Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds. You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.

Microservices architecture (MSA) is increasingly popular with software architects and engineers as it accelerates software solution design, development, and deployment in a risk-free manner. Placing a software system into a production environment is elegantly simplified and sped up with the use of MSA development platforms, runtime environments, acceleration engines, design patterns, integrated frameworks, and related tools. The MSA ecosystem is expanding with third-party products that automate as many tasks as possible. MSA is being positioned as the enterprise-grade and agile-application design method. This book covers in-depth the features and facilities that make up the MSA ecosystem. Beginning with an overview of Service-Oriented Architecture (SOA) that covers the Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), and Remote Method Invocation (RMI), the book explains the basic essentials of MSA and the continuous delivery of applications to customers. The book gives software developers insight into: Current and emerging communication models Key architectural elements of MSA-based applications Designing efficient APIs for microservices MSA middleware platforms such as REST, SOAP, Apache Thrift, and gRPC Microservice discovery and the API gateway Service orchestration and choreography for composing individual services to achieve a useful business process Database transactions in MSA-centric applications Design, composition, security, and deployment patterns MSA security Modernizing legacy applications The book concludes with a chapter on composing and building powerful microservices. With the exponential growth of IoT devices, microservices are being developed and deployed on resource-constrained but resource-intensive devices in order to provide people-centric applications. The book discusses the challenges of these applications. Finally, the book looks at the role of microservices in smart environments and upcoming trends including ubiquitous yet disappearing microservices.

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.

Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts introduces advanced software engineering to software engineers, scientists, postdoctoral researchers, academicians, software consultants, management executives, doctoral students, and advanced level postgraduate computer science students. This book contains research articles addressing numerous software engineering research challenges associated with various software development-related activities, including programming, testing, measurements, human factors (social software engineering), specification, quality, program analysis, software project management, and more. It provides relevant theoretical frameworks, empirical research findings, and evaluated solutions addressing the research challenges associated with the above-mentioned software engineering activities. To foster collaboration among the software engineering research community, this book also reports datasets acquired systematically through scientific methods and related to various software engineering aspects that are valuable to the research community. These datasets will allow other researchers to use them in their research, thus improving the quality of overall research. The knowledge disseminated by the research studies contained in the book will hopefully motivate other researchers to further innovation in the way software development happens in real practice.

This book meant for those who seek to apply evolutionary algorithms to problems in engineering and science. To this end, it provides the theoretical background necessary to the understanding of the presented evolutionary algorithms and their shortcomings, while also discussing themes that are pivotal to the successful application of evolutionary algorithms to real-world problems. The theoretical descriptions are illustrated with didactical Python implementations of the algorithms, which not only allow readers to consolidate their understanding, but also provide a sound starting point for those intending to apply evolutionary algorithms to optimization problems in their working fields. Python has been chosen due to its widespread adoption in the Artificial Intelligence community. Those familiar with high level languages such as MATLAB (TM) will not have any difficulty in reading the Python implementations of the evolutionary algorithms provided in the book. Instead of attempting to encompass most of the existing evolutionary algorithms, past and present, the book focuses on those algorithms that researchers have recently applied to difficult optimization problems, such as control problems with continuous action spaces and the training of high-dimensional convolutional neural-networks. The basic characteristics of real-world optimization problems are presented, together with recommendations on its proper application to evolutionary algorithms. The applied nature of the book is reinforced by the presentation of successful cases of the application of evolutionary algorithms to optimization problems. This is complemented by Python source codes, giving users an insight into the idiosyncrasies of the practical application of evolutionary algorithms.

"R and Data Mining "introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.

Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.

With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, "R and Data Mining" is a valuable, practical guide to a powerful method of analysis.
Presents an introduction into using R for data mining applications, covering most popular data mining techniquesProvides code examples and data so that readers can easily learn the techniquesFeatures case studies in real-world applicationsto help readers apply the techniques in their work"

This is the first book to explain Ripple-Down Rules, an approach to building knowledge-based systems which is more similar to machine learning methods than other rule-based systems but which depends on using an expert rather than applying statistics to data The book provides detailed worked examples and uses publicly available software to demonstrate Ripple-Down Rules The examples enable users to build their own RDR tools

This is the first book to explain Ripple-Down Rules, an approach to building knowledge-based systems which is more similar to machine learning methods than other rule-based systems but which depends on using an expert rather than applying statistics to data The book provides detailed worked examples and uses publicly available software to demonstrate Ripple-Down Rules The examples enable users to build their own RDR tools

This book provides readers with a single-source reference to static-single assignment (SSA)-based compiler design. It is the first (and up to now only) book that covers in a deep and comprehensive way how an optimizing compiler can be designed using the SSA form. After introducing vanilla SSA and its main properties, the authors describe several compiler analyses and optimizations under this form. They illustrate how compiler design can be made simpler and more efficient, thanks to the SSA form. This book also serves as a valuable text/reference for lecturers, making the teaching of compilers simpler and more effective. Coverage also includes advanced topics, such as code generation, aliasing, predication and more, making this book a valuable reference for advanced students and practicing engineers.

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

This book contains fundamental concepts on discrete mathematical structures in an easy to understand style so that the reader can grasp the contents and explanation easily. The concepts of discrete mathematical structures have application to computer science, engineering and information technology including in coding techniques, switching circuits, pointers and linked allocation, error corrections, as well as in data networking, Chemistry, Biology and many other scientific areas. The book is for undergraduate and graduate levels learners and educators associated with various courses and progammes in Mathematics, Computer Science, Engineering and Information Technology. The book should serve as a text and reference guide to many undergraduate and graduate programmes offered by many institutions including colleges and universities. Readers will find solved examples and end of chapter exercises to enhance reader comprehension. Features Offers comprehensive coverage of basic ideas of Logic, Mathematical Induction, Graph Theory, Algebraic Structures and Lattices and Boolean Algebra Provides end of chapter solved examples and practice problems Delivers materials on valid arguments and rules of inference with illustrations Focuses on algebraic structures to enable the reader to work with discrete structures

Originally published in 1966. Professor Rescher's aim is to develop a "logic of commands" in exactly the same general way which standard logic has already developed a "logic of truth-functional statement compounds" or a "logic of quantifiers". The object is to present a tolerably accurate and precise account of the logically relevant facets of a command, to study the nature of "inference" in reasonings involving commands, and above all to establish a viable concept of validity in command inference, so that the logical relationships among commands can be studied with something of the rigour to which one is accustomed in other branches of logic.

Explaining cryptography and its mathematical issues in terms specifically designed to reach computer programmers, this book covers all that is needed to write professional level cryptographic code. An expanded and improved version of the very well received first edition, it includes approximately 100 pages of new material as well as numerous improvements in the original text. The CD-ROM features tools and programs for use with the text.

Newly updated with over 150 pages of material on the latest Tcl extensions, "Tcl/Tk: A Developer s Gu"ide is a unique practical tutorial for professional programmers and beginners alike. Starting with a clear picture of the basics, Tcl/Tk covers the variety of tools in this "Swiss army knife" of programming languages, giving you the ability to enhance your programs, extend your application's capabilities, and become a more effective programmer.

This updated edition covers all of the new features of version 8.6, including object-oriented programming and the creation of megawidgets, existing data structure implementations, themed widgets and virtual events. Extensive code snippets and online tutorials in various languages will give you a firm grasp on how to use the Tcl/Tk libraries and interpreters and, most importantly, on what constitutes an effective strategy for using Tcl/Tk.
Includes the latest features of Tcl/Tk 8.6Covers Tcl development tools, popular extensions, and packages to allow developers to solve real-world problems with Tcl/Tk immediatelyProvides straightforward explanations for beginners and offers tips, style guidelines, and debugging techniques for advanced usersCompanion website includes Tcl/Tk tutorials, applications, distributions, and more tools"

In any serious engineering discipline, it would be unthinkable to construct a large system without having a precise notion of what is to be built and without verifying how the system is expected to function. Software engineering is no different in this respect.

Formal methods involve the use of mathematical notation and calculus in software development; such methods are difficult to apply to large-scale systems with practical constraints (e.g., limited developer skills, time and budget restrictions, changing requirements). Here Liu claims that formal engineering methods may bridge this gap. He advocates the incorporation of mathematical notation into the software engineering process, thus substantially improving the rigor, comprehensibility and effectiveness of the methods commonly used in industry.

This book provides an introduction to the SOFL (Structured Object-Oriented Formal Language) method that was designed and industry-tested by the author. Written in a style suitable for lecture courses or for use by professionals, there are numerous exercises and a significant real-world case study, so the readers are provided with all the knowledge and examples needed to successfully apply the method in their own projects.

A unique approach to "Intro CS." In a nutshell, the authors of this book's objective is to provide an introduction to computer science as an intellectually rich and vibrant field rather than focusing exclusively on computer programming. While programming is certainly an important and pervasive element of their approach, they emphasize concepts and problem-solving over syntax and programming language features. This book is a companion to the course "CS for All" developed at Harvey Mudd College and subsequently adopted at a variety of colleges and universities. At Mudd, this course is taken by almost every first-year student - irrespective of the student's ultimate major - as part of the college's core curriculum. The offering is also taken by many students at the Claremont Colleges, including students majoring in the humanities, social sciences, and the arts. Thus, it serves as a first computing course for students regardless of their major. This book is intended to be used with the substantial resources that we have developed for the course. These resources include complete lecture slides, a rich collection of weekly assignments, some accompanying software, documentation, and papers that have been published about the course. The authors have deliberately kept this book relatively short and have endeavored to make it fun and readable. The content of this book is an accurate reflection of the content of the course rather than an intimidating encyclopedic tome that can't possibly be covered in a single semester. The book has been written in the belief that a student can read all of it comfortably as the course proceeds.

Do modern programming languages, IDEs, and libraries make coding easy? Maybe, but coding is not design. Large-scale or expensive apps clearly require evaluation of design choices. Still, software design directly impacts code reuse and longevity even for small-scale apps with limited overhead. This text evaluates and contrasts common object-oriented designs. A given problem may have many solutions. A developer may employ different design techniques - composition, inheritance, dependency injection, delegation, etc. - to solve a particular problem. A skilled developer can determine the costs and benefits of different design responses, even amid competing concerns. A responsible developer documents design choices as a contract with the client, delineating external and internal responsibilities. To promote effective software design, this book examines contractual, object-oriented designs for immediate and sustained use as well as code reuse. The intent of identifying design variants is to recognize and manage conflicting goals such as short versus long-term utility, stability versus flexibility, and storage versus computation. Many examples are given to evaluate and contrast different solutions and to compare C# and C++ effects. No one has a crystal ball; however, deliberate design promotes software longevity. With the prominence of legacy OO code, a clear understanding of different object-oriented designs is essential. Design questions abound. Is code reuse better with inheritance or composition? Should composition rely on complete encapsulation? Design choices impact flexibility, efficiency, stability, longevity, and reuse, yet compilers do not enforce design and syntax does not necessarily illustrate design. Through deliberate design, or redesign when refactoring, developers construct sustainable, efficient code.

Do modern programming languages, IDEs, and libraries make coding easy? Maybe, but coding is not design. Large-scale or expensive apps clearly require evaluation of design choices. Still, software design directly impacts code reuse and longevity even for small-scale apps with limited overhead. This text evaluates and contrasts common object-oriented designs. A given problem may have many solutions. A developer may employ different design techniques - composition, inheritance, dependency injection, delegation, etc. - to solve a particular problem. A skilled developer can determine the costs and benefits of different design responses, even amid competing concerns. A responsible developer documents design choices as a contract with the client, delineating external and internal responsibilities. To promote effective software design, this book examines contractual, object-oriented designs for immediate and sustained use as well as code reuse. The intent of identifying design variants is to recognize and manage conflicting goals such as short versus long-term utility, stability versus flexibility, and storage versus computation. Many examples are given to evaluate and contrast different solutions and to compare C# and C++ effects. No one has a crystal ball; however, deliberate design promotes software longevity. With the prominence of legacy OO code, a clear understanding of different object-oriented designs is essential. Design questions abound. Is code reuse better with inheritance or composition? Should composition rely on complete encapsulation? Design choices impact flexibility, efficiency, stability, longevity, and reuse, yet compilers do not enforce design and syntax does not necessarily illustrate design. Through deliberate design, or redesign when refactoring, developers construct sustainable, efficient code.

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today. The Semantic Web treats data as a distributed resource on the scale of the World Wide Web, and incorporates features to address the challenges of massive data distribution as part of its basic design. The aim of the first two editions was to motivate the Semantic Web technology stack from end-to-end; to describe not only what the Semantic Web standards are and how they work, but also what their goals are and why they were designed as they are. It tells a coherent story from beginning to end of how the standards work to manage a world-wide distributed web of knowledge in a meaningful way. The third edition builds on this foundation to bring Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, bringing with him years of experience in global linked data, to open up the story to a modern view of global linked data. While the overall story is the same, the examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data. Also included with the third edition, all of the data sets and queries are available online for study and experimentation at data.world/swwo.

Using the implementation of a deep learning framework as an example, C++ Template Metaprogramming in Practice: A Deep Learning Framework explains the application of metaprogramming in a relatively large project and emphasizes ways to optimize systems performance. The book is suitable for developers with a basic knowledge of C++. Developers familiar with mainstream deep learning frameworks can also refer to this book to compare the differences between the deep learning framework implemented with metaprogramming and compile-time computing with deep learning frameworks using object-oriented methods. Consisting of eight chapters, the book starts with two chapters discussing basic techniques of metaprogramming and compile-time computing. The rest of the book's chapters focus on the practical application of metaprogramming in a deep learning framework. It examines rich types and systems, expression templates, and writing complex meta-functions, as well as such topics as: Heterogeneous dictionaries and policy templates An introduction to deep learning Type system and basic data types Operations and expression templates Basic layers Composite and recurrent layers Evaluation and its optimization Metaprogramming can construct flexible and efficient code. For C++ developers who are familiar with object-oriented programming, the main difficulty in learning and mastering C++ metaprogramming is establishing the thinking mode of functional programming. The meta-programming approach involved at compile time is functional, which means that the intermediate results of the construction cannot be changed, and the impact may be greater than expected. This book enables C++ programmers to develop a functional mindset and metaprogramming skills. The book also discusses the development cost and use cost of metaprogramming and provides workarounds for minimizing these costs.

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

Get an A grade in C As with any major language, mastery of C can take you to some very interesting new places. Almost 50 years after it first appeared, it's still the world's most popular programming language and is used as the basis of global industry's core systems, including operating systems, high-performance graphics applications, and microcontrollers. This means that fluent C users are in big demand at the sharp end in cutting-edge industries--such as gaming, app development, telecommunications, engineering, and even animation--to translate innovative ideas into a smoothly functioning reality. To help you get to where you want to go with C, this 2nd edition of C Programming For Dummies covers everything you need to begin writing programs, guiding you logically through the development cycle: from initial design and testing to deployment and live iteration. By the end you'll be au fait with the do's and don'ts of good clean writing and easily able to produce the basic--and not-so-basic--building blocks of an elegant and efficient source code. Write and compile source code Link code to create the executable program Debug and optimize your code Avoid common mistakes Whatever your destination: tech industry, start-up, or just developing for pleasure at home, this easy-to-follow, informative, and entertaining guide to the C programming language is the fastest and friendliest way to get there!

This book is the follow-up of the Boost Volume I book and it has been written for software developers who use Boost C++ libraries to create flexible applications. We discuss approximately 20 advanced libraries that can be classified into the following major categories: Mathematics: special functions, statistical distributions, interval arithmetic and matrix algebra. Special data structures: date and time, circular buffer, UUID, dynamic bitsets, pool memory. TCP and UDP portable network programming using the software interface. Interprocess communication and shared memory programming models. Three chapters on graphs, graph algorithms and their implementation in Boost. The focus is hands-on and each library is discussed in detail and numerous working examples are given to get the reader up to speed as soon as possible. Each library is described in a step-by-step fashion and you can use the corresponding code as a basis for more advanced applications. These libraries are the ideal basis for new applications. We shall use them in Volume III of the current series when we discuss applications to engineering, science and computational finance. About the Authors Robert Demming is software designer, developer and trainer and he has been involved with software projects in the areas of optical technology, process control, CAD and order processing systems since 1993. He has a BSc degree in technical computerscience from Amsterdam Hogeschool. Daniel J. Duffy is founder of Datasim Education BV and has been working with C++ and its applications since 1989 and with software development since 1979. He is designer, algorithm builder and trainer. He has a PhD in numerical analysis from Trinity College, Dublin. One of the projects that he is involved in is applying the Boost libraries to computational finance."