- Table View
- List View
Resource Discovery, Navigability and Trust Management Techniques for IoT and SIoT
by Venugopal K R Roopa M S Santosh PattarA ready reference for the next-generation discovery techniques, this book presents advanced research findings on resource discovery, network navigability, and trust management on the Internet of Things (IoT) and Social Internet of Things (SIoT) ecosystems. It discusses the benefits of integrating social networking concepts into the Internet of Things to find the preferable, reliable, scalable, and near-optimal detection of things or services. It explores the concepts of the Social Internet of Things in different domains of IoT, such as the Internet of Vehicles and the Industrial Internet of Things. This book works to recognize and respond to user queries and improve service provisioning, find the optimal solution for the link selection in the SIoT structure, develop large-scale platforms, and provide a smart mechanism for trust evaluation.· Covers the rapid advancements in low-cost sensor manufacturing, communication protocols, embedded systems, actuators, and hardware miniaturization that have contributed to the exponential growth of the IoT· Presents the fundamentals of a search system for sensor search and resource discovery in an IoT ecosystem· Includes the applicability of different search techniques across several application domains of the IoT under various use case scenarios· Discusses the thrust areas in SIoT (service discovery and composition, network navigability, relationship management, and trustworthiness management) and presents several prerequisites, challenges, and use case scenarios· Provides insights into current challenges in the domains of Internet of Things and Social Internet of ThingsThis book will be helpful to researchers, scholars, and postgraduate students in Computer Science and Information Technology departments.
Resource-Efficient Medical Image Analysis: First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13543)
by Xinxing Xu Xiaomeng Li Dwarikanath Mahapatra Li Cheng Caroline Petitjean Huazhu FuThis book constitutes the refereed proceedings of the first MICCAI Workshop on Resource-Efficient Medical Image Analysis, REMIA 2022, held in conjunction with MICCAI 2022, in September 2022 as a hybrid event. REMIA 2022 accepted 13 papers from the 19 submissions received. The workshop aims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations.
Resource Management and Performance Analysis of Wireless Communication Networks
by Shunfu Jin Wuyi YueWith the diversification of Internet services and the increase in mobile users, efficient management of network resources has become an extremely important issue in the field of wireless communication networks (WCNs). Adaptive resource management is an effective tool for improving the economic efficiency of WCN systems as well as network design and construction, especially in view of the surge in mobile device demands.This book presents modelling methods based on queueing theory and Markov processes for a wide variety of WCN systems, as well as precise and approximate analytical solution methods for the numerical evaluation of the system performance.This is the first book to provide an overview of the numerical analyses that can be gleaned by applying queueing theory, traffic theory and other analytical methods to various WCN systems. It also discusses the recent advances in the resource management of WCNs, such as broadband wireless access networks, cognitive radio networks, and green cloud computing. It assumes a basic understanding of computer networks and queueing theory, and familiarity with stochastic processes is also recommended.The analysis methods presented in this book are useful for first-year-graduate or senior computer science and communication engineering students. Providing information on network design and management, performance evaluation, queueing theory, game theory, intelligent optimization, and operations research for researchers and engineers, the book is also a valuable reference resource for students, analysts, managers and anyone in the industry interested in WCN system modelling, performance analysis and numerical evaluation.
Resource Management for Big Data Platforms
by Florin Pop Joanna Kołodziej Beniamino Di MartinoServing as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.
Resource Management for Device-to-Device Underlay Communication
by Chen Xu Zhu Han Lingyang SongDevice-to-Device (D2D) communication will become a key feature supported by next generation cellular networks, a topic of enormous importance to modern communication. Currently, D2D serves as an underlay to the cellular network as a means to increase spectral efficiency. Although D2D communication brings large benefits in terms of system capacity, it also causes interference as well as increased computation complexity to cellular networks as a result of spectrum sharing. Thus, efficient resource management must be performed to guarantee a target performance level of cellular communication. This brief presents the state-of-the-art research on resource management for D2D communication underlaying cellular networks. Those who work with D2D communication will use this book's information to help ensure their work is as efficient as possible. Along with the survey of existing work, this book also includes the fundamental theories, key techniques, and applications.
Resource Management for Energy and Spectrum Harvesting Sensor Networks
by Deyu Zhang Zhigang Chen Haibo Zhou Xuemin Sherman ShenThis SpringerBrief offers a comprehensive review and in-depth discussion of the current research on resource management. The authors explain how to best utilize harvested energy and temporally available licensed spectrum. Throughout the brief, the primary focus is energy and spectrum harvesting sensor networks (ESHNs) including energy harvesting (EH)-powered spectrum sensing and dynamic spectrum access. To efficiently collect data through the available licensed spectrum, this brief examines the joint management of energy and spectrum. An EH-powered spectrum sensing and management scheme for Heterogeneous Spectrum Harvesting Sensor Networks (HSHSNs) is presented in this brief. The scheme dynamically schedules the data sensing and spectrum access of sensors in ESHSNs to optimize the network utility, while considering the stochastic nature of EH process, PU activities and channel conditions. This brief also provides useful insights for the practical resource management scheme design for ESHSNs and motivates a new line of thinking for future sensor networking. Professionals, researchers, and advanced-level students in electrical or computer engineering will find the content valuable.
Resource Management for Internet of Things
by Flávia C. Delicato Paulo F. Pires Thais BatistaThis book investigates the pressing issue of resource management for Internet of Things (IoT). The unique IoT ecosystem poses new challenges and calls for unique and bespoke solutions to deal with these challenges. Using a holistic approach, the authors present a thorough study into the allocation of the resources available within IoT systems to accommodate application requirements. This is done by investigating different functionalities and architectural approaches involved in a basic workflow for managing the lifecycle of resources in an IoT system. Resource Management for the Internet of Things will be of interest to researchers and students as well as professional developers interested in studying the IoT paradigm from data acquisition to the delivery of value-added services for the end user.
Resource Management for Multimedia Services in High Data Rate Wireless Networks
by Ruonan Zhang Lin Cai Jianping PanThis brief offers a valuable resource on principles of quality-of-service (QoS) provisioning and the related link-layer resource management techniques for high data-rate wireless networks. The primary emphasis is on protocol modeling and analysis. It introduces media access control (MAC) protocols, standards of wireless local area networks (WLANs), wireless personal area networks (WPANs), and wireless body area networks (WBANs), discussing their key technologies, applications, and deployment scenarios. The main analytical approaches and models for performance analysis of the fundamental resource scheduling mechanisms, including the contention-based, reservation-based, and hybrid MAC, are presented. To help readers understand and evaluate system performance, the brief contains a range of simulation results. In addition, a thorough bibliography provides an additional tool. This brief is an essential resource for engineers, researchers, students, and users of wireless networks.
Resource Management in Real-Time Systems and Networks
by C. Siva Ram Murthy G. ManimaranReal-time systems and networks are of increasing importance in many applications, including automated factories, telecommunication systems, defense systems, and space systems. This book introduces the concepts and state-of-the-art research developments of resource management in real-time systems and networks. Unlike other texts in the field, it covers the entire spectrum of issues in resource management, including task scheduling in uniprocessor real-time systems; task scheduling, fault-tolerant task scheduling, and resource reclaiming in multiprocessor real-time systems; conventional task scheduling and object-based task scheduling in distributed real-time systems; message scheduling; QoS routing; dependable communication; multicast communication; and medium access protocols in real-time networks. It provides algorithmic treatments for all of the issues addressed, highlighting the intuition behind each algorithm and giving examples. The book also includes two chapters of case studies.
Resource Management in Utility and Cloud Computing
by Han Zhao Xiaolin LiThis SpringerBrief reviews the existing market-oriented strategies for economically managing resource allocation in distributed systems. It describes three new schemes that address cost-efficiency, user incentives, and allocation fairness with regard to different scheduling contexts. The first scheme, taking the Amazon EC2(tm) market as a case of study, investigates the optimal resource rental planning models based on linear integer programming and stochastic optimization techniques. This model is useful to explore the interaction between the cloud infrastructure provider and the cloud resource customers. The second scheme targets a free-trade resource market, studying the interactions amongst multiple rational resource traders. Leveraging an optimization framework from AI, this scheme examines the spontaneous exchange of resources among multiple resource owners. Finally, the third scheme describes an experimental market-oriented resource sharing platform inspired by eBay's transaction model. The study presented in this book sheds light on economic models and their implication to the utility-oriented scheduling problems.
Resource Management on Distributed Systems: Principles and Techniques
by Shikharesh MajumdarComprehensive guide to the principles, algorithms, and techniques underlying resource management for clouds, big data, and sensor-based systems Resource Management on Distributed Systems provides helpful guidance by describing algorithms and techniques for managing resources on parallel and distributed systems, including grids, clouds, and parallel processing-based platforms for big data analytics. The book focuses on four general principles of resource management and their impact on system performance, energy usage, and cost, including end-of-chapter exercises. The text includes chapters on sensors, autoscaling on clouds, complex event processing for streaming data, and data filtering techniques for big data systems. The book also covers results of applying the discussed techniques on simulated as well as real systems (including clouds and big data processing platforms), and techniques for handling errors associated with user predicted task execution times. Written by a highly qualified academic with significant research experience in the field, Resource Management on Distributed Systems includes information on sample topics such as: Attributes of parallel/distributed applications that have an intimate relationship with system behavior and performance, plus their related performance metrics.Handling a lack of a prior knowledge of local operating systems on individual nodes in a large system.Detection and management of complex events (that correspond to the occurrence of multiple raw events) on a platform for streaming analytics.Techniques for reducing data latency for multiple operator-based queries in an environment processing large textual documents. With comprehensive coverage of core topics in the field, Resource Management on Distributed Systems is a comprehensive guide to resource management in a single publication and is an essential read for professionals, researchers and students working with distributed systems.
Resource, Mobility, and Security Management in Wireless Networks and Mobile Communications (Wireless Networks and Mobile Communications)
by Yan Zhang Honglin Hu Masayuki FujiseOrganized into three parts, Resource, Mobility, and Security Management in Wireless Networks and Mobile Communications examines the inherent constraint of limited bandwidth and unreliable time-varying physical link in the wireless system, discusses the demand to realize the service continuity in the single-hop or multi-hop wireless networks, and ex
Resource-Oriented Computing with NetKernel: Taking REST Ideas to the Next Level
by Tom GeudensTake resource-oriented computing out for a spin with this hands-on introduction to NetKernel, and discover how ROC can improve the way you design and implement software and software systems. Learn how ROC’s new approach combines core ideas from the REST architectural style with the Unix development model. By using NetKernel to create and then string simple services together, you can develop complex systems that scale as easily as the Internet does. Author Tom Geudens helps you create several NetKernel modules right away, and then walks you through the results to demonstrate their effectiveness.Create, test, and document Netkernel modules from scratchLearn the basic principles of ROC’s abstract computing modelDesign an interface in NetKernel that lets you insert, update, delete, and select actions in MongoDBUse the Visualizer to trace information about root requests processed by NetKernelHandle resource requests with DPML—NetKernel’s Declarative-Request Process Markup LanguageCompose modular XML documents with the XML Recursion Language (XRL)Build solutions using nCoDE in NetKernel’s visual editor
Resource Proportional Software Design for Emerging Systems
by Suparna Bhattacharya Kanchi Gopinath Doug VoigtEfficiency is a crucial concern across computing systems, from the edge to the cloud. Paradoxically, even as the latencies of bottleneck components such as storage and networks have dropped by up to four orders of magnitude, software path lengths have progressively increased due to overhead from the very frameworks that have revolutionized the pace of information technology. Such overhead can be severe enough to overshadow the benefits from switching to new technologies like persistent memory and low latency interconnects. Resource Proportional Software Design for Emerging Systems introduces resource proportional design (RPD) as a principled approach to software component and system development that counters the overhead of deeply layered code without removing flexibility or ease of development. RPD makes resource consumption proportional to situational utility by adapting to diverse emerging needs and technology systems evolution. Highlights: Analysis of run-time bloat in deep software stacks, an under-explored source of power-performance wastage in IT systems Qualitative and quantitative treatment of key dimensions of resource proportionality Code features: Unify and broaden supported but optional features without losing efficiency Technology and systems evolution: Design software to adapt with changing trade-offs as technology evolves Data processing: Design systems to predict which subsets of data processed by an (analytics or ML) application are likely to be useful System wide trade-offs: Address interacting local and global considerations throughout software stacks and hardware including cross-layer co-design involving code, data and systems dimensions, and non-functional requirements such as security and fault tolerance Written from a systems perspective to explore RPD principles, best practices, models and tools in the context of emerging technologies and applications This book is primarily geared towards practitioners with some advanced topics for researchers. The principles shared in the book are expected to be useful for programmers, engineers and researchers interested in ensuring software and systems are optimized for existing and next generation technologies. The authors are from both industry (Bhattacharya and Voigt) and academic (Gopinath) backgrounds.
Resourceful Code Reuse
by Dmitry ZinovievReusing well-written, well-debugged, and well-tested code improves productivity, code quality, and software configurability and relieves pressure on software developers. When you organize your code into self-contained modular units, you can use them as building blocks for your future projects and share them with other programmers, if needed. Understand the benefits and downsides of seven code reuse models so you can confidently reuse code at any development stage. Create static and dynamic libraries in C and Python, two of the most popular modern programming languages. Adapt your code for the real world: deploy shared functions remotely and build software that accesses them using remote procedure calls. Avoid the drawbacks and harness the benefits associated with seven code reuse models. Create static and dynamic libraries in C and Python, deploy shared functions remotely, and build software that makes intelligent use of remote procedure calls. In no time at all, you'll develop the confidence to reuse code at any stage of real-world development. This one-stop solution covers the complete build cycle: editing, compiling, linking, and running a ready program. Apply Linux/macOS power software development tools, such as ld, ldd, ranlib, and nm, to construct and explore state-of-the-art function libraries in C that could be linked with application-specific code either permanently or for the duration of execution. Learn why Python has modules for reuse and how they differ from C object files and libraries. Understand the risks and other negative implications of sharing and reuse. As a bonus, distill the dependencies between your project's components and automate and optimize your build process with the "make" utility. Whether you are an amateur coder or an experienced developer, become a more productive and resourceful programmer by reusing previously written code. What You Need: To compile and run the C examples mentioned in the book, you need a decent C compiler (GCC is the best, but Intel and Microsoft would probably work, too) and a set of C development tools: maker (make), linker (ld), file, strip, ldd, and ranlib. Again, the GNU development toolset works marvels; other toolsets may or may not work. All examples in the book have been tested on a Linux computer but will most likely work on macOS. For the Python examples, a Python-3.x interpreter is all you want. No third-party modules are required.
Respawn: Gamers, Hackers, and Technogenic Life (Experimental Futures)
by Colin MilburnIn Respawn Colin Milburn examines the connections between video games, hacking, and science fiction that galvanize technological activism and technological communities. Discussing a wide range of games, from Portal and Final Fantasy VII to Super Mario Sunshine and Shadow of the Colossus, Milburn illustrates how they impact the lives of gamers and non-gamers alike. They also serve as resources for critique, resistance, and insurgency, offering a space for players and hacktivist groups such as Anonymous to challenge obstinate systems and experiment with alternative futures. Providing an essential walkthrough guide to our digital culture and its high-tech controversies, Milburn shows how games and playable media spawn new modes of engagement in a computerized world.
The Respiratory System in Equations
by Bertrand MauryThis book proposes an introduction to the mathematical modeling of the respiratory system. A detailed introduction on the physiological aspects makes it accessible to a large audience without any prior knowledge on the lung. Different levels of description are proposed, from the lumped models with a small number of parameters (Ordinary Differential Equations), up to infinite dimensional models based on Partial Differential Equations. Besides these two types of differential equations, two chapters are dedicated to resistive networks, and to the way they can be used to investigate the dependence of the resistance of the lung upon geometrical characteristics. The theoretical analysis of the various models is provided, together with state-of-the-art techniques to compute approximate solutions, allowing comparisons with experimental measurements. The book contains several exercises, most of which are accessible to advanced undergraduate students.
The Responsibilities of Online Service Providers
by Mariarosaria Taddeo Luciano FloridiThis volume focuses on the responsibilities of online service providers (OSPs) in contemporary societies. It examines the complexity and global dimensions of the rapidly evolving and serious challenges posed by the exponential development of Internet services and resources. It looks at the major actors - such as Facebook, Google, Twitter, and Yahoo! - and their significant influence on the informational environment and users' interactions within it, as well as the responsibilities and liabilities such influence entails. It discusses the position of OSPs as information gatekeepers and how they have gone from offering connecting and information-sharing services to paying members to providing open, free infrastructure and applications that facilitate digital expression and the communication of information. The book seeks consensus on the principles that should shape OSPs' responsibilities and practices, taking into account business ethics and policies. Finally, it discusses the rights of users and international regulations that are in place or currently lacking.
Responsible AI: Implementing Ethical and Unbiased Algorithms
by Sray Agarwal Shashin MishraThis book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination. The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter – providing the details that enable the business analysts and the data scientists to implement these fundamentals. AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it.
Responsible AI and Analytics for an Ethical and Inclusive Digitized Society: 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2021, Galway, Ireland, September 1–3, 2021, Proceedings (Lecture Notes in Computer Science #12896)
by Denis Dennehy Anastasia Griva Nancy Pouloudi Yogesh K. Dwivedi Ilias Pappas Matti MäntymäkiThis volume constitutes the proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2021, held in Galway, Ireland, in September 2021.*The total of 57 full and 8 short papers presented in these volumes were carefully reviewed and selected from 141 submissions. The papers are organized in the following topical sections: AI for Digital Transformation and Public Good; AI & Analytics Decision Making; AI Philosophy, Ethics & Governance; Privacy & Transparency in a Digitized Society; Digital Enabled Sustainable Organizations and Societies; Digital Technologies and Organizational Capabilities; Digitized Supply Chains; Customer Behavior and E-business; Blockchain; Information Systems Development; Social Media & Analytics; and Teaching & Learning. *The conference was held virtually due to the COVID-19 pandemic.
Responsible AI in Practice: A Practical Guide to Safe and Human AI
by Toju Duke Paolo GiudiciThis book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled “Safe Human-centered AI (SAFE-HAI),” which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks. This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will Learn Know the key principles behind Responsible AI and associated risks Become familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risks Be aware of the fundamentals of AI regulations and policies and how to adopt them Understand AI governance basics and implementation guidelines Who This Book Is For AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities
Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision Making
by Badrul H. Khan Joseph Rene Corbeil Maria Elena CorbeilRapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.
Responsible Artificial Intelligence: Challenges for Sustainable Management (CSR, Sustainability, Ethics & Governance)
by René Schmidpeter Reinhard AltenburgerArtificial intelligence - and social responsibility. Two topics that are at the top of the business agenda. This book discusses in theory and practice how both topics influence each other. In addition to impulses from the current often controversial scientific discussion, it presents case studies from companies dealing with the specific challenges of artificial intelligence.Particular emphasis is placed on the opportunities that artificial intelligence (AI) offers for companies from different industries. The book shows how dealing with the tension between AI and challenges caused by new corporate social responsibility creates strategic opportunities and also innovation opportunities. It highlights the active involvement of stakeholders in the design process, which is meant to build trust among customers and the public and thus contributes to the innovation and acceptance of artificial intelligence.The book is aimed at researchers and practitioners in the fields of corporate social responsibility as well as artificial intelligence and digitalization. The chapter "Exploring AI with purpose" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Responsible Data Science
by Peter C. Bruce Grant FlemingExplore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.
Responsible Data Science: Select Proceedings of ICDSE 2021 (Lecture Notes in Electrical Engineering #940)
by Jimson Mathew G. Santhosh Kumar Deepak P. Joemon M. JoseThis book comprises select proceedings of the 7th International Conference on Data Science and Engineering (ICDSE 2021). The contents of this book focus on responsible data science. This book tries to integrate research across diverse topics related to data science, such as fairness, trust, ethics, confidentiality, transparency, and accuracy. The chapters in this book represent research from different perspectives that offer novel theoretical implications that span multiple disciplines. The book will serve as a reference resource for researchers and practitioners in academia and industry.