4,285 research outputs found

    Intelligent computing : the latest advances, challenges and future

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    Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

    Reflections on the COVID-19 pandemic

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    Z-Numbers-Based Approach to Hotel Service Quality Assessment

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    In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

    Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

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    The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

    Fairness in Educational Assessment and Measurement

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    The importance of fairness, validity, and accessibility in assessment is greater than ever as testing expands to include more diverse populations, more complex purposes, and more sophisticated technologies. This book offers a detailed account of fairness in assessment, and illustrates the interplay between assessment and broader changes in education. In 16 chapters written by leading experts, this volume explores the philosophical, technical, and practical questions surrounding fair measurement. Fairness in Educational Assessment and Measurement addresses issues pertaining to the construction, administration, and scoring of tests, the comparison of performance across test takers, grade levels and tests, and the uses of educational test scores. Perfect for researchers and professionals in test development, design, and administration, Fairness in Educational Assessment and Measurement presents a diverse array of perspectives on this topic of enduring interest

    Caring in times of COVID-19: A global study on the impact of the pandemic on care work and gender equality

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    The coronavirus disease (COVID-19) pandemic, and the response to it, have brought to light the importance of care for the sustainability of life, and the central role that care plays in the functioning of our economies and societies. The pandemic has exacerbated existing care needs, transformed conditions of paid and unpaid care work and, ultimately, increased the volume of women’s unpaid care work, deepening the associated gender gaps. This study brings together evidence from across the globe on how the pandemic has impacted women’s unpaid care work, as well as exploring measures implemented by governments and the degree to which these mainstream a gender perspective. As the pandemic moves into its third year, these different experiences point to an opportunity to incorporate unpaid work and gender into recovery efforts, highlighting the care sector as an important driving force for building back better with more equality.Introduction .-- Part 1. Caring in times of COVID-19. I. The care economy and unpaid work: concepts and trends / Ana Ferigra Stefanović, Lucía Scuro, Iliana Vaca-Trigo. II. Crisis upon crisis / Ana Ferigra Stefanović, Lucía Scuro, Iliana Vaca-Trigo. III. Towards integral care systems for a transformative sustainable recovery / Ana Ferigra Stefanović, Lucía Scuro, Iliana Vaca-Trigo .-- Part II. Regional and national case studies. IV. The impact of COVID-19 on the care economy in Africa / Dr. Seithati Maria Motebang. V. Childcare, women’s employment and COVID-19 impacts in the Serbia / Sara Cantillon, Malinka Koparanova, Milana Rikanovic, Lidia Vujicic, Vladana Ajvaz, Nargis Azizova, Blerta Cela. VI. Childcare, women’s employment and COVID-19 impacts in the Kyrgz Republic / Mehrigiul Ablezova, Malinka Koparanova, Silke Steinhilber, Nargis Azizova. VII. The impact of the COVID-19 pandemic on care policies: experiences in Latin America / Catalina de la Cruz Pincetti, Lucia Scuro Somma. VIII. The impact of the COVID-19 crisis on the economic autonomy of women in Latin America and the Caribbean. IX. COVID-19 and the unpaid care economy in Asia and the Pacific / Deepta Chopra, Meenakshi Krishnan. X. Women’s economic empowerment in the Arab Region: advancing care economies / Muriel Sajoux, Abdulsalam Alsulaiman, Lena Torossian, Hala Attieh, Ghada Tabbah
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