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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77160完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成 | zh_TW |
| dc.contributor.author | 黃宇謙 | zh_TW |
| dc.contributor.author | Yu-Cian Huang | en |
| dc.date.accessioned | 2021-07-10T21:48:55Z | - |
| dc.date.available | 2024-12-09 | - |
| dc.date.copyright | 2019-12-17 | - |
| dc.date.issued | 2019 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | [1] S. Cohen and T. A. Wills, "Stress, social support, and the buffering hypothesis," in Psychological bulletin, 1985, p. 310.
[2] S. Shavitt, Y. I. Cho, T. P. Johnson, D. Jiang, A. Holbrook, and M. Stavrakantonaki, "Culture moderates the relation between perceived stress, social support, and mental and physical health," in Journal of Cross-Cultural Psychology, 2016, pp. 956-980. [3] W. Samter and B. R. Burleson, "Evaluations of communication skills as predictors of peer acceptance in a group living situation," in Communication Studies, 1990, pp. 311-326. [4] D. R. Lehman, K. J. Hemphill, and P. Relationships, "Recipients' perceptions of support attempts and attributions for support attempts that fail," in Journal of Social, 1990, pp. 563-574. [5] B. R. Burleson and W. Samter, "Consistencies in theoretical and naive evaluations of comforting messages," in Communications Monographs, 1985, pp. 103-123. [6] E. L. MacGeorge, R. M. Lichtman, and L. C. Pressey, "The evaluation of advice in supportive interactions: Facework and contextual factors," in Human Communication Research, 2002, pp. 451-463. [7] B. R. Burleson, "Comforting messages: Features, functions, and outcomes," in Strategic interpersonal communication, 1994, pp. 135-161. [8] A. L. Gilliland, "After praise and encouragement: Emotional support strategies used by birth doulas in the USA and Canada," in Midwifery, 2011, pp. 525-531. [9] A. L. Baylor, D. Warren, S. Park, E. Shen, and R. Perez, "The impact of frustration-mitigating messages delivered by an interface agent," in Artificial intelligence in education: supporting learning through intelligent socially informed technology, 2005, p. 73. [10] H. Prendinger and M. Ishizuka, "The empathic companion: A character-based interface that addresses users'affective states," in Applied Artificial Intelligence, 2005, pp. 267-285. [11] M. Dennis and J. Masthoff, "Adapting progress feedback and emotional support to learner personality," in International Journal of Artificial Intelligence in Education, 2016, pp. 877-931. [12] M. Dennis, P. Kindness, J. Masthoff, C. Mellish, and K. Smith, "Towards effective emotional support for community first responders experiencing stress," in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013, pp. 763-768. [13] P. Kindness, J. Masthoff, and C. Mellish, "Designing emotional support messages tailored to stressors," in International Journal of Human-Computer Studies, 2017, pp. 1-22. [14] C. E. Cutrona, B. B. Cohen, and S. Igram, "Contextual determinants of the perceived supportiveness of helping behaviors," in Journal of Social Personal Relationships, 1990, pp. 553-562. [15] S. Cobb, "Social support as a moderator of life stress," in Psychosomatic medicine, 1976, pp. 300-314. [16] J. House, Work stress and social support (Addison-Wesley Series on Occupational Stress). 1983. [17] L. S. Greenberg, L. N. Rice, and R. Elliott, Facilitating emotional change: The moment-by-moment process. Guilford Press, 1996. [18] N. Coupland, J. Coupland, and H. Giles, Language, society and the elderly: Discourse, identity and ageing. Basil Blackwell, 1991. [19] D. J. Goldsmith and E. L. MacGeorge, "The impact of politeness and relationship on perceived quality of advice about a problem," in Human Communication Research, 2000, pp. 234-263. [20] L. C. Callister, "The role of the nurse in childbirth: perceptions of the childbearing woman," in Clinical nurse specialist CNS, 1993, pp. 288-93, 317. [21] K. Wada and T. Shibata, "Living with seal robots—its sociopsychological and physiological influences on the elderly at a care house," in IEEE transactions on robotics, 2007, pp. 972-980. [22] D. Ullrich, S. Diefenbach, and A. Butz, "Murphy Miserable Robot: A Companion to Support Children's Well-being in Emotionally Difficult Situations," in Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2016, pp. 3234-3240. [23] E. Cambria, C. Havasi, and A. Hussain, "SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis," presented at the Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, 2012. [24] S. M. Mohammad and P. Turney, "Crowdsourcing a word–emotion association lexicon," in Computational Intelligence, 2013, pp. 436-465. [25] B. Agarwal, N. Mittal, P. Bansal, and S. Garg, "Sentiment analysis using common-sense and context information," in Computational intelligence neuroscience, 2015, p. 30. [26] R. Speer, J. Chin, and C. Havasi, "Conceptnet 5.5: An open multilingual graph of general knowledge," in Thirty-First AAAI Conference on Artificial Intelligence, 2017. [27] P. Castells, M. Fernandez, and D. Vallet, "An adaptation of the vector-space model for ontology-based information retrieval," in IEEE transactions on knowledge data engineering, 2006, pp. 261-272. [28] K. S. Jones, S. Walker, and S. E. Robertson, "A probabilistic model of information retrieval: development and comparative experiments: Part 2," in Information processing management, 2000, pp. 809-840. [29] H. Jung, S. Lee, J. Yim, S. Park, and J. Kim, "Joint fine-tuning in deep neural networks for facial expression recognition," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 2983-2991. [30] S. Ji, W. Xu, M. Yang, and K. Yu, "3D convolutional neural networks for human action recognition," in IEEE transactions on pattern analysis machine intelligence, 2012, pp. 221-231. [31] X. Xiong and F. De la Torre, "Supervised descent method and its applications to face alignment," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 532-539. [32] D. B. Lenat, "CYC: A large-scale investment in knowledge infrastructure," in Communications of the ACM, 1995, pp. 33-38. [33] P. Singh, T. Lin, E. T. Mueller, G. Lim, T. Perkins, and W. L. Zhu, "Open Mind Common Sense: Knowledge acquisition from the general public," in OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", 2002, pp. 1223-1237. [34] H. Liu and P. Singh, "ConceptNet—a practical commonsense reasoning tool-kit," in BT technology journal, 2004, pp. 211-226. [35] D. Koller and N. Friedman, "Probabilistic graphical models: principles and techniques," 2009. [36] H. Guo and W. Hsu, "A survey of algorithms for real-time Bayesian network inference," in Join Workshop on Real Time Decision Support and Diagnosis Systems, 2002. [37] W. R. Gilks, S. Richardson, and D. Spiegelhalter, Markov chain Monte Carlo in practice. Chapman and Hall/CRC, 1995. [38] R. C. Arkin, M. Fujita, T. Takagi, and R. Hasegawa, "An ethological and emotional basis for human–robot interaction," in Robotics Autonomous Systems, 2003, pp. 191-201. [39] M. H. Siddiqi, R. Ali, A. M. Khan, Y.-T. Park, and S. Lee, "Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields," in IEEE Transactions on Image Processing, 2015, pp. 1386-1398. [40] P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, "The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression," in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2010, pp. 94-101. [41] W. Che, Z. Li, and T. Liu, "Ltp: A chinese language technology platform," in Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, 2010, pp. 13-16. [42] J. Lafferty, A. McCallum, and F. C. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proceedings of the Eighteenth International Conference on Machine Learning, 2001, pp. 282-289. [43] T. Liu, J. Ma, and S. Li, "Building a Dependency Treebank for Improving Chinese Parser," in Journal of Chinese Language and Computing, 2006, pp. 207-224. [44] S. G. Hart, "NASA-task load index (NASA-TLX); 20 years later," in Proceedings of the human factors and ergonomics society annual meeting, 2006, pp. 904-908: Sage publications Sage CA: Los Angeles, CA. [45] B. M. Gadzella, "Student-life stress inventory: Identification of and reactions to stressors," in Psychological reports, 1994, pp. 395-402. [46] S. H. Hamaideh, "Stressors and reactions to stressors among university students," in International journal of social psychiatry, 2011, pp. 69-80. [47] T. H. Holmes and R. H. Rahe, "The social readjustment rating scale," in Journal of psychosomatic research, 1967, pp. 213-218. [48] B. Ajibade, O. Olabisi, B. Fabiyi, O. Ajao, and A. Ayeni, "Stress, types of stressors and coping strategies amongst selected nursing schools students in South-West, Nigeria," in European Journal of Biology Medical Science Research, 2016, pp. 1-15. [49] Daily Inventory of Stressful Events. Available: https://scienceofbehaviorchange.org/ [50] UCU model stress questionnaire. Available: https://www.ucu.org.uk/stress [51] ISMA Stress Questionnaire. Available: http://isma.org.uk/wp-content/uploads/2013/08/Stress-Questionnaire.pdf [52] 卓淑玲, 陳學志, and 鄭昭明, "台灣地區華人情緒與相關心理生理資料庫─ 中文情緒詞常模研究," in 中華心理學刊, 2013, pp. 493-523. [53] M. Kopacz, "Personality and music preferences: The influence of personality traits on preferences regarding musical elements," in Journal of Music Therapy, 2005, pp. 216-239. [54] R. C. Mulyanegara, Y. Tsarenko, and A. Anderson, "The Big Five and brand personality: Investigating the impact of consumer personality on preferences towards particular brand personality," in Journal of Brand Management, 2009, pp. 234-247. [55] 鄧景宜, 曾旭民, 李怡禎, and 游朝舜, ""International English Big-Five Mini-Markers" 之繁體中文版量表發展," in 管理學報, 2011, pp. 579-615. [56] L. R. Dice, "Measures of the amount of ecologic association between species," in Ecology, 1945, pp. 297-302. [57] PTT 中文語料. Available: https://github.com/zake7749/Gossiping-Chinese-Corpus [58] Y. Wu, W. Wu, C. Xing, M. Zhou, and Z. Li, "Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots," in Annual Meeting of the Association for Computational Linguistics, 2016. [59] S. Worswick. Mitsuku. Available: https://www.pandorabots.com/mitsuku/ [60] E. Thompson, "Development and validation of an international English big-five mini-markers," in Personality individual differences, 2008, pp. 542-548. [61] gRPC. Available: https://grpc.io/ | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77160 | - |
| dc.description.abstract | 情感支持是人與人之間協調關係最好的方式，人們在心情低落時，往往需要朋友、家人提供適當的關懷，來幫助自身度過負面的情感。在本論文中，我們開發一個能夠提供情感支持的人機互動系統。該系統能夠藉由觀察人的臉部表情與言語，在使用者是負面情緒時，主動給予關心，並基於使用者的自我披露，提供適合的情感支持回覆。
為了瞭解人的情感，我們應用深度學習的技術結合人臉語言語資訊識別出使用者的情緒，並以此作為主動關懷的觸發點。接著使用自然語言處理的技術來分析使用者的自我披露，並以分析出的資訊配合結合貝氏網路 (Bayesian Network) 的常識知識圖譜來推斷出人的心理情感以及其心情低落的壓力來源。而後應用神經網路 (Neural Network) 從壓力來源的種類推論出合適的情感支持類型。最後基於情感支持的類型和推論出的人之情感產生一個合適且具同理心的對話來與人互動，以此達到情感支持的效果。 | zh_TW |
| dc.description.abstract | Emotional support is a best way to coordinating relationship among people. When people are down in the dumps, appropriate care provided by friends or family usually can help people having upset. In this thesis, we aim to design a robot companion system which is able to provide emotional support to humans. While a human is in a bad mood, the system can generate an appropriate emotional support response based on his/her self-disclosure induced by the robot.
To understand human’s emotion, we apply deep learning techniques to recognize the facial expression and analyze the sentence sentiment, which in turn can be used as a cue for robot to proactively care humans. Given the information from the natural language processing as observations, the system can infer human’s feelings and stressors by applying a Bayesian Network which is constructed from commonsense knowledge graph. Afterwards, the system infers emotional support of a specific category from the human’s stressor by neural network. Based on the content of emotional support and inferred human’s feelings, the robot generates an appropriate dialogue which contain empathy to achieve the effective emotional support. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T21:48:55Z (GMT). No. of bitstreams: 1 ntu-108-R06922072-1.pdf: 3231837 bytes, checksum: bf69daa407d1ff2c8b516d5db089de0a (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Related work 4 1.3.1 Emotional Support Reviews 4 1.3.2 Emotion Inference 7 1.3.3 Dialogue System 8 1.4 Contribution 10 1.5 Thesis organization 10 Chapter 2 Preliminaries 12 2.1 Facial Expression Recognition 12 2.1.1 Convolutional Neural Network 13 2.1.2 Facial Expression Recognition 15 2.2 Commonsense Knowledge 17 2.2.1 ConceptNet 17 2.3 Probabilistic Graphical Model and Inference Algorithms 20 2.3.1 Bayesian Network 21 2.3.2 Markov Chain Monte Carlo Approximation Inference 22 Chapter 3 Emotional Support System 25 3.1 System Overview 25 3.2 Facial Expression Recognition 26 3.3 Natural Language Understanding 28 3.4 Stressor Knowledge Graph 30 3.4.1 Identification of Stressor Types 31 3.4.2 Knowledge Graph Structure 32 3.5 Probabilistic Graphical Model for Inferring Stressor and Feeling 35 3.5.1 Model Construction 35 3.5.2 Model Inference 38 3.6 Decision Module 40 3.7 Dialogue Manager 47 3.7.1 Dialogue Policy 48 3.7.2 Emotional Support Chat 48 3.7.3 General Chat 50 Chapter 4 Experiments 53 4.1 Stressor Inference Evaluation 53 4.1.1 Experimental Setup 53 4.1.2 Results and Discussion 55 4.2 Decision Module Evaluation 56 4.2.1 Data Description 56 4.2.2 Experimental Metrics 59 4.2.3 Data Processing 60 4.2.4 Results and Discussion 61 4.3 Human-Robot Interaction Experiment 63 4.3.1 Experimental Setup 64 4.3.2 Results and Discussion 69 Chapter 5 Conclusions 75 REFERENCE 77 | - |
| dc.language.iso | en | - |
| dc.title | 互動式陪伴型機器人之情感支持系統 | zh_TW |
| dc.title | Emotional Support System for Interactive Companion Robot | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 108-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 岳修平;黃從仁;林維真;李宏毅 | zh_TW |
| dc.contributor.oralexamcommittee | ;;; | en |
| dc.subject.keyword | 情感支持,社交陪伴機器人,貝氏網路,知識圖譜,機器學習, | zh_TW |
| dc.subject.keyword | Emotional Support,Social Robot Companion,Bayesian Network,Knowledge Graph,Machine Learning, | en |
| dc.relation.page | 83 | - |
| dc.identifier.doi | 10.6342/NTU201904364 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2019-12-09 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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