use of precise quantum simulation of trajectory movements of oil spills using a sequence of radar images, this book demonstrates the use of SARADA for contamination by oil spills as a promising novel technique. Key Features: Introduces basic concepts of a radar remote sensing. Fills a gap in the knowledge base of quantum theory and microwave remote sensing. Discusses the important aspects of oil spill imaging in radar data in relation to the quantum theory. Provides recent developments and progresses of automatic detection algorithms of oil spill from radar data. Presents 2-D oil spill radar data in 4-D images.

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.

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.

This two-volume book describes the most common IP routing protocols used today, explaining the underlying concepts of each protocol and how the protocol components and processes fit within the typical router. Unlike other books, this title is not vendor focused. Volume 1 discusses fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). Volume 2 focuses on link-state routing protocols (OSPF and IS-IS) and the only path-vector routing protocol in use today (BGP). The volumes explain the types of databases each routing protocol uses, how the databases are constructed and managed, and how the various protocol components and processes, relate and interact with the databases. They also describe the routing protocols from a systems perspective, recognizing the most important routing and packet forwarding components and functions of a router. An illustrated description of IP routing protocols is given using real-world network examples. The books are presented from a practicing engineer's perspective, linking theory and fundamental concepts to common practices and real-world examples. The discussion is presented in a simple style to make it comprehensible and appealing to undergraduate and graduate level students, research and practicing engineers, scientists, IT personnel, and network engineers.

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.

A successor to the first edition, this updated and revised book is a great companion guide for students and engineers alike, specifically software engineers who design reliable code. While succinct, this edition is mathematically rigorous, covering the foundations of both computer scientists and mathematicians with interest in algorithms. Besides covering the traditional algorithms of Computer Science such as Greedy, Dynamic Programming and Divide & Conquer, this edition goes further by exploring two classes of algorithms that are often overlooked: Randomised and Online algorithms - with emphasis placed on the algorithm itself. The coverage of both fields are timely as the ubiquity of Randomised algorithms are expressed through the emergence of cryptography while Online algorithms are essential in numerous fields as diverse as operating systems and stock market predictions. While being relatively short to ensure the essentiality of content, a strong focus has been placed on self-containment, introducing the idea of pre/post-conditions and loop invariants to readers of all backgrounds. Containing programming exercises in Python, solutions will also be placed on the book's website.

The science of biomedical measurements is experiencing a period of rapid development. Biomedical measuring systems are becoming increasingly accurate on the one hand and complex on the other. In order to make progress in this field, metrological problems must be solved using a systemic and formal approach. To this end, it is necessary to define the components of the system and the rules for their interaction, which allows the creation of a mathematical model. In this way, any technology or object can be presented in the form of a structure on which the necessary estimates can be formulated and synthesis, including metrological one, can be made. The authors have observed that despite the significance of the problem, few scientific centres deal with this issue in a generalised manner. Hence the idea of bringing together the achievements of the centres from Russia, Poland and Kazakhstan in one joint publication. The first and second volumes of Information Technology in Medical Diagnostics found readers not only in Poland, Ukraine, and Kazakhstan but also Spain, Russia and the Czech Republic. Following the readers' suggestions, in the third volume of ITMD we returned to the formula of closed chapters known from volume one. Due to its limited volume, the book deals with the aforementioned issues in only selected areas of biomedical engineering. The book will be of interest not only for academics and engineers but also for professionals involved in biomedical engineering, seeking solutions for the problems that cannot be solved using "traditional" technologies or trying to improve existing measurement systems.

A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.