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      Emotion Generation and Transition of Companion Robots Based on Plutchik’s Model and Quantum Circuit Schemes

      1 , 1 , 1 , 2 , 1
      Security and Communication Networks
      Hindawi Limited

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          Abstract

          Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik’s wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users’ social health by mitigating their negative emotions, especially during difficult times.

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          Most cited references24

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          A circumplex model of affect.

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            Quantum machine learning

            Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform
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              Deep Learning for Health Informatics

              With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.
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                Author and article information

                Contributors
                Journal
                Security and Communication Networks
                Security and Communication Networks
                Hindawi Limited
                1939-0122
                1939-0114
                August 18 2021
                August 18 2021
                : 2021
                : 1-15
                Affiliations
                [1 ]School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
                [2 ]College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
                Article
                10.1155/2021/6802521
                ed48fb9b-d804-4f3d-954e-6b5f80c63324
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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