Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today's digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

Ascend AI Processor Architecture and Programming: Principles and Applications of CANN offers in-depth AI applications using Huawei's Ascend chip, presenting and analyzing the unique performance and attributes of this processor. The title introduces the fundamental theory of AI, the software and hardware architecture of the Ascend AI processor, related tools and programming technology, and typical application cases. It demonstrates internal software and hardware design principles, system tools and programming techniques for the processor, laying out the elements of AI programming technology needed by researchers developing AI applications. Chapters cover the theoretical fundamentals of AI and deep learning, the state of the industry, including the current state of Neural Network Processors, deep learning frameworks, and a deep learning compilation framework, the hardware architecture of the Ascend AI processor, programming methods and practices for developing the processor, and finally, detailed case studies on data and algorithms for AI.

Throughout the 1990s, artists experimented with game engine technologies to disrupt our habitual relationships to video games. They hacked, glitched, and dismantled popular first-person shooters such as Doom (1993) and Quake (1996) to engage players in new kinds of embodied activity. In Unstable Aesthetics: Game Engines and the Strangeness of Art Modding, Eddie Lohmeyer investigates historical episodes of art modding practices-the alteration of a game system's existing code or hardware to generate abstract spaces-situated around a recent archaeology of the game engine: software for rendering two and three-dimensional gameworlds. The contemporary artists highlighted throughout this book-Cory Arcangel, JODI, Julian Oliver, Krista Hoefle, and Brent Watanabe, among others -- were attracted to the architectures of engines because they allowed them to explore vital relationships among abstraction, technology, and the body. Artists employed a range of modding techniques-hacking the ROM chips on Nintendo cartridges to produce experimental video, deconstructing source code to generate psychedelic glitch patterns, and collaging together surreal gameworlds-to intentionally dissect the engine's operations and unveil illusions of movement within algorithmic spaces. Through key moments in game engine history, Lohmeyer formulates a rich phenomenology of video games by focusing on the liminal spaces of interaction among system and body, or rather the strangeness of art modding.

In the digital economy, a new type of business activity, digital entrepreneurship, has developed rapidly and required breakthrough technologies such as blockchain, big data, cloud technologies, and more. There is a need for a comprehensive resource that provides all-encompassing insight into the essence, special aspects, models, and international best practices of e-business based on various digital technologies in various high-tech markets. Digital Technologies for Entrepreneurship in Industry 4.0 provides theoretical frameworks and recent results of research in this sphere. It substantiates digital entrepreneurship, discusses the practical experience of its implementation, and develops the scientific and methodological recommendations for the development of its infrastructural provision and regulation of provision of its competitiveness. Covering topics such as investment attractiveness, corporate reporting modernization, and public-private partnership mechanisms, this premier reference source is an excellent resource for entrepreneurs, business executives and managers, investors, IT managers, students and faculty of higher education, researchers, and academicians.

Advances in Computers, Volume 119, presents innovations in computer hardware, software, theory, design, and applications, with this updated volume including new chapters on Fast Execution of RDF Queries Using Apache Hadoop, A Study of DVFS Methodologies for Multicore Systems with Islanding Feature, Effectiveness of State-of-the-art Dynamic Analysis Techniques in Identifying Diverse Android Malware and Future Enhancements, Eyeing the Patterns: Data Visualization Using Doubly-Seriated Color Heatmaps, Eigenvideo for Video Indexing.

Methods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment provides theoretical concepts and practical techniques of AI and its applications in cancer management, building a roadmap on how to use AI in cancer at different stages of healthcare. It discusses topics such as the impactful role of AI during diagnosis and how it can support clinicians to make better decisions, AI tools to help pathologists identify exact types of cancer, how AI supports tumor profiling and can assist surgeons, and the gains in precision for oncologists using AI tools. Additionally, it provides information on AI used for survival and remission/recurrence analysis. The book is a valuable source for bioinformaticians, cancer researchers, oncologists, clinicians and members of the biomedical field who want to understand the promising field of AI applications in cancer management.

Due to the growing prevalence of artificial intelligence technologies, schools, museums, and art galleries will need to change traditional ways of working and conventional thought processes to fully embrace their potential. Integrating virtual and augmented reality technologies and wearable devices into these fields can promote higher engagement in an increasingly digital world. Virtual and Augmented Reality in Education, Art, and Museums is an essential research book that explores the strategic role and use of virtual and augmented reality in shaping visitor experiences at art galleries and museums and their ability to enhance education. Highlighting a range of topics such as online learning, digital heritage, and gaming, this book is ideal for museum directors, tour developers, educational software designers, 3D artists, designers, curators, preservationists, conservationists, education coordinators, academicians, researchers, and students.

Data has never mattered more. Our lives are increasingly shaped by it and how it is defined, collected and used. But who counts in the collection, analysis and application of data? This important book is the first to look at queer data - defined as data relating to gender, sex, sexual orientation and trans identity/history. The author shows us how current data practices reflect an incomplete account of LGBTQ lives and helps us understand how data biases are used to delegitimise the everyday experiences of queer people. Guyan demonstrates why it is important to understand, collect and analyse queer data, the benefits and challenges involved in doing so, and how we might better use queer data in our work. Arming us with the tools for action, this book shows how greater knowledge about queer identities is instrumental in informing decisions about resource allocation, changes to legislation, access to services, representation and visibility.

Virtual reality (VR) refers to technologies that use headsets to generate realistic images, sounds and other sensations that replicate a real-world environment or create an imaginary setting. VR also simulates a user's physical presence in this environment. In virtual reality, six degrees of freedom allows users to not only look around, but also to move around the virtual world and look from above, below or behind objects. To have a true VR experience, the hardware must provide six degrees of freedom, using both orientation tracking (rotational) and positional tracking (translation). This book is addressed to video experts who want to understand the basics of 3D representations and multi-camera video processing to target new immersive media applications. Unlike single camera video coding, future VR technologies address new challenges that arise beyond compression-only, including the pre- and post-processing (depth acquisition and 3D rendering). This book is inspired by the MPEG-I (immersive media) and JPEG-PLENO (plenoptic media) standardization activities, and offers a glimpse of their underlying technologies.

In today's digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity. The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.

Advanced computational intelligence techniques have been designed and developed in recent years to cope with various big data challenges and provide fast and efficient analytics that assist in making critical decisions. With the rapid evolution and development of internet-based services and applications, this technology is receiving attention from researchers, industries, and academic communities and requires additional study. Convergence of Big Data Technologies and Computational Intelligent Techniques considers recent advancements in big data and computational intelligence across fields and disciplines and discusses the various opportunities and challenges of adoption. Covering topics such as deep learning, data mining, smart environments, and high-performance computing, this reference work is crucial for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Cell phone apps share location information; software companies store user data in the cloud; biometric scanners read fingerprints; employees of some businesses have microchips implanted in their hands. In each of these instances we trade a share of privacy or an aspect of identity for greater convenience or improved security. What Robert M. Pallitto asks in Bargaining with the Machine is whether we are truly making such bargains freely - whether, in fact, such a transaction can be conducted freely or advisedly in our ever more technologically sophisticated world. Pallitto uses the social theory of bargaining to look at the daily compromises we make with technology. Specifically, he explores whether resisting these 'bargains' is still possible when the technologies in question are backed by persuasive, even coercive, corporate and state power. Who, he asks, is proposing the bargain? What is the balance of bargaining power? What is surrendered and what is gained? And are the perceived and the actual gains and losses the same - that is, what is hidden? At the center of Pallitto's work is the paradox of bargaining in a world of limited agency. Assurances that we are in control are abundant whether we are consumers, voters, or party to the social contract. But when purchasing goods from a technological behemoth like Amazon, or when choosing a candidate whose image is crafted and shaped by campaign strategists and media outlets, how truly free, let alone informed, are our choices? The tension between claims of agency and awareness of its limits is the site where we experience our social lives - and nowhere is this tension more pronounced than in the surveillance society. This book offers a cogent analysis of how that complex, contested, and even paradoxical experience arises as well as an unusually clear and troubling view of the consequential compromises we may be making.

5G NR: The Next Generation Wireless Access Technology, Second Edition, follows the authors' highly celebrated books on 3G and 4G and provides a new level of insight into 5G NR. After background discussion of 5G, including requirements, spectrum aspects, and the standardization timeline, all technology features of the first phase of NR are described in detail. The book covers the NR physical-layer structure and higher-layer protocols, RF and spectrum aspects, and co-existence and interworking with LTE. The book provides a good foundation in NR and different NR technology components, giving insight into why a certain solution has been selected. This second edition is updated to reflect the latest developments in Release 16 and includes brand new chapters on: NR in unlicensed spectrum; NR-U in Rel-16; IAB; V2X and sidelink in Rel-16; industrial IoT; IIoT and referring to the URLLC enhancements for PDCCH; RIM/CL; and positioning. Also included are the key radio-related requirements of NR; design principles; technical features of basic NR transmission structure-showing where it was inherited from LTE, where it deviates from it, and the reasons why- NR multi-antenna transmission functionality; detailed description of the signals and functionality of the initial NR access, including signals for synchronization and system information; random access and paging; LTE/NR co-existence in the same spectrum and the benefits of their interworking as one system; and different aspects of mobility in NR. RF requirements for NR are described for BS and UE, the legacy bands, and for the new mm-wave bands.