This book reports on innovative strategies for quality control, risk assessment and sustainable development in production processes, in the era of industry 4.0. Based on peer-reviewed contributions to the 7th International Scientific-Technical Conference MANUFACTURING 2022, held on May 1619, 2022, in Poznan, Poland, the chapters cover important topics relating to the use of quality management strategies in different stages of the production processes. They report on methods for statistical process control, vision control and inspection of machines, on the application of machine learning methods in quality control and/or risk assessment, on issues relating to digital transformation, and on methods to improve occupational safety. Besides industrial applications, the book also discusses the use quality management tools for educational purposes. By bridging between concepts in quality engineering, ergonomics, digitalization and industry 4.0, this book offers an authoritative source ofinformation for researchers, engineers and managers.

Reports on cutting-edge research in quality engineering Highlights environmental and human aspects issues in Industry 4.0 Reports on intelligent methods, including vision systems, for risk analysis and quality control

Klappentext
Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents. QA, posing a multitude of research challenges, has emerged as one of the most actively investigated topics in information retrieval, natural language processing, and the artificial intelligence communities today. The flip side of such diverse and active interest is that publications are highly fragmented across several venues in the above communities, making it very difficult for new entrants to the field to get a good overview of the topic. Through this book, we make an attempt towards mitigating the above problem by providing an overview of the state-of-the-art in question answering. We cover the twin paradigms of curated Web sources used in QA tasks trusted text collections like Wikipedia, and objective information distilled into large-scale knowledge bases. We discuss distinct methodologies that have been applied to solve the QA problem in both these paradigms, using instantiations of recent systems for illustration. We begin with an overview of the problem setup and evaluation, cover notable sub-topics like open-domain, multi-hop, and conversational QA in depth, and conclude with key insights and emerging topics. We believe that this resource is a valuable contribution towards a unified view on QA, helping graduate students and researchers planning to work on this topic in the near future.

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