Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing.

Because trainees need to learn about the underlying technologies to use automation safely and efficiently, the development of automated aviation systems training is a growing challenge. Task analysis has been singled out as the basis of the training, but it can be more time-consuming than traditional development techniques. Cases on Modern Computer Systems in Aviation is an essential reference source that covers new information technology use in aviation systems to streamline the cybersecurity, decision-making, planning, and design processes within the aviation industry. Featuring coverage on a broad range of topics such as computer systems in aviation, artificial intelligence, software-defined networking (SDN), air navigation systems, decision support systems (DSS), and more, this publication is ideally designed for aviation specialists and industry professionals, technicians, practitioners, researchers, and academicians seeking current research on modern modeling approaches to streamline management in aviation.

This textbook guides readers through their first steps into the challenging world of mimicking human vision with computational tools and techniques pertaining to the field of image processing and analysis. While today's theoretical and applied processing and analysis of images meet with challenging and complex problems, this primer is confined to a much simpler, albeit critical, collection of image-to-image transformations, including image normalisation, enhancement, and filtering.It serves as an introduction to beginners, a refresher for undergraduate and graduate students, as well as engineers and computer scientists confronted with a problem to solve in computer vision. The book covers basic image processing/computer vision pipeline techniques, which are widely used in today's computer vision, computer graphics, and image processing, giving the readers enough knowledge to successfully tackle a wide range of applied problems.

In this technological age, the information technology (IT) industry is an important facet of society and business. The IT industry is able to become more efficient and successful through the examination of its structure and a larger understanding of the individuals that work in the field. Multidisciplinary Perspectives on Human Capital and Information Technology Professionals is a critical scholarly resource that focuses on IT as an industry and examines it from an array of academic viewpoints. Featuring coverage on a wide range of topics, such as employee online communities, role stress, and competence frameworks, this book is targeted toward academicians, students, and researchers seeking relevant research on IT as an industry.

The technological advancements of today not only affect individual's personal lives. They also affect the way urban communities regard the improvement of their resident's lives. Research involving these autonomic reactions to the growing needs of the people is desperately needed to transform the cities of today into the cities of the future. Driving the Development, Management, and Sustainability of Cognitive Cities is a pivotal reference source that explores and improves the understanding of the strategic role of sustainable cognitive cities in residents' routine life styles. Such benefits to residents and businesses include having access to world-class training while sitting at home, having their wellbeing observed consistently, and having their medical issues identified before occurrence. This book is ideally designed for administrators, policymakers, industrialists, and researchers seeking current research on developing and managing cognitive cities.

Though in the past online learning was considered of poorer professional quality than classroom learning, it has become a useful and, in some cases, vital tool for promoting the inclusivity of education. Some of its benefits include allowing greater accessibility to educational resources previously unattainable by those in rural areas, and in current times, it has proven to be a critical asset as universities shut down due to natural disasters and pandemics. Examining the current state of distance learning and determining online assessment tools and processes that can enhance the online learning experience are clearly crucial for the advancement of modern education. The Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning is a collection of pioneering investigations on the methods and applications of digital technologies in the realm of education. It provides a clear and extensive analysis of issues regarding online learning while also offering frameworks to solve these addressed problems. Moreover, the book reviews and evaluates the present and intended future of distance learning, focusing on the societal and employer perspective versus the academic proposals. While highlighting topics including hybrid teaching, blended learning, and telelearning, this book is ideally designed for teachers, academicians, researchers, educational administrators, and students.

Sustainable Work in Europe brings together a strong core of Swedish working life research, with additional contributions from across Europe, and discussion of current issues such as digitalisation, climate change and the Covid pandemic. It bridges gaps between social science and medicine, and adds emphasis on age and gender. The book links workplace practice, theory and policy, and is intended to provide the basis for ongoing debate and dialogue.

Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book.

A groundbreaking treatise by one of the great mathematicians of our age, who outlines a style of thinking by which great ideas are conceived. What inspires and spurs on a great idea? Can we train ourselves to think in a way that will enable world-changing understandings and insights to emerge? Richard Hamming said we can. He first inspired a generation of engineers, scientists, and researchers in 1986 with "You and Your Research," an electrifying sermon on why some scientists do great work, why most don't, why he did, and why you can-and should-too. The Art of Doing Science and Engineering is the full expression of what "You and Your Research" outlined. It's a book about thinking; more specifically, a style of thinking by which great ideas are conceived. The book is filled with stories of great people performing mighty deeds-but they are not meant simply to be admired. Instead, they are to be aspired to, learned from, and surpassed. Hamming consistently returns to Shannon's information theory, Einstein's theory of relativity, Grace Hopper's work on high-level programming, Kaiser's work on digital fillers, and his own work on error-correcting codes. He also recounts a number of his spectacular failures as clear examples of what to avoid. Originally published in 1996 and adapted from a course that Hamming taught at the US Naval Postgraduate School, this edition includes an all-new foreword by designer, engineer, and founder of Dynamicland Bret Victor, plus more than 70 redrawn graphs and charts. The Art of Doing Science and Engineering is a reminder that a capacity for learning and creativity are accessible to everyone. Hamming was as much a teacher as a scientist, and having spent a lifetime forming and confirming a theory of great people and great ideas, he prepares the next generation for even greater distinction.

During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation.

Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing. Big Data Processing with Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.

Data is the most important commodity, which is why data protection has become a global priority. Data breaches and security flaws can jeopardize the global economy. Organizations face a greater risk of failing to achieve strategy and business goals as cyber threat behavior grows in frequency, sophistication, and destructiveness. A breach can result in data loss, business interruption, brand and reputation harm, as well as regulatory and legal consequences. A company needs a well-thought-out cybersecurity strategy to secure its critical infrastructure and information systems in order to overcome these challenges. Cross-Industry Applications of Cyber Security Frameworks provides an understanding of the specific, standards-based security controls that make up a best practice cybersecurity program. It is equipped with cross-industry applications of cybersecurity frameworks, best practices for common practices, and suggestions that may be highly relevant or appropriate in every case. Covering topics such as legal frameworks, cybersecurity in FinTech, and open banking, this premier reference source is an essential resource for executives, business leaders, managers, entrepreneurs, IT professionals, government officials, hospital administrators, educational administrators, privacy specialists, researchers, and academicians.

Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.