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explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability;

Here is a hands-on treatment of the subject of optimization, recommended for use by industry professionals, scientists, and students interested in optimization algorithms and their various applications. It provides a complete teaching package with MATLAB exercises and online solutions to end-of-chapter problems.

Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs.

This textbook treats graph colouring as an algorithmic problem, with a strong emphasis on practical applications. and whether they can produce better solutions than other algorithms for certain types of graphs, and why. The introductory chapters explain graph colouring, complexity theory, bounds and constructive algorithms.

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability;

This comprehensive Guide to Web Development with Java introduces the readers to the three-tiered, Model-View-Controller architecture by using Spring JPA, JSPs, and Spring MVC controllers. These three technologies use Java, so that a student with a background in programming will be able to master them with ease, with the end result of being able to create web applications that use MVC, validate user input,and save data to a database.Topics and features:¿ Presents web development topics in an accessible, easy-to-follow style, focusing on core information first, and allowing the reader to gain basic understanding before moving forwards¿ Contains many helpful pedagogical tools for students and lecturers, such as questions and exercises at the end of each chapter, detailed illustrations, chapter summaries, and a glossary¿ Uses existing powerful technologies that are freely available on the web to speedup web development, such as Spring Boot, Spring MVC, Spring JPA, Hibernate, JSP, JSTL, and Java 1.8¿ Discusses HTML, HTML forms, and Cascading Style Sheets¿ Starts with the simplest technology for web development (JSP) and gradually introduces the reader to more complex topics¿ Introduces core technologies from the outset, such as the Model-View-Controller architecture¿ Includes examples for accessing common web services¿ Provides supplementary examples and tutorials

This book presents a broad overview of computer graphics (CG), its history, and the hardware tools it employs. examines advanced techniques in CG, including the nature and properties of light and color, graphics standards and file formats, and fractals;

Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.More than just a source of ΓÇ£recipes,ΓÇ¥ this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.Topics and features:Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized coursesIncorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented realityPresents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projectsIncludes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decadeProvides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and softwareSuitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

This practically-oriented textbook presents an accessible introduction to discrete mathematics through a substantial collection of classroom-tested exercises. Each chapter opens with concise coverage of the theory underlying the topic, reviewing the basic concepts and establishing the terminology, as well as providing the key formulae and instructions on their use. This is then followed by a detailed account of the most common problems in the area, before the reader is invited to practice solving such problems for themselves through a varied series of questions and assignments.Topics and features: provides an extensive set of exercises and examples of varying levels of complexity, suitable for both laboratory practical training and self-study; offers detailed solutions to many problems, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus.This hands-on study guide is designed to address the core needs of undergraduate students training in computer science, informatics, and electronic engineering, emphasizing the skills required to develop and implement an algorithm in a specific programming language.

Algorithms are central to all areas of computer science, from compiler construction to numerical analysis to artificial intelligence. Throughout your academic and professional careers, you may be required to construct new algorithms, analyze existing algorithms, or modify algorithms to suit new purposes. How do we know that such algorithms are correct? One method involves making claims about how we expect our programs to operate, and then constructing code that carries out these tasks. The key component of such reasoning is the invariant, and is the topic of this book. In these pages, you will study how invariants are developed, how they are used to construct correct algorithms, and how they are helpful in analyzing existing programs. Along the way, you'll be introduced to some classic sorting, searching and mathematical algorithms, and even some solutions to games and logic puzzles. These examples, though, are only conduits for the loftier goal: understanding why algorithms work.

Logic for Artificial Intelligence and Information Technology is based on student notes used to teach logic to second year undergraduates and Artificial Intelligence to graduate students at the University of London since1984, first at Imperial College and later at King's College.Logic has been applied to a wide variety of subjects such as theoretical computer science, software engineering, hardware design, logic programming, computational linguistics and artificial intelligence. In this way it has served to stimulate the research for clear conceptual foundations.Over the past 20 years many extensions of classical logic such as temporal, modal, relevance, fuzzy, probabilistic and non-monotoinic logics have been widely used in computer science and artificial intelligence, therefore requiring new formulations of classical logic, which can be modified to yield the effect of the new applied logics.The text introduces classical logic in a goal directed way which can easily deviate into discussing other applied logics. It defines the many types of logics and differences between them.Dov Gabbay, FRSC, FAvH, FRSA, FBCS, is Augustus De Morgan Professor of Logic at the University of London. He has written over 300 papers in logic and over 20 books. He is Editor-in-Chief of several leading journals and has published over 50 handbooks of logic volumes. He is a world authority on applied logics and is one of the directors and founder of the UK charity the International Federation of Computational Logic

Automata and Dictionaries is aimed at students and specialists in natural language processing and related disciplines where efficient text analysis plays a role. Large linguistic resources, in particular lexica, are now recognized as a fundamental pre-requisite for all natural language processing tasks. Specialists in this domain cannot afford to be ignorant of the state-of-the-art lexicon-management algorithms. This monograph, which is also intended be used as an advanced text book in computational linguistics, fills a gap in natural language processing monographs and is complementary to other publications in this area.This book is also a source of examples, exercises and problems for software engineering in general. The algorithms that are presented are excellent examples of non-trivial problems of graph construction, graph handling and graph traversal. Even though published in scientific journals, they have not been presented in an easily accessible form so far to teachers and students. These algorithms will also be of interest for the training of software engineers.Chapter 1 of Automata and Dictionaries provides the application-oriented motivation for solving the problems studied in the rest of the book. It introduces and exemplifies several key notions of lexicon-based natural language processing in a way accessible to any computer science student. Chapter 2 surveys the main solutions of the problem, using as an example a very small toy lexicon. Chapter 3 defines the underlying mathematical notions, immediately illustrating theory with practical examples, which makes this part quite readable.Chapters 4 and 5 are dedicated to the two central notions of lexicon construction: the algorithms of determinization and minimization. The standard form of both algorithms is presented, but also their variants and some special cases that occur frequently in practice. The operation of the algorithms is described step by step in examples, introducing the beginner into the world of epsilon-transitions, state heights and reverse automata.Chapter 6 goes a step further into complexity. It is based on algorithms published by scholars from 1998 to now. They are presented here with the same clarity as the preceding, more classical, algorithms. This remarkable achievement owes much to the rigorous structuration of this chapter. These algorithms have variants for transducers, which are presented in Chapter 7 with the same pedagogical skill.The last chapter studies time and space complexity of the algorithms and explains several tricks useful to speed up their operation.

This classroom-tested and clearly-written textbook presents a focused guide to the conceptual foundations of compilation, explaining the fundamental principles and algorithms used for defining the syntax of languages, and for implementing simple translators.This significantly updated and expanded third edition has been enhanced with additional coverage of regular expressions, visibly pushdown languages, bottom-up and top-down deterministic parsing algorithms, and new grammar models.Topics and features: describes the principles and methods used in designing syntax-directed applications such as parsing and regular expression matching; covers translations, semantic functions (attribute grammars), and static program analysis by data flow equations; introduces an efficient method for string matching and parsing suitable for ambiguous regular expressions (NEW); presents a focus on extended BNF grammars with their general parser and with LR(1) and LL(1) parsers (NEW); introduces a parallel parsing algorithm that exploits multiple processing threads to speed up syntax analysis of large files; discusses recent formal models of input-driven automata and languages (NEW); includes extensive use of theoretical models of automata, transducers and formal grammars, and describes all algorithms in pseudocode; contains numerous illustrative examples, and supplies a large set of exercises with solutions at an associated website.Advanced undergraduate and graduate students of computer science will find this reader-friendly textbook to be an invaluable guide to the essential concepts of syntax-directed compilation. The fundamental paradigms of language structures are elegantly explained in terms of the underlying theory, without requiring the use of software tools or knowledge of implementation, and through algorithms simple enough to be practiced by paper and pencil.

The book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing.

This practically-oriented textbook presents an accessible introduction to discrete mathematics through a substantial collection of classroom-tested exercises. Each chapter opens with concise coverage of the theory underlying the topic, reviewing the basic concepts and establishing the terminology, as well as providing the key formulae and instructions on their use. This is then followed by a detailed account of the most common problems in the area, before the reader is invited to practice solving such problems for themselves through a varied series of questions and assignments.Topics and features: provides an extensive set of exercises and examples of varying levels of complexity, suitable for both laboratory practical training and self-study; offers detailed solutions to many problems, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus.This hands-on study guide is designed to address the core needs of undergraduate students training in computer science, informatics, and electronic engineering, emphasizing the skills required to develop and implement an algorithm in a specific programming language.

In its revised and updated edition, this book now offers coverage of such multimedia topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks and their attendant technologies. Includes study exercises.

Revised and updated, Specification of Software Systems builds upon the original focus on software specification with added emphasis on the practice of formal methods for specification and verification. The text covers a wide range of formal specification techniques.

Since the introduction of Hoares' Communicating Sequential Process (CSP) notation, powerful new tools have transformed CSP into a practical way of describing industrial-sized problems. This book presents the basic CSP knowledge needed to utilize those tools.

In its revised fifth edition, this book covers ethical, social and policy challenges arising from the convergence of computing and telecommunication and the spread of mobile information devices. Asks important questions about the impact of new technologies.

This book surveys theoretical computer science, presenting fundamental concepts and results. Updated and revised, the new edition includes two new chapters on nonuniform complexity, circuit complexity and parallel complexity, and randomized complexity.

The challenges of problems from international programming competitions are an effective way to improve your algorithmic and coding skills and understanding. This volume uses international programming competition-type problems to motivate the study of algorithms, programming, and other topics in computer science.

This practically-focused study guide introduces the fundamentals of discrete mathematics through an extensive set of classroom-tested problems. Each chapter presents a concise introduction to the relevant theory, followed by a detailed account of common challenges and methods for overcoming these. The reader is then encouraged to practice solving such problems for themselves, by tackling a varied selection of questions and assignments of different levels of complexity.This updated second edition now covers the design and analysis of algorithms using Python, and features more than 50 new problems, complete with solutions.Topics and features: provides a substantial collection of problems and examples of varying levels of difficulty, suitable for both laboratory practical training and self-study; offers detailed solutions to each problem, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including Turing machines, asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus.This hands-on workbook is an invaluable resource for undergraduate students of computer science, informatics, and electronic engineering. Suitable for use in a one- or two-semester course on discrete mathematics, the text emphasizes the skills required to develop and implement an algorithm in a specific programming language.

Explorations in Quantum Computing describes quantum computing in simple terms, and describes key technological hurdles that must be overcome in order to make quantum computers a reality. The text includes executable software simulations to illustrate the concepts and material.

This easy-to-follow textbook teaches Java programming from first principles, as well as covering design and testing methodologies. The text is divided into two parts. Each part supports a one-semester module, the first part addressing fundamental programming concepts, and the second part building on this foundation, teaching the skills required to develop more advanced applications.This fully updated and greatly enhanced fourth edition covers the key developments introduced in Java 8, including material on JavaFX, lambda expressions and the Stream API.Topics and features: begins by introducing fundamental programming concepts such as declaration of variables, control structures, methods and arrays; goes on to cover the fundamental object-oriented concepts of classes and objects, inheritance and polymorphism; uses JavaFX throughout for constructing event-driven graphical interfaces; includes advanced topics such as interfaces and lambda expressions, generics, collection classes and exceptions; explains file-handling techniques, packages, multi-threaded programs, socket programming, remote database access and processing collections using streams; includes self-test questions and programming exercises at the end of each chapter, as well as two illuminating case studies; provides additional resources at its associated website (simply go to springer.com and search for "Java in Two Semesters"), including a guide on how to install and use the NetBeans¿ Java IDE.Offering a gentle introduction to the field, assuming no prior knowledge of the subject, Java in Two Semesters is the ideal companion to undergraduate modules in software development or programming.

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content.

This book serves an introduction to data science, focusing on the skills and principles needed to build systems for collecting, analyzing, and interpreting data. As a discipline, data science sits at the intersection of statistics, computer science, and machine learning, but it is building a distinct heft and character of its own.In particular, the book stresses the following basic principles as fundamental to becoming a good data scientist: ΓÇ£Valuing Doing the Simple Things RightΓÇ¥, laying the groundwork of what really matters in analyzing data; ΓÇ£Developing Mathematical IntuitionΓÇ¥, so that readers can understand on an intuitive level why these concepts were developed, how they are useful and when they work best, and; ΓÇ£Thinking Like a Computer Scientist, but Acting Like a StatisticianΓÇ¥, following approaches which come most naturally to computer scientists while maintaining the core values of statistical reasoning. The book does not emphasize any particular language or suite of data analysis tools, but instead provides a high-level discussion of important design principles.This book covers enough material for an ΓÇ£Introduction to Data ScienceΓÇ¥ course at the undergraduate or early graduate student levels. A full set of lecture slides for teaching this course are available at an associated website, along with data resources for projects and assignments, and online video lectures.Other Pedagogical features of this book include: ΓÇ£War StoriesΓÇ¥ offering perspectives on how data science techniques apply in the real world; ΓÇ£False StartsΓÇ¥ revealing the subtle reasons why certain approaches fail; ΓÇ£Take-Home LessonsΓÇ¥ emphasizing the big-picture concepts to learn from each chapter; ΓÇ£Homework ProblemsΓÇ¥ providing a wide range of exercises for self-study; ΓÇ£Kaggle ChallengesΓÇ¥ from the online platform Kaggle; examples taken from the data science television show ΓÇ£The Quant ShopΓÇ¥, and; concluding notes in each tutorial chapter pointing readers to primary sources and additional references.