This book focuses on the fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). It discusses routing protocols from a practicing engineer's perspective, linking theory and fundamental concepts to common practices and everyday examples. The book benefits and reflects the author's more than 22 years of designing and working with IP routing devices and protocols (and Telecoms systems, in general). Every aspect of the book is written to reflect current best practices using real-world examples. This book describes the various methods used by routers to learn routing information. The author includes discussion of the characteristics of the different dynamic routing protocols, and how they differ in design and operation. He explains the processing steps involved in forwarding IP packets through an IP router to their destination and discusses the various mechanisms IP routers use for controlling routing in networks. The discussion is presented in a simple style to make it comprehensible and appealing to undergraduate and graduate level students, research and practicing engineers, scientists, IT personnel, and network engineers. It is geared toward readers who want to understand the concepts and theory of IP routing protocols, through real-world example systems and networks. Focuses on the fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). Describes the various methods used by routers to learn routing information. Includes discussion of the characteristics of the different dynamic routing protocols, and how they differ in design and operation. Provides detailed descriptions of the most common distance-vector routing protocols RIPv2 and EIGRP. Discusses the various mechanisms IP routers use for controlling routing in networks. James Aweya, PhD, is a chief research scientist at the Etisalat British Telecom Innovation Center (EBTIC), Khalifa University, Abu Dhabi, UAE. He has authored four books including this book and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).

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.

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.

Originally published in 1986 and a major contribution towards improving operations within transportation systems, this book provides detailed coverage of the theory of transportation networks as a general traffic and transportation discipline. It examines some of the daily difficulties encountered by traffic and transportation experts, uncovering the intricacies of vehicle routing and scheduling, crew planning and facilities placement. This problem-solving approach uses multiple numerical examples and simple mathematical methods to enable newcomers to apply the book's solutions to the situations they encounter on the job.

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models' concepts from the domain of formal mathematics into computer codes using MATLAB (R). The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB (R). This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach. Key Selling Points: Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory Covers the analysis of both continuous and discrete Markov chains Discusses the translation of HMM concepts from the realm of formal mathematics into computer code Offers many examples to supplement mathematical notation when explaining new concepts

This book covers the issues related to optimization of engineering and management problems using soft computing techniques with an industrial outlook. It covers a broad area related to real life complex decision making problems using a heuristics approach. It also explores a wide perspective and future directions in industrial engineering research on a global platform/scenario. The book highlights the concept of optimization, presents various soft computing techniques, offers sample problems, and discusses related software programs complete with illustrations. Features Explains the concept of optimization and relevance to soft computing techniques towards optimal solution in engineering and management Presents various soft computing techniques Offers problems and their optimization using various soft computing techniques Discusses related software programs, with illustrations Provides a step-by-step tutorial on how to handle relevant software for obtaining the optimal solution to various engineering problems

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.

Originally published in 1995, Large Deviations for Performance Analysis consists of two synergistic parts. The first half develops the theory of large deviations from the beginning, through recent results on the theory for processes with boundaries, keeping to a very narrow path: continuous-time, discrete-state processes. By developing only what is needed for the applications, the theory is kept to a manageable level, both in terms of length and in terms of difficulty. Within its scope, the treatment is detailed, comprehensive and self-contained. As the book shows, there are sufficiently many interesting applications of jump Markov processes to warrant a special treatment. The second half is a collection of applications developed at Bell Laboratories. The applications cover large areas of the theory of communication networks: circuit switched transmission, packet transmission, multiple access channels, and the M/M/1 queue. Aspects of parallel computation are covered as well including, basics of job allocation, rollback-based parallel simulation, assorted priority queueing models that might be used in performance models of various computer architectures, and asymptotic coupling of processors. These applications are thoroughly analysed using the tools developed in the first half of the book.

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.

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.

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.

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.

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.

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

Discrete Mathematics for Computer Science: An Example-Based Introduction is intended for a first- or second-year discrete mathematics course for computer science majors. It covers many important mathematical topics essential for future computer science majors, such as algorithms, number representations, logic, set theory, Boolean algebra, functions, combinatorics, algorithmic complexity, graphs, and trees. Features Designed to be especially useful for courses at the community-college level Ideal as a first- or second-year textbook for computer science majors, or as a general introduction to discrete mathematics Written to be accessible to those with a limited mathematics background, and to aid with the transition to abstract thinking Filled with over 200 worked examples, boxed for easy reference, and over 200 practice problems with answers Contains approximately 40 simple algorithms to aid students in becoming proficient with algorithm control structures and pseudocode Includes an appendix on basic circuit design which provides a real-world motivational example for computer science majors by drawing on multiple topics covered in the book to design a circuit that adds two eight-digit binary numbers Jon Pierre Fortney graduated from the University of Pennsylvania in 1996 with a BA in Mathematics and Actuarial Science and a BSE in Chemical Engineering. Prior to returning to graduate school, he worked as both an environmental engineer and as an actuarial analyst. He graduated from Arizona State University in 2008 with a PhD in Mathematics, specializing in Geometric Mechanics. Since 2012, he has worked at Zayed University in Dubai. This is his second mathematics textbook.

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.

This book discusses an important area of numerical optimization, called interior-point method. This topic has been popular since the 1980s when people gradually realized that all simplex algorithms were not convergent in polynomial time and many interior-point algorithms could be proved to converge in polynomial time. However, for a long time, there was a noticeable gap between theoretical polynomial bounds of the interior-point algorithms and efficiency of these algorithms. Strategies that were important to the computational efficiency became barriers in the proof of good polynomial bounds. The more the strategies were used in algorithms, the worse the polynomial bounds became. To further exacerbate the problem, Mehrotra's predictor-corrector (MPC) algorithm (the most popular and efficient interior-point algorithm until recently) uses all good strategies and fails to prove the convergence. Therefore, MPC does not have polynomiality, a critical issue with the simplex method. This book discusses recent developments that resolves the dilemma. It has three major parts. The first, including Chapters 1, 2, 3, and 4, presents some of the most important algorithms during the development of the interior-point method around the 1990s, most of them are widely known. The main purpose of this part is to explain the dilemma described above by analyzing these algorithms' polynomial bounds and summarizing the computational experience associated with them. The second part, including Chapters 5, 6, 7, and 8, describes how to solve the dilemma step-by-step using arc-search techniques. At the end of this part, a very efficient algorithm with the lowest polynomial bound is presented. The last part, including Chapters 9, 10, 11, and 12, extends arc-search techniques to some more general problems, such as convex quadratic programming, linear complementarity problem, and semi-definite programming.

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.

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.

With the growing interest in and use of big data analytics in many industries and in many research fields around the globe, this new volume addresses the need for a comprehensive resource on the core concepts of big data analytics along with the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media. Research Practitioner's Handbook on Big Data Analytics will be a valuable addition to the libraries of practitioners in data collection in many industries along with research scholars and faculty in the domain of big data analytics. The book can also serve as a handy textbook for courses in data collection, data mining, and big data analytics.

Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems - basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of aEURO~making things simpleaEURO (TM) and aEURO~divide and conqueraEURO (TM) to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.

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.

Modern computing relies on future and emergent technologies which have been conceived via interaction between computer science, engineering, chemistry, physics and biology. This highly interdisciplinary book presents advances in the fields of parallel, distributed and emergent information processing and computation. The book represents major breakthroughs in parallel quantum protocols, elastic cloud servers, structural properties of interconnection networks, internet of things, morphogenetic collective systems, swarm intelligence and cellular automata, unconventionality in parallel computation, algorithmic information dynamics, localized DNA computation, graph-based cryptography, slime mold inspired nano-electronics and cytoskeleton computers. Features Truly interdisciplinary, spanning computer science, electronics, mathematics and biology Covers widely popular topics of future and emergent computing technologies, cloud computing, parallel computing, DNA computation, security and network analysis, cryptography, and theoretical computer science Provides unique chapters written by top experts in theoretical and applied computer science, information processing and engineering From Parallel to Emergent Computing provides a visionary statement on how computing will advance in the next 25 years and what new fields of science will be involved in computing engineering. This book is a valuable resource for computer scientists working today, and in years to come.