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Robust Latent Feature Learning for Incomplete Big Data (SpringerBriefs in Computer Science)
by Di WuIncomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)
by Rachid Guerraoui Nirupam Gupta Rafael PinotToday, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.
Robust Modelling and Simulation
by Idalia Flores De La Mota Antoni Guasch Miguel Mujica Mota Miquel Angel PieraThis book presents for the first time a methodology that combines the power of a modelling formalism such as colored petri nets with the flexibility of a discrete event program such as SIMIO. Industrial practitioners have seen the growth of simulation as a methodology for tacking problems in which variability is the common denominator. Practically all industrial systems, from manufacturing to aviation are considered stochastic systems. Different modelling techniques have been developed as well as mathematical techniques for formalizing the cause-effect relationships in industrial and complex systems. The methodology in this book illustrates how complexity in modelling can be tackled by the use of coloured petri nets, while at the same time the variability present in systems is integrated in a robust fashion. The book can be used as a concise guide for developing robust models, which are able to efficiently simulate the cause-effect relationships present in complex industrial systems without losing the simulation power of discrete-event simulation. In addition SIMIO's capabilities allows integration of features that are becoming more and more important for the success of projects such as animation, virtual reality, and geographical information systems (GIS).
Robust Motion Detection in Real-Life Scenarios
by Ángel P. Pobil Ester Martínez-MartínThis work proposes a complete sensor-independent visual system that provides robust target motion detection. First, the way sensors obtain images, in terms of resolution distribution and pixel neighbourhood, is studied. This allows a spatial analysis of motion to be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. Two different situations are considered: a fixed camera observing a constant background where objects are moving; and a still camera observing objects in movement within a dynamic background. This distinction lies on developing a surveillance mechanism without the constraint of observing a scene free of foreground elements for several seconds when a reliable initial background model is obtained, as that situation cannot be guaranteed when a robotic system works in an unknown environment. Other problems are also addressed to successfully deal with changes in illumination, and the distinction between foreground and background elements.
Robust Multimodal Cognitive Load Measurement
by Fang Chen Jianlong Zhou Yang Wang Kun Yu Syed Z. Arshad Ahmad Khawaji Dan ConwayThis book explores robust multimodal cognitiveload measurement with physiological and behavioural modalities, which involve theeye, Galvanic Skin Response, speech, language, pen input, mouse movement andmultimodality fusions. Factors including stress, trust, and environmentalfactors such as illumination are discussed regarding their implications forcognitive load measurement. Furthermore, dynamic workload adjustment andreal-time cognitive load measurement with data streaming are presented in orderto make cognitive load measurement accessible by more widespread applicationsand users. Finally, application examples are reviewed demonstrating thefeasibility of multimodal cognitive load measurement in practical applications. This is thefirst book of its kind to systematically introduce various computationalmethods for automatic and real-time cognitive load measurement and by doing somoves the practical application of cognitive load measurement from the domainof the computer scientist and psychologist to more general end-users, ready forwidespread implementation. Robust Multimodal CognitiveLoad Measurement is intended for researchers and practitioners involved with cognitiveload studies and communities within the computer, cognitive, and socialsciences. The book will especially benefit researchers in areas like behaviouranalysis, social analytics, human-computer interaction (HCI), intelligentinformation processing, and decision support systems.
Robust Network Compressive Sensing (SpringerBriefs in Computer Science)
by Guangtao Xue Yi-Chao Chen Feng Lyu Minglu LiThis book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3 discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world traces. The analysis demonstrates that there are different factors that contribute to the violation of the low-rank property in real data. In particular, the authors find that (1) noise, errors, and anomalies, and (2) asynchrony in the time and frequency domains lead to network-induced ambiguity and can easily cause low-rank matrices to become higher-ranked. To address the problem of noise, errors and anomalies in Chap. 4, the authors propose a robust compressive sensing technique. It explicitly accounts for anomalies by decomposing real-world data represented in matrix form into a low-rank matrix, a sparse anomaly matrix, an error term and a small noise matrix. Chapter 5 addresses the problem of lack of synchronization, and the authors propose a data-driven synchronization algorithm. It can eliminate misalignment while taking into account the heterogeneity of real-world data in both time and frequency domains. The data-driven synchronization can be applied to any compressive sensing technique and is general to any real-world data. The authors illustrates that the combination of the two techniques can reduce the ranks of real-world data, improve the effectiveness of compressive sensing and have a wide range of applications. The networks are constantly generating a wealth of rich and diverse information. This information creates exciting opportunities for network analysis and provides insight into the complex interactions between network entities. However, network analysis often faces the problems of (1) under-constrained, where there is too little data due to feasibility and cost issues in collecting data, or (2) over-constrained, where there is too much data, so the analysis becomes unscalable. Compressive sensing is an effective technique to solve both problems. It utilizes the underlying data structure for analysis. Specifically, to solve the under-constrained problem, compressive sensing technologies can be applied to reconstruct the missing elements or predict the future data. Also, to solve the over-constraint problem, compressive sensing technologies can be applied to identify significant elementsTo support compressive sensing in network data analysis, a robust and general framework is needed to support diverse applications. Yet this can be challenging for real-world data where noise, anomalies and lack of synchronization are common. First, the number of unknowns for network analysis can be much larger than the number of measurements. For example, traffic engineering requires knowing the complete traffic matrix between all source and destination pairs, in order to properly configure traffic and avoid congestion. However, measuring the flow between all source and destination pairs is very expensive or even infeasible. Reconstructing data from a small number of measurements is an underconstrained proble
Robust Python: Write Clean And Maintainable Code
by Patrick ViaforeDoes it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes.In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code.Learn why types are essential in modern development ecosystemsUnderstand how type choices such as classes, dictionaries, and enums reflect specific intentsMake Python extensible for the future without adding bloatUse popular Python tools to increase the safety and robustness of your codebaseEvaluate current code to detect common maintainability gotchasBuild a safety net around your codebase with linters and tests
Robust Quality: Powerful Integration of Data Science and Process Engineering (Continuous Improvement Series)
by Rajesh JugulumHistorically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution
Robust Recognition via Information Theoretic Learning
by Ran He Baogang Hu Xiaotong Yuan Liang WangThis Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy. The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.
Robust Representation for Data Analytics
by Sheng Li Yun FuThis book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
Robust Sliding Mode Protocols for Formation of Quadcopter Swarm (Studies in Systems, Decision and Control #521)
by Axaykumar Mehta Akash ModiThis book presents several robust sliding mode protocols for achieving the formation and tracking of Quadcopter swarm for a given pattern. In entire book, the concept of leader-follower formation control of a multi-agent system is exploited for deriving the protocols and the graph theory is used to represent the communication between the Quadcopters. The book covers two types of formation protocols of the Quadcopter swarm namely, continuous-time sliding mode protocols and discrete-time sliding mode protocols. First, the continuous-time higher order sliding mode protocols using super-twisting algorithm are designed for formation using linear and non-linear models of Quadcopter. Then, the discrete-time sliding mode protocols using power rate reaching law, discrete-time super twisting algorithm, and exponential reaching law are presented. The protocols are thoroughly analysed for robustness, chattering, control effort, and convergence time for achieving the formation. Also, the stability conditions using the Lyapunov function are derived to ensure the stability of the swarm with each protocol. Further, each chapter includes extensive simulation and comparative studies to show the efficacy of each protocol. The book will be useful to graduate students, research scholars, and professionals working in the domain of civilian and military usage of the drone technology.
Robust Speaker Recognition in Noisy Environments
by K. Sreenivasa Rao Sourjya SarkarThis book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.
Robustness and Complex Data Structures
by Roland Fried Claudia Becker Sonja KuhntThis Festschrift in honour of Ursula Gather's 60th birthday deals with modern topics in the field of robust statistical methods, especially for time series and regression analysis, and with statistical methods for complex data structures. The individual contributions of leading experts provide a textbook-style overview of the topic, supplemented by current research results and questions. The statistical theory and methods in this volume aim at the analysis of data which deviate from classical stringent model assumptions, which contain outlying values and/or have a complex structure. Written for researchers as well as master and PhD students with a good knowledge of statistics.
Robustness-Related Issues in Speaker Recognition
by Thomas Fang Zheng Lantian LiThis book presents an overview of speaker recognition technologies with an emphasis on dealing with robustness issues. Firstly, the book gives an overview of speaker recognition, such as the basic system framework, categories under different criteria, performance evaluation and its development history. Secondly, with regard to robustness issues, the book presents three categories, including environment-related issues, speaker-related issues and application-oriented issues. For each category, the book describes the current hot topics, existing technologies, and potential research focuses in the future. The book is a useful reference book and self-learning guide for early researchers working in the field of robust speech recognition.
Rocket Surgery Made Easy: Finding And Fixing Usability Problems
by Steve KrugIn this how-to companion to "Don't Make Me Think: A Common Sense Approach to Web Usability," Krug spells out an approach to usability testing that anyone can easily apply to their own Web site, application, or other product. This work makes it realistic for teams to test early and often, catching problems while it's still easy to fix them.
The Rockstars of JVZoo.com: How Regular People Have Made a Fortune Buying and Selling on the World's Fastest Growing Ecommerce Platform
by Joel CommWhat Does it Take to Be an Online RockstarJVZoo is an amazing resource for entrepreneurs to turn their knowledge into digital products and recruit hundreds or thousands in their field to promote those products. Whether you are a business consultant, lawyer, real estate agent, or even someone who knows how to build the perfect birdhouse, JVZoo allows you to put your knowledge into a format that can be sold and profited from. While the rest of the world looks for jobs, JVZoo’s members create their own.In the three years that JVZoo has been around, it’s members have generated over $100 million in sales revenue. Our members are mainly people who started making money online as a part-time venture.Rockstars of JVZoo is a compilation of case studies, written by people just like you, people who listened to their inner voices and created jobs for themselves. Everything you are about to read in this book has been achieved by ordinary people who did that one thing that separates entrepreneurs from the rest of society: they took action.
Rogue Archives: Digital Cultural Memory and Media Fandom
by Abigail De KosnikAn examination of how nonprofessional archivists, especially media fans, practice cultural preservation on the Internet and how “digital cultural memory” differs radically from print-era archiving.The task of archiving was once entrusted only to museums, libraries, and other institutions that acted as repositories of culture in material form. But with the rise of digital networked media, a multitude of self-designated archivists—fans, pirates, hackers—have become practitioners of cultural preservation on the Internet. These nonprofessional archivists have democratized cultural memory, building freely accessible online archives of whatever content they consider suitable for digital preservation. In Rogue Archives, Abigail De Kosnik examines the practice of archiving in the transition from print to digital media, looking in particular at Internet fan fiction archives.De Kosnik explains that media users today regard all of mass culture as an archive, from which they can redeploy content for their own creations. Hence, “remix culture” and fan fiction are core genres of digital cultural production. De Kosnik explores, among other things, the anticanonical archiving styles of Internet preservationists; the volunteer labor of online archiving; how fan archives serve women and queer users as cultural resources; archivists' efforts to attract racially and sexually diverse content; and how digital archives adhere to the logics of performance more than the logics of print. She also considers the similarities and differences among free culture, free software, and fan communities, and uses digital humanities tools to quantify and visualize the size, user base, and rate of growth of several online fan archives.
Rogue Archives: Digital Cultural Memory and Media Fandom
by Abigail De KosnikThe task of archiving was once entrusted only to museums, libraries, and other institutions that acted as repositories of culture in material form. But with the rise of digital networked media, a multitude of self-designated archivists -- fans, pirates, hackers -- have become practitioners of cultural preservation on the Internet. These nonprofessional archivists have democratized cultural memory, building freely accessible online archives of whatever content they consider suitable for digital preservation. In Rogue Archives, Abigail De Kosnik examines the practice of archiving in the transition from print to digital media, looking in particular at Internet fan fiction archives.De Kosnik explains that media users today regard all of mass culture as an archive, from which they can redeploy content for their own creations. Hence, "remix culture" and fan fiction are core genres of digital cultural production. De Kosnik explores, among other things, the anticanonical archiving styles of Internet preservationists; the volunteer labor of online archiving; how fan archives serve women and queer users as cultural resources; archivists' efforts to attract racially and sexually diverse content; and how digital archives adhere to the logics of performance more than the logics of print. She also considers the similarities and differences among free culture, free software, and fan communities, and uses digital humanities tools to quantify and visualize the size, user base, and rate of growth of several online fan archives.
Roguelike Development with JavaScript: Build and Publish Roguelike Genre Games with JavaScript and Phaser
by Andre Alves GarziaGo on an adventure and build a roguelike from scratch using JavaScript. With the help of the battle-tested Phaser library, you’ll go through all the steps to build a small, fun, playable web roguelite game. The author will guide you on how to add further features to the game such as populating the game with enemies, adding treasures, and so on. You will acquire technical knowledge about procedural generation and tile-based mapping as well as learn game design skills such as what makes dungeons fun and how to evoke an emotion in your game. Roguelikes are very popular with indie developers because of their focus on gameplay over graphics. You’ll see why they appeal to game designers on a budget and discover that they serve as a good platform to experiment with novel ideas and designs. Along the way, you’ll cover the increasingly popular roguelite genre that provides a hyper casual form of the genre that is approachable and often mobile. After reading this book, you’ll be ready to create your own roguelikes, to dive deep into procedural generation, and also to bring some of the techniques shown here into other genres and game projects. What You Will Learn Make use of procedural generation for dungeons, mazes, monsters, and treasure Pick up skills to use Phaser to build games Implement turn-based mechanics Use tile-based graphics Who This Book Is For Game developers who want to build something fun and who have at least some prior JavaScript programming experience.
Role Competency Matrix: A Step-By-Step Guide to an Objective Competency Management System
by Mahesh KurubaThis book discusses the Role Competency Matrix (RCM), a competency management system framework that helps organizations to quantify and manage workforce competencies and align them to business needs. An objective and transparent system for both employees and managers alike, the RCM helps managers make quantitative decisions to develop strategies for staffing needs, competency development, career progression and succession planning in the workforce. It also helps employees to identify the roles suited to their aspirations and capabilities and to take responsibility for their development.“The right people for the right roles” is key to effective workforce management, and workforce competencies must be optimally deployed to improve productivity, enhance job satisfaction and reduce attrition. At the same time, information technology (IT) businesses are under constant pressure to respond quickly to the ever-changing technology and business trends. Although, some organizations take a myopic “hire and fire” approach, in order to succeed, an organization must continually upgrade its workforce’s competencies in line with new demands.
The Role of AI in Bio-Medical Translations’ Research for the Health Care Industry: First International Conference, AIBTR 2023, Nagpur, India, September 23, 2023, Revised Selected Papers (Communications in Computer and Information Science #1987)
by Sivaram Ponnusamy Vibha Rajesh BoraThis book constitutes the revised selected papers of the First International Conference, AIBTR 2023, held in Nagpur, India, during September 22–23, 2023.The 7 full papers included in this volume were carefully reviewed and selected from 37 submissions. The papers contained in this book discuss the application of Artificial Intelligence in Biomedical Engineering for the Health Care industry.
The Role of Criticism in Understanding Problem Solving
by Samuel Fee Brian BellandIn 1991, Denis Hlynka and John Belland released Paradigms Regained, a well received reader for graduate students in the field of educational technology. The Role of Criticism in Understanding Problem Solving updates some of those ideas initially proposed in Paradigms Regained, and extends the conversation into the contemporary discourse regarding problem based learning (PBL). Paradigms proposed the idea of criticism as a third method for the conduction of educational research, the first two being qualitative and qualitative. The concept of criticism as a tool for research is not well established in educational technology, although it is well established in other educational research traditions such as Curriculum Studies. Unfortunately, it is not always clear how criticism can be applied. This book views criticism as a way to step back and look at an educational intervention within educational technology through a particular critical lens. Criticism is viewed as a valuable approach to guiding meta analyses and theoretical studies, serving to prevent the proverbial "spinning of the wheels" that often happens in educational research. By indicating new potential research questions and directions, criticism approaches can invigorate educational research. This book revisits the ideals of criticism in order to establish their usefulness for studying educational technology interventions to support problem based learning. First, a few foundational chapters set the stage for the conversations on criticism. Then, the role criticism can play in enhancing analysis and interpretation of the PBL literature is explored. Finally, case studies addressing the central concepts of the text are presented and dissected. This book represents a complete overhaul and rethinking of the use of criticism as a method for understanding and furthering the research area of PBL within the field of Educational technology.
Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions (EAI/Springer Innovations in Communication and Computing)
by Sarvesh Pandey Udai Shanker Vijayalakshmi Saravanan Rajinikumar RamalingamThis book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, and in battling natural disasters, among others. Topics include real-time applications in the current big data perspective; improving security in IoT devices; data backup techniques for systems; artificial intelligence-based outlier prediction; machine learning in OpenFlow Network; and application of deep learning in blockchain enabled applications. This book is intended for a variety of readers from professional industries, organizations, and students.
The Role of Digital Technologies in Shaping the Post-Pandemic World: 21st IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2022, Newcastle upon Tyne, UK, September 13–14, 2022, Proceedings (Lecture Notes in Computer Science #13454)
by Savvas Papagiannidis Eleftherios Alamanos Suraksha Gupta Yogesh K. Dwivedi Matti Mäntymäki Ilias O. PappasThis book constitutes the refereed proceedings of the 21st IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2022, which took place Newcastle-upon-Tyne, UK, in September 2022. The 37 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Artificial intelligence; Data and Analytics; Careers and ICT; Digital Innovation and Transformation; Electronic Services; Health and Wellbeing; Pandemic; Privacy, Trust and Security.
The Role of e-Business during the Time of Grand Challenges: 19th Workshop on e-Business, WeB 2020, Virtual Event, December 12, 2020, Revised Selected Papers (Lecture Notes in Business Information Processing #418)
by Aravinda Garimella Prasanna Karhade Abhishek Kathuria Xiao Liu Jennifer Xu Kexin ZhaoThis book constitutes revised selected papers from the 19th Workshop on e-Business, WeB 2020, which took place virtually on December 12, 2020.The purpose of WeB is to provide a forum for researchers and practitioners to discuss findings, novel ideas, and lessons learned to address major challenges and map out the future directions for e-Business. The WeB 2020 theme was “The Role of e-Business during the Time of Grand Challenges.” The 12 papers included in this volume were carefully reviewed and selected from a total of 24 submissions. The contributions are organized in topical sections as follows: Cybersecurity and COVID-19 challenges; digital platforms; and managing human factors in e-business.