Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the "data-aptitude" built by the material in this book.

As the healthcare industry continues to expand, it must utilize technology to ensure efficiencies are maintained. Healthcare needs to move in a direction where computational methods and algorithms can relieve the routine work of medical doctors, leaving them more time to carry out more important and skilled tasks such as surgery. Computational Methods and Algorithms for Medicine and Optimized Clinical Practice discusses some of the most interesting aspects of theoretical and applied research covering complementary facets of computational methods and algorithms to achieve greater efficiency and support medical personnel. Featuring research on topics such as healthcare reform, artificial intelligence, and disease detection, this book will particularly appeal to medical professionals and practitioners, hospitals, administrators, students, researchers, and academicians.

This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book. This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.

This book systematically presents the operating principles and technical characteristics of the main radio navigating systems (RNSs) that make it possible to adequately evaluate the corresponding scratch indexes and levels of air safety for air vehicles, the chief concern of the International Civil Aviation Organization (ICAO). The book discusses how RNS systems substantially determine navigation accuracy and reliability, and therefore air safety; in addition, it presents practical solutions to problems arising in the operation and development of RNS systems.

This book features research presented and discussed during the Research & Innovation Forum (Rii Forum) 2020. As such, this book offers a unique insight into emerging topics, issues and developments pertinent to the fields of technology, innovation and education and their social impact. Papers included in this book apply inter- and multi-disciplinary approaches to query such issues as technology-enhanced teaching and learning, smart cities, information systems, cognitive computing and social networking. What brings these threads of the discussion together is the question of how advances in computer science - which are otherwise largely incomprehensible to researchers from other fields - can be effectively translated and capitalized on so as to make them beneficial for society as a whole. In this context, Rii Forum and Rii Forum proceedings offer an essential venue where diverse stakeholders, including academics, the think tank sector and decision-makers, can engage in a meaningful dialogue with a view to improving the applicability of advances in computer science.

This book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks.

This book contains extended and revised versions of the best papers presented at the 27th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2019, held in Cusco, Peru, in October 2019. The 15 full papers included in this volume were carefully reviewed and selected from the 28 papers (out of 82 submissions) presented at the conference. The papers discuss the latest academic and industrial results and developments as well as future trends in the field of System-on-Chip (SoC) design, considering the challenges of nano-scale, state-of-the-art and emerging manufacturing technologies. In particular they address cutting-edge research fields like heterogeneous, neuromorphic and brain-inspired, biologically-inspired, approximate computing systems.

This book provides information on digital audio watermarking, its applications, and its evaluation for copyright protection of audio signals - both basic and advanced. The author covers various advanced digital audio watermarking algorithms that can be used for copyright protection of audio signals. These algorithms are implemented using hybridization of advanced signal processing transforms such as fast discrete curvelet transform (FDCuT), redundant discrete wavelet transform (RDWT), and another signal processing transform such as discrete cosine transform (DCT). In these algorithms, Arnold scrambling is used to enhance the security of the watermark logo. This book is divided in to three portions: basic audio watermarking and its classification, audio watermarking algorithms, and audio watermarking algorithms using advance signal transforms. The book also covers optimization based audio watermarking. Describes basic of digital audio watermarking and its applications, including evaluation parameters for digital audio watermarking algorithms; Provides audio watermarking algorithms using advanced signal transformations; Provides optimization based audio watermarking algorithms.

This book highlights operation principles for Air Traffic Control Automated Systems (ATCAS), new scientific directions in design and application of dispatching training simulators and parameters of ATCAS radio equipment items for aircraft positioning. This book is designed for specialists in air traffic control and navigation at a professional and scientific level. The following topics are also included in this book: personnel actions in emergency, including such unforeseen circumstances as communication failure, airplane wandering off course, unrecognized aircraft appearance in the air traffic service zone, aerial target interception, fuel draining, airborne collision avoidance system (ACAS) alarm, emergency stacking and volcanic ash cloud straight ahead.

This book focuses on origami from the point of view of computer science. Ranging from basic theorems to the latest research results, the book introduces the considerably new and fertile research field of computational origami as computer science. Part I introduces basic knowledge of the geometry of development, also called a net, of a solid. Part II further details the topic of nets. In the science of nets, there are numerous unresolved issues, and mathematical characterization and the development of efficient algorithms by computer are closely connected with each other. Part III discusses folding models and their computational complexity. When a folding model is fixed, to find efficient ways of folding is to propose efficient algorithms. If this is difficult, it is intractable in terms of computational complexity. This is, precisely, an area for computer science research. Part IV presents some of the latest research topics as advanced problems. Commentaries on all exercises included in the last chapter. The contents are organized in a self-contained way, and no previous knowledge is required. This book is suitable for undergraduate, graduate, and even high school students, as well as researchers and engineers interested in origami.

This book discusses the application of metaheuristic algorithms in a number of important optimization problems in civil engineering. Advances in civil engineering technologies require greater accuracy, efficiency and speed in terms of the analysis and design of the corresponding systems. As such, it is not surprising that novel methods have been developed for the optimal design of real-world systems and models with complex configurations and large numbers of elements. This book is intended for scientists, engineers and students wishing to explore the potential of newly developed metaheuristics in practical problems. It presents concepts that are not only applicable to civil engineering problems, but can also used for optimizing problems related to mechanical, electrical, and industrial engineering. It is an essential resource for civil, mechanical and electrical engineers who use optimization methods for design, as well as for students and researchers interested in structural optimization.

This book describes a variety of test generation algorithms for testing crosstalk delay faults in VLSI circuits. It introduces readers to the various crosstalk effects and describes both deterministic and simulation-based methods for testing crosstalk delay faults. The book begins with a focus on currently available crosstalk delay models, test generation algorithms for delay faults and crosstalk delay faults, before moving on to deterministic algorithms and simulation-based algorithms used to test crosstalk delay faults. Given its depth of coverage, the book will be of interest to design engineers and researchers in the field of VLSI Testing.

This book contains contributions presented at the 12th International Conference on Complex Networks (CompleNet), 24-26 May 2021. CompleNet is an international conference on complex networks that brings together researchers and practitioners from diverse disciplines-from sociology, biology, physics, and computer science-who share a passion to better understand the interdependencies within and across systems. CompleNet is a venue to discuss ideas and findings about all types networks, from biological, to technological, to informational and social. It is this interdisciplinary nature of complex networks that CompleNet aims to explore and celebrate.

A comprehensive introduction to network flows that brings together the classic and the contemporary aspects of the field, and provides an integrative view of theory, algorithms, and applications.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression Hybrid Structural and Behavioral Diversity Methods in GP Multi-Population Competitive Coevolution for Anticipation of Tax Evasion Evolving Artificial General Intelligence for Video Game Controllers A Detailed Analysis of a PushGP Run Linear Genomes for Structured Programs Neutrality, Robustness, and Evolvability in GP Local Search in GP PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification Relational Structure in Program Synthesis Problems with Analogical Reasoning An Evolutionary Algorithm for Big Data Multi-Class Classification Problems A Generic Framework for Building Dispersion Operators in the Semantic Space Assisting Asset Model Development with Evolutionary Augmentation Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

Mapping and localization are two essential tasks in autonomous mobile robotics. Due to the unavoidable noise that sensors present, mapping algorithms usually rely on loop closure detection techniques, which entail the correct identification of previously seen places to reduce the uncertainty of the resulting maps. This book deals with the problem of generating topological maps of the environment using efficient appearance-based loop closure detection techniques. Since the quality of a visual loop closure detection algorithm is related to the image description method and its ability to index previously seen images, several methods for loop closure detection adopting different approaches are developed and assessed. Then, these methods are used in three novel topological mapping algorithms. The results obtained indicate that the solutions proposed attain a better performance than several state-of-the-art approaches. To conclude, given that loop closure detection is also a key component in other research areas, a multi-threaded image mosaicing algorithm is proposed. This approach makes use of one of the loop closure detection techniques previously introduced in order to find overlapping pairs between images and finally obtain seamless mosaics of different environments in a reasonable amount of time.

This book collects high-quality research papers presented at the International Conference on Computing Applications in Electrical & Electronics Engineering, held at Rajkiya Engineering College, Sonbhadra, India, on August 30-31, 2019. It provides novel contributions in computational intelligence, together with valuable reference material for future research. The topics covered include: big data analytics, IoT and smart infrastructures, machine learning, artificial intelligence and deep learning, crowd sourcing and social intelligence, natural language processing, business intelligence, high-performance computing, wireless, mobile and green communications, ad-hoc, sensor and mesh networks, SDN and network virtualization, cognitive systems, swarm intelligence, human-computer interaction, network and information security, intelligent control, soft computing, networked control systems, renewable energy sources and technologies, biomedical signal processing, pattern recognition and object tracking, and sensor devices and applications.

This book focuses on the design, implementation and applications of embedded systems and advanced industrial controls with microcontrollers. It combines classical and modern control theories as well as practical control programming codes to help readers learn control techniques easily and effectively. The book covers both linear and nonlinear control techniques to help readers understand modern control strategies. The author provides a detailed description of the practical considerations and applications in linear and nonlinear control systems. They concentrate on the ARM (R) Cortex (R)-M4 MCU system built by Texas Instruments (TM) called TM4C123GXL, in which two ARM (R) Cortex (R)-M4 MCUs, TM4C123GH6PM, are utilized. In order to help the reader develop and build application control software for a specified microcontroller unit. Readers can quickly develop and build their applications by using sample project codes provided in the book to access specified peripherals. The book enables readers to transfer from one interfacing protocol to another, even if they only have basic and fundamental understanding and basic knowledge of one interfacing function. Classical and Modern Controls with Microcontrollers is a powerful source of information for control and systems engineers looking to expand their programming knowledge of C, and of applications of embedded systems with microcontrollers. The book is a textbook for college students majored in CE, EE and ISE to learn and study classical and modern control technologies. The book can also be adopted as a reference book for professional programmers working in modern control fields or related to intelligent controls and embedded computing and applications. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity-in both time and memory requirements-for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don't wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.

Algorithms are now widely employed to make decisions that have increasingly far-reaching impacts on individuals and society as a whole ("algorithmic governance"), which could potentially lead to manipulation, biases, censorship, social discrimination, violations of privacy, property rights, and more. This has sparked a global debate on how to regulate AI and robotics ("governance of algorithms"). This book discusses both of these key aspects: the impact of algorithms, and the possibilities for future regulation.