Limited to a strict interpretation of its definition, open source consists of a set of rules which apply to a piece of software and which specify how the software and derivatives of it may be used. However, it is widely seen as much more than a simple licensing agreement, it is a ´´philoshophy´´, a ´´production model´´, a ´´way of organizing projects´´, or even ´´a new innovation model´´. But how are open source projects organized and how is work coordinated and distributed between its developers? This work contributes by examining actual source code changes, comparing 29 projects. Which developers collaborate in the same files and wich work exclusively in their own domain? Looking for patterns across projects, this work attempts to identify coordination styles in open source projects.
Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you´ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You´ll Learn Understand the fundamentals of the Python programming language Apply Python to numerical computational programming projects in engineering and science Discover the Pythonic way of life Apply data types, operators, and arrays Carry out plotting for visualization Work with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you´ll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python´s immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.
Work with blockchain and understand its potential application beyond cryptocurrencies in the domains of healthcare, Internet of Things, finance, decentralized organizations, and open science. Featuring case studies and practical insights generated from a start-up spun off from the author´s own lab, this book covers a unique mix of topics not found in others and offers insight into how to overcome real hurdles that arise as the market and consumers grow accustomed to blockchain based start-ups. You´ll start with a review of the historical origins of blockchain and explore the basic cryptography needed to make the blockchain work for Bitcoin. You will then learn about the technical advancements made in the surrounded ecosystem: the Ethereum virtual machine, Solidity, Colored Coins, the Hyperledger Project, Blockchain-as-a-service offered through IBM, Microsoft and more. This book looks at the consequences of machine-to-machine transactions using the blockchain socially, technologically, economically and politically. Blockchain Enabled Applications provides you with a clear perspective of the ecosystem that has developed around the blockchain and the various industries it has penetrated. What You´ll Learn Implement the code-base from Fabric and Sawtooth, two open source blockchain-efforts being developed under the Hyperledger Project Evaluate the benefits of integrating blockchain with emerging technologies, such as machine learning and artificial intelligence in the cloud Use the practical insights provided by the case studies to your own projects or start-up ideas Set up a development environment to compile and manage projects Who This Book Is For Developers who are interested in learning about the blockchain as a data-structure, the recent advancements being made and how to implement the code-base. Decision makers within large corporations (product managers, directors or CIO level executives) interested in implementing the blockchain who need more practical insights and not just theory.
This edited volume explores the intersection between philosophy and computing. It features work presented at the 2016 annual meeting of the International Association for Computing and Philosophy. The 23 contributions to this volume neatly represent a cross section of 40 papers, four keynote addresses, and eight symposia as they cut across six distinct research agendas. The volume begins with foundational studies in computation and information, epistemology and philosophy of science, and logic. The contributions next examine research into computational aspects of cognition and philosophy of mind. This leads to a look at moral dimensions of man-machine interaction as well as issues of trust, privacy, and justice. This multi-disciplinary or, better yet, a-disciplinary investigation reveals the fruitfulness of erasing distinctions among and boundaries between established academic disciplines. This should come as no surprise. The computational turn itself is a-disciplinary and no former discipline, whether scientific, artistic, or humanistic, has remained unchanged. Rigorous reflection on the nature of these changes opens the door to inquiry into the nature of the world, what constitutes our knowledge of it, and our understanding of our place in it. These investigations are only just beginning. The contributions to this volume make this clear: many encourage further research and end with open questions.
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition , presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. W hat You´ll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.
Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christen´s book is divided into three parts: Part I, ´´Overview´´, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, ´´Steps of the Data Matching Process´´, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, ´´Further Topics´´, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
The introduction of public key cryptography (PKC) was a critical advance in IT security. In contrast to symmetric key cryptography, it enables confidential communication between entities in open networks, in particular the Internet, without prior contact. Beyond this PKC also enables protection techniques that have no analogue in traditional cryptography, most importantly digital signatures which for example support Internet security by authenticating software downloads and updates. Although PKC does not require the confidential exchange of secret keys, proper management of the private and public keys used in PKC is still of vital importance: the private keys must remain private, and the public keys must be verifiably authentic. So understanding so-called public key infrastructures (PKIs) that manage key pairs is at least as important as studying the ingenious mathematical ideas underlying PKC. In this book the authors explain the most important concepts underlying PKIs and discuss relevant standards, implementations, and applications. The book is structured into chapters on the motivation for PKI, certificates, trust models, private keys, revocation, validity models, certification service providers, certificate policies, certification paths, and practical aspects of PKI. This is a suitable textbook for advanced undergraduate and graduate courses in computer science, mathematics, engineering, and related disciplines, complementing introductory courses on cryptography. The authors assume only basic computer science prerequisites, and they include exercises in all chapters and solutions in an appendix. They also include detailed pointers to relevant standards and implementation guidelines, so the book is also appropriate for self-study and reference by industrial and academic researchers and practitioners.
Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. What You Will Learn Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots Who This Book Is For Developers, programmers, and hobbyists who have basic programming knowledge. The book can be used by existing chatbot developers to gain a better understanding of analytics and the business side of bots.