The control-flow issues presented in this textbook are extremely relevant in modern computer languages and programming styles. In addition to the basic control-flow mechanisms, virtually all new computer languages provide some form of exceptional control flow to support robust programming introduced in this textbook. Also, concurrency capabilities are appearing with increasing frequency in both new and old programming languages, and are covered in this book. Understanding Control Flow: With Concurrent Programming Using C++ starts with looping, and works through each of the basic control-flow concepts, examining why each is fundamental and where it is useful. Time is spent on each concept according to its level of difficulty. Examples and exercises are also provided in this textbook. New programming methodologies are requiring new forms of control flow, and new programming languages are supporting these methodologies with new control structures, such as the concurrency constructs discussed in this textbook. Most computers now contain multi-threading and multi-cores, while multiple processors and distributed systems are ubiquitous - all of which require advanced programming methodologies to take full advantage of the available parallelism summarized in this textbook. Advance forms of control flow are becoming basic programming skills needed by all programmers, not just graduate students working in the operating systems or database disciplines. This textbook is designed for advanced-level students studying computer science and engineering. Professionals and researchers working in this field, specifically programming and software engineering, will find this book useful as a reference.

This book constitutes the proceedings of the 38th IFIP WG 6.1 International Conference on Formal Techniques for Distributed Objects, Components, and Systems, FORTE 2018, held in Madrid, Spain, in June 2018, as part of the 13th International Federated Conference on Distributed Computing Techniques, DisCoTec 2018. The 10 full papers presented were carefully reviewed and selected from 28 submissions. The conference is dedicated to fundamental research on theory, models, tools, and applications for distributed systems.

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language.The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

This open access book constitutes the proceedings of the 27th European Symposium on Programming, ESOP 2018, which took place in Thessaloniki, Greece in April 2018, held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018. The 36 papers presented in this volume were carefully reviewed and selected from 114 submissions. The papers are organized in topical sections named: language design; probabilistic programming; types and effects; concurrency; security; program verification; program analysis and automated verification; session types and concurrency; concurrency and distribution; and compiler verification.

This book completes the Apress Java learning journey and is a comprehensive approach to learning Java APIs, extensions, and modules such as Java EE integration, mobile Java modules, JavaFX, and JDBC. In this book, you'll learn how to build user interfaces with Swing and JavaFX as well as how to write network programs with the new Java 9 and much more. Java APIs, Extensions and Libraries is for Java programmers who are familiar with the fundamentals of the Java language and Java programming, who are now ready to call upon the power of extended Java functionality available from the huge array of Java APIs, extensions, and libraries. After reading and learning from this book you'll be ready to become a professional Java programmer. What You'll Learn Extend your Java skills beyond the fundamental object-oriented concepts and core language features Apply Java Swing for building Java front ends Get started with Java network programming Connect to databases and access data from Java programs using the JDBC API Work with JavaFX, RMI (Remote Method Invocation), and JNI (Java Native Interface) Use the new scripting features of Java Who This Book Is For Java programmers who are familiar with the fundamentals of the Java language and Java programming.

Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download. What You Will Learn Discover convolutional nets and how to use them Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs Master the various programming algorithms required Carry out multi-threaded gradient computations and memory allocations for this threading Work with CUDA code implementations of all core computations, including layer activations and gradient calculations Make use of the CONVNET program and manual to explore convolutional nets and case studies Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

Leverage your existing web development skills to learn the whole cycle of hybrid mobile app development. This edition is fully updated with the changes in Ionic 4, including Stencil, a new framework based on the web components standard. It explains Ionic and Firebase in detail, including how to create hybrid mobile apps using using React and Vue, and run those apps in an internal browser using a wrapper created by Apache Cordova. Build Mobile Apps with Ionic 4 and Firebase shows you how to focus on developing front-end code, without needing to manage any back-end code or servers. You'll learn in the context of building a Hacker News client app, which can view top stories in Hacker News, view comments of a story, and add stories to favorites. Explore how Ionic 4 uses Angular as the JavaScript framework to easily develop apps using an interface similar to native apps, and how to access Firebase, a real-time database, in web apps using JavaScript. What You'll Learn Create content-based Ionic mobile apps Work with new Ionic 4 compnents like gesture, text, and keyboard controller Manage your apps with RxJS & Redux Who This Book Is For Front-end developers and mobile app developers

Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Engage with chatbots using deep learning Who This Book Is For Data scientists and developers who want to adapt and build deep learning applications.

This book takes a foundational approach to the semantics of probabilistic programming. It elaborates a rigorous Markov chain semantics for the probabilistic typed lambda calculus, which is the typed lambda calculus with recursion plus probabilistic choice. The book starts with a recapitulation of the basic mathematical tools needed throughout the book, in particular Markov chains, graph theory and domain theory, and also explores the topic of inductive definitions. It then defines the syntax and establishes the Markov chain semantics of the probabilistic lambda calculus and, furthermore, both a graph and a tree semantics. Based on that, it investigates the termination behavior of probabilistic programs. It introduces the notions of termination degree, bounded termination and path stoppability and investigates their mutual relationships. Lastly, it defines a denotational semantics of the probabilistic lambda calculus, based on continuous functions over probability distributions as domains. The work mostly appeals to researchers in theoretical computer science focusing on probabilistic programming, randomized algorithms, or programming language theory.

Learn to program the Propeller in Spin and C and how to map the Propeller Assembler (PASM) language from other high-level languages you might know. The overall task you will pursue in the book is to implement a Delta Compression algorithm: first in Spin, then in PASM, then in C. Along the way, you'll review test driven development, a powerful technique for validating code, and conclude with a chapter on hardware manipulations. The book's main goal is to help you extend the capabilities of the Propeller processor by using the assembler language. What You'll Learn Use a data compression/decompression application to introduce PASM to the reader Integrate C and PASM code Review hardware interactions (setting and reading pins) Who This Book Is For Those who are familiar with Spin programming for the Parallax Propeller but have an interest in learning Propeller C and Propeller Assembler (PASM) programming. Some knowledge of C or Python is also helpful.

Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations. Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study. What You Will Learn Build basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suite Create several types of projects using Python and Google OR-Tools Who This Book Is For Developers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.

This book constitutes the proceedings of the 24th International Working Conference on Requirements Engineering - Foundation for Software Quality, REFSQ 2018, held in Utrecht, The Netherlands, in March 2018. The 23 full and 2 invited talks papers presented in this volume were carefully reviewed and selected from 57 submissions. The papers were organized in topical sections named: RE in Industrial Practice; NLP in Theory and Practice; Empirical Insights into Traceability; Taming Ambiguity; Large-Scale RE; Quality Requirements; User and Job Stories; Requirements Alignment; RE Previews and Visions; Big Data; Mindmapping and Requirements Modeling.

Gain the basics of Ruby's map, reduce, and select functions and discover how to use them to solve data-processing problems. This compact hands-on book explains how you can encode certain complex programs in 10 lines of Ruby code, an astonishingly small number. You will walk through problems and solutions which are effective because they use map, reduce, and select. As you read Ruby Data Processing, type in the code, run the code, and ponder the results. Tweak the code to test the code and see how the results change. After reading this book, you will have a deeper understanding of how to break data-processing problems into processing stages, each of which is understandable, debuggable, and composable, and how to combine the stages to solve your data-processing problem. As a result, your Ruby coding will become more efficient and your programs will be more elegant and robust. What You Will Learn Discover Ruby data processing and how to do it using the map, reduce, and select functions Develop complex solutions including debugging, randomizing, sorting, grouping, and more Reverse engineer complex data-processing solutions Who This Book Is For Those who have at least some prior experience programming in Ruby and who have a background and interest in data analysis and processing using Ruby.

This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises reinforce the learning objectives. Readers will learn to: * Write and run Python in the ArcGIS Python Window, the PythonWin IDE, and the PyScripter IDE * Work with Python syntax and data types * Call ArcToolbox tools, batch process GIS datasets, and manipulate map documents using the arcpy package * Read and modify proprietary and ASCII text GIS data * Parse HTML web pages and KML datasets * Create Web pages and fetch GIS data from Web sources. * Build user-interfaces with the native Python file dialog toolkit or the ArcGIS Script tools and PyToolboxes Python for ArcGIS is designed as a primary textbook for advanced-level students in GIS. Researchers, government specialists and professionals working in GIS will also find this book useful as a reference.

Quickly obtain solutions to common Java image processing problems, learn best practices, and understand everything OpenCV has to offer for image processing. You will work with a JVM image wrapper to make it very easy to run image transformation through pipelines and obtain instant visual feedback. This book makes heavy use of the Gorilla environment where code can be executed directly in the browser, and image transformation results can also be visualized directly in the browser. Java Image Processing Recipes includes recipes on more advanced image manipulation techniques, such as image smoothing, cartooning, sketching, and mastering masks to apply changes only to parts of the image. You'll see how OpenCV features provide instant solutions to problems such as edges detection and shape finding. Finally, the book contains practical recipes dealing with webcams and various video streams, giving you ready-made code with which to do real-time video analysis. What You Will Learn Create your personal real-time image manipulation environment Manipulate image characteristics with OpenCV Work with the Origami image wrapper Apply manipulations to webcams and video streams Who This Book Is For Developers that want to manipulate images and use other advanced imaging techniques, through code running in the JVM.

For courses in Java Programming. Effective step-by-step Java education Building Java Programs: A Back to Basics Approach introduces new concepts and syntax using a spiral approach, ensuring students are thoroughly prepared as they work through CS1 material. Through the first four editions, Building Java Programs and its back-to-basics approach have proven remarkably effective. The 5th Edition has been extensively updated with incorporation of JShell integration, improved loop coverage, rewritten and revised case studies, examples, updated collection syntax and idioms, expanded self-check and programming exercising sections, and new programming projects. Also available with MyLab Programming MyLab (TM) is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student.With MyLab Programming, students work through hundreds of short, auto-graded coding exercises and receive immediate and helpful feedback based on their work. If you would like to purchase both the physical text and MyLab Programming, search for: 0135862353 / 9780135862353 Building Java Programs: A Back to Basics Approach Plus MyLab Programming with Pearson eText -- Access Card Package, 5/e Package consists of: 0135472466 / 9780135472460 MyLab Programming Standalone Access Card 013547194X / 9780135471944 Building Java Programs: A Back to Basics Approach

This book constitutes the refereed post-conference proceedings of the 4th International Workshop on Accelerator Programming Using Directives, WACCPD 2017, held in Denver, CO, USA, in November 2017. The 9 full papers presented have been carefully reviewed and selected from 14 submissions. The papers share knowledge and experiences to program emerging complex parallel computing systems. They are organized in the following three sections: applications; environments; and program evaluation.

This book contains the thoroughly refereed technical papers presented in six workshops collocated with the International Conference on Software Technologies: Applications and Foundations, STAF 2017, held in Marburg, Germany, in July 2017. The 15 full and 22 short papers presented were carefully reviewed and selected from 37 submissions. The events whose papers are included in this volume are: BigMDE 2017: 5th International Workshop on Scalable Model Driven Engineering GCM 2017: 8th International Workshop on Graph Computation Models GRAND 2017: 1st International Workshop on Grand Challenges in Modeling MORSE 2017: 4th International Workshop on Model-driven Robot Software Engineering OCL 2017: 17th International Workshop in OCL and Textual Modeling STAF Projects Showcase 2017: 3rd event dedicated to international and national project dissemination and cooperation

This book constitutes the refereed proceedings of the 11th International Andrei P. Ershov Informatics Conference, PSI 2017, held in Moscow, Russia, in June 2017. The 31 full papers presented in this volume were carefully reviewed and selected from 57 submissions. The papers cover various topics related to the foundations of program and system development and analysis, programming methodology and software engineering and information technologies.

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you'll get a chance to revisit the concepts you've covered so far. What You Will Learn Use Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

This book constitutes the refereed proceedings of the 4th International Conference on Tools and Methods for Program Analysis, TMPA 2017, Moscow, Russia, March 3-4, 2017. The 12 revised full papers and 5 revised short papers presented together with three abstracts of keynote talks were carefully reviewed and selected from 51 submissions. The papers deal with topics such as software test automation, static program analysis, verification, dynamic methods of program analysis, testing and analysis of parallel and distributed systems, testing and analysis of high-load and high-availability systems, analysis and verification of hardware and software systems, methods of building quality software, tools for software analysis, testing and verification.

This volume contains a record of some of the lectures and seminars delivered at the Third International School on Engineering Trustworthy Software Systems (SETSS 2017), held in April 2017 at Southwest University in Chongqing, China. The six contributions included in this volume provide an overview of leading-edge research in methods and tools for use in computer system engineering. They have been distilled from six original courses delivered at the school on topics such as: rely/guarantee thinking; Hoare-style specification and verification of object-oriented programs with JML; logic, specification, verification, and interactive proof; software model checking with Automizer; writing programs and proofs; engineering self-adaptive software-intensive systems; and with an additional contribution on the challenges for formal semantic description. The material is useful for postgraduate students, researchers, academics, and industrial engineers, who are interested in the theory and practice of methods and tools for the design and programming of trustworthy software systems.

This volume constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Verified Software: Theories, Tools, and Experiments, VSTTE 2017, held in Heidelberg, Germany, in July 2017. The 12 full papers presented were carefully revised and selected from 20 submissions. The papers describe large-scale verification efforts that involve collaboration, theory unification, tool integration, and formalized domain knowledge as well as novel experiments and case studies evaluating verification techniques and technologies.