Provides complete update and organization of the previous books, with some material moving online; Includes new problems, projects, and exercises; Includes interactive coding resources to accompany the book, including examples in the text, exercises, projects, and refection questions.

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

This introduction to cryptography employs a programming-oriented approach to study the most important cryptographic schemes in current use and the main cryptanalytic attacks against them. Discussion of the theoretical aspects, emphasizing precise security definitions based on methodological tools such as complexity and randomness, and of the mathematical aspects, with emphasis on number-theoretic algorithms and their applications to cryptography and cryptanalysis, is integrated with the programming approach, thus providing implementations of the algorithms and schemes as well as examples of realistic size.

A distinctive feature of the author's approach is the use of Maple as a programming environment in which not just the cryptographic primitives but also the most important cryptographic schemes are implemented following the recommendations of standards bodies such as NIST, with many of the known cryptanalytic attacks implemented as well. The purpose of the Maple implementations is to let the reader experiment and learn, and for this reason the author includes numerous examples. The book discusses important recent subjects such as homomorphic encryption, identity-based cryptography and elliptic curve cryptography. The algorithms and schemes which are treated in detail and implemented in Maple include AES and modes of operation, CMAC, GCM/GMAC, SHA-256, HMAC, RSA, Rabin, Elgamal, Paillier, Cocks IBE, DSA and ECDSA. In addition, some recently introduced schemes enjoying strong security properties, such as RSA-OAEP, Rabin-SAEP, Cramer--Shoup, and PSS, are also discussed and implemented. On the cryptanalysis side, Maple implementations and examples are used to discuss many important algorithms, including birthday and man-in-the-middle attacks, integer factorization algorithms such as Pollard's rho and the quadratic sieve, and discrete log algorithms such as baby-step giant-step, Pollard's rho, Pohlig--Hellman and the index calculus method.

This textbook is suitable for advanced undergraduate and graduate students of computer science, engineering and mathematics, satisfying the requirements of various types of courses: a basic introductory course; a theoretically oriented course whose focus is on the precise definition of security concepts and on cryptographic schemes with reductionist security proofs; a practice-oriented course requiring little mathematical background and with an emphasis on applications; or a mathematically advanced course addressed to students with a stronger mathematical background. The main prerequisite is a basic knowledge of linear algebra and elementary calculus, and while some knowledge of probability and abstract algebra would be helpful, it is not essential because the book includes the necessary background from these subjects and, furthermore, explores the number-theoretic material in detail. The book is also a comprehensive reference and is suitable for self-study by practitioners and programmers."

This title includes a number of Open Access chapters. Data structure and software engineering is an integral part of computer science. This volume presents new approaches and methods to knowledge sharing, brain mapping, data integration, and data storage. The author describes how to manage an organization's business process and domain data and presents new software and hardware testing methods. The book introduces a game development framework used as a learning aid in a software engineering at the university level. It also features a review of social software engineering metrics and methods for processing business information. It explains how to use Pegasys to create and manage sequence analysis workflows.

This book provides insights into contemporary issues and challenges in soft computing applications and techniques in healthcare. It will be a useful guide to identify, categorise and assess the role of different soft computing techniques for disease, diagnosis and prediction due to technological advancements. The book explores applications in soft computing and covers empirical properties of artificial neural network (ANN), evolutionary computing, fuzzy logic and statistical techniques. It presents basic and advanced concepts to help beginners and industry professionals get up to speed on the latest developments in soft computing and healthcare systems. It incorporates the latest methodologies and challenges facing soft computing, examines descriptive, predictive and social network techniques and discusses analytics tools and their role in providing effective solutions for science and technology. The primary users for the book include researchers, academicians, postgraduate students, specialists and practitioners. Dr. Ashish Mishra is a professor in the Department of Computer Science and Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur, Madhya Pradesh, India. He has contributed in organising the INSPIRE Science Internship Camp. He is a member of the Institute of Electrical and Electronics Engineers and is a life member of the Computer Society of India. His research interests include the Internet of Things, data mining, cloud computing, image processing and knowledge-based systems. He holds nine patents in Intellectual Property, India. He has authored four books in the areas of data mining, image processing and LaTex. Dr. G. Suseendran is an assistant professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. His research interests include ad-hoc networks, the Internet of Things, data mining, cloud computing, image processing, knowledge-based systems, and Web information exploration. He has published more than 75 research papers in various international journals such as Science Citation Index, Springer Book Chapter, Scopus, IEEE Access and UGC-referred journals. Prof. Trung-Nghia Phung is an associate professor and Head of Academic Affairs, Thai Nguyen University of Information and Communication Technology (ICTU). He has published more than 60 research papers. His main research interest lies in the field of speech, audio, and biomedical signal processing. He serves as a technical committee program member, track chair, session chair, and reviewer of many international conferences and journals. He was a co-Chair of the International Conference on Advances in Information and Communication Technology 2016 (ICTA 2016) and a Session Chair of the 4th International Conference on Information System Design and Intelligent Applications (INDIA 2017).

Realism and Complexity in Social Science is an argument for a new approach to investigating the social world, that of complex realism. Complex realism brings together a number of strands of thought, in scientific realism, complexity science, probability theory and social research methodology. It proposes that the reality of the social world is that it is probabilistic, yet there exists enough invariance to make the discovery and explanation of social objects and causal mechanisms possible. This forms the basis for the development of a complex realist foundation for social research, that utilises a number of new and novel approaches to investigation, alongside the more traditional corpus of quantitative and qualitative methods. Research examples are drawn from research in sociology, epidemiology, criminology, social policy and human geography. The book assumes no prior knowledge of realism, probability or complexity and in the early chapters, the reader is introduced to these concepts and the arguments against them. Although the book is grounded in philosophical reasoning, this is in a direct and accessible style that will appeal both to social researchers with a methodological interest and philosophers with an interest in social investigation.

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

Cryptographic applications, such as RSA algorithm, ElGamal cryptography, elliptic curve cryptography, Rabin cryptosystem, Diffie -Hellmann key exchange algorithm, and the Digital Signature Standard, use modular exponentiation extensively. The performance of all these applications strongly depends on the efficient implementation of modular exponentiation and modular multiplication. Since 1984, when Montgomery first introduced a method to evaluate modular multiplications, many algorithmic modifications have been done for improving the efficiency of modular multiplication, but very less work has been done on the modular exponentiation to improve the efficiency. This research monograph addresses the question- how can the performance of modular exponentiation, which is the crucial operation of many public-key cryptographic techniques, be improved? The book focuses on Energy Efficient Modular Exponentiations for Cryptographic hardware. Spread across five chapters, this well-researched text focuses in detail on the Bit Forwarding Techniques and the corresponding hardware realizations. Readers will also discover advanced performance improvement techniques based on high radix multiplication and Cryptographic hardware based on multi-core architectures.

"Ask not what your compiler can do for you, ask what you can do for your compiler." --John Levesque, Director of Cray's Supercomputing Centers of Excellence The next decade of computationally intense computing lies with more powerful multi/manycore nodes where processors share a large memory space. These nodes will be the building block for systems that range from a single node workstation up to systems approaching the exaflop regime. The node itself will consist of 10's to 100's of MIMD (multiple instruction, multiple data) processing units with SIMD (single instruction, multiple data) parallel instructions. Since a standard, affordable memory architecture will not be able to supply the bandwidth required by these cores, new memory organizations will be introduced. These new node architectures will represent a significant challenge to application developers. Programming for Hybrid Multi/Manycore MPP Systems attempts to briefly describe the current state-of-the-art in programming these systems, and proposes an approach for developing a performance-portable application that can effectively utilize all of these systems from a single application. The book starts with a strategy for optimizing an application for multi/manycore architectures. It then looks at the three typical architectures, covering their advantages and disadvantages. The next section of the book explores the other important component of the target-the compiler. The compiler will ultimately convert the input language to executable code on the target, and the book explores how to make the compiler do what we want. The book then talks about gathering runtime statistics from running the application on the important problem sets previously discussed. How best to utilize available memory bandwidth and virtualization is covered next, along with hybridization of a program. The last part of the book includes several major applications, and examines future hardware advancements and how the application developer may prepare for those advancements.

Discover the foundations of software engineering with this easy and intuitive guide In the newly updated second edition of Beginning Software Engineering, expert programmer and tech educator Rod Stephens delivers an instructive and intuitive introduction to the fundamentals of software engineering. In the book, you'll learn to create well-constructed software applications that meet the needs of users while developing the practical, hands-on skills needed to build robust, efficient, and reliable software. The author skips the unnecessary jargon and sticks to simple and straightforward English to help you understand the concepts and ideas discussed within. He also offers you real-world tested methods you can apply to any programming language. You'll also get: Practical tips for preparing for programming job interviews, which often include questions about software engineering practices A no-nonsense guide to requirements gathering, system modeling, design, implementation, testing, and debugging Brand-new coverage of user interface design, algorithms, and programming language choices Beginning Software Engineering doesn't assume any experience with programming, development, or management. It's plentiful figures and graphics help to explain the foundational concepts and every chapter offers several case examples, Try It Out, and How It Works explanatory sections. For anyone interested in a new career in software development, or simply curious about the software engineering process, Beginning Software Engineering, Second Edition is the handbook you've been waiting for.

Justice apps - mobile and web-based programmes that can assist individuals with legal tasks - are being produced, improved, and accessed at an unprecedented rate. These technologies have the potential to reshape the justice system, improve access to justice, and demystify legal institutions. Using artificial intelligence techniques, apps can even facilitate the resolution of common legal disputes. However, these opportunities must be assessed in light of the many challenges associated with app use in the justice sector. These include the digital divide and other accessibility issues; the ethical challenges raised by the dehumanisation of legal processes; and various privacy, security, and confidentiality risks. Surveying the landscape of this emergent industry, this book explores the objectives, opportunities, and challenges presented by apps across all areas of the justice sector. Detailed consideration is also given to the use of justice apps in specific legal contexts, including the family law and criminal law sectors. The first book to engage with justice apps, this book will appeal to a wide range of legal scholars, students, practitioners, and policy-makers.

Anyone Can Code: The Art and Science of Logical Creativity introduces computer programming as a way of problem-solving through logical thinking. It uses the notion of modularization as a central lens through which we can make sense of many software concepts. This book takes the reader through fundamental concepts in programming by illustrating them in three different and distinct languages: C/C++, Python, and Javascript. Key features: Focuses on problem-solving and algorithmic thinking instead of programming functions, syntax, and libraries; Includes engaging examples, including video games and visual effects; Provides exercises and reflective questions. This book gives beginner and intermediate learners a strong understanding of what they are doing so that they can do it better and with any other tool or language that they may end up using later.

The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as "The best programming book of the decade." This new edition retains the same presentation but has been upgraded to Python 3, and there is a new section on neural network styles. Using a simple computational task (term frequency) to illustrate different programming styles, Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, interactivity, and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

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.

Disk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing. Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structures and MongoDB for unstructured document data. Designed for senior undergraduate and graduate students, as well as professionals, this book is useful for anyone interested in understanding the foundations and advances in big data storage and management, and big data analytics. About the Author Dr. Christopher G. Healey is a tenured Professor in the Department of Computer Science and the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics, both at North Carolina State University in Raleigh, North Carolina. He has published over 50 articles in major journals and conferences in the areas of visualization, visual and data analytics, computer graphics, and artificial intelligence. He is a recipient of the National Science Foundation's CAREER Early Faculty Development Award and the North Carolina State University Outstanding Instructor Award. He is a Senior Member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and an Associate Editor of ACM Transaction on Applied Perception, the leading worldwide journal on the application of human perception to issues in computer science.

Designed for a proof-based course on linear algebra, this rigorous and concise textbook intentionally introduces vector spaces, inner products, and vector and matrix norms before Gaussian elimination and eigenvalues so students can quickly discover the singular value decomposition (SVD)-arguably the most enlightening and useful of all matrix factorizations. Gaussian elimination is then introduced after the SVD and the four fundamental subspaces and is presented in the context of vector spaces rather than as a computational recipe. This allows the authors to use linear independence, spanning sets and bases, and the four fundamental subspaces to explain and exploit Gaussian elimination and the LU factorization, as well as the solution of overdetermined linear systems in the least squares sense and eigenvalues and eigenvectors. This unique textbook also includes examples and problems focused on concepts rather than the mechanics of linear algebra. The problems at the end of each chapter and in an associated website encourage readers to explore how to use the notions introduced in the chapter in a variety of ways. Additional problems, quizzes, and exams will be posted on an accompanying website and updated regularly. The Less Is More Linear Algebra of Vector Spaces and Matrices is for students and researchers interested in learning linear algebra who have the mathematical maturity to appreciate abstract concepts that generalize intuitive ideas. The early introduction of the SVD makes the book particularly useful for those interested in using linear algebra in applications such as scientific computing and data science. It is appropriate for a first proof-based course in linear algebra.

This book focuses on lattice-based cryptosystems, widely considered to be one of the most promising post-quantum cryptosystems and provides fundamental insights into how to construct provably secure cryptosystems from hard lattice problems. The concept of provable security is used to inform the choice of lattice tool for designing cryptosystems, including public-key encryption, identity-based encryption, attribute-based encryption, key change and digital signatures. Given its depth of coverage, the book especially appeals to graduate students and young researchers who plan to enter this research area.

This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.

Distributed Systems: An Algorithmic Approach, Second Edition provides a balanced and straightforward treatment of the underlying theory and practical applications of distributed computing. As in the previous version, the language is kept as unobscured as possible-clarity is given priority over mathematical formalism. This easily digestible text: Features significant updates that mirror the phenomenal growth of distributed systems Explores new topics related to peer-to-peer and social networks Includes fresh exercises, examples, and case studies Supplying a solid understanding of the key principles of distributed computing and their relationship to real-world applications, Distributed Systems: An Algorithmic Approach, Second Edition makes both an ideal textbook and a handy professional reference.

This compendium provides a detailed account of the lognormality principle characterizing the human motor behavior by summarizing a sound theoretical framework for modeling such a behavior, introducing the most recent algorithms for extracting the lognormal components of complex movements in 2, 2.5 and 3 dimensions. It also vividly reports the most advanced applications to handwriting analysis and recognition, signature and writer verification, gesture recognition and calligraphy generation, evaluation of motor skills, improvement/degradation with aging, handwriting learning, education and developmental deficits, prescreening of children with ADHD (Attention Development and Hyperactivity Disorder), monitoring of concussion recovery, diagnosis and monitoring of Alzheimer's and Parkinson's diseases and aging effects in speech and handwriting.The volume provides a unique and useful source of references on the lognormality principle, an update on the most recent advances and an outlook at the most promising future developments in e-Security, e-Learning and e-Health.

Algorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems.

This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java.

The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.

The vast circulations of mobile devices, sensors and data mean that the social world is now defined by a complex interweaving of human and machine agency. Key to this is the growing power of algorithms - the decision-making parts of code - in our software dense and data rich environments. Algorithms can shape how we are retreated, what we know, who we connect with and what we encounter, and they present us with some important questions about how society operates and how we understand it. This book offers a series of concepts, approaches and ideas for understanding the relations between algorithms and power. Each chapter provides a unique perspective on the integration of algorithms into the social world. As such, this book directly tackles some of the most important questions facing the social sciences today. This book was originally published as a special issue of Information, Communication & Society.

The introduction of public key cryptography (PKC) was a critical advance in IT security. In contrast to symmetric key cryptography, it enables confidential communication between entities in open networks, in particular the Internet, without prior contact. Beyond this PKC also enables protection techniques that have no analogue in traditional cryptography, most importantly digital signatures which for example support Internet security by authenticating software downloads and updates. Although PKC does not require the confidential exchange of secret keys, proper management of the private and public keys used in PKC is still of vital importance: the private keys must remain private, and the public keys must be verifiably authentic. So understanding so-called public key infrastructures (PKIs) that manage key pairs is at least as important as studying the ingenious mathematical ideas underlying PKC.

In this book the authors explain the most important concepts underlying PKIs and discuss relevant standards, implementations, and applications. The book is structured into chapters on the motivation for PKI, certificates, trust models, private keys, revocation, validity models, certification service providers, certificate policies, certification paths, and practical aspects of PKI.

This is a suitable textbook for advanced undergraduate and graduate courses in computer science, mathematics, engineering, and related disciplines, complementing introductory courses on cryptography. The authors assume only basic computer science prerequisites, and they include exercises in all chapters and solutions in an appendix. They also include detailed pointers to relevant standards and implementation guidelines, so the book is also appropriate for self-study and reference by industrial and academic researchers and practitioners.

Soft computing methods such as neural networks and genetic algorithms draw on the problem solving strategies of the natural world which differ fundamentally from the mathematically-based computing methods normally used in engineering. Human brains are highly effective computers with capabilities far beyond those of the most sophisticated electronic computers. The 'soft computing' methods they use can solve very difficult inverse problems based on reduction in disorder. This book outlines these methods and applies them to a range of difficult engineering problems, including applications in computational mechanics, earthquake engineering, and engineering design. Most of these are difficult inverse problems - especially in engineering design - and are treated in depth.

Find the right algorithm for your image processing application Exploring the recent achievements that have occurred since the mid-1990s, Circular and Linear Regression: Fitting Circles and Lines by Least Squares explains how to use modern algorithms to fit geometric contours (circles and circular arcs) to observed data in image processing and computer vision. The author covers all facets-geometric, statistical, and computational-of the methods. He looks at how the numerical algorithms relate to one another through underlying ideas, compares the strengths and weaknesses of each algorithm, and illustrates how to combine the algorithms to achieve the best performance. After introducing errors-in-variables (EIV) regression analysis and its history, the book summarizes the solution of the linear EIV problem and highlights its main geometric and statistical properties. It next describes the theory of fitting circles by least squares, before focusing on practical geometric and algebraic circle fitting methods. The text then covers the statistical analysis of curve and circle fitting methods. The last chapter presents a sample of "exotic" circle fits, including some mathematically sophisticated procedures that use complex numbers and conformal mappings of the complex plane. Essential for understanding the advantages and limitations of the practical schemes, this book thoroughly addresses the theoretical aspects of the fitting problem. It also identifies obscure issues that may be relevant in future research.