This book constitutes the proceedings of the 19th Asian Symposium on Programming Languages and Systems, APLAS 2021, held in Chicago, USA, in October 2021.* The 17 papers presented in this volume were carefully reviewed and selected from 43 submissions. They were organized in topical sections named: analysis and synthesis, compilation and transformation, language, and verification. * The conference was held in a hybrid format due to the COVID-19 pandemic.

This book presents a number of approaches to Fine-Kinney-based multi-criteria occupational risk-assessment. For each proposed approach, it provides case studies demonstrating their applicability, as well as Python coding, which will enable readers to implement them into their own risk assessment process. The book begins by giving a review of Fine-Kinney occupational risk-assessment methods and their extension by fuzzy sets. It then progresses in a logical fashion, dedicating a chapter to each approach, including the fuzzy best and worst method, interval-valued Pythagorean fuzzy VIKOR and interval type-2 fuzzy QUALIFLEX. This book will be of interest to professionals and researchers working in the field of occupational risk management, as well as postgraduate and undergraduate students studying applications of fuzzy systems.

With two new chapters and a new preface, the award-winning book "The Halo Effect "continues to unmask the delusions found in the corporate world and provides a sharp understanding of what drives business success and failure.
Too many of today's most prominent management gurus make steel-clad guarantees based on claims of irrefutable research, promising to reveal the secrets of why one company fails and another succeeds, and how you can become the latter. Combining equal measures of solemn-faced hype and a wide range of popular business delusions, statistical and otherwise, these self-styled experts cloud our ability to think critically about the nature of success.
Central among these delusions is the Halo Effect--the tendency to focus on the high financial performance of a successful company and then spread its golden glow to all its attributes--clear strategy, strong values, brilliant leadership, and outstanding execution. But should the same company's sales head south, the very same attributes are universally derided--suddenly the strategy was wrong, the culture was complacent, and the leader became arrogant.
"The Halo Effect" not only identifies these delusions that keep us from understanding business performance, but also suggests a more accurate way to think about leading a company. This approach--focusing on strategic choice and execution, while recognizing the inherent riskiness of both--clarifies the priorities that managers face.
Brilliant and unconventional, irreverent and witty, "The Halo Effect" is essential reading for anyone wanting to separate fact from fiction in the world of business.

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline. Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess. Key Features: Several theories and applications are provided, each with working Python scripts. All Python functions written for this book are archived on GitHub. Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app. About the Author Dr. Jesus Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Rust is a new systems programming language that gives you the low-level power of C with the elegance and ease of languages like Ruby and Python. Rust is thread safe, enabling "fearless concurrency". Rust in Action introduces the Rust programming language by exploring numerous systems programming concepts and techniques. You'll be learning Rust by delving into how computers work under the hood. You'll find yourself playing with persistent storage, memory, networking and even tinkering with CPU instructions. The book takes you through using Rust to extend other applications and teaches you tricks to write blindingly fast code. You'll also discover parallel and concurrent programming. * Concurrent and parallel programming Sharing resources with locks or atomic operations Avoiding programming with global state Message passing inside your applications Memory management and garbage collection Readers need intermediate programming skills and familiarity with general computer science concepts, the command line, and networking.

Making extensive use of examples, this textbook on Java programming teaches the fundamental skills for getting started in a command-line environment. Meant to be used for a one-semester course to build solid foundations in Java, Fundamentals of Java Programming eschews second-semester content to concentrate on over 180 code examples and 250 exercises.Key object classes (String, Scanner, PrintStream, Arrays, and File) are included to get started in Java programming. The programs are explained with almost line-by-line descriptions, also with chapter-by-chapter coding exercises. Teaching resources include solutions to the exercises, as well as digital lecture slides.

Address the most common integration challenges, by understanding the ins and outs of the choices and exemplifying the solutions with practical examples on how to create cloud native applications using Apache Camel. Camel will be our main tool, but we will also see some complementary tools and plugins that can make our development and testing easier, such as Quarkus, and tools for more specific use cases, such as Apache Kafka and Keycloak. You will learn to connect with databases, create REST APIs, transform data, connect with message oriented software (MOMs), secure your services, and test using Camel. You will also learn software architecture patterns for integration and how to leverage container platforms, such as Kubernetes. This book is suitable for those who are eager to learn an integration tool that fits the Kubernetes world, and who want to explore the integration challenges that can be solved using containers. What You Will Learn Focus on how to solve integration challenges Understand the basics of the Quarkus as it's the foundation for the application Acquire a comprehensive view on Apache Camel Deploy an application in Kubernetes Follow good practices Who This Book Is For Java developers looking to learn Apache Camel; Apache Camel developers looking to learn more about Kubernetes deployments; software architects looking to study integration patterns for Kubernetes based systems; system administrators (operations teams) looking to get a better understand of how technologies are integrated.

Written by two very experienced instructors, with more than thirty years of teaching experience between them; Presents material that is grounded in practical applications that are representative of the problems researchers encounter in real life; Teaches readers the core features of modern JavaScript; Covers programming with callbacks and promises; Describes how to build data services and data visualization;

Since its original introduction in 1997, the Unified Modeling Language has revolutionized software development. Every integrated software development environment in the world - open-source, standards-based and proprietary - now supports UML, and more importantly the model-driven approach to software development. This makes learning the newest UML standard UML 2.0, critical for all software developers - and there isn't a better choice than this clear, step-by-step guide to learning the language. - Richard Mark Soley, Chairman and CEO, OMG. If you're like most software developers you're building systems that are increasingly complex. Whether you're creating a desktop application or an enterprise system, complexity is the big hairy monster you must manage.

The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality. In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects. By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new. What You Will Learn Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs) Explore variations of GAN Understand the basics of other forms of content generation Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2 Who This Book Is For Machine learning developers and AI enthusiasts who want to understand AI content generation techniques

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing using R. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The final chapter presents the Transformer Architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years. Companion files with source code and figures are included. Features Covers extensive topics related to natural language processing using R Features companion files with source code and figures from the book

Learn how to gather, manipulate, and analyze data with Python. This book is a practical guide to help you get started with Python from ground zero and to the point where you can use coding for everyday tasks. Python, the most in-demand skill by employers, can be learned in a matter of months and a working knowledge will help you to advance your career. This book will teach you to crunch numbers, analyze big-data, and switch from spreadsheets to a faster and more efficient programming language. You'll benefit from the numerous real-life examples designed to meet current world challenges and from step-by-step guidance to become a confident Python user. Python is used in all aspects of financial industry, from algo trading, reporting and risk management to building valuations models and predictive machine learning programs. Basic Python for Data Management, Finance, and Marketing highlights how this language has become a useful skill with digital marketers, allowing them to analyze data more precisely and run more successful campaigns. What You'll Learn Get started with Python from square one Extend what's possible on excel with Python Automate tasks with Python Analyze data more precisely Who This Book Is For Professionals who want to find a job in the modern world or advance their careers within field of Python programming language.

MATLAB: A Practical Introduction to Programming and Problem Solving, winner of TAA's 2017 Textbook Excellence Award ("Texty"), guides the reader through both programming and built-in functions to easily exploit MATLAB's extensive capabilities for tackling engineering and scientific problems. Assuming no knowledge of programming, this book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. The sixth edition has been updated to reflect the functionality of the current version of MATLAB (R2021a), including the introduction of machine learning concepts and the Machine Learning Toolbox, and new sections on data formats and data scrubbing.

Learn the fundamentals of the Java 17 LTS or Java Standard Edition version 17 Long Term Support release, including basic programming concepts and the object-oriented fundamentals necessary at all levels of Java development. Authors Kishori Sharan and Adam L. Davis walk you through writing your first Java program step-by-step. Armed with that practical experience, you'll be ready to learn the core of the Java language. Beginning Java 17 Fundamentals provides over 90 diagrams and 240 complete programs to help you learn the topics faster. While this book teaches you the basics, it also has been revised to include the latest from Java 17 including the following: value types (records), immutable objects with an efficient memory layout; local variable type inference (var); pattern matching, a mechanism for testing and deconstructing values; sealed types, a mechanism for declaring all possible subclasses of a class; multiline text values; and switch expressions. The book continues with a series of foundation topics, including using data types, working with operators, and writing statements in Java. These basics lead onto the heart of the Java language: object-oriented programming. By learning topics such as classes, objects, interfaces, and inheritance you'll have a good understanding of Java's object-oriented model. The final collection of topics takes what you've learned and turns you into a real Java programmer. You'll see how to take the power of object-oriented programming and write programs that can handle errors and exceptions, process strings and dates, format data, and work with arrays to manipulate data. What You Will Learn Write your first Java programs with emphasis on learning object-oriented programming How to work with switch expressions, value types (records), local variable type inference, pattern matching switch and more from Java 17 Handle exceptions, assertions, strings and dates, and object formatting Learn about how to define and use modules Dive in depth into classes, interfaces, and inheritance in Java Use regular expressions Take advantage of the JShell REPL tool Who This Book Is For Those who are new to Java programming, who may have some or even no prior programming experience.

Designed for professionals and advanced students, Pointers On C provides a comprehensive resource for those needing in-depth coverage of the C programming language. An extensive explanation of pointer basics and a thorough exploration of their advanced features allows programmers to incorporate the power of pointers into their C programs. Complete coverage, detailed explanations of C programming idioms, and thorough discussion of advanced topics makes Pointers On C a valuable tutorial and reference for students and professionals alike.

Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask. By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. What You Will Learn Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies Look at different problem areas within the healthcare industry and solve them in a code-first approach Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks Understand the industry and learn ML Who This Book Is For Data scientists and software developers interested in machine learning and its application in the healthcare industry

Work with Db2 to write SQL and access databases using optimized code for the fastest response. This book will give you complete documentation on DB2 via Python for the IBM_db module and provide a number of examples for the usage of each module API. Begin by getting your free version of Db2 for Linux and Windows. While the book concentrates more on the Linux version of Db2, it also covers enough of the Windows version so that you're comfortable with obtaining and installing Db2 on your version of Windows. Next, you'll see how to install the sample database that comes with Db2, and take some data from the web to design a database around it, including tables and indexes. For Db2 to be really useful you need to use strong SQL expressions. This book provides specific examples of how to avoid using poor ones that can cause extra processing time for the query. Lastly, you'll look at each API in the ibm_db and ibm_db_dbi module. This module is not sponsored by IBM and must be installed separately from the Db2 database. After reading Foundation Db2 and Python you'll be able to install Db2 on Windows or Linux, and perform backups and restore data. What You'll Learn Obtain and install Db2 properly on Linux and Windows Create databases and load them on Db2 Work with ibm_db and ibm_db_dbi API modules for Python Write SQL for Db2 Review the future of the ibm_db Python module Who This Book Is For Python programmers and DB2 administrators interested in building application with Python and DB2

Developing a large-scale software system in C++ requires more than just a sound understanding of the logical design issues covered in most books on C++ programming. To be successful, you will also need a grasp of physical design concepts that, while closely tied to the technical aspects of development, include a dimension with which even expert software developers may have little or no experience.

This is the definitive book for all C++ software professionals involved in large development efforts such as databases, operating systems, compilers, and frameworks. It is the first C++ book that actually demonstrates how to design large systems, and one of the few books on object-oriented design specifically geared to practical aspects of the C++ programming language.

In this book, Lakos explains the process of decomposing large systems into physical (not inheritance) hierarchies of smaller, more manageable components. Such systems with their acyclic physical dependencies are fundamentally easier and more economical to maintain, test, and reuse than tightly interdependent systems. In addition to explaining the motivation for following good physical as well as logical design practices, Lakos provides you with a catalog of specific techniques designed to eliminate cyclic, compile-time, and link-time (physical) dependencies. He then extends these concepts from large to very large systems. The book concludes with a comprehensive top-down approach to the logical design of individual components. Appendices include a valuable design pattern "Protocol Hierarchy" designed to avoid fat interfaces while minimizing physical dependencies; the details of implementing an ANSI C compatible C++procedural interface; and a complete specification for a suite of UNIX-like tools to extract and analyze physical dependencies. Practical design rules, guidelines, and principles are also collected in an appendix and indexed for quick reference.
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Do you have creative ideas that you wish you could transform into code? Do you want to boost your problem solving and logic skills? Do you want to enhance your career by adopting an algorithmic mindset? In our increasingly digital world, coding is an essential skill. Communicating an algorithm to a machine to perform a set of tasks is vital. Beginner's Guide to Code Algorithms: Experiments to Enhance Productivity and Solve Problems written by Deepankar Maitra teaches you how to think like a programmer. The author unravels the secret behind writing code - building a good algorithm. Algorithmic thinking leads to asking the right question and enables a shift from issue resolution to value creation. Having this mindset will make you more marketable to employers. This book takes you on a problem-solving journey to expand your mind and increase your willingness to experiment with code. You will: Learn the art of building an algorithm through hands-on exercises Understand how to develop code for inspiring productivity concepts Build a mentality of developing algorithms to solve problems Develop, test, review, and improve code through guided experimentation This book is designed to develop a culture of logical thinking through intellectual stimulation. It will benefit students and teachers of programming, business professionals, as well as experienced users of Microsoft Excel who wish to become proficient with macros.

Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. What You Will Learn Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud Who This Book Is For Data scientists, machine learning engineers and architects, and AI and machine learning business leaders.

For use as a core text supplement in any course covering common LISP such as Artificial Intelligence or Concepts of Programming Languages.

Teaching students new and more powerful ways of thinking about programs, this text contains a tutorial—full of examples—that explains all the essential concepts of Lisp programming, plus an up- to-date summary of ANSI Common Lisp, listing every operator in the language. Informative and fun, it gives students everything they need to start writing programs in Lisp both efficiently and effectively, and highlights such innovative Lisp features as automatic memory management, manifest typing, closures, and more.

Make the most of R's extensive toolset R Projects For Dummies offers a unique learn-by-doing approach. You will increase the depth and breadth of your R skillset by completing a wide variety of projects. By using R's graphics, interactive, and machine learning tools, you'll learn to apply R's extensive capabilities in an array of scenarios. The depth of the project experience is unmatched by any other content online or in print. And you just might increase your statistics knowledge along the way, too! R is a free tool, and it's the basis of a huge amount of work in data science. It's taking the place of costly statistical software that sometimes takes a long time to learn. One reason is that you can use just a few R commands to create sophisticated analyses. Another is that easy-to-learn R graphics enable you make the results of those analyses available to a wide audience. This book will help you sharpen your skills by applying them in the context of projects with R, including dashboards, image processing, data reduction, mapping, and more. Appropriate for R users at all levels Helps R programmers plan and complete their own projects Focuses on R functions and packages Shows how to carry out complex analyses by just entering a few commands If you're brand new to R or just want to brush up on your skills, R Projects For Dummies will help you complete your projects with ease.