This new edition provides step-by-step instruction on modern 3D graphics shader programming in OpenGL with Java, along with its theoretical foundations. It is appropriate both for computer science graphics courses, and for professionals interested in mastering 3D graphics skills. It has been designed in a 4-color, "teach-yourself" format with numerous examples that the reader can run just as presented. Every shader stage is explored, from the basics of modeling, textures, lighting, shadows, etc., through advanced techniques such as tessellation, normal mapping, noise maps, as well as new chapters on simulating water, stereoscopy, and ray tracing. FEATURES Covers modern OpenGL 4.0+ shader programming in Java, with instructions for both PC/Windows and Macintosh Illustrates every technique with running code examples. Everything needed to install the libraries, and complete source code for each example Includes step-by-step instruction for using each GLSL programmable pipeline stage (vertex, tessellation, geometry, and fragment) Explores practical examples for modeling, lighting and shadows (including soft shadows), terrain, water, and 3D materials such as wood and marble Adds new chapters on simulating water, stereoscopy, and ray tracing with compute shaders Explains how to optimize code with tools such as Nvidia's Nsight debugger Includes companion files with code, object models, figures, and more

This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions-Chebyshev, Legendre, Gegenbauer, and Jacobi-are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.

Visual languages have long been lit pursuitofeffective communication 00 tween human and machine. Today, they are suecessfully employed for e: nd user progmmming, modeliog, rapid prototypmg, and design activities by people ofmany disciplines including arehitects, artists, children, engi neers, and scientists. Furthermore. with rapid advances ofthe Internet and Web technology, human human communication through the Web or eleo tronie mobile deviees is becoming more and moreprevalent This manuscript provides a comprehensive introduetion to diagmmmatiooI visual programming languages and the technologyofautomatie genemtion ofsnch languages. It covers a broad rangeofcontents from the underlying theoryofgraph grammars to the applications in various domains. Thecon tents were ex: l: l: aeted from the papers that my Ph. D. students and I have published in the last 10 years. and are updated and organized in a coherent fashion. The manuseript gives an in. -depth treatmentof all the topic areas. Pointers to related work and further readings are also faeilitated at the end ofeverychapterexeeptChapter 9. Rather than describing how to program visually, the manuscript discusses what are visual programming languages, and how sooh languages and their underlying foundations can be usefully applied to other fields incomputer science that need graphs as the p: rimary meansofrepresentation. Assuming the basic knowledge of computer programming and compiler co: nstruetion, the manuscript can be used as a textbook for senior orgradu ate computer science classes on visual languages, or a reference book for programming language classes, practitioners, and researchers inthe related field. The manuscript cannot be completed without the helps of many people.

Compiler technology is fundamental to computer science since it provides the means to implement many other tools. It is interesting that, in fact, many tools have a compiler framework - they accept input in a particular format, perform some processing and present output in another format. Such tools support the abstraction process and are crucial to productive systems development. The focus of Compiler Technology: Tools, Translators and Language Implementation is to enable quick development of analysis tools. Both lexical scanner and parser generator tools are provided as supplements to this book, since a hands-on approach to experimentation with a toy implementation aids in understanding abstract topics such as parse-trees and parse conflicts. Furthermore, it is through hands-on exercises that one discovers the particular intricacies of language implementation. Compiler Technology: Tools, Translators and Language Implementation is suitable as a textbook for an undergraduate or graduate level course on compiler technology, and as a reference for researchers and practitioners interested in compilers and language implementation.

Explains the basic concepts of Python and its role in machine learning Provides comprehensive coverage of feature-engineering including real-time case studies Perceive the structural patterns with reference to data science and statistics and analytics Includes machine learning based structured exercises Appreciates different algorithmic concepts of machine learning including unsupervised, supervised and reinforcement learning

The formal study of program behavior has become an essential ingredient in guiding the design of new computer architectures. Accurate characterization of applications leads to efficient design of high performing architectures. Quantitative and analytical characterization of workloads is important to understand and exploit the interesting features of workloads. This book includes ten chapters on various aspects of workload characterizati on. File caching characteristics of the industry-standard web-serving benchmark SPECweb99 are presented by Keller et al. in Chapter 1, while value locality of SPECJVM98 benchmarks are characterized by Rychlik et al. in Chapter 2. SPECJVM98 benchmarks are visited again in Chapter 3, where Tao et al. study the operating system activity in Java programs. In Chapter 4, KleinOsowski et al. describe how the SPEC2000 CPU benchmark suite may be adapted for computer architecture research and present the small, representative input data sets they created to reduce simulation time without compromising on accuracy. Their research has been recognized by the Standard Performance Evaluation Corporation (SPEC) and is listed on the official SPEC website, http: //www. spec. org/osg/cpu2000/research/umnl. The main contribution of Chapter 5 is the proposal of a new measure called locality surface to characterize locality of reference in programs. Sorenson et al. describe how a three-dimensional surface can be used to represent both of programs. In Chapter 6, Thornock et al

This is the book for Gophers who want to learn how to build distributed systems. You know the basics of Go and are eager to put your knowledge to work. Build distributed services that are highly available, resilient, and scalable. This book is just what you need to apply Go to real-world situations. Level up your engineering skills today. Take your Go skills to the next level by learning how to design, develop, and deploy a distributed service. Start from the bare essentials of storage handling, then work your way through networking a client and server, and finally to distributing server instances, deployment, and testing. All this will make coding in your day job or side projects easier, faster, and more fun. Create your own distributed services and contribute to open source projects. Build networked, secure clients and servers with gRPC. Gain insights into your systems and debug issues with observable services instrumented with metrics, logs, and traces. Operate your own Certificate Authority to authenticate internal web services with TLS. Automatically handle when nodes are added or removed to your cluster with service discovery. Coordinate distributed systems with replicated state machines powered by the Raft consensus algorithm. Lay out your applications and libraries to be modular and easy to maintain. Write CLIs to configure and run your applications. Run your distributed system locally and deploy to the cloud with Kubernetes. Test and benchmark your applications to ensure they're correct and fast. Dive into writing Go and join the hundreds of thousands who are using it to build software for the real world. What You Need: Go 1.13+ and Kubernetes 1.16+

The Forum on Design Languages (FDL) is the European Forum to exchange experiences and learn new trends, in the application of languages and the associated design methods and tools, to design complex electronic systems. By offering several co-located workshops, this multi-faceted event gives an excellent opportunity to gain up-to-date knowledge across main aspects of such a wide field. All the workshops address as their common denominator the different application domains of system-design languages with the presentation of the latest research results and design experiences.
FDL'02 was organized as four focused workshops, Languages for Analog and Mixed-Signal system design, UML-based system specification and design, C/C++-based system design and Specification Formalisms for Proven design.

FDL served once more as the European Forum for electronic system design languages and consolidates as the main place in Europe where designers interested in design languages and their applications can meet and interchange experiences.

In this fourth book in the CHDL Series, a selection of the best papers presented in FDL'02 is published. System Specification and Design Languages contains outstanding research contributions in the four areas mentioned above. So, The Analog and Mixed-Signal system design contributions cover the new methodological approaches like AMS behavioral specification, mixed-signal modeling and simulation, AMS reuse and MEMs design using the new modeling languages such as VHDL-AMS, Verilog-AMS, Modelica and analog-mixed signal extensions to SystemC.

UML is the de-facto standard for SW development covering the early development stages of requirement analysis and system specification. The UML-based system specification and design contributions address latest results on hot-topic areas such as system profiling, performance analysis and UML application to complex, HW/SW embedded systems and SoC design.C/C++-for HW/SW systems design is entering standard industrial design flows. Selected papers cover system modeling, system verification and SW generation.

The papers from the Specification Formalisms for Proven design workshop present formal methods for system modeling and design, semantic integrity and formal languages such as ALPHA, HANDLE and B.

What's this AOP thing anyway, really-when you get right down to it-and can someone please explain what an aspect actually is?
Aspect-Oriented Programming with the e Verification Language takes a pragmatic, example based, and fun approach to unraveling the mysteries of AOP. In this book, you'll learn how to:
- Use AOP to organize your code in a way that makes it easy to deal with the things you really care about in your verification environments. Forget about organizing by classes, and start organizing by functionality, layers, components, protocols, functional coverage, checking, or anything that you decide is important to you
- Easily create flexible code that eases your development burden, and gives your users the power to quickly do what they need to do with your code
- Truly create a plug-and-play environment that allows you to add and remove functionality without modifying your code. Examples include how to use AOP to create pluggable debug modules, and a pluggable module that lets you check that your testbench is still working before you begin a regression
- Utilize AOP to sidestep those productivity roadblocks that seem to plague all projects at the most inconvenient of times
- Discover why "return" is evil, and some other "gotchas" with the AOP features of e
All of the methodologies, tips, and techniques described in this book have been developed and tested on real projects, with real people, real schedules and all of the associated problems that come with these. Only the ones that worked, and worked well, have made it in, so by following the advice given in this book, you'll gain access to the true power of AOP while neatly avoiding the effort of workingit all out yourself.
- Use AOP to organize your code in a way that makes it easy to deal with the things you really care about in your verification environments. Forget about organizing by classes, and start organizing by functionality, layers, components, protocols, functional coverage, checking, or anything that you decide is important to you
- Easily create flexible code that eases your development burden, and gives your users the power to quickly do what they need to do with your code
- Truly create a plug-and-play environment that allows you to add and remove functionality without modifying your code. Examples include how to use AOP to create pluggable debug modules, and a pluggable module that lets you check that your testbench is still working before you begin a regression
- Utilize AOP to sidestep those productivity roadblocks that seem to plague all projects at the most inconvenient of times
- Discover why "return" is evil, and some other "gotchas" with the AOP features of e

VHDL Answers to Frequently Asked Questions, Second Edition is a follow up to the author's books VHDL Answers to Frequently Asked Questions (ISBN 0-7923-9791-6) and VHDL Coding Styles and Methodologies (ISBN 0-7923-9598-0). This book addresses: misinterpretations in the use of the language; methods for writing error-free, and simulation-efficient, code for testbench designs and for synthesis; and general principles and guidelines for design verification. This second edition includes the following additions to the first edition: a new chapter on design for reuse that defines coding and design techniques that are impermeable to new technologies and are malleable to new requirements; more questions and answers including discussions on applications of guarded signals and shared variables; more models including the design of a reusable priority encoder, and a switch; more packages including an enhancement of image package to convert values to text strings in binary, hexadecimal, and decimal formats, and the complex package that defines complex numbers and overloaded operators.The book differs from other VHDL books in many respects. This bookemphasizes real VHDL, rather than philosophical or introductory types of informationemphasizes application of VHDL for synthesisuses complete examples to demonstrate problems and solutionsprovides a disk that includes all the book examples and other useful VHDL reference materialuses easy to remember symbology notation to emphasize language rules, good and poor methodology and coding stylesidentifies obsolete VHDL constructs that must be avoidedidentifies synthesizable/non-synthesizable structuresuses a question and answer format to clarify and emphasizethe concerns of VHDL users.VHDL Answers to Frequently Asked Questions, Second Edition is intended for those who are seeking an enhanced proficiency in VHDL.

Parts 1-4 of Robert Sedgewick's work provide extensive coverage of fundamental data structures and algorithms for sorting, searching, and related applications. They reflect the third edition's greater emphasis on abstract data types (ADTs). Coverage includes more than 100 key algorithms for sorting, selection, priority queue ADT implementations, and symbol table ADT (searching) implementations. Also included are new implementations of binomial queues, multiway radix sorting, Batcher's sorting networks, randomized BSTs, splay trees, skip lists, and multiway tries. Increased quantitative information gives students a more solid basis for comparing algorithms, and hundreds of new exercises reinforce their learning. Algorithms and data structures described in the book are expressed in concise implementations in C, so that students can both appreciate their fundamental properties and test them on real applications.

"Beautiful C++ presents the C++ Core Guidelines from a developer's point of view with an emphasis on what benefits can be obtained from following the rules and what nightmares can result from ignoring them. For true geeks, it is an easy and entertaining read. For most software developers, it offers something new and useful." --Bjarne Stroustrup, inventor of C++ and co-editor of the C++ Core Guidelines

Writing great C++ code needn't be difficult. The C++ Core Guidelines can help every C++ developer design and write C++ programs that are exceptionally reliable, efficient, and well-performing. But the Guidelines are so jam-packed with excellent advice that it's hard to know where to start. Start here, with Beautiful C++.

Expert C++ programmers Guy Davidson and Kate Gregory identify 30 Core Guidelines you'll find especially valuable and offer detailed practical knowledge for improving your C++ style. For easy reference, this book is structured to align closely with the official C++ Core Guidelines website.

Throughout, Davidson and Gregory offer useful conceptual insights and expert sample code, illuminate proven ways to use both new and longstanding language features more successfully, and show how to write programs that are more robust and performant by default.

This reference is intended for experienced practitioners, consultants and students working on building practical applications. It discusses the most widely-used programming languages and their fuctional pros and cons for application and development. The author provides: a brief overview of programming languages principles and concepts; numerous diagrams, charts and sample programs; coverage of object-oriented programming and visual programming; and tables rating languages on such subjects as simplicity, data structuring, portability and efficiency.

While compilers for high-level programming languages are large complex software systems, they have particular characteristics that differentiate them from other software systems. Their functionality is almost completely well-defined - ideally there exist complete precise descriptions of the source and target languages, while additional descriptions of the interfaces to the operating system, programming system and programming environment, and to other compilers and libraries are often available. The implementation of application systems directly in machine language is both difficult and error-prone, leading to programs that become obsolete as quickly as the computers for which they were developed. With the development of higher-level machine-independent programming languages came the need to offer compilers that were able to translate programs into machine language. Given this basic challenge, the different subtasks of compilation have been the subject of intensive research since the 1950s.

This book is not intended to be a cookbook for compilers, instead the authors' presentation reflects the special characteristics of compiler design, especially the existence of precise specifications of the subtasks. They invest effort to understand these precisely and to provide adequate concepts for their systematic treatment. This is the first book in a multivolume set, and here the authors describe what a compiler does, i.e., what correspondence it establishes between a source and a target program. To achieve this the authors specify a suitable virtual machine (abstract machine) and exactly describe the compilation of programs of each source language into the language of the associated virtual machine for an imperative, functional, logic and object-oriented programming language.

This book is intended for students of computer science. Knowledge of at least one imperative programming language is assumed, while for the chapters on the translation of functional and logic programming languages it would be helpful to know a modern functional language and Prolog. The book is supported throughout with examples, exercises and program fragments.

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Blake Brown eased his car out of the parking lot and onto the highway. Unseen, the idling evil slowly left its resting place to follow discreetly behind him. Blake rounded corners and went through lights and the black, metallic beast stayed on his tail. As Blake finally turned into his apartment complex, his shadow turned with him. He never noticed the sleek beast behind him as he pulled into his parking spot and turned off the ignition. The evil trailing him inched to a stop on the other side of the parking lot. Blake turned to walk across to his building. Just as he started across the lot, the black car started again. It honed in on him. It moved gracefully through the lot, smelling blood in the water. As it got close to Blake, it slowed, menacingly creeping toward him. The window lowered silently. Blake, sensing its presence, turned and his eyes lit with a hint of recognition.

'Squ?" was all he got out. Before he could finish the sentence, three shots quietly erupted from the silencer- equipped gun inside the black car. Blake Brown staggered backward. He was hit three times in the chest. The deal was off."

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience-from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).

An introduction to advanced C++, specialized for scientific programmers

C++ Scientific Programming teaches the design of programs for scientific computation in C++. It introduces unique C++ classes for vectors, dense matrices, and sparse matrices, and defines the particular relationships among these classes. It then demonstrates how to use these fundamental classes in a dozen of the most powerful current applications.

The author limits the scope of the book to features that are specifically useful to scientific programmers. He presents a set of practices that allows programmers to embrace the attractive features of C++ without incurring undesired side effects and hidden costs. He illustrates these practices by implementing major contemporary numerical methods and providing examples for execution with a C++ compiler. A collection of source code files correlated with the content of the book can be downloaded from the Wiley ftp site.

C++ Scientific Programming shows how C++ improves on both FORTRAN and C and affords greater convenience and efficiency in coding mathematics. It serves as a vital companion to more general C++ texts and treats the unique needs of scientific programmers.

For courses in introductory C# programming. Motivate students with clear, down-to-earth explanations and familiar graphical elements Starting Out With Visual C# is an ideal introductory Visual C# text for students with no prior programming experience. Students who are new to programming will appreciate the clear, down-to-earth explanations and the detailed walk-throughs that are provided by the hands-on tutorials. Gaddis's hallmark, step-by-step instructions are supported by a GUI-based approach that motivates students as they learn to create GUI-based, event-driven, Visual C# applications. Topics are examined progressively in each chapter, with objects taught before classes. The 5th Edition adds an abundance of new material and improvements with updates for compatibility with Visual Studio 2017. Two new chapters include Chapter 13: Delegates and Lambda Expressions and Chapter 14: Language-Integrated Query (LINQ).

Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon's Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.

Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the "data-aptitude" built by the material in this book.

Electronic Chips & Systems Design Languagesoutlines and describes the latest advances in design languages. The challenge of System on a Chip (SOC) design requires designers to work in a multi-lingual environment which is becoming increasingly difficult to master. It is therefore crucial for them to learn, almost in real time, from the experiences of their colleagues in the use of design languages and how these languages have become more advanced to cope with system design. System designers, as well as students willing to become system designers, often do not have the time to attend all scientific events where they could learn the necessary information. This book will bring them a selected digest of the best contributions and industry strength case studies. All the levels of abstraction that are relevant, from the informal user requirements down to the implementation specifications, are addressed by different contributors. The author, together with colleague authors who provide valuable additional experience, presents examples of actual industrial world applications. Furthermore the academic concepts presented in this book provide excellent theories to student readers and the concepts described are up to date and in so doing provide most suitable root information for Ph.D. postgraduates.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

If you're an experienced programmer, you already have a rock-solid foundation for learning Java. All you need is a resource that takes your experience into account and explains Java's key principles and techniques in an intelligent, efficient way.
Java: Practical Guide for Programmers is precisely that resource. Here, you won't have to wade through hundreds of pages of overly simplistic material to learn the basics of Java programming. Instead, you get highly focused instruction in the core elements of Java 1.4, accompanied by carefully chosen examples and line-by-line analyses that are right to the point. You'll be astonished at how soon you can begin productive coding in Java, and how quickly your skills will progress.
Web resources:
Source code from the book is no longer available at
http: //www.zm.sikora.btinternet.co.uk/source.html
Please press the 'Companion Page' button on the upper right side of this page to access a zip file containing it.
Sun J2SE download page
http: //java.sun.com/j2se/downloads.html
Sun J2SE API documentation
http: //java.sun.com/j2se/1.4.1/docs/index.html
* Written expressly for people who already know a procedural or object-oriented programming language.
* Takes a concise approach designed to make the most of the experience you already have.
* Covers the core elements of Java 1.4, including language syntax, OO features, collections, exception handling, input/output, threads, event handling, and Swing components.
* Filled with incisive coding examples and line-by-line analyses.
* Via a companion Web site, provides downloadable example code and links to additional resources.