Chapters authored
Machine Translation and the Evaluation of Its Quality
Machine translation has already become part of our everyday life. This chapter gives an overview of machine translation approaches. Statistical machine translation was a dominant approach over the past 20 years. It brought many cases of practical use. It is described in more detail in this chapter. Statistical machine translation is not equally successful for all language pairs. Highly inflectional languages are hard to process, especially as target languages. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This chapter also describes the evaluation of machine translation quality. It covers manual and automatic evaluations. Traditional and recently proposed metrics for automatic machine translation evaluation are described. Human translation still provides the best translation quality, but it is, in general, time-consuming and expensive. Integration of human and machine translation is a promising workflow for the future. Machine translation will not replace human translation, but it can serve as a tool to increase productivity in the translation process.
Part of the book: Recent Trends in Computational Intelligence
Perspective Chapter: Recognition of Activities of Daily Living for Elderly People in the Era of Digital Health
People around the world are living longer. The question arises of how to help elderly people to live longer independently and feel safe in their homes. Activity of Daily Living (ADL) recognition systems automatically recognize the daily activities of residents in smart homes. Automated monitoring of the daily routine of older individuals, detecting behavior patterns, and identifying deviations can help to identify the need for assistance. Such systems must ensure the confidentiality, privacy, and autonomy of residents. In this chapter, we review research and development in the field of ADL recognition. Breakthrough advancements have been evident in recent years with advances in sensor technology, the Internet of Things (IoT), machine learning, and artificial intelligence. We examine the main steps in the development of an ADL recognition system, introduce metrics for system evaluation, and present the latest trends in knowledge transfer and detection of behavior changes. The literature overview shows that deep learning approaches currently provide promising results. Such systems will soon mature for more diverse practical uses as transfer learning enables their fast deployment in new environments.
Part of the book: A Comprehensive Overview of Telemedicine [Working title]