Computer Science Programming Basics in Ruby

This textbook provides in-depth coverage of the fundamentals of the C and C++ programming languages and the object-oriented programming paradigm. It follows an example-driven approach to facilitate understanding of theoretical concepts. Essential concepts, including functions, arrays, pointers and inheritance, are explained, while complex topics, such as dynamic memory allocation, object slicing, vtables, and upcasting and downcasting, are examined in detail. Concepts are explained with the help of line diagrams, student-teacher conversations and flow charts, while other useful features, such as quiz questions and points to remember, are included. Solved examples, review questions and useful case studies are interspersed throughout the text, and explanations of the logic used to implement particular functionality is also provided. This book will be useful for undergraduate students of computer science and engineering, and information technology.

This new resource is written to follow the updated IGSCE Computer Science syllabus 0478 with examination in June and November 2016.

There has been an enormous increase in research activity aimed at elucidating the basis for cortical activity in the brain. Among modern techniques used in this area of scientific endeavour, few have proved as popular as computer simulation. Model neural networks are the subject of intense study, and some remarkable properties have already come to light: these networks are able to discriminate, remember and associate. Professor Cotterill has assembled leading experts in this burgeoning field to produce an exciting review of advances. The volume covers the creation of computer models of neural networks, and their use in the study of neural function, of cognition, memory and vision. The results and future directions explored here will have an important bearing on research into brain function, physiology, psychology, biophysics and artificial intelligence.

Linear logic is a branch of proof theory which provides refined tools for the study of the computational aspects of proofs. These tools include a duality-based categorical semantics, an intrinsic graphical representation of proofs, the introduction of well-behaved non-commutative logical connectives, and the concepts of polarity and focalisation. These various aspects are illustrated here through introductory tutorials as well as more specialised contributions, with a particular emphasis on applications to computer science: denotational semantics, lambda-calculus, logic programming and concurrency theory. The volume is rounded-off by two invited contributions on new topics rooted in recent developments of linear logic. The book derives from a summer school that was the climax of the EU Training and Mobility of Researchers project 'Linear Logic in Computer Science'. It is an excellent introduction to some of the most active research topics in the area.

There has been an enormous increase in research activity aimed at elucidating the basis for cortical activity in the brain. Among modern techniques used in this area of scientific endeavour, few have proved as popular as computer simulation. Model neural networks are the subject of intense study, and some remarkable properties have already come to light: these networks are able to discriminate, remember and associate. Professor Cotterill has assembled leading experts in this burgeoning field to produce an exciting review of advances. The volume covers the creation of computer models of neural networks, and their use in the study of neural function, of cognition, memory and vision. The results and future directions explored here will have an important bearing on research into brain function, physiology, psychology, biophysics and artificial intelligence.

This comprehensive text provides a modern and technically precise exposition of the fundamental theory and applications of temporal logics in computer science. Part I presents the basics of discrete transition systems, including constructions and behavioural equivalences. Part II examines the most important temporal logics for transition systems and Part III looks at their expressiveness and complexity. Finally, Part IV describes the main computational methods and decision procedures for model checking and model building - based on tableaux, automata and games - and discusses their relationships. The book contains a wealth of examples and exercises, as well as an extensive annotated bibliography. Thus, the book is not only a solid professional reference for researchers in the field but also a comprehensive graduate textbook that can be used for self-study as well as for teaching courses.

Nominal sets provide a promising new mathematical analysis of names in formal languages based upon symmetry, with many applications to the syntax and semantics of programming language constructs that involve binding, or localising names. Part I provides an introduction to the basic theory of nominal sets. In Part II, the author surveys some of the applications that have developed in programming language semantics (both operational and denotational), functional programming and logic programming. As the first book to give a detailed account of the theory of nominal sets, it will be welcomed by researchers and graduate students in theoretical computer science.

The logic and methodology of design is examined in this book from the perspective of computer science. Computers provide the context for this examination both by discussion of the design process for hardware and software systems and by consideration of the role of computers in design in general. The central question posed by the author is whether or not we can construct a theory of design. This book concentrates upon the relationship between design, mathematics and science and thus its audience must include designers and software designers as well as computer scientists.

First published in 1996, this collection of essays by distinguished computer scientists celebrates the achievements of research and speculates about the unsolved problems in computer science that require future investigation. Since the subject stretches from technology in the field, through engineering design to foundations in mathematics, there is a wide variety of concerns and approaches among the authors. The book's purpose is to show that long-term research in computer science is crucial and that it must not be driven solely by commercial considerations. The authors do not shirk the difficult aspects of their topics, but try to expose them in the simplest terms possible without diluting them, in order that the reader can understand the issues involved. Thus the book also represents a broad overview of much of the state of knowledge and future expectations of computer science, illustrating that it is much more than a technology and it is a fully fledged and growing intellectual disc

First published in 1996, this collection of essays by distinguished computer scientists celebrates the achievements of research and speculates about the unsolved problems in computer science that require future investigation. Since the subject stretches from technology in the field, through engineering design to foundations in mathematics, there is a wide variety of concerns and approaches among the authors. The book's purpose is to show that long-term research in computer science is crucial and that it must not be driven solely by commercial considerations. The authors do not shirk the difficult aspects of their topics, but try to expose them in the simplest terms possible without diluting them, in order that the reader can understand the issues involved. Thus the book also represents a broad overview of much of the state of knowledge and future expectations of computer science, illustrating that it is much more than a technology and it is a fully fledged and growing intellectual disc

Category theory has become increasingly important and popular in computer science, and many universities now have introductions to category theory as part of their courses for undergraduate computer scientists. The author is a respected category theorist and has based this textbook on a course given over the last few years at the University of Sydney. The theory is developed in a straightforward way, and is enriched with many examples from computer science. Thus this book meets the needs of undergradute computer scientists, and yet retains a level of mathematical correctness that will broaden its appeal to include students of mathematics new to category theory.

Category theory has become increasingly important and popular in computer science, and many universities now have introductions to category theory as part of their courses for undergraduate computer scientists. The author is a respected category theorist and has based this textbook on a course given over the last few years at the University of Sydney. The theory is developed in a straightforward way, and is enriched with many examples from computer science. Thus this book meets the needs of undergradute computer scientists, and yet retains a level of mathematical correctness that will broaden its appeal to include students of mathematics new to category theory.

The logic and methodology of design is examined in this book from the perspective of computer science. Computers provide the context for this examination both by discussion of the design process for hardware and software systems and by consideration of the role of computers in design in general. The central question posed by the author is whether or not we can construct a theory of design. This book concentrates upon the relationship between design, mathematics and science and thus its audience must include designers and software designers as well as computer scientists.

Using basic category theory, this Element describes all the central concepts and proves the main theorems of theoretical computer science. Category theory, which works with functions, processes, and structures, is uniquely qualified to present the fundamental results of theoretical computer science. In this Element, readers will meet some of the deepest ideas and theorems of modern computers and mathematics, such as Turing machines, unsolvable problems, the P=NP question, Kurt Gödel's incompleteness theorem, intractable problems, cryptographic protocols, Alan Turing's Halting problem, and much more. The concepts come alive with many examples and exercises.

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

This collection of papers presents a series of in-depth examinations of a variety of advanced topics related to Boolean functions and expressions. The chapters are written by some of the most prominent experts in their respective fields and cover topics ranging from algebra and propositional logic to learning theory, cryptography, computational complexity, electrical engineering, and reliability theory. Beyond the diversity of the questions raised and investigated in different chapters, a remarkable feature of the collection is the common thread created by the fundamental language, concepts, models, and tools provided by Boolean theory. Many readers will be surprised to discover the countless links between seemingly remote topics discussed in various chapters of the book. This text will help them draw on such connections to further their understanding of their own scientific discipline and to explore new avenues for research.

Concurrent Programming in ML focuses on the practical use of concurrency to implement naturally concurrent applications. In addition to a tutorial introduction to programming in Concurrent ML (CML), the book presents three extended examples using CML for practical systems programming: a parallel software build system, a simple concurrent window manager, and an implementation of distributed tuple spaces. CML, which is included as part of the SML of New Jersey (SML/NJ) distribution, combines the best features of concurrent programming and functional programming. This book also illustrates advanced SML programming techniques, and includes a chapter on the implementation of concurrency using features provided by the SML/NJ system. It will be of interest to programmers, students, and professional researchers working in computer language development.

Epistemic logic has grown from its philosophical beginnings to find diverse applications in computer science as a means of reasoning about the knowledge and belief of agents. This book, based on courses taught at universities and summer schools, provides a broad introduction to the subject; many exercises are included together with their solutions. The authors begin by presenting the necessary apparatus from mathematics and logic, including Kripke semantics and the well-known modal logics K, T, S4 and S5. Then they turn to applications in the contexts of distributed systems and artificial intelligence: topics that are addressed include the notions of common knowledge, distributed knowledge, explicit and implicit belief, the interplays between knowledge and time, and knowledge and action, as well as a graded (or numerical) variant of the epistemic operators. The problem of logical omniscience is also discussed extensively. Halpern and Moses' theory of honest formulae is covered, and a d

Concurrent Programming in ML focuses on the practical use of concurrency to implement naturally concurrent applications. In addition to a tutorial introduction to programming in Concurrent ML (CML), the book presents three extended examples using CML for practical systems programming: a parallel software build system, a simple concurrent window manager, and an implementation of distributed tuple spaces. CML, which is included as part of the SML of New Jersey (SML/NJ) distribution, combines the best features of concurrent programming and functional programming. This book also illustrates advanced SML programming techniques, and includes a chapter on the implementation of concurrency using features provided by the SML/NJ system. It will be of interest to programmers, students, and professional researchers working in computer language development.

Game theory is the science of interaction. This textbook, derived from courses taught by the author and developed over several years, is a comprehensive, straightforward introduction to the mathematics of non-cooperative games. It teaches what every game theorist should know: the important ideas and results on strategies, game trees, utility theory, imperfect information, and Nash equilibrium. The proofs of these results, in particular existence of an equilibrium via fixed points, and an elegant direct proof of the minimax theorem for zero-sum games, are presented in a self-contained, accessible way. This is complemented by chapters on combinatorial games like Go; and, it has introductions to algorithmic game theory, traffic games, and the geometry of two-player games. This detailed and lively text requires minimal mathematical background and includes many examples, exercises, and pictures. It is suitable for self-study or introductory courses in mathematics, computer science, or econo

Game theory is the science of interaction. This textbook, derived from courses taught by the author and developed over several years, is a comprehensive, straightforward introduction to the mathematics of non-cooperative games. It teaches what every game theorist should know: the important ideas and results on strategies, game trees, utility theory, imperfect information, and Nash equilibrium. The proofs of these results, in particular existence of an equilibrium via fixed points, and an elegant direct proof of the minimax theorem for zero-sum games, are presented in a self-contained, accessible way. This is complemented by chapters on combinatorial games like Go; and, it has introductions to algorithmic game theory, traffic games, and the geometry of two-player games. This detailed and lively text requires minimal mathematical background and includes many examples, exercises, and pictures. It is suitable for self-study or introductory courses in mathematics, computer science, or econo

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

Essentials of Programming in Mathematica® provides an introduction suitable for readers with little or no background in the language as well as for those with some experience using programs such as C, Java, or Perl. The author, an established authority on Mathematica® programming, has written an example-driven text that covers the language from first principles, as well as including material from natural language processing, bioinformatics, graphs and networks, signal analysis, geometry, computer science, and many other applied areas. The book is appropriate for self-study or as a text for a course in programming in computational science. Readers will benefit from the author's tips, which provide insight and suggestions on small and large points. He also provides more than 350 exercises from novice through to advanced level with all of the solutions available online.

Games provide mathematical models for interaction. Numerous tasks in computer science can be formulated in game-theoretic terms. This fresh and intuitive way of thinking through complex issues reveals underlying algorithmic questions and clarifies the relationships between different domains. This collection of lectures, by specialists in the field, provides an excellent introduction to various aspects of game theory relevant for applications in computer science that concern program design, synthesis, verification, testing and design of multi-agent or distributed systems. Originally devised for a Spring School organised by the GAMES Networking Programme in 2009, these lectures have since been revised and expanded, and range from tutorials concerning fundamental notions and methods to more advanced presentations of current research topics. This volume is a valuable guide to current research on game-based methods in computer science for undergraduate and graduate students. It will also in

In our day-to-day lives we constantly make decisions which are simply 'good enough' rather than optimal. Most computer-based decision-making algorithms, on the other hand, doggedly seek only the optimal solution based on rigid criteria and reject any others. In this book, Professor Stirling outlines an alternative approach, using novel algorithms and techniques which can be used to find satisficing solutions. Building on traditional decision and game theory, these techniques allow decision-making systems to cope with more subtle situations where self and group interests conflict, perfect solutions can't be found and human issues need to be taken into account - in short, more closely modelling the way humans make decisions. The book will therefore be of great interest to engineers, computer scientists and mathematicians working on artificial intelligence and expert systems.

In our day-to-day lives we constantly make decisions which are simply 'good enough' rather than optimal. Most computer-based decision-making algorithms, on the other hand, doggedly seek only the optimal solution based on rigid criteria and reject any others. In this book, Professor Stirling outlines an alternative approach, using novel algorithms and techniques which can be used to find satisficing solutions. Building on traditional decision and game theory, these techniques allow decision-making systems to cope with more subtle situations where self and group interests conflict, perfect solutions can't be found and human issues need to be taken into account - in short, more closely modelling the way humans make decisions. The book will therefore be of great interest to engineers, computer scientists and mathematicians working on artificial intelligence and expert systems.

Basics of Holography is a general introduction to the subject written by a leading worker in the field. It begins with the theory of holographic imaging, the characteristics of the reconstructed image, and the various types of holograms. Practical aspects of holography are then described, including light sources, the characteristics of recording media and recording materials, as well as methods for producing different types of holograms and computer-generated holograms. Finally, important applications of holography are discussed, such as high-resolution imaging, holographic optical elements, information processing, and holographic interferometry. The book includes comprehensive reference sections and appendices summarizing some useful mathematical results. Numerical problems with their solutions are provided at the ends of chapters. This is an invaluable resource for advanced undergraduate and graduate students as well as researchers in science and engineering who would like to learn m

Basics of Holography is a general introduction to the subject written by a leading worker in the field. It begins with the theory of holographic imaging, the characteristics of the reconstructed image, and the various types of holograms. Practical aspects of holography are then described, including light sources, the characteristics of recording media and recording materials, as well as methods for producing different types of holograms and computer-generated holograms. Finally, important applications of holography are discussed, such as high-resolution imaging, holographic optical elements, information processing, and holographic interferometry. The book includes comprehensive reference sections and appendices summarizing some useful mathematical results. Numerical problems with their solutions are provided at the ends of chapters. This is an invaluable resource for advanced undergraduate and graduate students as well as researchers in science and engineering who would like to learn m

Computer algebra systems have revolutionized the use of computers within mathematics research, and are currently extending that revolution to the undergraduate mathematics curriculum. But the power of such systems goes beyond simple algebraic or numerical manipulation. This book shows how computer-aided mathematics has reached a level where it can support effectively many of the computations in science and engineering. In addition to treating traditional computer science topics, an introductory course should show scientists and engineers how these computer-based tools can be used to do scientific computations. A valuable text for computer science courses for scientists and engineers, this book should also prove useful to Mathematica users at all levels. Covering the latest release of Mathematica, the book includes useful tips and techniques to help even seasoned users.

This textbook is designed as a first book on concurrent programming for computer science undergraduates, and provides a comprehensive introduction to the problems of concurrency. Concurrency is of vital importance in many areas of computer science, particularly in operating systems. It is also increasingly being taught in undergraduate courses. The book builds on the student's familiarity with sequential programming in a high level language, which will make it very accessible to computer science students. The book is concerned mainly with the high level aspects of concurrency, which will be equally applicable to traditional time sliced or more recent truly parallel systems.

Games Programming has been written for the personal computer owner who is setting out to write fams programs. It charts the territory which lies beyond the simplistic arcade game and which leads to the more intellectually exciting game moderators and learning programs. This is not another book of program listings, nor does it present game algorithms in isolation from the computer methods which make them work. The aim is to equip the reader with an awareness of modern techniques of structured programming, a robust attitude to computer languages, and a source of valuable 'know how'.

Computing is an exact science and the systematic study of any aspect necessarily involves the use of mathematical models. Moreover, the rate at which the subject is evolving demands a facility for developing new mathematical systems to keep pace with new computing systems and this requires an appreciation of how mathematics works. An understanding of the underlying mathematical structure facilitates the construction of suitable computer programs to perform computations. Assuming no specific knowledge of mathematics, the authors describe all the basic concepts required and progress from sets (rather than numbers) through a variety of algebraic structures that permit the precise description, specification and subsequent analysis of many problems in computing. The material included provides the essential mathematical foundations for core topics of computer science and extends into the areas of language theory, abstract machine theory and computer geometry. Computer Mathematics will be of

Computer scientists often need to learn new programming languages quickly. The best way to prepare for this is to understand the foundational principles that underlie even the most complicated industrial languages. This text for an undergraduate programming languages course distills great languages and their design principles down to easy-to-learn 'bridge' languages implemented by interpreters whose key parts are explained in the text. The book goes deep into the roots of both functional and object-oriented programming, and it shows how types and modules, including generics/polymorphism, contribute to effective programming. The book is not just about programming languages; it is also about programming. Through concepts, examples, and more than 300 practice exercises that exploit the interpreter, students learn not only what programming-language features are but also how to do things with them. Substantial implementation projects include Milner's type inference, both copying and mark-an

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes wit

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-p

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

This textbook provides a comprehensive overview of the human-computer interface in clear, non-technical language, making it an ideal introduction for students of both psychology and computer science. Covering the past, present, and future developments in technology and psychology, it combines cutting-edge academic research with engaging illustrations and examples that show students how the material relates to their lives. Topics addressed include: human factors of input devices, and the basics of sensation and perception; memory and cognitive issues of users navigating their way through interfaces; communication via programming languages and natural speech interaction; cyberpathologies such as techno-stress and Internet addiction disorders; and challenges surrounding automation and artificial intelligence. This thoroughly updated second edition features new chapters on virtual reality and cybersecurity; expanded coverage of social media, mobile computing, e-learning, and video games; a

This textbook provides a comprehensive overview of the human-computer interface in clear, non-technical language, making it an ideal introduction for students of both psychology and computer science. Covering the past, present, and future developments in technology and psychology, it combines cutting-edge academic research with engaging illustrations and examples that show students how the material relates to their lives. Topics addressed include: human factors of input devices, and the basics of sensation and perception; memory and cognitive issues of users navigating their way through interfaces; communication via programming languages and natural speech interaction; cyberpathologies such as techno-stress and Internet addiction disorders; and challenges surrounding automation and artificial intelligence. This thoroughly updated second edition features new chapters on virtual reality and cybersecurity; expanded coverage of social media, mobile computing, e-learning, and video games; a

Since their inception, the Perspectives in Logic and Lecture Notes in Logic series have published seminal works by leading logicians. Many of the original books in the series have been unavailable for years, but they are now in print once again. This volume, the sixth publication in the Lecture Notes in Logic series, collects the proceedings of the conference 'Logical Foundations of Mathematics, Computer Science, and Physics - Kurt Gödel's Legacy', held in Brno, Czech Republic, on the 90th anniversary of Gödel's birth. The broad range of speakers who participated in this event affirms the continuing importance of Gödel's work in logic, physics, and the philosophy and foundations of mathematics and computer science. The papers in this volume range over all these topics and contribute to our present understanding of them.