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Fault-Tolerant Design
by Elena DubrovaThis textbook serves as an introduction to fault-tolerance, intended for upper-division undergraduate students, graduate-level students and practicing engineers in need of an overview of the field. Readers will develop skills in modeling and evaluating fault-tolerant architectures in terms of reliability, availability and safety. They will gain a thorough understanding of fault tolerant computers, including both the theory of how to design and evaluate them and the practical knowledge of achieving fault-tolerance in electronic, communication and software systems. Coverage includes fault-tolerance techniques through hardware, software, information and time redundancy. The content is designed to be highly accessible, including numerous examples and exercises. Solutions and powerpoint slides are available for instructors.
Fault-Tolerant Search Algorithms
by Ferdinando CicaleseWhy a book on fault-tolerant search algorithms? Searching is one of the fundamental problems in computer science. Time and again algorithmic and combinatorial issues originally studied in the context of search find application in the most diverse areas of computer science and discrete mathematics. On the other hand, fault-tolerance is a necessary ingredient of computing. Due to their inherent complexity, information systems are naturally prone to errors, which may appear at any level - as imprecisions in the data, bugs in the software, or transient or permanent hardware failures. This book provides a concise, rigorous and up-to-date account of different approaches to fault-tolerance in the context of algorithmic search theory. Thanks to their basic structure, search problems offer insights into how fault-tolerant techniques may be applied in various scenarios. In the first part of the book, a paradigmatic model for fault-tolerant search is presented, the Ulam--Rényi problem. Following a didactic approach, the author takes the reader on a tour of Ulam--Rényi problem variants of increasing complexity. In the context of this basic model, fundamental combinatorial and algorithmic issues in the design of fault-tolerant search procedures are discussed. The algorithmic efficiency achievable is analyzed with respect to the statistical nature of the error sources, and the amount of information on which the search algorithm bases its decisions. In the second part of the book, more general models of faults and fault-tolerance are considered. Special attention is given to the application of fault-tolerant search procedures to specific problems in distributed computing, bioinformatics and computational learning. This book will be of special value to researchers from the areas of combinatorial search and fault-tolerant computation, but also to researchers in learning and coding theory, databases, and artificial intelligence. Only basic training in discrete mathematics is assumed. Parts of the book can be used as the basis for specialized graduate courses on combinatorial search, or as supporting material for a graduate or undergraduate course on error-correcting codes.
Faux Paw's Adventures In The Internet: Keeping Children Safe Online
by Jacalyn Leavitt Sally Linford Laura Bush J. Chad Erekson"I encourage all adults to teach children the basic principles of online safety that are found in this book. " --First Lady Laura Bush, from the Foreword <P><P> Wait 'til you hear what almost happened to me! The Internet is like a big city with great places to go, but you have to be careful! I know. I had a REAL adventure on the Internet, and it wasn't the fun kind. When I tell you what almost happened, you'll see why it's so important to follow the rules for online safety! Read this book and you'll find out what I'm talking about. Keep safe, okay? G2G (that's "got to go" online--but you knew that!) These "3 KEEPs" help me keep safe online I KEEP SAFE all my personal information I KEEP AWAY from online strangers I KEEP TELLING my parents or a trusted adult what I see on the Internet <P> A word to adults If there's a child in your life who uses the Internet, it's your job to help them keep safe from online predators. <P> Created for the Internet Keep Safe Coalition, Faux Paw--an adventurous, six-toed, Web-surfing cat--is here to help! With a foreword by First Lady Laura Bush, this colorful book and animated movie will give kids wise advice and you a place to start talking to them.
FBML Essentials: Facebook Markup Language Fundamentals
by Jesse StayDo you have an idea for a Facebook application? With FBML Essentials, you'll learn how to build it quickly using the Facebook Markup Language (FBML) and other easy-to-use tools in the site's framework. If you can develop a website with HTML, writing a Facebook application with the help of this book will be a breeze. Of course, Facebook is not just another website. Any applications you write for it will have a potential audience of 16 million dedicated users. It's not just another social networking site, either. Under the surface, Facebook is pretty sophisticated, with a development toolkit that includes it's own modified version of HTML -- FBML -- to customize the look and feel of your Facebook applications. With FBML Essentials, you not only learn how to get started with this toolkit, you also get a complete reference on every FBML tag Facebook has ever written, with examples of how each tag works and advice on the best ways to use these tags in your code. This book includes:A walkthrough of a sample Facebook application Design rules for using images, CSS, JavaScript, and forms Specific chapters on tags -- authorization tags, logic tags, user/group tags, profile-specific tags, and more Messaging and alerts using FBML Creating forms with FBML Facebook navigation Notifications and requests Dynamic FBML attributes, including MockAJAX How to test your FBML code A chapter on FBJS, Facebook's version of JavaScript If you want to try your hand at writing a Facebook application, you have a willing audience, an easy-to-use toolkit, and the perfect guide to get you started. FBML Essentials will help you take your idea from conception to working application in no time.
F'D Companies: Spectacular Dot-com Flameouts
by Philip J. KaplanNot long ago, the world was awash with venture capital in search of the next Yahoo! or Amazon.com. No product, no experience, no technology, no business plan -- no problem. You could still get $40 million from investors to start up your dot-com. And you could get people to work around the clock for stock options and the promise of millions. Then, around April 2000, it all came crashing down. Smart investors, esteemed analysts, and the business press found themselves asking: Who knew people wouldn't rush out to trade in their U.S. dollars for a virtual currency called Flooz? Who knew people wouldn't blow all their Flooz on a used car from the guys at iMotors.com? And who needed a used car from iMotors.com when they could just sit at home and have 40-lb. bags of dog food delivered to them by a sock puppet? F'd Companies captures the waste, greed, and human stupidity of more than 100 dot-com companies. Written in Philip J. Kaplan's popular, cynical style, these profiles are filled with colorful anecdotes, factoids, and information unavailable anywhere else. Together they form a gleeful encyclopedia of how not to run a business. They also capture a truly remarkable period of history. F'd Companies is required reading for everyone involved in the "new economy" -- assuming your severance check can cover the cost.
FE Computation on Accuracy Fabrication of Ship and Offshore Structure Based on Processing Mechanics
by Hong ZHOU Jiangchao WANGThis book provides insight on processing mechanics during ship and offshore structure, and researchers, scientists, and engineers in the field of manufacturing process mechanics can benefit from the book. This book is written by subject experts based on the recent research results in FE computation on accuracy fabrication of ship and offshore structures based on processing mechanics. In order to deal with actual engineering problems during construction of ship and offshore structure, it proposes advanced computational approaches such as thermal elastic–plastic and elastic FE computations and employed to examine physical behavior and clarifies generation mechanism of mechanical response. As such, this book provides valuable knowledge, useful methods, and practical algorithms that can be considered in manufacturing process mechanics.
Fear, Hate, and Victimhood: How George Wallace Wrote the Donald Trump Playbook (Race, Rhetoric, and Media Series)
by Andrew E. StonerWhen Donald Trump announced his campaign for president in 2015, journalists, historians, and politicians alike attempted to compare his candidacy to that of Governor George Wallace. Like Trump, Wallace, who launched four presidential campaigns between 1964 and 1976, utilized rhetoric based in resentment, nationalism, and anger to sway and eventually captivate voters among America’s white majority. Though separated by almost half a century, the campaigns of both Wallace and Trump broke new grounds for political partisanship and divisiveness.In Fear, Hate, and Victimhood: How George Wallace Wrote the Donald Trump Playbook, author Andrew E. Stoner conducts a deep analysis of the two candidates, their campaigns, and their speeches and activities, as well as their coverage by the media, through the lens of demagogic rhetoric. Though past work on Wallace argues conventional politics overcame the candidate, Stoner makes the case that Wallace may in fact be a prelude to the more successful Trump campaign.Stoner considers how ideas about “in-group” and “out-group” mentalities operate in politics, how anti-establishment views permeate much of the rhetoric in question, and how expressions of victimhood often paradoxically characterize the language of a leader praised for “telling it like it is.” He also examines the role of political spectacle in each candidate’s campaigns, exploring how media struggles to respond to—let alone document—demagogic rhetoric.Ultimately, the author suggests that the Trump presidency can be understood as an actualized version of the Wallace presidency that never was. Though vast differences exist, the demagogic positioning of both men provides a framework to dissect these times—and perhaps a valuable warning about what is possible in our highly digitized information society.
Fearless Cross-Platform Development with Delphi: Expand your Delphi skills to build a new generation of Windows, web, mobile, and IoT applications
by David CorneliusLearn to rapidly build and deploy cross-platform applications from a single codebase with practical, real-world solutions using the mature Delphi 10.4 programming environmentKey FeaturesImplement Delphi's modern features to build professional-grade Windows, web, mobile, and IoT applications and powerful serversBecome a Delphi code and project guru by learning best practices and techniques for cross-platform developmentDeploy your complete end-to-end application suite anywhereBook DescriptionDelphi is a strongly typed, event-driven programming language with a rich ecosystem of frameworks and support tools. It comes with an extensive set of web and database libraries for rapid application development on desktop, mobile, and internet-enabled devices. This book will help you keep up with the latest IDE features and provide a sound foundation of project management and recent language enhancements to take your productivity to the next level.You'll discover how simple it is to support popular mobile device features such as sensors, cameras, and GPS. The book will help you feel comfortable working with FireMonkey and styles and incorporating 3D user interfaces in new ways. As you advance, you'll be able to build cross-platform solutions that not only look native but also take advantage of a wide array of device capabilities. You'll also learn how to use embedded databases, such as SQLite and InterBase ToGo, synchronizing them with your own custom backend servers or modules using the powerful RAD Server engine. The book concludes by sharing tips for testing and deploying your end-to-end application suite for a smooth user experience.By the end of this book, you'll be able to deliver modern enterprise applications using Delphi confidently.What you will learnDiscover the latest enhancements in the Delphi IDEOvercome the barriers that hold you back from embracing cross-platform developmentBecome fluent with FireMonkey controls, styles, LiveBindings, and 3D objectsBuild Delphi packages to extend RAD Server or modularize your applicationsUse FireDAC to get quick and direct access to any dataLeverage IoT technologies such as Bluetooth and Beacons and learn how to put your app on a Raspberry PiEnable remote apps with backend servers on Windows and Linux through REST APIsDevelop modules for IIS and Apache web serversWho this book is forThis book is for Delphi developers interested in expanding their skillset beyond Windows programming by creating professional-grade applications on multiple platforms, including Windows, Mac, iOS, Android, and back-office servers. You'll also find this book useful if you're a developer looking to upgrade your knowledge of Delphi to keep up with the latest changes and enhancements in this powerful toolset. Some Delphi programming experience is necessary to make the most out of this book.
Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)
by Frederic Ros Rabia RiadThis book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
A Feature-Centric View of Information Retrieval (The Information Retrieval Series #27)
by Donald MetzlerCommercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model. In addition, he describes several extensions, such as an automatic feature selection algorithm and a query expansion framework. The resulting model and extensions provide a flexible framework for highly effective retrieval across a wide range of tasks and data sets. A Feature-Centric View of Information Retrieval provides graduate students, as well as academic and industrial researchers in the fields of information retrieval and Web search with a modern perspective on information retrieval modeling and Web searches.
Feature Coding for Image Representation and Recognition
by Yongzhen Huang Tieniu TanThis brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) Discuss the applications of feature coding in different visual tasks, analyze the influence of some key factors in feature coding with intensive experimental studies; (5) Provide the suggestions of how to apply different feature coding methods and forecast the potential directions for future work on the topic. It is suitable for students, researchers, practitioners interested in object recognition.
Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series)
by Max Kuhn Kjell JohnsonThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Feature Engineering Bookcamp
by Sinan OzdemirDeliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book&’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results.In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models to extract hidden patterns in data Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You&’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model&’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you&’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more. About the technology Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline. About the book Feature Engineering Bookcamp walks you through six hands-on projects where you&’ll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You&’ll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains—from natural language processing to time-series analysis. What's inside Identify and implement feature transformations Build machine learning pipelines with unstructured data Quantify and minimize bias in ML pipelines Use feature stores to build real-time feature engineering pipelines Enhance existing pipelines by manipulating input data About the reader For experienced machine learning engineers familiar with Python. About the author Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning. Table of Contents 1 Introduction to feature engineering 2 The basics of feature engineering 3 Healthcare: Diagnosing COVID-19 4 Bias and fairness: Modeling recidivism 5 Natural language processing: Classifying social media sentiment 6 Computer vision: Object recognition 7 Time series analysis: Day trading with machine learning 8 Feature stores 9 Putting it all together
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
by Alice Zheng Amanda CasariFeature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.You’ll examine:Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transformsNatural text techniques: bag-of-words, n-grams, and phrase detectionFrequency-based filtering and feature scaling for eliminating uninformative featuresEncoding techniques of categorical variables, including feature hashing and bin-countingModel-based feature engineering with principal component analysisThe concept of model stacking, using k-means as a featurization techniqueImage feature extraction with manual and deep-learning techniques
Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Guozhu Dong Huan LiuFeature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems
by Michael Smith Sinan Ozdemir Divya SusarlaA perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book Description Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is for If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.
Feature Extraction in Medical Image Retrieval: A New Design of Wavelet Filter Banks
by Aswini Kumar Samantaray Amol D. RahulkarMedical imaging is fundamental to modern healthcare, and its widespread use has resulted in creation of image databases. These repositories contain images from a diverse range of modalities, multidimensional as well as co-aligned multimodality images. These image collections offer opportunity for evidence-based diagnosis, teaching, and research. Advances in medical image analysis over last two decades shows there are now many algorithms and ideas available that allow to address medical image analysis tasks in commercial solutions with sufficient performance in terms of accuracy, reliability and speed. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. This book emphasizes the design of wavelet filter-banks as efficient and effective feature descriptors for medical image retrieval.Firstly, a generalized novel design of a family of multiplier-free orthogonal wavelet filter-banks is presented. In this, the dyadic filter coefficients are obtained based on double-shifting orthogonality property with allowable deviation from original filter coefficients. Next, a low complex symmetric Daub-4 orthogonal wavelet filter-bank is presented. This is achieved by slightly altering the perfect reconstruction condition to make designed filter-bank symmetric and to obtain dyadic filter coefficients. In third contribution, the first dyadic Gabor wavelet filter-bank is presented based on slight alteration in orientation parameter without disturbing remaining Gabor wavelet parameters. In addition, a novel feature descriptor based on the design of adaptive Gabor wavelet filter-bank is presented. The use of Maximum likelihood estimation is suggested to measure the similarity between the feature vectors of heterogeneous medical images. The performance of the suggested methods is evaluated on three different publicly available databases namely NEMA, OASIS and EXACT09. The performance in terms of average retrieval precision, average retrieval recall and computational time are compared with well-known existing methods.
Feature Learning and Understanding: Algorithms and Applications (Information Fusion and Data Science)
by Henry Leung Haitao Zhao Zhihui Lai Xianyi ZhangThis book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.
Feature Management with LaunchDarkly: Discover safe ways to make live changes in your systems and master testing in production
by Michael Gillett John KodumalMake code deployments completely safe and change your application in production in real time with LaunchDarkly using percentage-based rollouts, kill switches, and A/B and multi-variant testingKey FeaturesLearn how to work with LaunchDarkly to turn features on and off within your production applicationsExplore the ways in which feature management can change how software is built and how teams workMaster every aspect of LaunchDarkly's functionality to test in production and learn from your usersBook DescriptionOver the past few years, DevOps has become the de facto approach for designing, building, and delivering software. Feature management is now extending the DevOps methodology to allow applications to change on demand and run experiments to validate the success of new features. If you want to make feature management happen, LaunchDarkly is the tool for you. This book explains how feature management is key to building modern software systems. Starting with the basics of LaunchDarkly and configuring simple feature flags to turn features on and off, you'll learn how simple functionality can be applied in more powerful ways with percentage-based rollouts, experimentation, and switches. You'll see how feature management can change the way teams work and how large projects, including migrations, are planned. Finally, you'll discover various uses of every part of the tool to gain mastery of LaunchDarkly. This includes tips and tricks for experimentation, identifying groups and segments of users, and investigating and debugging issues with specific users and feature flag evaluations. By the end of the book, you'll have gained a comprehensive understanding of LaunchDarkly, along with knowledge of the adoption of trunk-based development workflows and methods, multi-variant testing, and managing infrastructure changes and migrations.What you will learnGet to grips with the basics of LaunchDarkly and feature flagsRoll out a feature to a percentage or group of usersFind out how to experiment with multi-variant and A/B testingDiscover how to adopt a trunk-based development workflowExplore methods to manage infrastructure changes and migrationsGain an in-depth understanding of all aspects of the LaunchDarkly toolWho this book is forThis book is for developers, quality assurance engineers and DevOps engineers. This includes individuals who want to decouple the deployment of code from the release of a feature, run experiments in production, or understand how to change processes to build and deploy software. Software engineers will also benefit from learning how feature management can be used to improve products and processes. A basic understanding of software is all that you need to get started with this book as it covers the basics before moving on to more advanced topics.
Feature-Oriented Software Product Lines
by Sven Apel Don Batory Christian Kästner Gunter SaakeWhile standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments, nor the needs and wishes of individual customers. Software product lines reconcile mass production and standardization with mass customization in software engineering. Ideally, based on a set of reusable parts, a software manufacturer can generate a software product based on the requirements of its customer. The concept of features is central to achieving this level of automation, because features bridge the gap between the requirements the customer has and the functionality a product provides. Thus features are a central concept in all phases of product-line development. The authors take a developer's viewpoint, focus on the development, maintenance, and implementation of product-line variability, and especially concentrate on automated product derivation based on a user's feature selection. The book consists of three parts. Part I provides a general introduction to feature-oriented software product lines, describing the product-line approach and introducing the product-line development process with its two elements of domain and application engineering. The pivotal part II covers a wide variety of implementation techniques including design patterns, frameworks, components, feature-oriented programming, and aspect-oriented programming, as well as tool-based approaches including preprocessors, build systems, version-control systems, and virtual separation of concerns. Finally, part III is devoted to advanced topics related to feature-oriented product lines like refactoring, feature interaction, and analysis tools specific to product lines. In addition, an appendix lists various helpful tools for software product-line development, along with a description of how they relate to the topics covered in this book. To tie the book together, the authors use two running examples that are well documented in the product-line literature: data management for embedded systems, and variations of graph data structures. They start every chapter by explicitly stating the respective learning goals and finish it with a set of exercises; additional teaching material is also available online. All these features make the book ideally suited for teaching - both for academic classes and for professionals interested in self-study.
Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering (Studies in Computational Intelligence #816)
by Laith Mohammad AbualigahThis book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities.Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature.
Feature Selection for Data and Pattern Recognition
by Urszula Stańczyk Lakhmi C. JainThis research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
Feature Store for Machine Learning: Curate, discover, share and serve ML features at scale
by Jayanth Kumar JLearn how to leverage feature stores to make the most of your machine learning modelsKey FeaturesUnderstand the significance of feature stores in the ML life cycleDiscover how features can be shared, discovered, and re-usedLearn to make features available for online models during inferenceBook DescriptionFeature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started.Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time.By the end of this book, you'll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud.What you will learnUnderstand the significance of feature stores in a machine learning pipelineBecome well-versed with how to curate, store, share and discover features using feature storesExplore the different components and capabilities of a feature storeDiscover how to use feature stores with batch and online modelsAccelerate your model life cycle and reduce costsDeploy your first feature store for production use casesWho this book is forIf you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.
Federal IT Capital Planning and Investment Control (with CD)
by Thomas G. Kessler DBA Cisa Patricia A. Kelley DPAReduce risk and improve the overall performance of IT assets! Federal IT Capital Planning and Investment Control is the first book to provide a comprehensive look at the IT capital planning and investment control (CPIC) process. Written from a practitioner's perspective, this book covers a range of topics designed to provide both strategic and operational perspectives on IT CPIC. From planning to evaluation, this valuable resource helps managers and analysts at all levels realize the full benefits of the CPIC process. •Explore the full range of IT investment principles and practices •Learn CPIC project management techniques including earned-value management, integrated baseline review, cost-benefit analysis, and risk-adjusted cost and schedule estimates •Identify strategies to improve how your organization manages its IT portfolio and selects, controls, and evaluates investments •Discover how to leverage scarce IT resources and align investments with program priorities •Benefit from the in-depth coverage—excellent for the experienced as well as those new to the CPIC process
Federal Trade Commission Privacy Law and Policy
by Chris Jay HoofnagleThe Federal Trade Commission, a US agency created in 1914 to police the problem of 'bigness', has evolved into the most important regulator of information privacy - and thus innovation policy - in the world. Its policies profoundly affect business practices and serve to regulate most of the consumer economy. In short, it now regulates our technological future. Despite its stature, however, the agency is often poorly understood by observers and even those who practice before it. This volume by Chris Jay Hoofnagle - an internationally recognized scholar with more than fifteen years of experience interacting with the FTC - is designed to redress this confusion by explaining how the FTC arrived at its current position of power. It will be essential reading for lawyers, legal academics, political scientists, historians and anyone else interested in understanding the FTC's privacy activities and how they fit in the context of the agency's broader consumer protection mission.