This book provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions. A unified framework is provided for learning algorithms that are based on the stochastic gradient (a golden standard in learning), including random simultaneous perturbations and the response surface the methodology. Original algorithms include model-free learning of short decision sequences as well as long sequences-relying on model-supported gradient estimation. Learning is based on whole sequences of a process observation that are either vectors or images. This methodology is applicable to repetitive processes, covering a wide range from (additive) manufacturing to decision making for COVID-19 waves mitigation. A distinctive feature of the algorithms is learning between repetitions-this idea extends the paradigms of iterative learning and run-to-run control. The main ideas can be extended to other decision learning tasks, not included in this book. The text is written in a comprehensible way with the emphasis on a user-friendly presentation of the algorithms, their explanations, and recommendations on how to select them. The book is expected to be of interest to researchers, Ph.D., and graduate students in computer science and engineering, operations research, decision making, and those working on the iterative learning control.

Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods. The book's three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount to specific design choices for the model, data, and loss of a ML method.

The Complexity Theory Companion is an accessible, algorithmically oriented, research-centered, up-to-date guide to some of the most interesting techniques of complexity theory. The book's thesis is that simple algorithms are at the heart of complexity theory. From the tree-pruning and interval-pruning algorithms that shape the first chapter to the query simulation procedures that dominate the last chapter, the central proof methods of the book are algorithmic. And to more clearly highlight the role of algorithmic techniques in complexity theory, the book is - unlike other texts on complexity - organized by technique rather than by topic. Each chapter of this book focuses on one technique: what it is, and what results and applications it yields. This textbook was developed at the University of Rochester in courses given to graduate students and advanced undergraduates. Researchers also will find this book a valuable source of reference due to the comprehensive bibliography of close to five hundred entries, the thirty-five page subject index, and the appendices giving overviews of complexity classes and reductions.

This open access book gives an overview of cutting-edge work on a new paradigm called the "sublinear computation paradigm," which was proposed in the large multiyear academic research project "Foundations of Innovative Algorithms for Big Data." That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as "fast," but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required.The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms.

Aspects of Robust Statistics are important in many areas. Based on the International Conference on Robust Statistics 2001 (ICORS 2001) in Vorau, Austria, this volume discusses future directions of the discipline, bringing together leading scientists, experienced researchers and practitioners, as well as younger researchers. The papers cover a multitude of different aspects of Robust Statistics. For instance, the fundamental problem of data summary (weights of evidence) is considered and its robustness properties are studied. Further theoretical subjects include e.g.: robust methods for skewness, time series, longitudinal data, multivariate methods, and tests. Some papers deal with computational aspects and algorithms. Finally, the aspects of application and programming tools complete the volume.

Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science - not only in tourism - and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau

This contributed volume offers practical solutions and design-, modeling-, and implementation-related insights that address current research problems in memristors, memristive devices, and memristor computing. The book studies and addresses related challenges in and proposes solutions for the future of memristor computing. State-of-the-art research on memristor modeling, memristive interconnections, memory circuit architectures, software simulation tools, and applications of memristors in computing are presented. Utilising contributions from numerous experts in the field, written in clear language and illustrated throughout, this book is a comprehensive reference work. Memristor Computing Systems explains memristors and memristive devices in an accessible way for graduate students and researchers with a basic knowledge of electrical and control systems engineering, as well as prompting further research for more experienced academics.

Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs. The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader's immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis. With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis.

This book includes high-quality papers presented at the Second International Conference on Data Science and Management (ICDSM 2021), organized by the Gandhi Institute for Education and Technology, Bhubaneswar, from 19 to 20 February 2021. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.

Swarm intelligence algorithms are a form of nature-based optimization algorithms. Their main inspiration is the cooperative behavior of animals within specific communities. This can be described as simple behaviors of individuals along with the mechanisms for sharing knowledge between them, resulting in the complex behavior of the entire community. Examples of such behavior can be found in ant colonies, bee swarms, schools of fish or bird flocks. Swarm intelligence algorithms are used to solve difficult optimization problems for which there are no exact solving methods or the use of such methods is impossible, e.g. due to unacceptable computational time. This book thoroughly presents the basics of 24 algorithms selected from the entire family of swarm intelligence algorithms. Each chapter deals with a different algorithm describing it in detail and showing how it works in the form of a pseudo-code. In addition, the source code is provided for each algorithm in Matlab and in the C ++ programming language. In order to better understand how each swarm intelligence algorithm works, a simple numerical example is included in each chapter, which guides the reader step by step through the individual stages of the algorithm, showing all necessary calculations. This book can provide the basics for understanding how swarm intelligence algorithms work, and aid readers in programming these algorithms on their own to solve various computational problems. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning the basics of these algorithms efficiently and quickly. 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 already has basic knowledge of swarm intelligence algorithms, we recommend the book: "Swarm Intelligence Algorithms: Modifications and Applications" (Edited by A. Slowik, CRC Press, 2020), which describes selected modifications of these algorithms and presents their practical applications.

This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.

This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.

Machine Vision Algorithms in Java provides a comprehensive introduction to the algorithms and techniques associated with machine vision systems. The Java programming language is also introduced, with particular reference to its imaging capabilities. The book contains explanations of key machine vision techniques and algorithms, along with the associated Java source code.Special features include: - A complete self-contained treatment of the topics and techniques essential to the understanding and implementation of machine vision.- An introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities.- Java source code for a wide range of practical image processing and analysis functions.- Readers will be given the opportunity to download a fully functional Java-based visual programming environment for machine vision, available via the WWW. This contains over 200 image processing, manipulation and analysis functions and will enable users to implement many of the ideas covered in this book. - Details relating to the design of a Java-based visual programming environment for machine vision.- An introduction to the Java 2D imaging and Java Advanced Imaging (JAI) APIs- A wide range of illustrative examples.- Practical treatment of the subject matter. This book is aimed at senior undergraduate and postgraduate students in engineering and computer science as well as practitioners in machine vision who may wish to update or expand their knowledge of the subject. The techniques and algorithms of machine vision are expounded in a way that will be understood not only by specialists but also by those who are less familiar with the topic.

This book is a groundbreaking resource that covers both algorithms and technologies of interactive videos. It presents recent research and application work for building and browsing interactive digital videos. The book deals mainly with low-level semi-automatic and full-automatic processing of the video content for intelligent human computer interaction. There is a special focus on eye tracking methods.

Is the exponential function computable? Are union and intersection of closed subsets of the real plane computable? Are differentiation and integration computable operators? Is zero finding for complex polynomials computable? Is the Mandelbrot set decidable? And in case of computability, what is the computational complexity? Computable analysis supplies exact definitions for these and many other similar questions and tries to solve them. - Merging fundamental concepts of analysis and recursion theory to a new exciting theory, this book provides a solid basis for studying various aspects of computability and complexity in analysis. It is the result of an introductory course given for several years and is written in a style suitable for graduate-level and senior students in computer science and mathematics. Many examples illustrate the new concepts while numerous exercises of varying difficulty extend the material and stimulate readers to work actively on the text.

This book describes concepts and tools needed for water resources management, including methods for modeling, simulation, optimization, big data analysis, data mining, remote sensing, geographical information system, game theory, conflict resolution, System dynamics, agent-based models, multiobjective, multicriteria, and multiattribute decision making and risk and uncertainty analysis, for better and sustainable management of water resources and consumption, thus mitigating the present and future global water shortage crisis. It presents the applications of these tools through case studies which demonstrate its benefits of proper management of water resources systems. This book acts as a reference for students, professors, industrial practitioners, and stakeholders in the field of water resources and hydrology.

The papers in this volume comprise the refereed proceedings of the Second IFIP International Conference on Computer and Computing Technologies in Agriculture (CCTA2008), in Beijing, China, 2008. The conference on the Second IFIP International Conference on Computer and Computing Technologies in Agriculture (CCTA 2008) is cooperatively sponsored and organized by the China Agricultural University (CAU), the National Engineering Research Center for Information Technology in Agriculture (NERCITA), the Chinese Society of Agricultural Engineering (CSAE) , International Federation for Information Processing (IFIP), Beijing Society for Information Technology in Agriculture, China and Beijing Research Center for Agro-products Test and Farmland Inspection, China. The related departments of China's central government bodies like: Ministry of Science and Technology, Ministry of Industry and Information Technology, Ministry of Education and the Beijing Municipal Natural Science Foundation, Beijing Academy of Agricultural and Forestry Sciences, etc. have greatly contributed and supported to this event. The conference is as good platform to bring together scientists and researchers, agronomists and information engineers, extension servers and entrepreneurs from a range of disciplines concerned with impact of Information technology for sustainable agriculture and rural development. The representatives of all the supporting organizations, a group of invited speakers, experts and researchers from more than 15 countries, such as: the Netherlands, Spain, Portugal, Mexico, Germany, Greece, Australia, Estonia, Japan, Korea, India, Iran, Nigeria, Brazil, China, etc.

This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started. By the end of the book, the reader should be proficient in both software. Content-wise, this book focuses on the syntax, semantics and proof theory of various logics; automata theory, formal languages, computability and complexity. The final chapter closes the gap with a discussion on the insight that links logic with computation. This book is written for a high-level undergraduate course or a Master's course. The hybrid skill set of practical theorem proving and model checking should be helpful for the future of readers should they pursue a research career or engineering in formal methods.

bridges ML and Optimisation; discusses optimisation techniques to improve ML algorithms for big data problems; identifies key research areas to solve large-scale machine learning problems; identifies recent research directions to solve major areas to tackle the challenge

This book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply. In addition, the basics of Bayesian data analysis and its Python implementation are presented. Since surveys are widely used as the primary method to collect data, and ultimately information, on attitudes, interests, and opinions of customers and constituents, these tools are vital for private or public sector policy decisions. As a compact volume, this book uses case studies to illustrate methods of analysis essential for those who work with survey data in either sector. It focuses on two overarching objectives: Demonstrate how to extract actionable, insightful, and useful information from survey data; and Introduce Python and Pandas for analyzing survey data.

This stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: This book provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics such as the nature of theorem proving; examines the field of software engineering, including software reliability and dependability and describes formal methods; investigates probability and statistics and presents an overview of operations research and financial mathematics.

This book presents a new understanding on how control systems truly operate, and explains how to recognize, simulate, and improve control systems in all fields of activity. It also reveals the pervasive, ubiquitous and indispensable role of control processes in our life and the need to develop a "control-oriented thinking"-based on uncomplicated but effective models derived from systems thinking-that is, a true "discipline of control." Over the book's thirteen chapters, Piero Mella shows that there are simple control systems (rather than complex ones) that can easily help us to manage complexity without drawing upon more sophisticated control systems. It begins by reviewing the basic language of systems thinking and the models it allows users to create. It then introduces the control process, presenting the theoretical structure of three simple control systems we all can observe in order to gain fundamental knowledge from them about the basic structure of a control system. Then, it presents the anatomy of the simplest "magic ring" and the general theoretical model of any control system. This is followed by an introduction to a general typology of control systems and a broader view of control systems by investigating multi-lever control systems and multi-objective systems. The book undertakes the concepts through various environments, increasingly broader in scope to suggest to readers how to recognize therein control systems manifestations in everyday life and in natural phenomena. Updated for the 2nd edition, new chapters explore control systems regulating the biological environment and the organizations, with an in-depth study of the control of quality, productivity, production, stocks and costs. Finally, it concludes by dealing with the learning process, problem-solving, and designing the logical structure of control systems.

This book provides awareness of methods used for functional encryption in the academic and professional communities. The book covers functional encryption algorithms and its modern applications in developing secure systems via entity authentication, message authentication, software security, cyber security, hardware security, Internet of Thing (IoT), cloud security, smart card technology, CAPTCHA, digital signature, and digital watermarking. This book is organized into fifteen chapters; topics include foundations of functional encryption, impact of group theory in cryptosystems, elliptic curve cryptography, XTR algorithm, pairing based cryptography, NTRU algorithms, ring units, cocks IBE schemes, Boneh-Franklin IBE, Sakai-Kasahara IBE, hierarchical identity based encryption, attribute based Encryption, extensions of IBE and related primitives, and digital signatures. Explains the latest functional encryption algorithms in a simple way with examples; Includes applications of functional encryption in information security, application security, and network security; Relevant to academics, research scholars, software developers, etc.

This introductory book on quantum computing includes an emphasis on the development of algorithms. Appropriate for both university students as well as software developers interested in programming a quantum computer, this practical approach to modern quantum computing takes the reader through the required background and up to the latest developments. Beginning with introductory chapters on the required math and quantum mechanics, Fundamentals of Quantum Computing proceeds to describe four leading qubit modalities and explains the core principles of quantum computing in detail. Providing a step-by-step derivation of math and source code, some of the well-known quantum algorithms are explained in simple ways so the reader can try them either on IBM Q or Microsoft QDK. The book also includes a chapter on adiabatic quantum computing and modern concepts such as topological quantum computing and surface codes. Features: o Foundational chapters that build the necessary background on math and quantum mechanics. o Examples and illustrations throughout provide a practical approach to quantum programming with end-of-chapter exercises. o Detailed treatment on four leading qubit modalities -- trapped-ion, superconducting transmons, topological qubits, and quantum dots -- teaches how qubits work so that readers can understand how quantum computers work under the hood and devise efficient algorithms and error correction codes. Also introduces protected qubits - 0- qubits, fluxon parity protected qubits, and charge-parity protected qubits. o Principles of quantum computing, such as quantum superposition principle, quantum entanglement, quantum teleportation, no-cloning theorem, quantum parallelism, and quantum interference are explained in detail. A dedicated chapter on quantum algorithm explores both oracle-based, and Quantum Fourier Transform-based algorithms in detail with step-by-step math and working code that runs on IBM QisKit and Microsoft QDK. Topics on EPR Paradox, Quantum Key Distribution protocols, Density Matrix formalism, and Stabilizer formalism are intriguing. While focusing on the universal gate model of quantum computing, this book also introduces adiabatic quantum computing and quantum annealing. This book includes a section on fault-tolerant quantum computing to make the discussions complete. The topics on Quantum Error Correction, Surface codes such as Toric code and Planar code, and protected qubits help explain how fault tolerance can be built at the system level.

This book provides an essential update for experienced data processing professionals, transaction managers and database specialists who are seeking system solutions beyond the confines of traditional approaches. It provides practical advice on how to manage complex transactions and share distributed databases on client servers and the Internet. Based on extensive research in over 100 companies in the USA, Europe, Japan and the UK, topics covered include : * the challenge of global transaction requirements within an expanding business perspective *how to handle long transactions and their constituent elements *possible benefits from object-oriented solutions * the contribution of knowledge engineering in transaction management * the Internet, the World Wide Web and transaction handling * systems software and transaction-processing monitors * OSF/1 and the Encina transaction monitor * active data transfers and remote procedure calls * serialization in a transaction environment * transaction locks, two-phase commit and deadlocks * improving transaction-oriented database management * the successful development of an increasingly complex transaction environment.