A riveting account of espionage for the digital age, from one of America's leading intelligence experts Spying has never been more ubiquitous-or less understood. The world is drowning in spy movies, TV shows, and novels, but universities offer more courses on rock and roll than on the CIA and there are more congressional experts on powdered milk than espionage. This crisis in intelligence education is distorting public opinion, fueling conspiracy theories, and hurting intelligence policy. In Spies, Lies, and Algorithms, Amy Zegart separates fact from fiction as she offers an engaging and enlightening account of the past, present, and future of American espionage as it faces a revolution driven by digital technology. Drawing on decades of research and hundreds of interviews with intelligence officials, Zegart provides a history of U.S. espionage, from George Washington's Revolutionary War spies to today's spy satellites; examines how fictional spies are influencing real officials; gives an overview of intelligence basics and life inside America's intelligence agencies; explains the deadly cognitive biases that can mislead analysts; and explores the vexed issues of traitors, covert action, and congressional oversight. Most of all, Zegart describes how technology is empowering new enemies and opportunities, and creating powerful new players, such as private citizens who are successfully tracking nuclear threats using little more than Google Earth. And she shows why cyberspace is, in many ways, the ultimate cloak-and-dagger battleground, where nefarious actors employ deception, subterfuge, and advanced technology for theft, espionage, and information warfare. A fascinating and revealing account of espionage for the digital age, Spies, Lies, and Algorithms is essential reading for anyone who wants to understand the reality of spying today.

1) Provides a levelling approach, bringing students at all stages of programming experience to the same point 2) Focuses Python, a general language, to an engineering and scientific context 3) Uses a classroom tested, practical approach to teaching programming 4) Teaches students and professionals how to use Python to solve engineering calculations such as differential and algebraic equations

Highlights the importance and applications of Swarm Intelligence and Machine learning in Healthcare industry. Elaborates Swarm Intelligence and Machine Learning for Cancer Detection. Focuses on applying Swarm Intelligence and Machine Learning for Heart Disease detection and diagnosis. Explores of the concepts of machine learning along with swarm intelligence techniques, along with recent research developments in healthcare sectors. Investigates how healthcare companies can leverage the tapestry of big data to discover new business values. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms.

Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.

Features contributions from thought leaders across academia, industry, and government Focuses on novel algorithms and practical applications

- New advancements of fractal analysis with applications to many scientific, engineering, and societal issues - Recent changes and challenges of fractal geometry with the rapid advancement of technology - Attracted chapters on novel theory and recent applications of fractals. - Offers recent findings, modelling and simulations of fractal analysis from eminent institutions across the world - Analytical innovations of fractal analysis - Edited collection with a variety of viewpoints

Provides guidance on performance enhancement and reliability of IC chips. Provides a detailed hybrid optimization strategy for the optimal arrangement of IC chips on a board. The MATLAB program for the hybrid optimization strategy along with its stability analysis is carried out in a detailed manner.

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.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB (R) Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

We live in an algorithmic society. Algorithms have become the main mediator through which power is enacted in our society. This book brings together three academic fields - Public Administration, Criminal Justice and Urban Governance - into a single conceptual framework, and offers a broad cultural-political analysis, addressing critical and ethical issues of algorithms. Governments are increasingly turning towards algorithms to predict criminality, deliver public services, allocate resources, and calculate recidivism rates. Mind-boggling amounts of data regarding our daily actions are analysed to make decisions that manage, control, and nudge our behaviour in everyday life. The contributions in this book offer a broad analysis of the mechanisms and social implications of algorithmic governance. Reporting from the cutting edge of scientific research, the result is illuminating and useful for understanding the relations between algorithms and power.Topics covered include: Algorithmic governmentality Transparency and accountability Fairness in criminal justice and predictive policing Principles of good digital administration Artificial Intelligence (AI) in the smart city This book is essential reading for students and scholars of Sociology, Criminology, Public Administration, Political Sciences, and Cultural Theory interested in the integration of algorithms into the governance of society.

This book exposes how inequalities based on class and social background arise from employment practices in the digital age. It considers instances where social media is used in recruitment to infiltrate private lives and hide job advertisements based on locality; where algorithms assess socio-economic data to filter candidates; where human interviewers are replaced by artificial intelligence with design that disadvantages users of classed language; and where already vulnerable groups become victims of digitalisation and remote work. The author examines whether these practices create risks of discrimination based on certain protected attributes, including ‘social origin’ in international labour law and laws in Australia and South Africa, ‘social condition’ and ‘family status’ in laws within Canada, and others. The book proposes essential law reform and improvements to workplace policy.

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.

Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences.

The volume addresses a selection of diverse topics in such areas as: machine learning based intelligent systems for healthcare applications of artificial intelligence the Internet of Things, intelligent data analytics techniques, intelligent network systems and applications, and inequalities and process control systems.

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.

This is the first comprehensive overview of the 'science of science,' an emerging interdisciplinary field that relies on big data to unveil the reproducible patterns that govern individual scientific careers and the workings of science. It explores the roots of scientific impact, the role of productivity and creativity, when and what kind of collaborations are effective, the impact of failure and success in a scientific career, and what metrics can tell us about the fundamental workings of science. The book relies on data to draw actionable insights, which can be applied by individuals to further their career or decision makers to enhance the role of science in society. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists and graduate students, policymakers, and administrators with an interest in the wider scientific enterprise.

The detailed survey on constraint handling techniques specifically penalty function approach is presented in the book; presents the Cohort Intelligence (CI) algorithm incorporated with a novel self-adaptive penalty function (SAPF) approach which helped in avoiding preliminary trials of selecting penalty parameter. The approach is referred to as CI-SAPF; CI-SAPF is further hybridized with Colliding Bodies Optimization (CBO) algorithm to promote a parameter less metaheuristic algorithm; presents solutions to several problems from discrete truss structure domain, mixed variable design engineering domain, and linear & nonlinear domain validating the CI-SAPF and CI-SAPF-CBO; behavior of SAPF approach on pseudo objective function, constraint violations, penalty function and penalty parameter have been analyzed and discussed in very detail; presents the in-depth analysis and comparison of the CI-SAPF, CI-SAPF-CBO and CBO algorithms with other contemporary techniques; provides the solution to real-world manufacturing problems of optimizing multi pass milling and turning processes using CI-SPF, CI-SAPF and CI-SAPF-CBO approaches.

Introducing strong foundations to practical Cyber-Physical Systems Leveraging CPS for pandemic affected society Ensuring Secured and Privacy aware CPS for Sensitive Data in a pandemic situation Providing methodologies to deploy CPS in industries affect by a pandemic

Crypto-Finance, Law and Regulation investigates whether crypto-finance will cause a paradigm shift in regulation from a centralised model to a model based on distributed consensus. This book explores the emergence of a decentralised and disintermediated crypto-market and investigates the way in which it can transform the financial markets. It examines three components of the financial market - technology, finance, and the law - and shows how their interrelationship dictates the structure of a crypto-market. It focuses on regulators' enforcement policies and their jurisdiction over crypto-finance operators and participants. The book also discusses the latest developments in crypto-finance, and the advantages and disadvantages of crypto-currency as an alternative payment product. It also investigates how such a decentralised crypto-finance system can provide access to finance, promote a shared economy, and allow access to justice. By exploring the law, regulation and governance of crypto-finance from a national, regional and global viewpoint, the book provides a fascinating and comprehensive overview of this important topic and will appeal to students, scholars and practitioners interested in regulation, finance and the law.

Provides a comprehensive introduction to multi-robot systems planning and task allocation; Explores multi robot aerial planning, flight planning, orienteering and coverage, and deployment, patrolling, and foraging; Includes real-world case studies; Treats different aspects of cooperation in multi-agent systems.

1) Focuses on the concepts and implementation strategies of various Deep Learning algorithms through properly curated examples. 2) The subject area will be valid for the next 10 years or so, as Deep Learning theory/algorithms and their applications will not be outdated easily. Hence there will be demand for such a book in the market. 3) In comparison to other titles, this book rigorously covers mathematical and conceptual details of relevant topics.

This is a how-to book for solving geometric problems robustly or error free in actual practice. The contents and accompanying source code are based on the feature requests and feedback received from industry professionals and academics who want both the descriptions and source code for implementations of geometric algorithms. The book provides a framework for geometric computing using several arithmetic systems and describes how to select the appropriate system for the problem at hand. Key Features: A framework of arithmetic systems that can be applied to many geometric algorithms to obtain robust or error-free implementations Detailed derivations for algorithms that lead to implementable code Teaching the readers how to use the book concepts in deriving algorithms in their fields of application The Geometric Tools Library, a repository of well-tested code at the Geometric Tools website, https://www.geometrictools.com, that implements the book concepts

Nature-based algorithms play an important role among artificial intelligence algorithms. Among them are global optimization algorithms called swarm intelligence algorithms. These algorithms that use the behavior of simple agents and various ways of cooperation between them, are used to solve specific problems that are defined by the so-called objective function. Swarm intelligence algorithms are inspired by the social behavior of various animal species, e.g. ant colonies, bird flocks, bee swarms, schools of fish, etc. The family of these algorithms is very large and additionally includes various types of modifications to enable swarm intelligence algorithms to solve problems dealing with areas other than those for which they were originally developed. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning these algorithms, along with their modifications and practical applications. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work. If the reader wishes to expand his knowledge beyond the basics of swarm intelligence algorithms presented in this book and is interested in more detailed information, we recommend the book "Swarm Intelligence Algorithms: A Tutorial" (Edited by A. Slowik, CRC Press, 2020). It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.

Covers deep learning fundamentals; Focuses on applications; Covers human emotion analysis and deep learning; Explains how to use web based techniques for deep learning applications; Includes coverage of autonomous vehicles and deep learning