Fang Chen – författare

Designing Human Interface in Speech Technology bridges a gap between the needs of the technical engineer and cognitive researchers working in the multidisciplinary area of speech technology applications. The approach is systematic and the focus is on the utility of developing and designing speech related products.

Included is coverage of topics such as neuroscience on the multimodal cortex, cognitive theories on multi-task performance, stress and workload, as well as human information process theory and ecological interface design theory for evaluating speech-related human-system interfaces.

Of special emphasis are topics such as spoken dialogue system design, in-vehicle communication system design and speech technology in military applications. Also included are tools on how to analyze the design, different design theories and process, methods about how to understand users. The material systematically describes the user-center design process and usability evaluation methods.

Designing Human Interface in Speech Technology is appropriate for designers, engineers, and decision makers working in the area of speech technology research. It is also a good text book for senior university students and postgraduate students in the respective interaction design areas.

New Trends in Speech Based Interactive Systems will give an overview of the research and application of speech technologies in different areas. The basic technology development areas include: Automatic speech recognition technology, speech synthesis, spoken interaction, natural language understanding, speaker recognition, emotion in spoken dialogue systems, expressive speech synthesis, affective computing, multimodal communication, interaction technologies, and animated agents. The application areas include: in-vehicle information system and interaction, military application, other industrial applications such as for space, air traffic control, or complicated control room interaction, simulator control, etc., and application for special users and in entertainment systems. General design and usability evaluation methodologies from the user’s perspective will also be included in the book.

The overview of each technology includes: 1. The history and the development of the technology. 2. What are the hot research topics/interests in the technology? 3. What kind of mistakes/assumption that people has tried, so to avoid repeating the mistakes. 4. What can be the special interests/problems in different application areas? 5. The trend of the technical development in the coming few years.

The overview of each application area includes: 1. The history of the application studies. 2. What kind of speech technologies are interested in the application. 3. The analysis of the factors that affect the real-time application. 4. What can be the possible usability requirements and interaction design problems? 5. The trend of the research focus in the coming few years.

One of the special characteristics of the book is that the authors will not just present their own research work, but take a broad view of the multiple research areas and take the multidisciplinary approach to the topics. One of the goals in this book is to emphasize the application. User experience, human factors and usability issues are the focus in this book.

This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade’s experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management.

The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks.

This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.

This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade’s experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management.

The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks.

This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.

Artificial Intelligence (AI) is changing the world around us, and it is changing the way people are living, working, and entertaining. As a result, demands for understanding how AI functions to achieve and enhance human goals from basic needs to high level well-being (whilst maintaining human health) are increasing. This edited book systematically investigates how AI facilitates enhancing human needs in the digital age, and reports on the state-of-the-art advances in theories, techniques, and applications of humanity driven AI. Consisting of five parts, it covers the fundamentals of AI and humanity, AI for productivity, AI for well-being, AI for sustainability, and human-AI partnership.

Humanity Driven AI creates an important opportunity to not only promote AI techniques from a humanity perspective, but also to invent novel AI applications to benefit humanity. It aims to serve as the dedicated source for the theories, methodologies, and applications on humanity drivenAI, establishing state-of-the-art research, and providing a ground-breaking book for graduate students, research professionals, and AI practitioners.

This is the first book of its kind to systematically introduce various computational methods for automatic and real-time cognitive load measurement and by doing so moves the practical application of cognitive load measurement from the domain of the computerscientist and psychologist to more general end-users, ready for widespread implementation.

Robust Multimodal Cognitive Load Measurement is intended for researchers and practitioners involved with cognitive load studies and communities within the computer, cognitive, and social sciences. The book will especially benefit researchers in areas like behaviour analysis, social analytics, human-computer interaction (HCI), intelligent information processing, and decision support systems.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.

This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.

This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.