This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

This book collects high-quality research papers presented at the International Conference on Computing Applications in Electrical & Electronics Engineering, held at Rajkiya Engineering College, Sonbhadra, India, on August 30-31, 2019. It provides novel contributions in computational intelligence, together with valuable reference material for future research. The topics covered include: big data analytics, IoT and smart infrastructures, machine learning, artificial intelligence and deep learning, crowd sourcing and social intelligence, natural language processing, business intelligence, high-performance computing, wireless, mobile and green communications, ad-hoc, sensor and mesh networks, SDN and network virtualization, cognitive systems, swarm intelligence, human-computer interaction, network and information security, intelligent control, soft computing, networked control systems, renewable energy sources and technologies, biomedical signal processing, pattern recognition and object tracking, and sensor devices and applications.

This book gathers selected high-quality research papers presented at the International Conference on Paradigms of Communication, Computing and Data Sciences (PCCDS 2021), held at the National Institute of Technology, Kurukshetra, India, during May 07-09, 2021. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications, and data science techniques. The book is a collection of latest research articles in computation algorithm, communication, and data sciences, intertwined with each other for efficiency.

Credit Data and Scoring: The First Triumph of Big Data and Big Algorithms illuminates the often-hidden practice of predicting an individual's economic responsibility. Written by a leading practitioner, it examines the international implications of US leadership in credit scoring and what other countries have learned from it in building their own systems. Through its comprehensive contemporary perspective, the book also explores how algorithms and big data are driving the future of credit scoring. By revealing a new big picture and data comparisons, it delivers useful insights into legal, regulatory and data manipulation.

This book aims to systematically review and design different intelligent control algorithms for the small-signal stability assessment of HPS. With the growing consciousness of global warming and the fast depletion of natural power generation resources, the existing power system is on the verge of transitions to a "hybrid power system (HPS)" integrated with distributed energy resources. The recent results and requirements for the developments of intelligent control algorithms have motivated the authors to introduce this book for extensively analyzing the performance of HPS against unknown/uncertain disturbances. This book introduces fractional-order resilient control methodologies for arresting small-signal instability of HPS. The prospective investigation has been performed on the MATLAB platform. This book is helpful for undergraduate, postgraduate students, and research scholars working in power system stability, control applications, and soft computing in particular.

The Rust programming language is consistent and does its best to avoid surprising the programmer. Like all languages, though, Rust still has its quirks. But these quirks present a teaching opportunity. In this book, you'll work through a series of brain teasers that will challenge your understanding of Rust. By understanding the gaps in your knowledge, you can become better at what you do and avoid mistakes. Many of the teasers in this book come from the author's own experience creating software. Others derive from commonly asked questions in the Rust community. Regardless of their origin, these brain teasers are fun, and let's face it: who doesn't love a good puzzle, right? What better way to exercise your brain and increase your Rust programming knowledge than with a collection of dynamic brain teasers? As you read through each of these puzzles and try to work out the answers, you'll not only learn about Rust's unique quirks and peculiarities, you'll also have loads of fun along the way. Dive right in and get started with example code and sample problems that cover numbers and text, shadowing and memory, and everything in between. Try to figure out why a particular program won't compile, why it produces unexpected output, or why it panics and terminates with an error message. Once you've run the code and read the answer, it's time to get to the heart of the matter with a detailed explanation. Learn why a program produced the result it did, and discover how similar issues might affect the code you write in your own programs, even in production. Sourced from engaging discussions within the Rust community, real-world problems, and even reader feedback, these challenges will certainly surprise, enlighten, and entertain you. Are you ready to experience Rust like never before? Then sharpen your brain and get ready for a challenge! What You Need: This book assumes you have some knowledge of the Rust programming language. To work through the brain teasers in this book, you'll need a working Rust environment on any platform. You can install Rust by visiting https: //rustup.rs/. You'll also need a text editor or Rust-friendly IDE.

With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. Suitable for an introductory course on cluster analysis or data mining, with an in-depth mathematical treatment that includes discussions on different measures, primitives (points, lines, etc.) and optimization-based clustering methods, Cluster Analysis and Applications also includes coverage of deep learning based clustering methods. With clear explanations of ideas and precise definitions of concepts, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications may be used by students and researchers in various disciplines, working in data analysis or data science.

This book provides a broad overview of the many card systems and solutions that are in practical use today. This new edition adds content on RFIDs, embedded security, attacks and countermeasures, security evaluation, javacards, banking or payment cards, identity cards and passports, mobile systems security, and security management. A step-by-step approach educates the reader in card types, production, operating systems, commercial applications, new technologies, security design, attacks, application development, deployment and lifecycle management. By the end of the book the reader should be able to play an educated role in a smart card related project, even to programming a card application. This book is designed as a textbook for graduate level students in computer science. It is also as an invaluable post-graduate level reference for professionals and researchers. This volume offers insight into benefits and pitfalls of diverse industry, government, financial and logistics aspects while providing a sufficient level of technical detail to support technologists, information security specialists, engineers and researchers.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks. "

Provides sound knowledge of optimal decision making in statistics and operations research problems. Serves a quick reference by exploring the research literature on the subject with commercial value-added research applications in statistics and operations research. Provides sound knowledge of optimisations and statistical techniques in modelling of real-world problems. Reviews recent developments and contributions in optimal decision-making problems using optimisation and statistical techniques. Provides an understanding of formulations of decision-making problems and their solution procedures. Describes latest developments in modelling of real-world problems and their solution approaches.

This is a central topic in any computer science curriculum. To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm. Data structures are discussed to the extent needed for the implementation of the algorithms. The specific algorithms examined were chosen because of their wide field of application. This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises.

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Bela Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics.

This is a comprehensive study of various time-dependent scheduling problems in single-, parallel- and dedicated-machine environments. In addition to complexity issues and exact or heuristic algorithms which are typically presented in scheduling books, the author also includes more advanced topics such as matrix methods in time-dependent scheduling, time-dependent scheduling with two criteria and time-dependent two-agent scheduling. The reader should be familiar with the basic notions of calculus, discrete mathematics and combinatorial optimization theory, while the book offers introductory material on theory of algorithms, NP-complete problems, and the basics of scheduling theory. The author includes numerous examples, figures and tables, he presents different classes of algorithms using pseudocode, he completes all chapters with extensive bibliographies, and he closes the book with comprehensive symbol and subject indexes. The previous edition of the book focused on computational complexity of time-dependent scheduling problems. In this edition, the author concentrates on models of time-dependent job processing times and algorithms for solving time-dependent scheduling problems. The book is suitable for researchers working on scheduling, problem complexity, optimization, heuristics and local search algorithms.

A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.

The information infrastructure - comprising computers, embedded devices, networks and software systems - is vital to operations in every sector: inf- mation technology, telecommunications, energy, banking and ?nance, tra- portation systems, chemicals, agriculture and food, defense industrial base, public health and health care, national monuments and icons, drinking water and water treatment systems, commercial facilities, dams, emergency services, commercial nuclear reactors, materials and waste, postal and shipping, and government facilities. Global business and industry, governments, indeed - ciety itself, cannot function if major components of the critical information infrastructure are degraded, disabled or destroyed. This book, Critical Infrastructure Protection IV, is the fourth volume in the annual series produced by IFIP Working Group 11.10 on Critical Infr- tructure Protection, an active international community of scientists, engineers, practitioners and policy makers dedicated to advancing research, development and implementation e?orts related to critical infrastructure protection. The book presents original research results and innovative applications in the area of infrastructure protection. Also, it highlights the importance of weaving s- ence, technology and policy in crafting sophisticated, yet practical, solutions that will help secure information, computer and network assets in the various critical infrastructure sectors. This volume contains seventeen edited papers from the Fourth Annual IFIP Working Group 11.10 International Conference on Critical Infrastructure P- tection, held at the National Defense University, Washington, DC, March 15- 17, 2010. The papers were refereed by members of IFIP Working Group 11.10 and other internationally-recognized experts in critical infrastructure prot- tion.

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity-in both time and memory requirements-for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

Algorithms are now widely employed to make decisions that have increasingly far-reaching impacts on individuals and society as a whole ("algorithmic governance"), which could potentially lead to manipulation, biases, censorship, social discrimination, violations of privacy, property rights, and more. This has sparked a global debate on how to regulate AI and robotics ("governance of algorithms"). This book discusses both of these key aspects: the impact of algorithms, and the possibilities for future regulation.

This book gathers selected high-quality research papers presented at International Conference on Renewable Technologies in Engineering (ICRTE 2021) organized by Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India, during 15-16 April 2021. The book includes conference papers on the theme "Computational Techniques for Renewable Energy Optimization", which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of renewable energy integration, planning, control and optimization. It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends and concerns as well as practical challenges encountered and solutions adopted in the fields of smart structures in energy infrastructure.

This book focuses on Krylov subspace methods for solving linear systems, which are known as one of the top 10 algorithms in the twentieth century, such as Fast Fourier Transform and Quick Sort (SIAM News, 2000). Theoretical aspects of Krylov subspace methods developed in the twentieth century are explained and derived in a concise and unified way. Furthermore, some Krylov subspace methods in the twenty-first century are described in detail, such as the COCR method for complex symmetric linear systems, the BiCR method, and the IDR(s) method for non-Hermitian linear systems. The strength of the book is not only in describing principles of Krylov subspace methods but in providing a variety of applications: shifted linear systems and matrix functions from the theoretical point of view, as well as partial differential equations, computational physics, computational particle physics, optimizations, and machine learning from a practical point of view. The book is self-contained in that basic necessary concepts of numerical linear algebra are explained, making it suitable for senior undergraduates, postgraduates, and researchers in mathematics, engineering, and computational science. Readers will find it a useful resource for understanding the principles and properties of Krylov subspace methods and correctly using those methods for solving problems in the future.

This book features selected research papers presented at the International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2020), held at the Sir M. Visvesvaraya Institute of Technology on 20-21 February 2020. Discussing advances in evolutionary computing technologies, including swarm intelligence algorithms and other evolutionary algorithm paradigms which are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks.

Following an introduction to the basis of the fast Fourier transform (FFT), this book focuses on the implementation details on FFT for parallel computers. FFT is an efficient implementation of the discrete Fourier transform (DFT), and is widely used for many applications in engineering, science, and mathematics. Presenting many algorithms in pseudo-code and a complexity analysis, this book offers a valuable reference guide for graduate students, engineers, and scientists in the field who wish to apply FFT to large-scale problems.Parallel computation is becoming indispensable in solving the large-scale problems increasingly arising in a wide range of applications. The performance of parallel supercomputers is steadily improving, and it is expected that a massively parallel system with hundreds of thousands of compute nodes equipped with multi-core processors and accelerators will be available in the near future. Accordingly, the book also provides up-to-date computational techniques relevant to the FFT in state-of-the-art parallel computers. Following the introductory chapter, Chapter 2 introduces readers to the DFT and the basic idea of the FFT. Chapter 3 explains mixed-radix FFT algorithms, while Chapter 4 describes split-radix FFT algorithms. Chapter 5 explains multi-dimensional FFT algorithms, Chapter 6 presents high-performance FFT algorithms, and Chapter 7 addresses parallel FFT algorithms for shared-memory parallel computers. In closing, Chapter 8 describes parallel FFT algorithms for distributed-memory parallel computers.

Though the reductionist approachto biology and medicine has led to several imp- tant advances, further progresses with respect to the remaining challenges require integration of representation, characterization and modeling of the studied systems along a wide range of spatial and time scales. Such an approach, intrinsically - lated to systems biology, is poised to ultimately turning biology into a more precise and synthetic discipline, paving the way to extensive preventive and regenerative medicine [1], drug discovery [20] and treatment optimization [24]. A particularly appealing and effective approach to addressing the complexity of interactions inherent to the biological systems is provided by the new area of c- plex networks [34, 30, 8, 13, 12]. Basically, it is an extension of graph theory [10], focusing on the modeling, representation, characterization, analysis and simulation ofcomplexsystemsbyconsideringmanyelementsandtheirinterconnections.C- plex networks concepts and methods have been used to study disease [17], tr- scription networks [5, 6, 4], protein-protein networks [22, 36, 16, 39], metabolic networks [23] and anatomy [40].

This book includes original research findings in the field of memetic algorithms for image processing applications. It gathers contributions on theory, case studies, and design methods pertaining to memetic algorithms for image processing applications ranging from defence, medical image processing, and surveillance, to computer vision, robotics, etc. The content presented here provides new directions for future research from both theoretical and practical viewpoints, and will spur further advances in the field.

This book focuses on picturing B-IoT techniques from a few perspectives, which are architecture, key technologies, security and privacy, service models and framework, practical use cases and more. Main contents of this book derive from most updated technical achievements or breakthroughs in the field. A number of representative IoT service offerings will be covered by this book, such as vehicular networks, document sharing system, and telehealth. Both theoretical and practical contents will be involved in this book in order to assist readers to have a comprehensive and deep understanding the mechanism of using blockchain for powering up IoT systems. The blockchain-enabled Internet of Things (B-IoT) is deemed to be a novel technical alternative that provides network-based services with additional functionalities, benefits, and implementations in terms of decentralization, immutability, and auditability. Towards the enhanced secure and privacy-preserving Internet of Things (IoT), this book introduces a few significant aspects of B-IoT, which includes fundamental knowledge of both blockchain and IoT, state-of-the-art reviews of B-IoT applications, crucial components in the B-IoT system and the model design, and future development potentials and trends. IoT technologies and services, e.g. cloud data storage technologies and vehicular services, play important roles in wireless technology developments. On the other side, blockchain technologies are being adopted in a variety of academic societies and professional realms due to its promising characteristics. It is observable that the research and development on integrating these two technologies will provide critical thinking and solid references for contemporary and future network-relevant solutions. This book targets researchers and advanced level students in computer science, who are focused on cryptography, cloud computing and internet of things, as well as electrical engineering students and researchers focused on vehicular networks and more. Professionals working in these fields will also find this book to be a valuable resource.

Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.