Currently, we see a variety of tools and techniques for specifying and implementing business processes. The problem is that there are still gaps and tensions between the different disciplines needed to improve business process execution and improvement in enterprises. Business process modeling, workflow execution and application programming are examples of disciplines that are hosted by different communities and that emerged separately from each other. In particular, concepts have not yet been fully elaborated at the system analysis level. Therefore, practitioners are faced again and again with similar questions in concrete business process projects: Which decomposition mechanism to use? How to find the correct granularity for business process activities? Which implementing technology is the optimal one in a given situation? This work offers an approach to the systematization of the field. The methodology used is explicitly not a comparative analysis of existing tools and techniques - although a review of existing tools is an essential basis for the considerations in the book. Rather, the book tries to provide a landscape of rationales and concepts in business processes with a discussion of alternatives.

For courses in computer programming. Evaluates the fundamentals of contemporary computer programming languages Concepts of Computer Programming Languages introduces students to the fundamental concepts of computer programming languages and provides them with the tools necessary to evaluate contemporary and future languages. Through a critical analysis of design issues, the text teaches students the essential differences between computing with specific languages, while the in-depth discussion of programming language structures also prepares them to study compiler design. The 12th Edition includes new material on contemporary languages like Swift and Python, replacing discussions of outdated languages.

Get started quickly with Qt, the popular open source C++ framework for building C++-based applications and games. This book will have you building both fully functional desktop and mobile applications in no time, including some simple game applications. Introducing Qt 6 begins by guiding you in setting up your tools and environment, and then walks you through the first "baby steps" of Qt framework. Next, you'll learn the basics of how project and app structure are set up using Qt. Then, you'll begin your first real hands-on projects using Qt, including a task and problem management application and two games. As you progress, you can enhance these apps and games using additional Qt components and features. The book then delves into advanced topics in Qt, learning above and beyond what the Qt docs can offer, including local storage, C++ integration, deployment to Windows and Android, custom components and how to work with them. Upon completing this book, you'll come away knowing how to build a C++ application from design to deployment, top to bottom. And, you'll have actual application and game examples that you can apply to your own work or hobby. What You Will Learn Learn to build your first applications and games using Qt 6 framework Design, create, build and deploy your first Qt applications or games as finished products Explore local storage integration in theory and practice Cover deployment on Windows and on Android Integrate with C++ language to leverage additional functionality Dive into Custom Components and how to work with them Explore different project structures and more Who This Book Is For Software programmers, developers who are new to C++ or the Qt framework. Some prior programming experience though may be helpful.

The art, craft, discipline, logic, practice and science of developing large-scale software products needs a professional base. The textbooks in this three-volume set combine informal, engineeringly sound approaches with the rigor of formal, mathematics-based approaches.

This volume covers the basic principles and techniques of specifying systems and languages. It deals with modelling the semiotics (pragmatics, semantics and syntax of systems and languages), modelling spatial and simple temporal phenomena, and such specialized topics as modularity (incl. UML class diagrams), Petri nets, live sequence charts, statecharts, and temporal logics, including the duration calculus. Finally, the book presents techniques for interpreter and compiler development of functional, imperative, modular and parallel programming languages.

This book is targeted at late undergraduate to early graduate university students, and researchers of programming methodologies. Vol. 1 of this series is a prerequisite text.

For courses in Java Programming Layered, Back-to-Basics Approach to Java Programming This edition of Building Java Programs: A Back to Basics Approach uses a layered strategy to introduce Java programming and overcome the high failure rates that are common in introductory computer science courses. The authors' proven and class-tested "back to basics" approach introduces programming fundamentals first, with new syntax and concepts added over multiple chapters. Object-oriented programming is discussed only once students have developed a basic understanding of Java programming. Previous editions have established the text's reputation as an excellent choice for two-course sequences in introductory computer science, and new material in the 4th Edition incorporates concepts related to Java 8, functional programming, and image manipulation.

This is a one-semester, introductory programming textbook in Java that uses game applications as a central pedagogical tool to improve student engagement, learning outcomes, and retention. Game programming is incorporated into the text in a way that does not compromise the amount of material traditionally covered in a basic programming course and permits instructors who are not familiar with game programming and computer graphics concepts to realize the verified pedagogical advantages of game applications. The companion disc includes a game environment that is easily integrated into projects created with the popular Java Development Environments, including Eclipse, NetBeans, and JCreator in a student-friendly way and also includes a set of executable student games to peak their interest by giving them a glimpse into their future capabilities. The material presented in the book is in full compliance with the 2013 ACM/IEEE computer science curriculum guidelines. It has been used to teach programming to students whose majors are within and outside of the computing fields. Ancillaries include a comprehensive instructor's resource disc with programming solutions, slides, quizzes, projects, and more. Features: Uses an objects-early approach to learning Java Follows the 2013 ACM/IEEE computer science curriculum guidelines Integrates game applications as a central pedagogical tool to improve student engagement, learning outcomes, and retention Includes a companion disc with projects created with the popular Java Development Environments; also includes a set of executable student games, source code, and figures Uses working programs to illustrate concepts under discussion Complete instructor's resource package available upon adoption

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. What You Will Learn How to start developing web applications using Streamlit What are Streamlit's components Media elements in Streamlit How to visualize data using various interactive and dynamic Python libraries How to implement models in Streamlit web applications Who This Book Is ForProfessionals working in data science and machine learning domains who want to showcase and deploy their work in a web application with no prior knowledge of web development.

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.

Because of its portability and platform-independence, Java is the ideal computer programming language to use when working on graph algorithms and other mathematical programming problems. Collecting some of the most popular graph algorithms and optimization procedures, A Java Library of Graph Algorithms and Optimization provides the source code for a library of Java programs that can be used to solve problems in graph theory and combinatorial optimization. Self-contained and largely independent, each topic starts with a problem description and an outline of the solution procedure, followed by its parameter list specification, source code, and a test example that illustrates the usage of the code. The book begins with a chapter on random graph generation that examines bipartite, regular, connected, Hamilton, and isomorphic graphs as well as spanning, labeled, and unlabeled rooted trees. It then discusses connectivity procedures, followed by a paths and cycles chapter that contains the Chinese postman and traveling salesman problems, Euler and Hamilton cycles, and shortest paths. The author proceeds to describe two test procedures involving planarity and graph isomorphism. Subsequent chapters deal with graph coloring, graph matching, network flow, and packing and covering, including the assignment, bottleneck assignment, quadratic assignment, multiple knapsack, set covering, and set partitioning problems. The final chapters explore linear, integer, and quadratic programming. The appendices provide references that offer further details of the algorithms and include the definitions of many graph theory terms used in the book.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan-Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood's formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow's method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Shading, as part of the creation of realistic computer-generated images, is currently bringing major advances to computer graphics, with important practical applications in computer game design and animation. Shaders are a more sophisticated way of making 3D objects look more realistic. While most shaders are created using proprietary shading languages, Sh, a new open-source system, simplifies the shader programming process by making it part of the C++ language. This book introduces Sh and describes how to program a GPU using C++ to implement both basic and advanced shading techniques. Readers of this book will be able to start writing advanced, modular shaders using Sh within a day

Turn futuristic ideas about computer vision and machine learning into demonstrations that are both functional and entertaining Key Features Build OpenCV 4 apps with Python 2 and 3 on desktops and Raspberry Pi, Java on Android, and C# in Unity Detect, classify, recognize, and measure real-world objects in real-time Work with images from diverse sources, including the web, research datasets, and various cameras Book DescriptionOpenCV 4 is a collection of image processing functions and computer vision algorithms. It is open source, supports many programming languages and platforms, and is fast enough for many real-time applications. With this handy library, you'll be able to build a variety of impressive gadgets. OpenCV 4 for Secret Agents features a broad selection of projects based on computer vision, machine learning, and several application frameworks. To enable you to build apps for diverse desktop systems and Raspberry Pi, the book supports multiple Python versions, from 2.7 to 3.7. For Android app development, the book also supports Java in Android Studio, and C# in the Unity game engine. Taking inspiration from the world of James Bond, this book will add a touch of adventure and computer vision to your daily routine. You'll be able to protect your home and car with intelligent camera systems that analyze obstacles, people, and even cats. In addition to this, you'll also learn how to train a search engine to praise or criticize the images that it finds, and build a mobile app that speaks to you and responds to your body language. By the end of this book, you will be equipped with the knowledge you need to advance your skills as an app developer and a computer vision specialist. What you will learn Detect motion and recognize gestures to control a smartphone game Detect car headlights and estimate their distance Detect and recognize human and cat faces to trigger an alarm Amplify motion in a real-time video to show heartbeats and breaths Make a physics simulation that detects shapes in a real-world drawing Build OpenCV 4 projects in Python 3 for desktops and Raspberry Pi Develop OpenCV 4 Android applications in Android Studio and Unity Who this book is forIf you are an experienced software developer who is new to computer vision or machine learning, and wants to study these topics through creative projects, then this book is for you. The book will also help existing OpenCV users who want upgrade their projects to OpenCV 4 and new versions of other libraries, languages, tools, and operating systems. General familiarity with object-oriented programming, application development, and usage of operating systems (OS), developer tools, and the command line is required.

Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. What You Will Learn Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models Who This Book Is For AI engineers, data scientists, and software developers interested in XAI

Learn to program with Rust 2021 Edition, in an easy, step-by-step manner on Unix, the Linux shell, macOS, and the Windows command line. As you read this book, you'll build on the knowledge you gained in previous chapters and see what Rust has to offer. Beginning Rust starts with the basics of Rust, including how to name objects, control execution flow, and handle primitive types. You'll see how to do arithmetic, allocate memory, use iterators, and handle input/output. Once you have mastered these core skills, you'll work on handling errors and using the object-oriented features of Rust to build robust Rust applications in no time. Only a basic knowledge of programming in C or C++ and familiarity with a command console are required. After reading this book, you'll be ready to build simple Rust applications. What You Will Learn Get started programming with Rust Understand heterogeneous data structures and data sequences Define functions, generic functions, structs, and more Work with closures, changeable strings, ranges and slices Use traits and learn about lifetimes Who This Book Is For Those who are new to Rust and who have at least some prior experience with programming in general: some C/C++ is recommended particularly.

Bring Elixir into your company, with real-life strategies from the people who built Elixir and use it successfully at scale. See how Ben Marx and Bleacher Report maintain one of the highest-traffic Elixir applications by selling the concept to management and delivering on that promise. Find out how Bruce Tate and icanmakeitbetter hire and train Elixir engineers, and the techniques they've employed to design and ensure code consistency since Elixir's early days. Explore customer challenges in deploying and monitoring distributed applications with Jose Valim and Plataformatec. This book has all the information you need to take your application from concept to production. Adoption is more than programming. Elixir is an exciting new language, but to successfully get your application from start to finish, you're going to need to know more than just the language. The case studies and strategies in this book will get you there. Make a business case and build a team before you finish your first prototype. Once you're in development, form strategies for organizing your code and learning the constraints of the runtime and ecosystem. Convince stakeholders, both business and technical, about the value they can expect. Prepare to make the critical early decisions that will shape your application for years to come. Finally, manage your deployment with all of the knobs and gauges that good DevOps teams demand. Decide between the many options available for deployment, and how to best prepare yourself for the challenges of running a production application. This book picks up where most Elixir books let off. It won't teach you to program Elixir, or any of its tools. Instead, it guides you through the broader landscape and shows you a holistic approach to adopting the language. What You Need: This book works with any version of Elixir.

This in-depth guide covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. In this book, you'll implement numerical algorithms in Kotlin using NM Dev, an object-oriented and high-performance programming library for applied and industrial mathematics. Discover how Kotlin has many advantages over Java in its speed, and in some cases, ease of use. In this book, you'll see how it can help you easily create solutions for your complex engineering and data science problems. After reading this book, you'll come away with the knowledge to create your own numerical models and algorithms using the Kotlin programming language. What You Will Learn Program in Kotlin using a high-performance numerical library Learn the mathematics necessary for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solutions Build solvers for industrial optimization problems Perform data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.

This book constitutes the refereed proceedings of the 7th International IFIP WG 2.13 Conference on Open Source Systems, OSS 2010, held in Salvador, Brazil, in October 2011. The 20 revised full papers presented together with 4 industrial full papers and 8 lightning talks were carefully reviewed and selected from 56 submissions. The papers are organized in the following topical sections: OSS quality and reliability, OSS products, review of technologies of and for OSS, knowledge and research building in OSS, OSS reuse, integration, and compliance, OSS value and economics, OSS adoption in industry, and mining OSS repositories.

Series Information:
Advanced Information Processing Technology

Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them. Among this group of seven, you'll explore the most critical programming models of our time. Learn the dynamic typing that makes Ruby, Python, and Perl so flexible and compelling. Understand the underlying prototype system that's at the heart of JavaScript. See how pattern matching in Prolog shaped the development of Scala and Erlang. Discover how pure functional programming in Haskell is different from the Lisp family of languages, including Clojure. Explore the concurrency techniques that are quickly becoming the backbone of a new generation of Internet applications. Find out how to use Erlang's let-it-crash philosophy for building fault-tolerant systems. Understand the actor model that drives concurrency design in Io and Scala. Learn how Clojure uses versioning to solve some of the most difficult concurrency problems. It's all here, all in one place. Use the concepts from one language to find creative solutions in another-or discover a language that may become one of your favorites.

Choose the right programmable logic devices for your projects with this guide to the proprietary details of the alternative architectures and processes of Complex Programmable Logic Devices (CPLD) and Field Programmable Gate Arrays (FPGA). Engineers learn how to select the appropriate development tools and resources and how to optimize and increase the reliability of the design along with the appropriate use of simulation and testing. Engineering managers will gain insight into how to plan, schedule, and budget a CPLD-based or FPGA-based design.

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 provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You'll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you'll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare. What You Will Learn Understand the components required to facilitate precision health and personalized care Apply and implement precision health systems Overcome the challenges of delivering precision healthcare at scale Reconcile ethical and moral implications of delivering precision healthcare Gain insight into the hurdles providers face while implementing precision healthcare Who This Book Is For Healthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students

This tutorial offers a full complement of AutoLISP programming skills. Beginning with an introduction to the language, the programming environment, and the Visual LISP editor, succeeding chapters address data manipulation, user input and output options, programming dialog boxes, modifying AutoCAD drawing database objects, and using advanced features of the Visual LISP programming tool kit. Demonstration projects can also serve as core applications for reader's own use.

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.