The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20)

October 10-12, 2020 Online

Conference Proceedings

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Organizing Comittee

Conference Chair Fuji Ren (Japan) Advisory Committee Co-Chairs Program Committee Chairss Qionghai Dai (China) Hans Uszkoreit (Germany) Mengchu Zhou (USA) Tetsuya Tanioka (Japan) Deyi Li (China) Tsujii Junichi (Japan) Maosong Sun (China) Guodong Zhou (China) Christian Boitet (France) Eduard Hovy (USA) Nicolata Calzolari (Italy) Xiaojie Wang (China) Nanning Zheng (China) Jon George Hall (UK) Key-Sun Choi (Korea) Mohammad Golam Sohrab Makoto Nagao（Japan） Yixin Zhong (China) (Japan)

Organizing Committee Chairs Sponsor Chair Forum Chairs Weining Wang (China) Hao Yu (China) Shumin Shi (China) Kyoko Osaka (Japan) Kuzuyuki Matsumoto (Japan) Caixia Yuan (China) Mingwen Wang (China) Hirokazu Ito (Japan) Yoshihiro KAI (Japan) Lei Li (China)

Publication Co-Chairs General Secretari Sponsor Zhenjiang Dong (China) Xiao Sun (China) Chinese Association for Xin Kang (Japan) Changqin Quan (Japan) Artificial Intelligence Yuko Yasuhara (Japan) Yaru Zou (China)

Co-Sponsors Organizers Conference Contact Beijing Univ. of Posts and Natural Language Understanding [email protected] Telecommunications Committee of CAAI, China Tokushima University, Affective Computing and Intelligent Robot Laboratory Hefei University of Technology

Program Committee

Adonbek Gulilla (Xinjiang University) Adonbek Gulilla (Xinjiang University) Baobao Chang (Beijing University) Baobao Chang (Beijing University) Bin Dong (Ricoh Software Research Institute) Bin Dong (Ricoh Software Research Institute) Bing Qin (Harbin Institute of Technology) Bing Qin (Harbin Institute of Technology) Bingquan Liu (Harbin Institute of Technology) Bingquan Liu (Harbin Institute of Technology) Caixia Yuan (Beijing University of Posts and Telecommunications) Caixia Yuan (Beijing University of Posts and Changliang Li (Director of Kingsoft Artificial Intelligence Telecommunications) Laboratory) Changliang Li (Director of Kingsoft Artificial Intelligence Changqin Quan (Hefei University of Technology) Laboratory) Chengjie Sun (Harbin Institute of Technology) Changqin Quan (Hefei University of Technology) Chengqing Zong (Chinese Academy of Sciences) Chengjie Sun (Harbin Institute of Technology) Degen Huang (Dalian University of Technology) Chengqing Zong (Chinese Academy of Sciences) Fuji Ren (Tokushima University) Degen Huang (Dalian University of Technology) Guodong Zhou (Suzhou University) Fuji Ren (Tokushima University) Haiqing Hu (Xi'an University of Technology) Guodong Zhou (Suzhou University) Haitao Yu (University of Tsukuba) Haiqing Hu (Xi'an University of Technology) Hao Yu (Ricoh Software) Haitao Yu (University of Tsukuba) Heyan Huang (Beijing Institute of Technology) Hao Yu (Ricoh Software) Hong Chen (Ant Financial) Heyan Huang (Beijing Institute of Technology) Hongying Zan (Zhengzhou University) Hong Chen (Ant Financial) Houfeng Wang (Beijing University) Hongying Zan (Zhengzhou University) Hua Wu (Baidu) Houfeng Wang (Beijing University) Huixing Jiang (Meituan) Hua Wu (Baidu) Jian Sun (Ali Group) Huixing Jiang (Meituan) Keliang Zhang (Information Engineering University) Jian Sun (Ali Group) Lei Li (Beijing University of Posts and Telecommunications) Keliang Zhang (Information Engineering University) Linmei Hu (Beijing University of Posts and Telecommunications) Lei Li (Beijing University of Posts and Telecommunications) Liping Mi (Shaoguan University of Guangdong Province) Linmei Hu (Beijing University of Posts and Maosheng Zhong (Jiangxi Normal University) Telecommunications) Maosong Sun (Tsinghua University) Liping Mi (Shaoguan University of Guangdong Province) Mingwen Wang (Jiangxi Normal University) Maosheng Zhong (Jiangxi Normal University) Peng Jin (Leshan Normal University) Maosong Sun (Tsinghua University) Qing Li (Southwestern University of Finance and Economics) Mingwen Wang (Jiangxi Normal University) Peng Jin (Leshan Normal University) Qing Li (Southwestern University of Finance and Economics)

Contents

Welcoming Message from the General Chair……………………………………………….……. 1

Guest profile…………………………………………………………………………………………. 1

The Preliminary Construction of Tibetan Semantic Knowledge Base Based on HowNet——The Combination of Tibetan Cases and HowNet Yiyuan ...... 1 Zhou Yao, Xiaobing Zhao

Construction and Evaluation of QOL Specialized Dictionary SqolDic Utilizing Vocabulary Meaning and QOL Scale ...... 3 Satoshi Nakagawa, Minlie Huang, Yasuo Kuniyoshi

Corpus-based Research on Translation Features of the Japanese Versions of “Analects” ...... 7 Ye Yang, Zihan Wang

Emotion Wheel and Affective Lexicon based Label Enhancement for Emotion Distribution Learning ...... 9 Xue-Qiang Zeng, Qi-Fan Chen, Ping-Sheng Liu, Jia-Li Zuo, Ming-Wen Wang

Improved of RelGAN for Text Generation ...... 10 Jiao Ziyun, Fuji Ren

Potential Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering ...... 11 Qian Zhang, Fuji Ren

Predicting Personality of Social Media Users based on Big Five Theory ...... 12 Fanli Meng, Bo Xu, Chaoliang Peng, Hongfei Lin

Detecting Early Stage Depressions Based on Compound Neural Language Understanding. 13 Rongyu Dou, Fuji Ren

Local Label Correlation Learning for Multi-Label Classification ...... 15 Jiawen Deng, Fuji Ren

Capsule Network with TC Loss for Intention Detection ...... 16 Siyuan Xue, Fuji Ren

Prediction of Hepatocellular Carcinoma with Machine Learning Algorithms ...... 18 Yakun Lu, Qiong Wu, Bo Qiu, Chitty Chen, Zongnan Tan, Mengci Li, Guanjie Xiang

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A humanoid robot based on neural network architecture for accompanying children with autism ...... 19 Tianhao She, Fuji Ren

Deficiencies and development trends of rehabilitation robots ...... 20 Yue Xu, Peiyong Ni

Construction of emergency medical support system based on understanding dialect intention ...... 21 Kyoya Abe, Fuji Ren, Shun Nishide, Xin Kang

Surface Defects Classification of Steel Strips based on Transfer Learning and Naive Bayes Model ...... 22 Jiaqiao Zhang, Xin Kang, Hongjun Ni, Fuji Ren

Thermal Fault Area Recognition and Location System based on Infrared Image ...... 24 Kaixuan Wang, Hongjun Ni, Fuji Ren, Jiaqiao Zhang, Shuaishuai Lv, Xingxing Wang

Real-time Dynamic Facial Expression Recognition System Based On CNN-GRU ...... 26 Duo Feng, Fuji Ren

Multi-scale Receptive Field Fusion Residual Network for Image Classification ...... 28 Wenjie Liu, Fuji Ren, Guoqing Wu

Binary K-means Tree Ensemble Classifiers ...... 30 Quan Wang, Fei Wang, Peilin Jiang,Fuji Ren

Design and Control of an Elbow Joint Assist Suit with a Velocity-Based Mechanical Safety Device ...... 31 Atsushi Kaneta, Tsubasa Kaneda, Keisuke Ikeda, Yoshihiro Kai, Kenichi Sugawara, Masayoshi Tomizuka, Tetsuya Tanioka, Kensaku Takase

Development of a Velocity-Based Mechanical Safety Brake for Wheeled Mobile Nursing Robots ...... 35 Yoshiaki Sato, Hiroki Mishima, Yoshihiro Kai, Tetsuya Tanioka, Yuko Yasuhara, Kyoko Osaka, Yueren Zhao

A Walking Support System Equipped with a Variable Speed Treadmill and a Lift Device ... 39 Yuya Yokouchi, Yoshihiro Kai, Masayuki Tsuchida, Kenichi Sugawara

Human operator's "Characteristic of autonomic nerve activity as psychological burden" and "Clarification of thinking process for AI development" while operating the humanoid robot conversation program ...... 41

Tomoya Yokotani, Ryuichi Tanioka, Chihiro Kawai, Feni Betriana, Hirokazu Ito, Yuko Yasuhara, Kazuyuki Matsumoto, Fuji Ren, Tetsuya Tanioka

Developmental issues on communication robots' application for the use of older people care ...... 46 Yuki Obayashi, Feni Betriana, Tetsuya Tanioka, Tomoya Yokotani, Ryuichi Tanioka, Chihiro Kawai, Hirokazu Ito, Yuko Yasuhara, Kyoko Osaka, Kazuyuki Matsumoto, Fuji Ren, Yoshihiro Kai

The effects of gaze and paralanguage for communication in nursing: A systematic review .. 51 Kanon Fukuta, Chiharu Fukutomi, Misaki Yoshimatsu, Hirokazu Ito, Rozzano Locsin,

Issues in developing natural language processing applications for healthcare robots to motivate older people while rehabilitation ...... 55 Ryuichi Tanioka, Kazuyuki Matsumoto, Kenichi Sugawara, Kensaku Takase, Yoshihiro Kai, Madahito Tomotake, Tetsuya Tanioka,

Examination to develop the artificial intelligence through transforming tacit knowledge of nurses' dialogue for patients with dementia towards explicit knowledge ...... 59 Hirokazu Ito, Kazuyuki Matsumoto, Xin Kang, Tetsuya Tanioka, Yuko Yasuhara, Rozzano Locsin, Fuji Ren

Construction of Annotated TOBYO Blog Corpus for Lifestyle Disease Analysis of Diabetic PATIENT ...... 63 Mopuaa Ryu, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita

Towards Automatic Dental Diagnosis System Based on Deep Learning ...... 64 Kohei Torii, Minoru Yoshida, Kazuyuki Matsumoto, Jiro Tsuruki, Kenichiro Kobayashi, Eiichi Honda, Kenji Kita,

Towards Analyzing Relations Between Sleeping Time and SNS Texts: Prediction of Tweet Time Span Using The Last Tweet of the Day ...... 66 Minoru Yoshida, Takumi Kojima, Kazuyuki Matsumoto, Kenji Kita

The 15th International Conference on Natural Language Welcome Message from the General Chair Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Welcoming Message from the General Chair

Good afternoon, everyone,

It is my pleasure to welcome you to NLP-KE-2020, the 15th International Conference on Natural Language Processing and Knowledge Engineering.

The Conference serves as an international forum for academic and industrial researchers to discuss and advance the applications of new technologies to natural language processing and knowledge engineering. The main objective of this conference is to bring together researchers from both natural language processing and knowledge engineering to exchange ideas and findings in their respective fields.

We have successfully held the conference in the past years, 2003, in Beijing, 2005, in Wuhan, 2007 and 2008, in Beijing, 2009, in Dalian, 2010, in Beijing, 2011, in Tokushima, 2012, in Hefei, 2014, in Shenzhen, 2015, in Sapporo, 2016, in Okinawa, 2017, in Chengdu, 2018, in Nanjing, 2019, in Singapore.

Because of the COVID-19 pandemic, at some moments in the spring and summer, we wondered whether we should cancel this year’s meeting. However, we received tremendous support and encouragement from our scientific community, our authors, and NLP-KE Fans to hold the conference. Their support greatly helped us prepare this conference, although this time the conference is held online.

About 5 papers will be selected as best paper awards, all papers will be recommended to be published by the International Journal of Advanced Intelligence.

The success of NLP-KE-2020 is due to the tremendous efforts and good-will of all those who were involved in organizing the conference. I would like to express my sincere gratitude to the program committee members, organization committee members, and our contributors and our reviewers. Many thanks to give our distinguish keynote speakers. Last but not least, I would like to thank all who submitted their work to NLP-KE2020 and who are attending the conference.

This time, as we can't meet face-to-face, I'll put some photos and activity videos taken at past conferences online. I hope you enjoy it.

Thank you!

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Professor Hans Uszkoreit

Hans Uszkoreit is Founder and Chief Scientist of GIANCE Technologies in Beijing. He is also Scientific Director at the German Research Center for Artificial Intelligence (DFKI) in Berlin, where he used to lead the Berlin DFKI site and is currently coordinating international activities. Uszkoreit has worked in AI for more than 30 years in the US, Germany and China as professor, research leader and entrepreneur. He is

Member of the European Academy of Sciences, Hans Uszkoreit Honorary Professor of Technical University Berlin, Permanent Member of the International Committee of Scientific Director Computational Linguistics (ICCL) and Past President of GIANCE and DFKI the Association of Logic, Language and Information. Hehas co-founded several AI startups and served as advisor for small and large corporations. His main research interests in AI are methods and applications of language and knowledge technologies and AI applications for enterprises. His research is documented in more than 250internationalpublications.

Title: AI for Intelligent Transformation: The case of Enterprise Intelligence The role of AI in the intelligent transformation of an enterprise is as complex as its entire system of business processes. Narrow AI applications, based on machine learning, can immensely improve many processes by automating tasks and optimizing the design and control of the entire process. But the vision of the Intelligent Enterprise extends far beyond these existing applications. It is based on the idea that processes, which combine automated execution and control with intellectual high-level human decision making, utilize both explicit corporate knowledge and ML-acquired experience for a quality of tactical and strategic decision making that neither individual experts nor collectives of people could achieve. Since such enterprise solutions require the clever integration of machine learning, knowledge technologies and human-IT interaction, this revolution of the enterprise world is still in its very beginnings. In my presentation, I will provide examples of such promising developments. One application area for such integrated solutions will be explained in more detail: Enterprise Intelligence, which enables the inclusion of enterprise-external data and knowledge for enhanced decision processes. I will demonstrate how various NLP functions based on machine learning interact with knowledge graphs for the acquisition and use of explicit corporate knowledge.The solution has been successfully applied in several enterprise support functions.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Professor Tetsuya Tanioka

He is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the

BEd from Meisei University Japan, in 1997. He was Tetsuya Tanioka, visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting Institute of Biomedical S professor of St. Paul University Philippines. He is ciences, Tokushima University, currently a board member of the Anne Boykin Institute Japan for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots. He was the lead editor of the book, Nursing Robots: Robotic Technology and Human Caring for the Elderly published by Fukuro Publishing, Japan, in March 2017.

Title: Nursing and Caring Robot Sciences: Relating Natural Language Processing and Knowledge Engineering Healthcare for the increasing older adult population is a significant concern in Japan, and in other developed countries. This challenge was further affected by the decreasing numbers of healthcare workers who are also getting older, resulting in high turnover rates of healthcare workers. In this kind of situation, it is appropriate to consider the use of healthcare robots which is are increasingly recognized as a potential solution to meet older person care as well as psychiatric patients’ healthcare and welfare needs. In a discussion among engineers who are collaborative researchers, questions arose: "What are the feasible content areas of engagement among older persons that support using robots?" "What are the barriers to introducing robots to hospitals or elderly institutions?" Challenges to developing nursing science using robots exists. Even with new devices and technologies developed by engineers and introduced and use in nursing care it is necessary to promote research with the goal of systematizing the technological competency, ethical thinking, safety measure, and effects of using robots in the nursing settings.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute A typical question may be asked, “which one is needed for nursing care the anthropomorphic

or non-anthropomorphic robots?” Anthropomorphic robots are not necessary if its use is for measuring blood pressure and body temperature, it is necessary when robot involvement includes having a warm anthropomorphic form in order to talk to older persons while taking vital signs much like a human nurse. Thus, in the healthcare field, each specialized occupation carries out their job as defined by the law. Also, following questions arose: "what are the tasks of nursing care that can be programmed for robots?" "What is the core competency that only human nurses and professional caregivers can do?" It is critical to establish an academic discipline of nursing science that answer the former and the latter questions together. When nurses' role is simply to do only simple tasks, nursing might simply succumb to being replaced by humanoid robots equipped with the higher level of intelligence. In order to satisfy the required performances described it is important that the anthropomorphic robot acts with high quality dialogue functions. In terms of hardware, by allowing independent range of action and degree of freedom, the burden is reduced among the nurses and professional caregivers. Furthermore, it is important to develop a friendlier robot by equipping it with non-verbal expressions that can be accessed by older persons linked with dialogue functions. If these functions can be combined, this robot can serve as a possible instructor for rehabilitation and recreation activities for older people. In this way, the robot can play an active role in healthcare and in the welfare fields more than ever.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Yoshihiro Kai

He received his Bachelor of Engineering degree, Master

of Engineering degree, and Doctor of Engineering degree in mechanical engineering from Doshisha University, Japan, in 1994, 1996, and 1999, respectively. From 1999 to 2002, he was a research associate at Kochi University of Technology, Japan. Since 2003, he has

been with Tokai University, Japan, where he is currently a Professor in the Department of Mechanical Engineering. He was a visiting scholar at the University of California, Berkeley's Department of Mechanical

Engineering in 2012. His research interests include human-friendly robots, exoskeletal robots, and drone Yoshihiro Kai systems. He is a member of the Japan Society of Mechanical Engineers (JSME), the Robotics Society of Japan (RSJ), the Institute of Systems, Control and Information Engineers (ISCIE), the Japan Society for Design Engineering (JSDE), the Japan Society of Nursing Research (JSNR), the International Federation for the Theory of Machines and Mechanisms (IFToMM), the Japanese Society for Regenerative Medicine and Rehabilitation (JSRMR), and the Society of Biomechanisms Japan (SOBIM Japan). He is currently the chairperson of the Mechanical Design Committee of Machine Design & Tribology Division in the Japan Society of Mechanical Engineering. Speech Title: What Can We Do When A Robot’s Computer Does Not Work? Recently, due to the rapid development of computer technologies, robots have been working not only within factories but also within human environments. In industrial robots, which work within factories, safety is guaranteed by isolating the robots from humans by using various types of barriers. However, when considering human-friendly robots which work within human environments, isolating the robots from humans is not possible. Therefore, safety is one of the most important problems in human-friendly robots. We can improve safety of human-friendly robots by using computer technologies. However, what can we do when a robot’s computer does not work? In this keynote speech, safety measures to ensure safety when the computer does not work are introduced. Not only the traditional safety measures but also hardware-based mechanical safety devices which this presenter et al. have developed are explained. Furthermore, the effectiveness of these safety measures is discussed.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Associate Professor Xian-Ling Mao

Xian-Ling Mao is Associate Professor of Computer Science at Beijing Institute of Technology. He received his PhD from Peking University in 2012. He focuses on the fundamental problem: how to satisfy the information need of users efficiently and effectively? His current research focuses on Learn to Hashing, Scientific Mining and Dialogue. His work has been published in TOIS, TKDE, SIGIR, AAAI, IJCAI, EMNLP, and many other leading conferences. Xian-Ling Mao Beijing Institute of Technology

Title: Similarity-preserved Hashing: Diffusing from Image to Text In the past decade, we have witnessed an explosive growth of data on the Internet, and it brings both challenges and opportunities to traditional algorithms developed on small to median scale data sets. Particularly, nearest neighbor search (NN) has become a key ingredient in many large-scale machine learning and data management tasks. In fact, approximate nearest neighbors (ANN) are enough to achieve satisfactory performance in many applications, such as the image retrieval task in search engines. Due to the low storage cost and fast retrieval speed, similarity-preserved hashing is one of the popular solutions for ANN search. This talk will first review related methods for images, then introduce the ways how similarity-preserved hashing is enabling natural language processing. It will also highlight open problems that are being addressed by emerging research.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Professor Zhiyuan Liu

He is an associate professor at the Department of Computer Science and Technology, Tsinghua University. He received his Ph.D. degree in Computer Science from Tsinghua in 2011. His research interests include representation learning, knowledge graphs and social computation, and has published more than 80 papers in top-tier conferences and journals of AI and NLP including ACL, IJCAI and AAAI, cited by more than 10,000 according to Google Scholar. Zhiyuan Liu

Associate Professor Tsinghua University Title: Knowledge-Guided Natural Language Processing Recent years have witnessed the advances of deep learning techniques in various areas of NLP. However, as a typical data-driven approach, deep learning suffers from the issue of poor interpretability. A potential solution is to incorporate large-scale symbol-based knowledge graphs into deep learning. In this talk, I will present recent works on knowledge-guided deep learning methods for NLP.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Professor Jiajun Zhang

He received his PhD degrees in Computer Science from Institute of Automation Chinese Academy of Sciences. His research interests include machine translation andmulti-lingual natural language processing. He has published more than 80 papers in top conference including AAAI, IJCAI, ACL, EMNLP, COLING and in international journals including Artificial Intelligence, IEEE/ACM TASLP, IEEE Intelligent Systems, IEEE TKDE and TACL. He also received several best papers from PACLIC-2009, NLPCC-2012, CWMT-2014, NLPCC-2017 Jiajun Zhang and CCMT-2019. He servers as SPC for AAAI (2019-2020) Institute of Automation Chinese and IJCAI (2017-2021), PC co-chair for CWMT-2018 and Academy of Sciences area chair for COLING (2018, 2020), EMNLP-2019, ACL (2020-2021). He is an editorial board member of Machine Translation journal.

Title: Towards End-to-End Speech-to-Text Translation Nowadays, speech translation becomes more and more important in many scenarios, such as global e-commerce, multilingual media subtitles and global communications. Traditionally, speech translation is composed of three modules, namely automatic speech recognition, machine translation and speech synthesis. Recently, end-to-end modeling, in particular end-to-end speech-to-text translation attracts much attention due to its superiority of efficiency. In this talk, I will introduce the overall framework of end-to-end speech-to-text translation and analyze its key challenges. Next, I will mainly introduce some of our solutions to address the challenges, leading to much better translation performance.

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The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

The Preliminary Construction of Tibetan Semantic Knowledge Base Based on HowNet——The Combination of Tibetan Cases and HowNet Yiyuan

Zhou Yao Center of Minority Languages—National Language Resource Monitoring & Research, Minzu University of China, 27 Zhongguancun, South Avenue Beijing, 100081, China [email protected]

Xiaobing Zhao Center of Minority Languages—National Language Resource Monitoring & Research, Minzu University of China, 27 Zhongguancun, South Avenue Beijing, 100081, China [email protected]

Corpus is essential for natural language processing and in this paper, we introduce a theoretically language-independent semantic knowledge system, HowNet, to guide the construction of Tibetan knowledge base. It is assumed that word meanings can be described by a limited semantic prime set which is smaller than the word. In such a theoretical assumption, Yiyuan are put forward to describe the concepts with a semantic perspective. Yiyuan are organized and structured in a hierarchical system which deals with natural language processing. HowNet is called universal, so we try to add Tibetan to it. Chinese and English are included in HowNet, but Tibetan is totally different from them. The Tibetan case is the unity of syntactics and semantics which we can use to get semantic information from the surface structure. Case is so important in Tibetan that it is used in nearly every sentence, and every single case is both semantic and syntactic. Yet, we cannot see the same linguistic phenomenon in Chinese or English. In order to get semantic information, we extract semantic information from Tibetan cases and do the semantic role labeling. Secondly, we match them with the Yiyuan system. Besides, with the help of cases, we put them in the relationship of hypernymy & hyponymy of HowNet orderly and activate the related events which contributes to the understanding of Tibetan sentences.

References

1. Zhendong Dong and Qiang Dong.2003. HowNet - A hybrid language and knowledge resource. In Proceedings of 2003 International Conference on Natural Language Processing and Knowledge Engineering, October 26 – 29, 2003 Beijing, China. Institute of Electronical and Electronics Engineers, Middlesex, New Jersey, 820-824. DOI: 10.1109/NLPKE.2003.1276017 . 2. Zhendong Dong and Qiang Dong. 2006. Hownet And the Computation of Meaning. World Scientific Publising Co., Inc, Singapore, SG. 3. Qi Kunyu. 2004. Research on the Design of Modern Tibetan Semantic Dictionary for Machine Translation. Journal of Northwest Minorities University for Nationalities (Natural Science) 25, 3 (Sept. 2004), 33-37. DOI: 10.14084/j.cnki.cn62-1188/n.2004.03.010. 4. Zhendong Dong, Qiang Dong and Changling Hao. 2007. Theoretical Findings of HowNet. Journal of Chinese Information Processing 21, 4 (July. 2007), 3-9.

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5. Dexi Zhu. 1999. Questions & Answers of Grammar (2nd. ed.). The Commercial Press, Beijing, China. 6. Kunyu Qi. 2014. Research on Tibetan Semantic Role Labeling Based on Dependency Relationship. Journal of Northwest University for Nationalities (Philosophy and Social Science), 1 (Jan. 2014), 139-143. 7. Ge Sangjumian, Ge Sangyangjing. 2004. Practical Tibetan Grammar Course. Sichuan Minority Press, Chengdu, China. 8. Jinwu Ma. 2008. Four Clear Structures of Tibetan Grammar. Publishing House of Minority Nationalities, Beijing, China. 9. Zhendong Dong, Qiang Dong and Changling Hao. 2010. HowNet and its computation of meaning. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING '10), August 23 - 27, 2010, Beijing, China. Association for Computational Linguistics, Monroe, Pennsylvania, 53–56. DOI: https://dl.acm.org/doi/abs/10.5555/1944284.1944298. 10. Tan Hu. 1984. Analysis of Several Verb Sentences in Lhasa Tibetan. Minority Languages of China, 1 (Jan. 1984), 1-16. 11. Di Jiang. 2006. The Classification of Tibetan Verbs and Relative Patterns Based on Semantics and Syntax. Journal of Chinese Information Processing 20, 1 (Jan. 2006), 37-43. 12. Xijie Huang, Dan Zhenduojie. 2007. Comparison of Chinese and Tibetan Grammar. Publishing House of Minority Nationalities, Beijing, China. 13. E. Bach and R. T. Harms (Ed.). 1968. Universals in Linguistic Theory. The Case for Case. Holt, Rinehart, and Winston, New York, America. 14. Mingyang Hu. 1996. Study of Parts of Speech. Beijing Language and Culture University Press, Beijing, China. 15. Daniel Jurafsky and James H. Martin. 2009. Speech and Language Processing (2nd. ed.). Prentice Hall, New Jersey, America. 16. Christopher S. Butler. 2003. Structure and Function: Approaches to the simplex clause. John Benjamins Publishing, Amsterdam, Netherland. 17. Joseph H. Greenberg. 1966. Universals of language (2nd. ed.). The MIT Press, Massachusetts, America. 18. Zhendong Dong and Qiang Dong. 2001. Some issues on IT-oriented lexica1 semantics. Applied Linguistics, 3 (Aug. 2001), 27-32. DOI: 10.16499/j.cnki.1003-5397.2001.03.007. 19. Caihua1, Choenor and Ngodrup .2018. Studying on the Tibetan case structure and its grammatical functions. Plateau Science Research, 1 (Jan. 2018), 94-100. 20. Duo Jiezhuoma. 2010. Analyses of Semantic Relations between Tibetan Framework of Semantic Knowledge. Journal of Northwest University for Nationalities (Natural Science) 31, 1 (Sept. 2010), 16-19. DOI: 10.14084/j.cnki.cn62-1188/n.2010.01.020. 21. Tongling Zhang. 2011. The Constructional Research on Semantic Network of Zaoshi Words. Computer Development & Applications 24, 7 (June. 2011), 25-27.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Construction and Evaluation of QOL Specialized Dictionary SqolDic Utilizing Vocabulary Meaning and QOL Scale

Satoshi Nakagawa Grad. School of Information Sci. and Tech., The University of Tokyo, 113-8656 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan [email protected]

Minlie Huang Dept. of Computer Science, Tsinghua University, Beijing 100084, China [email protected]

Yasuo Kuniyoshi Grad. School of Information Sci. and Tech., The University of Tokyo, 113-8656 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan [email protected]

Agents that build interactive relationships with people can provide appropriate support and generate behaviors by accurately grasping the state of the person. This study focuses on the quality of life (QOL), which can be assessed multidimensionally, and aims to estimate QOL scores in the process of human interaction. Although vision-based estimation has been the main method for QOL estimation, we proposed a new text-based estimation method. We created a QOL-specific dictionary called SqolDic, which is based on large-scale textual data. To evaluate the effectiveness of SqolDic, we implemented a system that outputs the time-series variation of a user's conversation content and the QOL scores based on it. In an experiment for estimating the content of user conversations based on a QOL scale by inputting data from actual human conversations, we achieved a maximum estimation accuracy of 91.2¥%. Additionally, in an experiment to estimate QOL score variability, we successfully estimated the mental health state and one of the QOL scales with a smaller distribution of error than that in previous studies. The experimental results demonstrated the effectiveness of our system in estimating conversation content and QOL scores as well as the effectiveness of our newly proposed QOL dictionary.

Acknowledgments

This study was supported by the KDDI Foundation Research Grant Program 2019, the Graduate Program for Social ICT Global Creative Leaders (GCL) of The University of Tokyo by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Chair for Frontier AI Education in School of Information Science and Technology, and Next Generation AI Research Center, The University of Tokyo.

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References

1. T. Hu, A. Xu, Z. Liu, Q. You, Y. Guo, V. Sinha, J. Luo and R. Akkiraju. Touch your heart: A tone-aware chatbot for customer care on social media. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-12, 2018. 2. S. Fukuhara, S. Bito, J. Green, A. Hsiao and K. Kurokawa. Translation, adaptation, and validation of the sf-36 health survey for use in japan. Journal of clinical epidemiology, vol. 51, no. 11, pp. 1037-1044, 1998. 3. S. Nakagawa, S. Yonekura, H. Kanazawa, S. Nishikawa and Y. Kuniyoshi. Estimation of Mental Health Quality of Life using Visual Information during Interaction with a Communication Agent. In The 29th IEEE International Conference on Robot and Human Interactive Communication, 2020. 4. S. Nakagawa, D. Enomoto, S. Yonekura, H. Kanazawa and Y. Kuniyoshi. A telecare system that estimates quality of life through communication. In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 325-330, 2018. doi:10.1109/CCIS.2018.8691360 5. S. Nakagawa, D. Enomoto, S. Yonekura, H. Kanazawa and Y. Kuniyoshi. New telecare approach based on 3D convolutional neural network for estimating quality of life. Neurocomputing 397, pp. 464-476, 2020. 10.1016/j.neucom.2019.09.112 6. DeVault, D., Artstein, R., Benn, G., Dey, T., Fast, E., Gainer, A., Georgila, K., Gratch, J., Hartholt, A., Lhommet, M., Lucas, G. SimsenSei Kiosk: A virtual human interviewer for health- care decision support. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, pp. 1061-1068. International Foundation for Autonomous Agents and Multiagent Systems, 2014. 7. S. N. Shivhare and S. Khethawat, “Emotion detection from text,” arXiv preprint arXiv:1205.4944, 2012. 8. S. Shaheen, W. El-Hajj, H. Hajj and S. Elbassuoni. Emotion recognition from text based on automatically generated rules. In 2014 IEEE International Conference on Data Mining Workshop, pp. 383-392, 2014. 9. F. Ren, X. Kang and C. Quan, Examining accumulated emotional traits in suicide blogs with an emotion topic model. IEEE J. Biomed. Health Inform. 20(5), pp. 1384-1396, 2015. doi:10.1109/JBHI.2015.2459683 10. F. Ren and Y. Wu. Predicting user-topic opinions in Twitter with social and topical context. IEEE Trans. Affect. Comput. 4(4), pp. 412-424, 2013. doi:10.1109/T-AFFC.2013.22 11. Uchida, T., Takahashi, H., Ban, M., Shimaya, J., Yoshikawa, Y., and Ishiguro, H. A robot counseling system - What kinds of topics do we prefer to disclose to robots?. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication, pp. 207-212, 2017. 12. Rashkin, H., Smith, E. M., Li, M., and Boureau, Y. L. (2018). Towards empathetic open- domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207. 13. Perez-Rosas, V., Mihalcea, R., Resnicow, K., Singh, S., and An, L. Understanding and predicting empathic behavior in counseling therapy. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics Vol. 1, pp. 1426-1435, 2017. 14. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pp. 3111-3119, 2013. 15. T. Kudo, K. Yamamoto and Y. Matsumoto. Applying Conditional Random Fields to Japanese Morphological Analysis. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP-2004), pp. 230-237, 2004.

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16. H. Takamura, T. Inui and M. Okumura. Extracting Semantic Orientations of Words using Spin Model. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL2005), pp. 133-140, 2005.

Satoshi Nakagawa Satoshi Nakagawa received the bachelor’s degree and master’s degree from the University of Tokyo, Tokyo, Japan, in 2018 and 2020 respectively. He is currently a Ph.D. candidate in the Department of Mechano-Informatics, Graduate School of Information Science and Technology, the University of Tokyo. He was a collaborator at Center of Mathematical Sciences and Applications at Harvard University in 2018 and a visiting researcher at the Department of Computer Science and Technology at Tsinghua University in 2019. His research interests include human robot interaction, deep learning, and elderly welfare.

Minlie Huang Dr. Minlie Huang is an associate professor at Tsinghua University. He won Wuwenjun AI award in 2019, Hanvon Youngth Innovation Award in 2018, MSRA collaborative research grant in 2019, and Alibaba Innovative Research Award in 2019. He won SIGDIAL 2020 best paper, NLPCC 2020 best student paper, IJCAI-ECAI 2018 distinguished paper, NLPCC 2015 best paper, and CCL 2018 best demo award. His work on Emotional Chatting Machine was reported by MIT Technology Review, the Guardian, NVIDIA, Cankao Xiaoxi, Xinhua News Agency, etc. He has published 80+ papers in premier conferences such as ACL, AAAI, IJCAI, EMNLP, WWW, SIGIR, and highly-impacted journals like ACM TOIS, IEEE TASLP, TACL etc. He served as ACL 2021 diversity&inclusion cochair, EMNLP 2021 workshop cochair, area chairs for ACL 2020/2016, EMNLP 2020/2019/2014/2011, AACL 2020, and Senior PC of IJCAI 2020-2017/IJCAI 2018(Distinguished SPC), AAAI 2020-2017, and associate editor for TNNLS, action editor for TACL. He was supported by several NSFC projects and one key NSFC project. His homepage is at: http://coai.cs.tsinghua.edu.cn/hml/ .

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Yasuo Kuniyoshi Yasuo Kuniyoshi received Ph.D. from The University of Tokyo in 1991 and joined Electrotechnical Laboratory, AIST, MITI, Japan. From 1996 to 1997 he was a Visiting Scholar at MIT AI Lab. In 2001 he was appointed as an Associate Professor and then full Professor in 2005 at The University of Tokyo. He is also the Director of RIKEN CBS-Toyota Collaboration Center since 2012, the Director of Next Generation Artificial Intelligence Research Center of The University of Tokyo since 2016, and an affiliate member of International Research Center for Neurointelligence (IRCN) of The University of Tokyo since 2018. He published over 300 refereed academic papers and received IJCAI 93 Outstanding Paper Award, Gold Medal; Tokyo Techno-Forum21; Award, Best Paper Awards from Robotics Society of Japan, IEEE ROBIO T.-J. Tarn Best Paper Award in Robotics, Okawa Publications Prize, and other awards. He is a Fellow of Robotics Society of Japan, President of the Japan Society of Developmental Neuroscience, and a member of IEEE, Science Council of Japan (affiliate), Japan Society of Artificial Intelligence, Information Processing Society of Japan, Japanese Society of Baby Science. For further information about his research, visit http://www.isi.imi.i.u-tokyo.ac.jp/ and http://www.ai.u-tokyo.ac.jp/ .

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Corpus-based Research on Translation Features of the Japanese Versions of “Analects”

Ye Yang Harbin university of Science and Technolegy Department China [email protected]

Zihan Wang Harbin university of Science and Technolegy Department China [email protected]

Translation texts include L2 translation texts and L1 translation texts. On the similarities and differences between the two, it is rare to use computer technology to conduct related research from the perspective of statistical analysis. Besides, there is no corpus-based translation research on the Japanese versions of "Analects" yet.

In order to further clarify the similarities and differences between L2 translation texts and L1 translation texts in Japanese versions, this paper uses self-built Chinese-Japanese parallel corpus of "Analects" to select text features that can be quantitatively analyzed, and compares one L2 translation text and three L1 translation texts of the Japanese versions of “Analects”. The selected L2 translation text is the Japanese version of "Analects" from “Library of Chinese Classics” which is officially organized by the Chinese government. The selected L1 translation texts are "Analects” translated and annotated by three Japanese sinologists.Through statistical analysis, this paper analyzes it from macro perspective based on quantitative data. The results can be summarized as follows:

①. Since the L2 translation text selected in this paper is translated with reference to "Analects" (mandarin Chinese version) by Yang Bojun, the author first compares the two. The result from Antconc3.5.7 search shows that the vocabulary richness and sentence length of L2 translation text is far lower than that of "Analects" (mandarin Chinese version) in terms of standardized type-token ratio, average sentence length and bigram co-occurrence. They are also different in terms of standardized TTR std.dev. The vocabulary discreteness of the L2 translation text is much lower than that of "Analects" (mandarin Chinese version), which shows that the vocabulary richness is distributed more evenly in the latter than in the former in full text.

②. By comparing the L2 translation text and the L1 translation texts, it is found that the L2 translation text tends to have a lower vocabulary richness in terms of standardized type-token ratio and bigram co-occurrence. For the standardized TTR std.dev, the value of the L2 translation text is relatively high, indicating that its vocabulary discreteness is larger. In terms of average sentence length, the L2 translation text is basically the same as the L1 translation texts.

③. In terms of word frequencies, by counting the high-frequency words in the four translation texts, it can be seen that the topic features of the L2 translation text is similar to the original text of “Analects”, and they show different tendencies with those of the L1 translation texts. From the statistical result of the Middle frequency words of the four translation texts, it can be concluded that the language-use characteristics of the four translation texts are

7 Instructions for Typesetting Manuscripts 8 different. The statistical result of the low-frequency words in the four translation texts shows that the four translation texts also present personalized stylistic features.

④. Through further statistical analysis on the bigram concurrences in the four translation texts, it is found that in the L2 translation text and the L1 translation texts, besides the differences in Japanese expressions (especially Japanese phases), unique tendencies can also be seen in the selection of translation words for some Chinese Culture-specific Words.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Emotion Wheel and Affective Lexicon based Label Enhancement for Emotion Distribution Learning

Xue-Qiang Zeng School of Computer & Information Engineering, Jiangxi Normal University, Ziyang Road 99, Nanchang, Jiangxi 330022, China [email protected]

Qi-Fan Chen School of Computer & Information Engineering, Jiangxi Normal University, Ziyang Road 99, Nanchang, Jiangxi 330022, China [email protected]

Ping-Sheng Liu School of Computer & Information Engineering, Jiangxi Normal University, Ziyang Road 99, Nanchang, Jiangxi 330022, China [email protected]

Jia-Li Zuo School of Computer & Information Engineering, Jiangxi Normal University, Ziyang Road 99, Nanchang, Jiangxi 330022, China [email protected]

Ming-Wen Wang School of Computer & Information Engineering, Jiangxi Normal University, Ziyang Road 99, Nanchang, Jiangxi 330022, China [email protected]

Emotion Distribution Learning (EDL) is a recently proposed effective multi-emotion analysis model, which handles the emotional fuzziness by associating each instance with an emotion distribution (each component of the distribution is the expression degree of the corresponding emotion on the instance). Nowadays, one of the most important difficulties of EDL is the lack of emotion distribution marked text datasets. Utilizing the existed single-labeled emotion datasets in EDL is a promising way to solve this problem, where label enhancement method is required to be applied to convert traditional emotion label into emotion distribution. This paper proposed an Emotion Wheel and Lexicon based emotion distribution Label Enhancement (EWLLE) method by utilizing the affective words’ linguistic emotional information and the psychological knowledge of the Plutchick’s Emotion Wheel. Based on the psychological emotion distances, EWLLE generates the discrete Gaussian distributions for the sentence emotion label and the affective words’ emotion labels respectively. Then, the two kinds of distribution were superposed into a unified emotion distribution. Extensive comparative experiments on 4 commonly used text emotional datasets showed that the proposed EWLLE method is superior to the existing EDL label enhancement methods in the emotion recognition task.

9 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Improved of RelGAN for Text Generation

Jiao Ziyun Faculty of Engineering, Tokushima University Japan [email protected]

Fuji Ren Faculty of Engineering, Tokushima University Japan [email protected]

Generative Adversarial Network (GAN) was proposed in 2014, and GAN is widely used in the field of computer vision, such as image generation and other tasks. It generally includes two parts: generator and discriminator. The generator is used to generate data, and the discriminator is used to distinguish true and false data. Through adversarial training, the generator and the discriminator can improve their performance. Since pictures are naturally continuous data, GAN can directly obtain gradients and backpropagation. Due to the sampling process in text generation, the output is discretized, the gradient cannot directly pass from the discriminator to the generator. Moreover, the complicated model structures and learning strategies limit their performance and exacerbate the training instability. So, the development of GAN in the field of text generation is relatively slow. Although development is slower than image generation, many excellent models have emerged in the field of GAN for text generation, such as SeqGAN, LeakGAN, RelGAN.

In this paper, we propose an improved neural network based on RelGAN. Our experiments base on the mr15, COCO Image Captions datasets and synthetic data and different from the RelGAN, we modified the network structure of the discriminator, correspondingly, we also modified the loss function of the network. As compared with other network, empirical results show that BLEU- [2,3,4,5] , NLLgen and NLLdiv scores are all improved with our novel method.

10 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Potential Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering

Qian Zhang Faculty of Engineering, Tokushima University 2-1, Minamijyousanjima-cho, Tokushima, Japan [email protected]

Fuji Ren Faculty of Engineering, Tokushima University 2-1, Minamijyousanjima-cho, Tokushima, Japan [email protected] Recommender system has become an essential tool in providing users things of interest from the overload information on the website. In many applications, only implicit feedback, which is also called one-class feedback, can be observed. Therefore, the one-class problem in recommender systems has attracted more and more attention. Bayesian Personalized Ranking1 is a well-known pairwise recommendation method. It is also a powerful generic solution for the one-class recommendation and assumes that the importance of the unobserved items is equal. However, this assumption may not match the real condition. In this paper, we propose a Potential Preference Based Bayesian Personalized Ranking (BPR+) approach, which relaxes the assumption above by taking the potential preference scores between users and items to measure the preference difference of unobserved items. Moreover, we calculate the similarity between users and the similarity between items at the item level and entity level for further studying the calculation methods of potential preference scores. Experimental results demonstrate that our proposed BPR+ can provide more accurate recommendation results than BPR on the real-world dataset.

Acknowledgments

This work was partially supported by the Research Clusters program of Tokushima University (No. 2003002).

References

1. S. Rendle, C. Freudenthaler, Z. Gantner and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback, In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp.452-461, 2009.

11 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Predicting Personality of Social Media Users based on Big Five Theory

Fanli Meng Vocational College of Industry and Technology Jilin, China

Bo Xu, Chaoliang Peng, Hongfei Lin Dalian University of Technology Dalian, China {xubo,hflin}@dlut.edu.cn

User profile modeling aims to model users based on abundant features of users so as to mining personalities of different users. In recent years, related technologies about user profile modeling become one of the hottest research fields in the intersection of text mining, natural language processing and machine learning, which owns much value for various applications. Big five theories, as one of the classic theories to model personalities in psychology, have not yet been well applied in user profile modeling research, which is potentially beneficial to solve the problem. To deal with the problem, we use test questionnaires of big five modeling to annotate the personalities of different users, and take the annotated big five values as the learning target for prediction. Based on the user generated texts on social media, we adopt machine learning methods, including ridge regression, lasso regression and ElasticNet regression, integrated with sentimental semantic resources to predict the big five predicting models of these users. We conduct extensive experiments on Sina Microblogs and Tencent Says, and experimental results show that our method is effective to mine the big five personalities for users in social media, which would be of great value for user profile modeling in relevant tasks.

Acknowledgments

This work is partially supported by a grant from the Natural Science Foundation of China (No.62006034，61702080) and the Fundamental Research Funds for the Central Universities (No.DUT18ZD102, No. DUT19RC(4)016), the Ministry of Education Humanities and Social Science Project (No.19YJCZH199).

12 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Detecting Early Stage Depressions Based on Compound Neural Language Understanding

Rongyu Dou, Fuji Ren Faculty of Engineering, Tokushima University Tokushima, 770-0851, Japan [email protected]; [email protected]

Depression has been reported by the world health organization in 2020 as the second most serious public health problem in the world, which causes over one million deaths every year [1]. Although there are different types of research on the diagnosis and treatment of depression in many aeras, using machine learning models to distinguish the separate depression symptoms [2] and to detect depression at an early stage [3] has been proved to be a promising approach for prescribing the right medicine for depression treatment. In this paper, we propose a deep neural network model which learns to detect the depression symptoms based on the compound natural language understanding and employ an attention mechanism for aggregating the sequential language features for depression detection in the network. This method could assist the medical instruments by automatically detecting the early stage depression for a very large group of people. Researchers found that the depression patients are less interested in communicating with other people [4] or seeking for medical help, which makes detecting this disease very difficult through direct evaluations. However, the difference in online communication for the depressed people and the healthy people is found not as significant. Indeed, these people more often upload messages in text, emoji, picture, and video through social media than talking with the other people around. In this work, we employ the social media data from the 2018 CLEF eRisk [4] task and learn a deep neural network model for the early state depression detection. The deep neural network consists of two components, which are the BERT network for natural language understanding and the attention network for aggregating the understandings. The BERT network extract feature vectors which could take the low-level expression of a depression symptom in the same sequential order as the input word sequences from the social media message. The attention network learns to evaluate a weight for these feature vectors and aggregates them into one abstract feature vector which represents the high-level expression of multiple depression symptoms in the message. The aggregated feature vector is then employed for a binary label prediction. Based on the two components, we propose a combined learning target by minimizing the mean cross entropy loss in predicting the depression labels while fine-tuning a masked language model on the BERT network. The combined learning target allows us to train this network to better understand the language pattern of the depressed and the control people and to make more accurate predictions on the early stage depression.

References

1. World Health Organization home-page〔DB/OL〕https://www.who.int/en 2. Hollon, S. D., Cohen, Z. D., Singla, D. R., & Andrews, P. W. (2019). Recent developments in the treatment of depressionBehavior Therapy, 50( 2), 257-269.

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3. Bedi G, Carrillo F, Cecchi G A, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths[J]. npj Schizophrenia, 2015, 1(1): 1-7. 4. World Health Organization website〔DB/OL〕https://www.who.int/news-room 5. Losada D E, Crestani F, Parapar J. Overview of eRisk 2018: Early Risk Prediction on the Internet (extended lab overview) [C]. Proceedings of the 9th International Conference of the CLEF Association, CLEF. 2018.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Local Label Correlation Learning for Multi-Label Classification

Jiawen Deng Faculty of Engineering, Tokushima University Tokushima, 770-8501, Japan [email protected]

Fuji Ren Faculty of Engineering, Tokushima University Tokushima, 770-8501, Japan [email protected]

In multi-label learning, each input instance is associated with a set of labels, and there is no constraint on how many of the labels the instance can be assigned to. In deep-learning based models, multi-class classification algorithms can be easily extended to multi-labels classification models by simply changing the prediction layer, such as modify the number of neurons and activation function in the output layer. In most cases, the emotional information of text is often encoded together into a representation vector and then directly fed into multiple binary classifiers. They treated multi-label problem as a general classification without considering label correlation information. However, the labels assigned to each instance are usually related to each other, and exploiting label correlation can significantly contribute to classification performance. To learn correlation information among label classes, some works try to exploit effective neural network architecture for label correlation learning. For example, a fully connection layer is often utilized to learn the label co-occurrence information. Some works try to train an additional label embedding matrix to learn label representation, and label correlation information can be obtained by calculating the similarity of each label embedding. However, these networks are usually trained with binary cross-entropy, and ignore the importance of integrating label correlation information into training objectives, which could limit model’s performance to a certain extent.

In this paper, Local Label Correlation Aware (LLCA) training objective is proposed for multi-label learning. Instead of concentrating on individual label discrimination like traditional cross-entropy loss function, the true positive-negative pairwise label correlation of each sample is discovered during the training stage. LLCA loss aimed to maximizing the difference of the prediction probability of each positive-negative label pair, in this way, a higher probability could be assigned to positive label while lower probability to negative label. To make the loss put more focus on hard and misclassified labels during training, a dynamic modulating factor is assigned to each label. In multi-label learning, for a certain instance, the number of its positive labels are usually far smaller than negative labels. To reduce the influence of imbalance distribution of positive-negative label pairs, a harmonic factor is introduced to make the loss focus more on positive labels. In the experiments, an attention mechanism based GRU network is trained with proposed local label correlation aware training objective for multi label learning. The experiments are conducted on multi-label emotion corpus: RenCECps. The results indicate the considerable potential of proposed training objective, which were more promising than usually utilized binary cross-entropy on multi-label learning tasks.

15 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Capsule Network with TC Loss for Intention Detection

Siyuan Xue Faculty of Engineering, Tokushima University Tokushima,770-8506, Japan [email protected]

Fuji Ren Faculty of Engineering, Tokushima University Tokushima, 770-8506, Japan [email protected]

With the increasing the technology of the dialogue system, lots of companies delicate to develop the intelligent conversation understanding system with software or applications that run on mobile device via natural language interface, such Google Home, Apple Siri and Amazon’s Alexa. The intention detection plays a critical role for the spoken language understanding module in the dialogue system and question answering system. Understanding intent helps modern search engines to improve recognition result and reply reliable feedback. In this paper, we present out the intention detection task based on the capsule network architecture and the metric learning approach to learn the intent behind the utterance.

Recently, it has been shown that capsule networks are effective to the face recognition, their validity in the domain of natural language processing has not been wildly explored. Capsule is a group of neurons which are locally invariant groups that learn to recognize the existence of visual entities and encode their properties into vectors. Capsule consider the spatial relationships between entities and learn these relationships via dynamic routing mechanism. The dynamic routing determines the connection strength between lower-level and upper level capsules through repetitive routing-based on a coupling coefficient parameter. The coupling coefficient parameter is utilized to measure the similarity between vectors that predict the upper-level capsule and lower-level capsule and learns the importance interaction between lower capsule layer and higher capsule layer. Moreover, in order to make the dialogue system has better recognition ability, we utilize the metric learning to learn discriminative feature. In this work, we use the triplet loss and center loss to joint optimize the model. The simple SoftMax loss is limited to find discriminative features because is cannot consider the intra-class compactness of features. The triplet center loss can leverage the advantages of triplet loss and center loss, like efficiently minimize the intra-class distances of the deeply learned features as well as maximize the inter-class distances of deep features simultaneously.

In summary, the contributions of this work are:

 The capsule network architecture can learn a higher-level utterance semantic representation by a hierarchical manner to show the elements of utterance that are important to the intent class.

 We joint learn the triplet center loss and SoftMax loss as the objective function. The SoftMax loss focus on mapping the utterance to discrete labels, which TCL aims to learn the discriminate embedding feature set independently without supervision.

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However, these two losses can also be combined together to achieve more distinguished utterance embedding features.

 We utilize the static routing mechanism to assign a proper contribution of each semantic and aggregate to get an intent representation. The static routing mechanism is more effectively reduce the computational complexity of dynamic routing.

In the experiment, we evaluate the proposed model on the benchmark SNIPS Natural language understanding dataset. The Snips dataset is collected from the Snips personal voice assistant and contain 7 intent types. The number of samples for each intention label is balanced.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Prediction of Hepatocellular Carcinoma with Machine Learning Algorithms

Yakun Lu Hebei University of Technology China [email protected]

Qiong Wu Shengjing Hospital of China Medical University China [email protected]

Bo Qiu Hebei University of Technology China [email protected]

Chitty Chen SysDiagno Biotech Co.,Ltd. Belgium [email protected]

Zongnan Tan SysDiagno Biotech Co.,Ltd. China [email protected]

Mengci Li, Guanjie Xiang Hebei University of Technology China [email protected], [email protected]

Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. According to the needs of clinical diagnosis of HCC, we constructed a machine learning model for prediction of HCC. Our data comes from a biotechnology company. Through feature analysis and combined with a variety of machine learning classification models, we carried out two classification experiments on HCC patients and non-HCC patients. Experimental results show that the XGBoost model can achieve an optimal classification accuracy of 75%, with a sensitivity of 70% and a specificity of 77%. In order to balance the number of training data sets and improve the accuracy of the model, the experiment uses GAN network to expand the data set of HCC patients. After data expansion, the accuracy of XGBoost model is 82%, the sensitivity and specificity are 85% and 80% respectively.

Acknowledgments

This work is supported by the Joint Research Fund in Astronomy, National Natural Science Foundation of China, U1931134, and the Natural Science Foundation of Hebei, A2020202001.

18 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

A humanoid robot based on neural network architecture for accompanying children with autism

Tianhao She Graduate School of Advanced Technology and Science, Tokushima University, 2-24 Shinkuracho Tokushima, Japan [email protected]

Fuji Ren Faculty of Engineering, Tokushima University, 2-24 Shinkuracho Tokushima, Japan [email protected]

Autism spectrum disorder (ASD) is a life-long neurological disability that is characterized by significant social-communication and behavioral deficits. In this study, we present to use a neural network model as the generative conversational agent, and train the model on English dialogue corpus of children with autism. The main contribution of our work is incorporation of conversation model into an actual robot system for supporting children with autism. We chose the method of generation-based agents which uses recurrent neural networks to create effective models, which aims at generating meaningful and coherent dialogue responses given the dialogue history. We validated the results of the proposed model in the general source discourse, and compared the results of the proposed model and baseline for children with autism.

19 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Deficiencies and development trends of rehabilitation robots

Yue Xu School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

Peiyong Ni School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

As the global aging degree continues to deepen, the demand for social elderly care, rehabilitation training and health auxiliary treatment in various countries has increased significantly, and the research of rehabilitation robots has received more and more attention. Since the rehabilitation robot can provide continuous, high-intensity, and repeatable treatment potential, it has shown significant advantages in the rehabilitation of sports injuries, amputations, spinal cord injuries, strokes, traumatic brain injuries and other diseases, and has important practical application value. According to the structure, rehabilitation robots can be divided into terminal control type and exoskeleton type. Although rehabilitation robots have many advantages, the design of unreasonable mobility assistance rehabilitation robots also has an adverse effect on the rehabilitation training of patients. The design of more reasonable and efficient rehabilitation robots is a problem that needs to be solved urgently. The shortcomings of current rehabilitation robots are mainly reflected in the following points: ①The rehabilitation robots that use gravity weight reduction devices limit the range of movement of the patient's limbs and are likely to make the patient dependent. ②The rigid structure of the rehabilitation robot is large in size and weight, which is easy to cause discomfort for the patient to wear and cause huge energy consumption, thus limiting the patient's rehabilitation training time. ③The training safety of rehabilitation robots needs to be improved. Because most patients have poor muscle strength and low bone density, if the control system and feedback device cannot adjust the angle and speed of the patient’s joint movement in time, it is easy to cause secondary damage to the patient. ④At present, there is no individualized and precise quantitative design for the training time and training intensity of rehabilitation robots, and there are very few truly effective training evaluation systems that can be used in clinical systems. ⑤The virtual reality technology adopted by the new generation of rehabilitation robots has a low sense of reality, poor integration, weak immersion, and poor interactivity. The development trend of rehabilitation robots is mainly concentrated in the following aspects: ①Intelligent and lightweight. Use lighter and flexible materials to achieve the integration of rigid body mechanisms and flexible structures, and reduce unnecessary equipment.②The control system and feedback mechanism of the rehabilitation robot can combine EEG and EMG, while achieving remote control and network service, so that patients can share medical data and better meet the needs of patients for medical rehabilitation.③ Design more superior human-computer interaction systems, mainly including individualized and precise training systems and evaluation systems. At the same time, it strengthens the matching degree of the interactive hardware interface and improves the humanization and interactivity of the software interface.

20 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Construction of emergency medical support system based on understanding dialect intention

Kyoya Abe Faculty of Engineering, Tokushima University, Tokushima, 770-8506, Japan [email protected]

Fuji Ren Faculty of Engineering, Tokushima University, Tokushima, 770-8506, Japan [email protected]

Shun Nishide Faculty of Engineering, Tokushima University, Tokushima, 770-8506, Japan [email protected]

Xin Kang School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

In recent years, many disasters such as earthquakes have occurred, and prompt communication between disaster victims and medical staff is required. However, most victims use dialect, which may not be understood by medical staff. In this paper, we propose a system that analyzes sentences including dialects by natural language processing and displays body parts from the analysis results. In addition, since disasters are urgent, it is necessary to have a lightweight system that can be used with mobile phones and the like. For that reason, the voice recognition engine “Julius” was used to build a lightweight and easy-to-add system. Using the word dictionary of the voice recognition engine “Julius”, we will experiment how many dialects can be recognized. As a result of voice recognition using a microphone, the accuracy was almost 100%. I will also mention dialects that I could not recognize.

21 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Surface Defects Classification of Steel Strips based on Transfer Learning and Naive Bayes Model

Jiaqiao Zhang School of Mechanical Engineering, Nantong University, Nantong 226019, China Faculty of Engineering, Tokushima University, Tokushima 770-8506, Japan [email protected]

Xin Kang Faculty of Engineering, Tokushima University, Tokushima 770-8506, Japan [email protected]

Hongjun Ni School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

Fuji Ren Faculty of Engineering, Tokushima University, Tokushima 770-8506, Japan [email protected]

Steel strip is an common material in the machinery industry and is widely used in production and processing. However, due to the effects of equipment and technology, some defects will inevitably occur on the surface of the steel strips [1]. The traditional method of classifying steel strip defects is for workers to observe with their eyes, which is time-consuming and labor-intensive.

In the present research, transfer learning and naive Bayes models are used to classify the surface defects of steel strips and the classification results are compared. The data set contains 6 types of steel strip surface defects, namely crazing, inclusion, patches, pitched surface, rolled-inscales and scratches. There are 300 images for each type of defect, and each image contains one of the above 6 types of defects. When the transfer learning model was used for defect classification, the convolutional network was pre-trained on the ImageNet data set, and then the network was replaced and the classifier was retrained on the data set of steel strip surface defects, and the weight of the pre-trained network was fine-tuned by continuing back propagation. When the naive Bayes model was used for defect classification, the feature attributes were divided manually to form a training set. The frequency of occurrence of each category in the training samples and the estimation of the conditional probability of each feature attribute division were calculated. Besides, the results were recorded to form a classifier. Finally, the classifier was used to classify the surface defects.

The research results show that the transfer learning network has no classification ability after the initialization is completed, and the accuracy of the training set is initially 0.65. Then, as the number of iterations increases, the accuracy gradually increases, and finally converges to 1.

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However, the loss value decreases and finally converges to 0. The average classification accuracy of six types of steel strip surface defects can reach 0.97, and the running time of each epoch is 3.75min. The average accuracy of defect classification based on naive Bayes model is 0.62, and the defect with the highest classification accuracy of 0.96 is patch. The running time of naive Bayes model is 23.76s. Therefore, when classifying the surface defects of steel strips, the classification accuracy of the transfer learning is 1.56 times that of the naive Bayes model, but the classification speed is much lower than that of the naive Bayes model.

Acknowledgments

This work was partially supported by the Research Clusters program of Tokushima University (No. 2003002) and the JSPS KAKENHI (No. 19K20345).

References

1. Zhang J, Kang X, Ni H and Ren F. Surface defect detection of steel strips based on classification priority YOLOv3-dense network. Ironmaking & Steelmaking, pp.1-12, 2020.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Thermal Fault Area Recognition and Location System based on Infrared Image

Kaixuan Wang School of Mechanical Engineering, Nantong University, Nantong 226019, China Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima 770-8506, Japan [email protected]

Hongjun Ni School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

Fuji Ren Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima 770-8506, Japan [email protected]

Jiaqiao Zhang School of Mechanical Engineering, Nantong University, Nantong 226019, China Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima 770-8506, Japan [email protected]

Shuaishuai Lv School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

Xingxing Wang School of Mechanical Engineering, Nantong University, Nantong 226019, China [email protected]

Power equipment is prone to produce various faults caused by the complex environment and generated heat. Recently, the infrared image of the power equipment is widely used to detect faults by judging the temperature distribution on the surface of the equipment based on the infrared radiation information.

In order to solve the problem of low efficiency and high error rate of traditional fault detection method, it is of great significance to use machine vision technology for automatic identification, fault detecting and statistical recording of infrared image temperature values of power equipment.

In this paper we proposed a temperature values recognition and defect location system for infrared image. This system is divided into four parts: image preprocessing, temperature values recognition, defect location, and result output. Firstly, we developed an image

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enhancement method based on LAB model at image preprocessing stage. The infrared image is transformed into LAB color model to get L, A and B channels, which are processed by adaptive histogram equalization (CLAHE) for L channel, Gaussian filtering for A channel and bilateral filtering for B channel respectively to realize image denoising and image detail enhancement. Secondly, we established temperature values recognition method by combining the Histogram of Oriented Gradient (HOG) and binary support vector machine (SVM) to recognize the maximum and minimum temperature values. Thirdly, we established the relationship between the temperature map and the temperature value to locate the defect. In order to eliminate the burr and break the small connected area, morphological processing is used to smooth the image contour, and the connected region marking method is used to locate the high temperature area that is the defect location. Finally, the denoising and detail enhancement infrared image as well as defect location are output as pictures, the maximum and minimum temperature values are output as pictures text.

In experiments, the infrared images were collected by infrared thermal imager, 1000 temperature values samples were trained and tested according to the ratio of 7:3 and 200 infrared images were tested for the temperature values recognition experiments. The results showed that the overall accuracy of temperature values recognition system was 96% and the defect locations with abnormal temperature were accurate, which can effectively reduce labor intensity and improve work efficiency of the power grid inspectors. The proposed system provided an effective way to locate and recognition the temperature values of infrared images.

Acknowledgments

This work was partially supported by the Research Clusters program of Tokushima University (No. 2003002).

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Real-time Dynamic Facial Expression Recognition System Based On CNN-GRU

Duo Feng Faculty of Engineering, Tokushima University, Tokushima, Japan. [email protected]

Fuji Ren Faculty of Engineering, Tokushima University, Tokushima, Japan. [email protected]

Create Human Machine Interaction (HMI) systems that able to reach the full emotional and social capabilities for rich and robust interaction with human beings will be a long and arduous but important task. Facial expression recognition (FER) is a vital research field to reach this goal since the facial expression is one of the most important nonverbal channels for expressing internal emotions and intentions. One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. Recognizing facial expression from consecutive frames is more natural and proved to be more effective in recent years. And over the past few years, models based on deep convolutional network (CNN) have dominated static FER tasks. But only CNN is difficult to deal with temporal features, methods such as Recurrent Neural Network (RNN) has been applicated on dynamic FER.

In this paper, we propose a real-time dynamic facial expression recognition (FER) system based on MobileNetV2 and Gate Recurrent Unit (GRU). For the real-time capability of the system, we chose the MobileNetV2 architecture to minimize the system parameters. The proposed system is extended to a frame-to-sequence approach by exploiting temporal information with GRU. Finally, we demonstrate the performance improvement by using the proposed system on some public datasets.

The motivation of this paper was to create a lightweight network deal with dynamic facial ex-pression sequence. Based on this idea, we did not choose 3D-CNN in order to avoid many training parameters but chose the cascade networks of CNN-RNN. In the CNN part, we selected MobileNetV2 architecture through experiments. And reduce the problem of overfitting through transfer learning. At the same time, as the input of CNN, we reduce the size of the image into 96×96. Although the smaller image size cannot reduce the training parameters of the model, but it can reduce the amount of calculation required. In the RNN part, we did not choose the feature vector extracted by CNN as inputs but chose the probability distribution of each expression classes after the softmax layer processed. We combine the probability distribution of each frame in videos into a sequence, as the input of the GRU. Compared with the feature vector, the probability distribution has a smaller length, and the GRU utilized probability distribution as input also has fewer parameters.

In the whole system, we intercepted a certain length of frames as the basic unit of expression

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recognition. In this frame sequence, we first calculate the probability distribution of each frame through CNN in real time. When the last frame of the sequence is reached, the probability distribution of each frame is combined into a sequence, and the expressions of this sequence are calculated through GRU. In the expression database, we also cut the video based on this standard, and obtained 6 basic expressions and data under natural conditions. The experimental results prove the effectiveness of the proposed system.

Duo Feng (Member) He received the bachelor’s degree from the Xidian University, China in 2012, and the master’s degree from Tokushima University, Japan in 2016. He is a PhD student at Tokushima University. His research interests include affective computing, deep learning and emotional visualization. He is a student member of IEEE.

Fuji Ren (Member)

He received his Ph.D. degree in 1991 from the Faculty of Engineering, Hokkaido University, Japan. From 1991 to1994, he worked at CSK as a chief researcher. In 1994, he joined the Faculty of Information Sciences, Hiroshima City University, as an Associate Professor. Since 2001, he has been a Professor of the Faculty of Engineering, Tokushima University. His current research interests include Natural Language Processing, Artificial Intelligence, Affective Computing, Emotional Robot. He is the Academician of The Engineering Academy of Japan and EU Academy of Sciences. He is a senior member of IEEE, Edi-tor-in-Chief of International Journal of Advanced Intelligence, a vice president of CAAI, and a Fellow of The Japan Federation of Engineering Societies ， a Fellow of IEICE, a Fellow of CAAI. He is the President of International Advanced Information Institute, Japan.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Multi-scale Receptive Field Fusion Residual Network for Image Classification

Wenjie Liu School of Transportation and Civil Engineering, Nantong University Nantong, 226019, China [email protected]

Fuji Ren Faculty of Engineering, Tokushima University Tokushima, 770-8506, Japan [email protected]

Guoqing Wu School of Information Science and Technology, Nantong University Nantong, 226019, China [email protected]

Computer vision has achieved tremendous improvement in recent years. Receptive filed (RF) is an important factor when we design a convolution neural network. The convolution layer with big RF can extract feature for big object recognition, and the convolution layer with small RF tend to extract the feature for small object recognition. How to fuse the feature from the convolution block with different RF size is a challenge problem. In this paper, we proposed a new residual block variant, which fuse multi-scale feature with different receptive field in model depth direction to enhance the capacity of feature extraction for image classification tasks based on ResNet.

The proposed residual block contains four parts: the first 1x1 convolution layer, the 5x5 RF size convolution block, the 3x3 RF size convolution block, and the last 1x1 convolution layer. The first 1x1 convolution layer is used to adjust the information flow to following branches. The 5x5 RF size convolution block is used to extract feature for big object recognition. The 3x3 RF size convolution block is used to extract feature for small object recognition. The last 1x1 convolution layer is applied to adjust the feature map dimension to match the identity branch. In each residual block, it can fuse the feature from the convolution layer with different RF size. Thus, we name our model as Multi-scale Receptive Field Residual Network (MRF-ResNet).

For the sake of evaluating the effectiveness of our method, we test our model on CIFAR-100 benchmark dataset. The experimental results show that our model can achieve better accuracy performance than ResNet when the total number of parameters is similar. Compared with the baseline model, our model can achieve a 76.43% test accuracy on CIFAR-100 dataset, which outperforms the 110-layer ResNet by 2.99%.

Meanwhile, in order to explore the properties of our model, we implement some ablation experiments. First, to explore the parameter efficiency, we replace the 5x5 kernel size convolution layer with two successive 3x3 kernel size convolution layer. Second, we explored

28 Instructions for Typesetting Manuscripts 29 the impact the order of 5x5 RF size convolution block and the 3x3 RF size convolution block in each MRF residual block. Third, we also experiment the impact of model width on model performance when the total number of parameters is similar. As the experimental results shown, our model can achieve a better test accuracy on CIFAR-100 dataset when adopting the two successive 3x3 convolution layer. It demonstrated that two successive 3x3 convolution layer have a more parameter efficiency than one 5x5 convolution layer. Therefore, we use two successive 3x3 convolution layer in our experiments. In terms of the order of the two RF size convolution block, our experiments showed that the information pass through the 5x5 RF size convolution block first can achieve a better accuracy performance. It verified that feeding the information to the 5x5 RF size convolution block first can extract more feature for image classification task, and our model also adopt this operation in our experiments. In terms of the model width, our model had a better accuracy performance when doubling the model width. Accordingly, we double the model width in our experiments.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Binary K-means Tree Ensemble Classifiers

Quan Wang Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University Xi'an, 710049, China Faculty of Engineering, Tokushima University Tokushima, 770-8506, Japan [email protected]

Fei Wang Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University Xi'an, 710049, China [email protected]

Peilin Jiang School of Software Engineering, Xi'an Jiaotong University Xi'an, 710049, China [email protected]

Fuji Ren Faculty of Engineering, Tokushima University Tokushima, 770-8506, Japan [email protected]

Classification, a mainstream data mining technique, plays a significant role in human perceiving, understanding, and analyzing the surrounding matters. As an important branch of classification methods, ensemble classifiers have attracted more and more attention because the classification performance can be enhanced by combining multiple single classifiers to form the multi-classifier system. Decision forest is a type of ensemble classifiers which is composed of a collection of decision trees. Different tree models, randomness strategies, or combination methods can produce different decision forests. The classification performance of decision forests is determined by the predictive ability of individual trees and the diversity between these trees. This paper presents new decision forests named Binary K-means Tree Ensemble Classifiers (BKTEC) in which each binary decision tree is constructed by applying K-means (K=2) clustering for node splitting. K-means splitting enables the tree to own powerful representation ability, thus ensuring the predictive performance of individual trees. Random subspace concept is adopted to randomly select feature subsets at each splitting node to guarantee the diversity of individual trees. Experiments on several data sets have demonstrated that BKTEC outperforms the state-of-the-art decision forest as well as the original single decision tree.

30 International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Design and Control of an Elbow Joint Assist Suit with a Velocity-Based Mechanical Safety Device

Atsushi Kaneta, Tsubasa Kaneda, Keisuke Ikeda, Yoshihiro Kai Department of Mechanical Engineering, Tokai University, 4-1-1 Kitakaname Hiratsuka-shi, Kanagawa 259-1292, Japan [email protected], [email protected]

Kenichi Sugawara Department of Rehabilitation, Kanagawa University of Human Services, 1-10-1 Heisei-cho Yokosuka-shi, Kanagawa 238-8522, Japan [email protected]

Masayoshi Tomizuka Department of Mechanical Engineering, University of California, Berkeley Berkeley, CA94720, USA [email protected]

Tetsuya Tanioka Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Kensaku Takase Faculty of Health Science, Butsuryo Collage of Osaka, 3 3-3 Otorikitamachi Nishiku Sakai-shi, Osaka 593-8328, Japan [email protected]

In rehabilitation assist suits, supporting patients’ physical training safely and effectively is important. In this paper, we describe the design and control of an elbow joint assist suit equipped with a velocity-based mechanical safety device. The assist suit aids flexion and extension of a patient’s elbow joint. The safety device stops the assist suit when it detects an unexpected elbow joint angular velocity. The safety device works even when the computer breaks down, because it consists of only passive mechanical components. Firstly, we describe patients considered in this study. Secondly, we introduce the elbow joint assist suit designed for the patients. Thirdly, we propose a control method of the assist suit for supporting the patients’ training effectively. Finally, we present experimental results to verify the effectiveness of the designed assist suit and the proposed control method.

Acknowledgments

This work was partially supported by JKA and its promotion funds from KEIRIN RACE.

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Atsushi Kaneta

Atsushi Kaneta is currently a Master student at the Graduate School of Course of Mechanical Engineering in Tokai University, Japan. He received his Bachelor’s degree of Mechanical Engineering in 2020 from Tokai University. His research interests include rehabilitation assist suits and safety devices. He is a student member of the JSME.

Tsubasa Kaneda Tsubasa Kaneda received his B.E. and M.E. degrees in Mechanical Engineering from Tokai University, Japan, in 2018 and 2020, respectively. He studied rehabilitation assist suits and safety devices.

Keisuke Ikeda

Tsubasa Kaneda received his B.E. and M.E. degrees in Mechanical Engineering from Tokai University, Japan, in 2018 and 2020, respectively. He studied rehabilitation assist suits and safety devices.

Yoshihiro Kai Yoshihiro Kai received his Doctor of Engineering degree in 1999 from Doshisha University, Japan. From 1999 to 2002, he was a research associate at Kochi University of Technology, Japan. Since 2003, he has been with Tokai University, Japan, where he is currently a Professor in the Department of Mechanical Engineering. His research interests include Human-Friendly Robots, Exoskeleton Robots, and Drone Systems. He is a member of the JSME, RSJ, ISCIE, JSDE, JSNR, and SIBOM. Kenichi Sugawara

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Kenichi Sugawara, RPT, PhD is the chief professor of the Physical Therapy Division at the Department of Rehabilitation of the Faculty of Health and Social Services at Kanagawa University Human Services in Yokosuka, Japan. He earned his PhD from Hiroshima University. His research focuses on neurophysiology, motor learning, motor control of motor cortex, and spinal motoneuron for paretic or normal limbs in rehabilitation situations.

Masayoshi Tomizuka Masayoshi Tomizuka received his B.S. and M.S. degrees from Keio University, Tokyo, Japan, in 1968 and 1970, respectively. He received his Ph.D. degree from the Massachusetts Institute of Technology, Cambridge, in 1974, after which he joined the Department of Mechanical Engineering, University of California, Berkeley, where he is currently the Cheryl and John Neerhout Jr. Distinguished Professor Chair. His current research interests include optimal and adaptive control, digital control, signal processing, motion control, mechatronics and their applications in robotics, manufacturing, data storage devices, vehicles, and human-machine systems. He is a Fellow of the ASME and the SME. He was the recipient of the Charles Russ Richards Memorial Award (ASME, 1997), the Rufus Oldenburger Medal (ASME, 2002), the John R. Ragazzini Award (2006), the AACC Richard E. Bellman Heritage Award (AACC, 2018), and the Soichiro Honda Medal (ASME, 2019).

Tetsuya Tanioka Tetsuya Tanioka is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the BEd from Meisei University Japan, in 1997. He was visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting professor of St. Paul University Philippines. He is currently a board member of the Anne Boykin Institute for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots. He was the lead editor of the book, Nursing Robots: Robotic Technology and Human

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Caring for the Elderly published by Fukuro Publishing, Japan, in March 2017.

Kensaku Takase

Kensaku Takase is a Professor of Radiotherapy and anatomy, Faculty of Health Science, Butsuryo Collage of Osaka, Sakai, Japan. He has been engaged in stroke treatment for 35 years as a certified neurosurgeon of the Japanese Society of Neurosurgery, who is also currently the chairperson of the Japan Academy of Neurosonology.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Development of a Velocity-Based Mechanical Safety Brake for Wheeled Mobile Nursing Robots

Yoshiaki Sato, Hiroki Mishima, Yoshihiro Kai Department of Mechanical Engineering, Tokai University 4-1-1 Kitakaname, Hiratsuka-shi, Kanagawa 259-1292, Japan [email protected], [email protected], [email protected]

Tetsuya Tanioka, Yuko Yasuhara Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected], [email protected]

Kyoko Osaka Department of Psychiatric Nursing, Graduate School of Integrated and Sciences, Kochi University 185-1 Oko-cho kohasu, Nankoku-shi, Kochi 783-0043, Japan [email protected]

Yueren Zhao Department of Psychiatry, Fujita Health University 1-98 Dengakugakubo, Kutsukake-cho, Toyoake-shi, Aichi 470-1192, Japan [email protected]

In recent years, the demand for robots which work in the field of nursing or nursing care has been increasing. Human safety when interacting with robots is important because these robots work closely with humans. When considering nursing robots that move in hospitals, wheels or legs will be used as their movement mechanism. Since barrier-free conditions of wheeled chairs have been mostly achieved in hospitals, and legged robots have the risk of falling, we consider that wheeled mobile robots should be used as the nursing robots. However, if their computer breaks down, there is the possibility that they will move unintentionally and collide with humans at high velocities. Furthermore, in the wheeled mobile nursing robots, if the batteries die while descending on a slope, the robots may roll down the slope uncontrollably and collide with humans at high velocities due to gravity. To solve these problems, we can use (i) emergency switches, (ii) shock absorbing materials, and (iii) holding brakes that activate when the batteries die. However, (i) when using the emergency switch, humans might not be able to push the emergency switch when the robot moves at high velocities. (ii) Attaching shock absorbing materials on the robot’s surface is useful in order to reduce the impact force when the robot collides with humans. However, even when attaching the shock absorbing materials, preventing the robot from colliding with humans at high velocities is vital. (iii) The holding brakes can stop the robot when the batteries die on the slope. However, if a human is pressed against a wall by the robot after the emergency switch is pushed, it will be difficult to rescue the human because the brakes are activated. To address these problems, Sato et al. developed a compact velocity-based mechanical lock device. If this lock device detects the angular velocity of a robot’s driveshaft exceeds a preset level (hereinafter referred to as “detection velocity level”), it switches off all the robot’s motors and then locks the driveshaft. The lock device has a function to easily rescue a human if the human is being pressed against a wall by the robot locked by the device. The lock device needs no power supply, because it

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consists of only passive mechanical components. However, wheeled mobile nursing robots may fall down when their wheel driveshaft is suddenly locked by this lock device. Therefore, it is desirable to stop the driveshaft by gradually reducing the velocity of the driveshaft after the lock device detects the detection velocity level. In this paper, we propose a velocity-based mechanical safety brake that first gradually reduces the velocity of the driveshaft after detecting the detection velocity level, and then stops the driveshaft. Firstly, we explain the design of the velocity-based mechanical safety brake. Secondly, we show the fabricated safety brake. Thirdly, we conduct an experiment to check if the safety brake works as intended by using an experimental wheeled mobile robot equipped with the safety brake. Then, based on the results of the experiment, we examine the effectiveness of the safety brake. Finally, we discuss the effectiveness and the problems of nursing robots equipped with this safety brake.

Acknowledgments

This work was partially supported by JSPS KAKENHI Grant Number JP18K04056.

Yoshiaki Sato Yoshiaki Sato is currently a Master student at the Graduate School of Course of Mechanical Engineering in Tokai University, Japan. He received his Bachelor’s degree of Mechanical Engineering in 2019 from Tokai University. His research interests include Human-Friendly Robots and their Safety Measures. He is a student member of the JSME.

Hiroki Mishima Hiroki Mishima is currently a Master student at the Graduate School of Course of Mechanical Engineering in Tokai University, Japan. He received his Bachelor’s degree of Mechanical Engineering in 2020 from Tokai University. His research interests include Wheeled Mobile Robots and their Safety Measures.

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Yoshihiro Kai Yoshihiro Kai received his Doctor of Engineering degree in 1999 from Doshisha University, Japan. From 1999 to 2002, he was a research associate at Kochi University of Technology, Japan. Since 2003, he has been with Tokai University, Japan, where he is currently a Professor in the Department of Mechanical Engineering. His research interests include Human-Friendly Robots, Exoskeleton Robots, and Drone Systems. He is a member of the JSME, RSJ, ISCIE, JSDE, JSNR, and SIBOM.

Tetsuya Tanioka Tetsuya Tanioka is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the BEd from Meisei University Japan, in 1997. He was visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting professor of St. Paul University Philippines. He is currently a board member of the Anne Boykin Institute for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots. He was the lead editor of the book, Nursing Robots: Robotic Technology and Human Caring for the Elderly published by Fukuro Publishing, Japan, in March 2017.

Yuko Yasuhara Yuko Yasuhara, RN: PLIN, PhD, is an associate professor of nursing at the Department of Nursing Outcome Management of the Tokushima University Graduate School in Japan. She received her MSN from the Kobe City College of Nursing in 2003, and her PhD from the Kawasaki University of Medical Welfare in 2013. She worked as a stull nurse at a hospital in Kobe, Japan from 1993 to 2000. She has worked at the Tokushima University since 2001. Her research focuses on sleep and the activity of people, care for patients with ischemic heart disease, safe intramuscular injection techniques, and caring as nursing.

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Kyoko Osaka Kyoko Osaka, RN; PHN, PhD, is a professor, Nursing Science, Graduate School of Integrated Arts and Sciences, Kochi University, Japan since 2019. She earned her Doctor of Engineering, Graduate School of Advanced Technology and Science, Information Science and Intelligent System, Tokushima University in 2008. Her interdisciplinary collaborative research will be a prospective visioning seen and realized from the perspective of Japanese human caring ideas for an aging society. She found her passion for nursing research focused on studying caring for older adults with dementia, older adults and caring robot interaction/transaction, and empathic understanding in nursing. Yueren Zhao Yueren Zhao, MD: PhD, is an Associate Professor at the Department of Psychiatry, Fujita Health University in Aichi, Japan. He obtained his PhD degree in 2013 from Kumamoto University. He started his carrier as an anesthesiologist in 1994, and bridged to psychiatry in 1997. He continues to work as a clinical psychiatrist, focusing on promoting good patient-physician relationship through open patient-oriented dialogue among multidisciplinary team members using approaches which strengthen anti-stigma activities. He is a former board member of the Japan Young Psychiatrists Organization.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

A Walking Support System Equipped with a Variable Speed Treadmill and a Lift Device

Yuya Yokouchi, Yoshihiro Kai Department of Mechanical Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka-shi, Kanagawa 259-1292, Japan [email protected], [email protected]

Masayuki Tsuchida Department of Rehabilitation, Shonan University of Medical Sciences, 16-48 Kamishinano Totsuka-ku, Yokohama-shi, Kanagawa 224-0806, Japan [email protected]

Kenichi Sugawara Department of Physical Therapy, Kanagawa University of Human Services, 1-10-1 Heisei-cho, Yokosuka-shi, Kanagawa 238-8522, Japan [email protected]

The world's population is aging, and this trend is expected to continue in the future. It is said that the ability to change walking speed decreases with age, which may lead to falls. Therefore, walking support systems to improve the elderly's ability to change their walking speed are needed more and more in the future. Various kinds of walking support systems have been developed. However, they are not systems to improve the elderly's ability to change their walking speed. In this paper, we propose a walking support system to improve the elderly's walking speed control ability. The system consists of a Variable Speed Treadmill (hereinafter referred to as “VST”) and a Lift Device. The VST's belt speed is controlled by a computer in order for the elderly to improve their walking speed control ability. Physical therapists can set the VST's belt speed according to each elderly person’s physical condition. The lift device is a device already developed by Kai Laboratory. The lift device is used to prevent the elderly from falling down, because changing the VST's belt speed increases the risk of falling. The lift device has an electromagnetic powder brake controlled by the computer. The lift device can softly prevent the elderly from falling down by applying impedance control to the powder brake. Firstly, we describe the walking support system. Secondly, we explain the VST in more detail. Finally, we discuss the effectiveness of this walking support system.

Yuya Yokouchi Yuya Yokouchi is currently a first-year master’s student at the Graduate School of Course of Mechanical Engineering in Tokai University, Japan. He received his bachelor’s degree of Mechanical Engineering in 2020 from Tokai University. His research interests include the development of a walking support system to improve the elderly's walking speed control ability. He is a student member of the JSME.

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Yoshihiro Kai Yoshihiro Kai received his Doctor of Engineering degree in 1999 from Doshisha University, Japan. From 1999 to 2002, he was a research associate at Kochi University of Technology, Japan. Since 2003, he has been with Tokai University, Japan, where he is currently a Professor in the Department of Mechanical Engineering. His research interests include Human-Friendly Robots, Exoskeleton Robots, and Drone Systems. He is a member of the JSME, RSJ, ISCIE, JSDE, JSNR, and SIBOM.

Masayuki Tsuchida Masayuki Tsuchida, RPT, PhD is the Assistant professor of the Department of Rehabilitation, Shonan University of Medical Sciences. He earned his PhD from Kanagawa University of Human Services. His research focuses on anatomy, motor learning, electrophysical agents, and the development of rehabilitation methods improving gait stability of the elderly.

Kenichi Sugawara Kenichi Sugawara, RPT, PhD is the chief professor of the Physical Therapy Division at the Department of Rehabilitation of the Faculty of Health and Social Services at Kanagawa University Human Services in Yokosuka, Japan. He earned his PhD from Hiroshima University. His research focuses on neurophysiology, motor learning, motor control of motor cortex, and spinal motoneuron for paretic or normal limbs in rehabilitation situations.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Human operator's "Characteristic of autonomic nerve activity as psychological burden" and "Clarification of thinking process for AI development" while operating the humanoid robot conversation program

Tomoya Yokotani Tokushima University Hospital, 2-50-1, Kuramoto-cho, Tokushima 770-8503, JAPAN [email protected]

Ryuichi Tanioka

Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Chihiro Kawai

Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Feni Betriana

Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Hirokazu Ito

Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Yuko Yasuhara

Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Kazuyuki Matsumoto

Graduate School of Technology, Industrial and Social Science, Tokushima University 2-1 Minamijyousanjima-cho,Tokushima 770-8506,JAPAN [email protected]

Fuji Ren

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Graduate School of Technology, Industrial and Social Science, Tokushima University 2-1 Minamijyousanjima-cho,Tokushima 770-8506,JAPAN [email protected]

Tetsuya Tanioka Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Background: The sphere where artificial intelligence (AI) is making its presence felt as a real and tangible entity is when it has a voice of its own. Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. Our research team is working on the development of dialogue applications for rehabilitation and dialogue to prevent dementia among older people. Thus, it is important to physiologically clarify the basic thinking process and psychological burden for humans to interact with people or to interact with robot.

Purpose: The purpose of this study is to clarify changes in human operator's autonomic nerve activity as psychological burden and thinking process during operate the humanoid robot conversation program, as a basic study for the development of AI for NLP to interact with older adults.

Method: Two rooms consisting of an experiment and an operating room for the robot conversation application were prepared for this experiment. One Pepper robot and two female subjects were placed in the experiment room. Two video cameras and three observers were placed in the experiment room. In the separate operating room, one operator and an operator’s assistant were assigned to input phrases remotely into Pepper's conversation application program through the keyboards. The operator's assistant assisted the operator for inputting actions such as acknowledgment gestures and nodding during conversation. The one operator was measured the heart rate variability (HRV) device to measure autonomic nervous activity during operations for the conversation application. After the experiment was completed, based on the recorded video data, the contents of conversations, thinking process of why and how operator responded for their conversation in certain situations were organized and listed in a table. These contents were listed chronologically and synchronized with the recorded results of the operator's HRV. Operator's autonomic nervous activity was analyzed for the psychological strain during operation.

In this presentation, authors provide the following: (1) the cognitive process involved during conversations with the operator of the humanoid robot conversation program, (2) changes in autonomic nerve activity as a result of psychological strain, and (3) challenges in AI development of the humanoid robot conversation program.

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP17H01609, and the Research Clusters program of Tokushima University (No. 2003002).

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TomoyaYokotani (Member) He is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. He earned

his MSN degrees from Tokushima University. He is working as a nurse at the Tokushima University, Japan. His research interests in her PhD course is the nurses’ perception of the Technological Competency as Caring in Nursing.

Ryuichi Tanioka (Member) He is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. He received

his Master of Policy Studies of Tokushima Bunri University, Tokushima. His research interests include Rehabilitation and Robotic Technology, AI.

Chihiro Kawai (Member)

She is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. She earned

his MSN degrees from Tokushima University. She works as a public health nurse in Japanese administration. Her research interest is the roles and services of welfare commissioners expected by public health nurses in providing support to children.

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Feni Betriana (Member)

She is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. She received her Master of Nursing Science from Prince of Songkla University, Thailand. She worked as a lecturer in Nursing Department, University of Fort de Kock, Indonesia. Her research interest is the development of the grief state scale for nurses.

Hirokazu Ito (Member)

Dr. Ito earned his PhD, MSN and BSN from the University of Tokushima in 2016, 2013, and in 2007

respectively. He worked as a staff nurse at the Tokushima University Hospital in Japan from 2007 to 2013. He is has been an Assistant Professor since 2013. His current research focus is on developing the Psychiatric Nursing Assessment Classification and Nursing Care Planning System (PsyNACS©), a patient database in psychiatric nursing. He has presented his research work in international conferences of nursing and engineering. Yuko Yasuhara (Member) Yuko Yasuhara, RN; PHN, PhD, is an associate professor of nursing at the Department of Nursing Outcome Management of the Tokushima University Graduate School in Japan. She received her MSN from the Kobe City College of Nursing in 2003, and her PhD from the Kawasaki University of Medical Welfare in 2013. She worked as a stuff nurse at hospital in Kobe, Japan from 1993 to 2000. She has worked at the Tokushima University since 2001. Her research focuses on sleep and the activity of people, care for patients with ischemic heart disease, safe intramuscular injection techniques, and caring as nursing.

Kazuyuki Matsumoto He received the Ph.D degree in 2008 from Faculty of Engineering, the University of Tokushima. He is currently an associate professor at the university of Tokushima. His research interests include affective computing, Emotion Recognition and Natural Language Processing. He is a member of IPSJ.

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Fuji Ren

He received the Ph.D. degree in 1991 from Faculty of Engineering, Hokkaido University, Japan. He worked at CSK, Japan, where he was a chief researcher of NLP. From 1994 to 2000, he was an associate professor in the Faculty of Information Sciences. From 2001 he joined the faculty of engineering, the University of Tokushima as a professor. His research interests include Natural Language Processing, Artificial Intelligence, Language Understanding and Communication. He is a member of the IEICE, CAAI, IEEJ, IPSJ, JSAI, AAMT and a senior member of IEEE.

Tetsuya Tanioka (Member)

He is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the BEd from Meisei University Japan, in 1997. He was visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting professor of St. Paul University Philippines. He is currently a board member of the Anne Boykin Institute for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots. He was the lead editor of the book, Nursing Robots: Robotic Technology and Human Caring for the Elderly published by Fukuro Publishing, Japan, in March 2017.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Developmental issues on communication robots' application for the use of older people care

Yuki Obayashi School of Health Sciences, Tokushima University 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Feni Betriana Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Tetsuya Tanioka Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Tomoya Yokotani Tokushima University Hospital, 2-50-1, Kuramoto-cho, Tokushima 770-8503, JAPAN [email protected]

Ryuichi Tanioka Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Chihiro Kawai Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Hirokazu Ito Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Yuko Yasuhara Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramoto-cho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Kyoko Osaka Nursing Science, Graduate School of Integrated Arts and Sciences, Kochi University, Japan

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Kohasu, Oko-cho, Nankoku-shi, Kochi 783-8505, Japan [email protected]

Kazuyuki Matsumoto Graduate School of Technology, Industrial and Social Sciences, Information Science and Intelligent Systems, Tokushima University, Graduate School [email protected]

Fuji Ren Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima 770-8506, Japan [email protected]

Yoshihiro Kai Department of Mechanical Engineering,Tokai University 4-1-1 Kitakaname, Hiratsuka-shi, Kanagawa 259-1292, Japan [email protected]

If anthropomorphic robots will be used for nursing care for older people, it is critical to consider how to develop these robots through interdisciplinary research based on scientific evidences. This development should be aimed to improve the performance of humanoid robots, thereby enabling them to care for older people and communicate to prevent the progression of dementia. The aim of this presentation is to discuss the developmental issues of communication robots from the application development perspective in introducing them to the elderly institutions. One of the developmental issues is low performance of robot dialogue application functions for older people. Therefore, long-time dialogue between user and robot is difficult without an intermediary role. In addition, there is a need for anthropomorphization of communication robots for older people, and its appearance requires face and arms to express their emotion and nonverbal language. The required performance of the application for dialogue with older people is the robot's receiver function that accurately reads the user's situation from multiple information such as user's voice, facial expression, and gesture. By contrast, the robot's sender function expresses response sentences, voices, and actions that matched with older person's conversation contents and emotions. These functions place older person-robot as the sender-receiver and vice versa in the dialogue. As future challenges, it is necessary to establish robots that can listen user's voice and convey artificial compassion by improving the performance such nonverbal expression and speech matched with the dialogue scene with such integrated functions. This work was supported by JSPS KAKENHI Grant Number JP17H01609, and the Research Clusters program of Tokushima University (No. 2003002).

Yuki Obayashi

She is an undergraduate nursing student at School of Health Sciences, Tokushima University. She received her Bachelor of Science in Nutrition from Tokushima University.

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Feni Betriana

She is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. She received her Master of Nursing Science from Prince of Songkla University, Thailand. She worked as a lecturer in Nursing Department, University of Fort de Kock, Indonesia. Her research interest is the development of the grief state scale for nurses.

Tetsuya Tanioka He is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the BEd from Meisei University Japan, in 1997. He was a visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting professor of St. Paul University Philippines. He is currently a board member of the Anne Boykin Institute for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots.

Tomoya Yokotani

He is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. He earned his MSN degrees from Tokushima University. He is working as a nurse at the Tokushima University Hospital, Japan. His research interests in his PhD course is the nurses’ perception of the Technological Competency as Caring in Nursing.

Ryuichi Tanioka

He is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. He received his Master of Policy Studies from Tokushima Bunri University, Tokushima, Japan. His research interests include Rehabilitation and Robotic Technology, and AI.

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Chihiro Kawai

She is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. She earned his MSN degrees from Tokushima University. She works as a public health nurse in Japanese administration. Her research interest is the roles and services of welfare commissioners expected by public health nurses in providing support to children.

Hirokazu Ito Dr. Ito earned his PhD, MSN and BSN from the University of Tokushima in 2016, 2013, and in 2007 respectively. He worked as a staff nurse at the Tokushima University Hospital in Japan from 2007 to 2013. He has been an Assistant Professor since 2013. His current research focus is on developing the Psychiatric Nursing Assessment Classification and Nursing Care Planning System (PsyNACS©), a patient database in psychiatric nursing. He has presented his research work in international conferences of nursing and engineering.

Yuko Yasuhara

Yuko Yasuhara, RN; PHN, PhD, is an associate professor of nursing at the Department of Nursing Outcome Management of the Tokushima University Graduate School in Japan. She received her MSN from the Kobe City College of Nursing in 2003, and her PhD from the Kawasaki University of Medical Welfare in 2013. She worked as a staff nurse at hospital in Kobe, Japan from 1993 to 2000. She has worked at the Tokushima University since 2001. Her research focuses on sleep and the activity of people, care for patients with ischemic heart disease, safe intramuscular injection techniques, and caring as nursing.

Kyoko Osaka

Kyoko Osaka, RN; PHN, MSN, PhD, Professor of Nursing Science, Graduate School of Integrated Arts and Sciences, Kochi University, Japan since 2019. She worked as a Lecturer of Nursing, Graduate School of Health Biosciences, Tokushima University, Tokushima, Japan during August 2013 to July 2019; and an Assistant Professor, Faculty of Nursing, Kochi Prefectural University, Kochi, Japan, from April 2009 to July 2013. She is known for her research on the Transactive Phenomenon in Relationships among Older Adults with Dementia, Nurses as Intermediaries, and Communication Robot.

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Kazuyuki Matsumoto

He received his PhD degree in 2008 from Faculty of Engineering, the University of Tokushima. He is currently an associate professor at the university of Tokushima. His research interests include affective computing, Emotion Recognition and Natural Language Processing. He is a member of IPSJ.

Fuji Ren He received the PhD degree in 1991 from Faculty of Engineering, Hokkaido University, Japan. He worked at CSK, Japan, where he was a chief researcher of NLP. From 1994 to 2000, he was an associate professor in the Faculty of Information Sciences. From 2001 he joined the faculty of engineering, the University of Tokushima as a professor. His research interests include Natural Language Processing, Artificial Intelligence, Language Understanding and Communication. He is a member of the IEICE, CAAI, IEEJ, IPSJ, JSAI, AAMT and a senior member of IEEE.

Yoshihiro Kai

Yoshihiro Kai, Dr. Eng., is a Professor of Robotics at the Department of Mechanical Engineering in Tokai University, Japan. He received his Bachelor’s, Master’s degree, and his Doctor of Engineering degree in Mechanical Engineering from Doshisha University. From 1999 to 2002, he was a research associate at the Kochi University of Technology, Japan. Since 2003, he has been with Tokai University. His current research interests focus on the development of human-friendly robots such as walking support robots for older people, exoskeletal robots, and service robots which support human daily activities.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

The effects of gaze and paralanguage for communication in nursing: A systematic review

Kanon Fukuta Student,Department of Nursing,Faculty of Health Sciences,Tokushima University,Tokushima, Japan, [email protected]

Chiharu Fukutomi Student,Department of Nursing,Faculty of Health Sciences,Tokushima University,Tokushima, Japan, [email protected]

Misaki Yoshimatsu Student,Department of Nursing,Faculty of Health Sciences,Tokushima University,Tokushima, Japan, [email protected]

Hirokazu Ito Assistant Professor of Nursing, Institute of Biomedical Sciences, Faculty of Health Sciences, Tokushima University, Tokushima, Japan [email protected]

Rozzano Locsin Professor Emeritus of Nursing, Institute of Biomedical Sciences Graduate School [email protected]

Language is a critical aspect of Communication, particularly among human beings. While language is popularly classified into two categories, namely verbal (articulated words) and nonverbal language (visual/gaze, facial expressions (of emotions) and body language, paralanguage, an emerging third category emphasize voice (sound), pitch, loudness, speed and tone. Birdwhistell has expressed that in human conversations, only 35% of all of messages are conveyed by verbal means, while the remaining 65% are conveyed by nonverbal communication. Mehrabian also explained that in estimating human attitudes and personality tendencies during dialogues, only 7% are judged by the language, 38% by peripheral language and 55% by facial expressions. Therefore, it has been shown that nonverbal communication accounts for a large proportion of communication. For Nurses, nonverbal communication is important in order to build relationships of trust with patients, and to deepen their understanding of patient conditions. These provided the impetus to review the current state of influence of paralanguage (such as gaze and voice in nonverbal language), and identify factors necessary for well-intentioned communication of nurses in order to understand their patients’ nursing care requisites.

The purpose of this study was to clarify how gaze and voice characteristics (paralanguage) affect nurse-patient communication.

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The literature reviewed were those published between 2010 and August 2020. Three available search engines were used, PubMed, CINAHL, and Google Scholar, which have a large collection of medical and nursing literature. For searching in the PubMed and CINAHL databases, two combinations of keywords were used: "gaze & nursing" and "eye-tracking & nursing" related to the gaze expression. For Google Scholar, “gaze", "nursing", "eye-tracking", "communication", "nurse", "patient & eye-contact" were the keywords used. For PubMed and CINAHL, three combinations of "nurse", "patient", "voice & communication", "paralanguage", "nursing & communication", "paralinguistic", and "nursing & communication" as keywords related to voice were used. For Google Scholar, “nursing", "voice", "communication", "patient", "nurse", "paralanguage & paralinguistic" were used. All English-language articles that met the selection criteria were retrieved and analyzed. In addition, by doing selective hand searching of other available articles which were not available on-line, necessary contents were also included. The literature research was conducted in accordance with the PRISMA process. This study is part of a larger study approved by the Tokushima University Hospital Medical Research Ethics Review Committee (approval number 3568).

Results of the literature search revealed 23 articles of which 7 of these were related to gaze while 16 were related to voice. The results showed the following themes: "the importance of communicating with deep attention" is more important in nursing situations where voice is not effective, such as in intensive care units. Similarly, it was found that studies were conducted to "clarify the abilities of nurses," e.g. the importance of knowing their own characteristics, and the different characteristics and influencing factors between nurses and students. Furthermore "intervention in communication by advanced devices" establish communication by using devices and apps. There were also considered to be important factors in nursing care practice in recent years. Importantly, it was also clearly shown that "difficult communication is not possible when using a common language" particularly in multi-ethnic countries which are not often experienced by nurses in Japan.

These findings clearly illuminated that the gaze (line of sight) and paralanguage are critical factors for nurses to engage when in communication with their patients to better their clinical practices. This finding was supported by the following themes: "the importance of communicating with deep attention", "clarify the abilities of nurses themselves," "intervention in communication by technological devices", and "difficulty when communication is not possible using a common language."

For nurses, gaze and voice (paralanguage) were always necessary elements for communicating with patients. In future clinical situations it is thought that patients’ intentions can be understood best by nurses when their thoughts and ideas are expressed well to include congruence with their gaze and their non-verbal language. These elements are critical for enhancing better understanding, thereby supporting the need to apprise nurses that communication through verbal language, paralanguage, and non-verbal language is critical to assure stronger patient communication thereby enhancing better understanding of nursing care processes towards quality healthcare. This work was supported by JSPS KAKENHI Grant Number 19K1973500.

References

1. Ray L. Birdwhistell, Kinesics and Contex t: Essays on Body Motion Communication. University of Pennsylvania Press, Philadelphia, 1970 2. A. Mehrabian, Silent messages. Wadsworth, Belmont, California, 1971

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3. www.prisma-statement.org/ PRISMA, TRANSPARENT REPORTING of SYSTEMATIC REVIEWS and META-ANALYSES(2020.06.12)

Kanon Fukuta She is currently a student and fourth grade in the Department of Nursing, Faculty of Health Sciences, Tokushima University. She is interested in nursing communication. Especially, she focused on gaze and paralanguage.

Chiharu Fukutomi She is currently a student and fourth grade in the Department of Nursing, Faculty of Health Sciences, Tokushima University. She is interested in nursing communication. Especially, she focused on gaze and paralanguage.

Misaki Yoshimatsu She is currently a student and fourth grade in the Department of Nursing, Faculty of Health Sciences, Tokushima University. She is interested in nursing communication. Especially, she focused on gaze and paralanguage.

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Hirokazu Ito Dr. Ito earned his PhD, MSN and BSN from the University of Tokushima in 2016, 2013, and in 2007

respectively. He worked as a staff nurse at the Tokushima University Hospital in Japan from 2007 to 2013. He is has been an Assistant Professor since 2013. His current research focus is on developing the Psychiatric Nursing Assessment Classification and

Nursing Care Planning System (PsyNACS©), a patient database in psychiatric nursing. He has presented his research work in international conferences of nursing and engineering.

Rozzano Locsin Rozzano C. Locsin, RN; PhD, FAAN is Professor emeritus of Nursing at the Institute of Biomedical

Sciences, Tokushima University Graduate School, Tokushima, Japan, and Professor Emeritus of Florida Atlantic University, Christine E. Lynn College of Nursing in Boca Raton. Florida. He holds Visiting Professorial positions at colleges of nursing in

Thailand, Uganda, and the Philippines. As a nurse theorist and scholar of caring science in nursing, he authored the middle-range theory, Technological Competency as Caring in Nursing has edited and co-authored books, such as Advancing Technology, Caring and Nursing Technology and Nursing Practice A Contemporary Nursing Practice.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Issues in developing natural language processing applications for healthcare robots to motivate older people while rehabilitation

Ryuichi Tanioka Department of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Kazuyuki Matumoto Department of Science and Engineering, Tokushima University, Graduate School 2-1 Zyousanzima-tyo, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Kenichi Sugawara Department of Rehabilitation, Graduate School of Health and Welfare, Kanagawa University of Human services, Graduate School 1-10-1 Heisei-tyo, Yokosuka-shi, Kanagawa 221-8686, Japan [email protected]

Kensaku Takase Faculty of Health Science, Butsuryo Collage of Osaka 3 3-3 Otorikitamachi Nishiku, Sakai-shi,Osaka 593-8328, Japan [email protected]

Yoshihiro Kai Department of Mechanical Engineering, Tokai University 4-1-1 Kitakaname, Hiratsuka-shi, Kanagawa 259-1292, Japan [email protected]

Masahito Tomotake Department of Psychiatry, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Tetsuya Tanioka Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

If anthropomorphic robots will be used for nursing care and rehabilitation for older people, it will critical to consider how to develop it by interdisciplinary research based on scientific evidences. It should be aimed to improve the performance of humanoid robots, in order to heal and care the older people, and prevent the progression of dementia. The purpose of this study is to consider the issues in developing natural language processing applications for healthcare robots to motivate older people while rehabilitation. In this presentation, (1)

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Summarize the results of clinical evaluation of applications for rehabilitation of Pepper robots. (2) Conduct literature research from previous research on motivation (encouragement) in rehabilitation for the older people. (3) Based on the mentioned contents, we will examine the application development issues for robots by natural language processing that motivate the older people while rehabilitation. Motivation was a frequently used concept and was described as an important determinant of rehabilitation outcome. Maclean et al. (2002) suggested that motivation was a frequently used concept and was described as an important determinant of rehabilitation outcome. The proactivity was equated with motivation, passivity with lack of motivation and older adult's compliance with rehabilitation was seen as indicative of motivation, noncompliance as a lack of motivation. Impotently, the determinants of motivation were located partly in personality factors but also in social factors. Central among the social factors were aspects of the professionals’ own behavior taken to positively and negatively affect motivation. In rehabilitation for the older people, in order for developing humanoid robots which make the best choices in speaking (encouragement) and observation while rehabilitation, when creating the NLP database, it is necessary to collect data to encourage people regarding communication patterns and observation points of healthcare staff.

Ryuichi Tanioka He is a PhD student at the Graduate School of Health Sciences in Tokushima University, Japan. He received his Master of Policy Studies of Tokushima Bunri University, Tokushima. His research interests include Rehabilitation and Robotic Technology, AI.

Kazuyuki Matsumoto He received the Ph.D degree in 2008 from Faculty of Engineering, the University of Tokushima. He is currently an associate professor at the university of Tokushima. His research interests include affective computing, Emotion Recognition and Natural Language Processing. He is a member of IPSJ.

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Kenichi Sugawara Kenichi Sugawara, RPT, PhD is the chief professor of the Physical Therapy Division at the Department of Rehabilitation of the Faculty of Health and Social Services at Kanagawa University Human Services in Yokosuka, Japan. He earned his PhD from Hiroshima University. His research focuses on neurophysiology, motor learning, motor control of motor cortex, and spinal motoneuron for paretic or normal limbs in rehabilitation situations.

Kensaku Takase Kensaku Takase is a Professor of Radiotherapy and anatomy, Faculty of Health Science, Butsuryo Collage of Osaka, Sakai, Japan. He has been engaged in stroke treatment for 35 years as a certified neurosurgeon of the Japanese Society of Neurosurgery, who is also currently the chairperson of the Japan Academy of Neurosonology.

Yoshihiro Kai Yoshihiro Kai, Dr. Eng., is Professor of Robotics at the Department of Mechanical Engineering in Tokai University, Japan. He received his Bachelor’s Master’s degree, and his Doctor of Engineering degree in Mechanical Engineering from Doshisha University. From 1999 to 2002, he was a research associate at the Kochi University of Technology, Japan. Since 2003, he has been with Tokai University. His current research interests focus on the development of human-friendly robots such as walking support robots for elderly people, exoskeletal robots, and service robots which support human daily activities.

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Masahito Tomotake He is Professor of Psychiatry at Tokushima University of Institute of Biomedical Sciences, Tokushima, Japan. He earned his PhD in Medicine from the University of Tokushima in 1997. He was an assistant professor until 2006 and became an associate professor in the same year at Tokushima University. At Tokushima University, he became a professor of the Institute of Health Biosciences from 2009 and a professor of the Institute of Biomedical Sciences in 2015. His research fields are psychiatry and mental health.

Tetsuya Tanioka He is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the BEd from Meisei University Japan, in 1997. He was visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting professor of St. Paul University Philippines. He is currently

a board member of the Anne Boykin Institute for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots. He was the lead editor of the book, Nursing Robots: Robotic Technology and Human Caring for the Elderly published by Fukuro Publishing, Japan, in March 2017.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Examination to develop the artificial intelligence through transforming tacit knowledge of nurses' dialogue for patients with dementia towards explicit knowledge

Hirokazu Ito Assistant Professor of Nursing, Institute of Biomedical Sciences, Faculty of Health Sciences, Tokushima University, Tokushima, Japan [email protected]

Kazuyuki Matsumoto Graduate School of Technology, Industrial and Social Sciences, Tokushima University 2-1 Minamijyousanjima-cho, 770-8502, Japan [email protected]

Xin Kang Department of Information Science and Intelligent Systems, Tokushima University 2-1 Minamijyousanjima-cho, Tokushima, Japan [email protected]

Tetsuya Tanioka Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Yuko Yasuhara Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School 2-24 Kuramotocho, Tokushima-shi, Tokushima 770-0042, Japan [email protected]

Rozzano Locsin Professor Emeritus of Nursing, Institute of Biomedical Sciences Graduate School [email protected]

Fuji Ren Department of Information Science and Intelligent Systems, Tokushima University 2-1 Minamijyousanjima-cho, Tokushima, Japan [email protected]

The shortage of medical and welfare human resources has been serious status in recent years in Japan, and the actions needed to address them, the introduction of communication robots in clinical and welfare field is progressing. Professional nurses make decisions on dialogue

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based on complex thinking processes and on a lot of information from patients with dementia. In particular, care for patient with dementia is extremely difficult, because they have reduction in function accompanying old age, and nurse’s competency is required to deal their symptoms well. In order for a communication robot to have similar dialogue functions as the professional nurses, it is critical to convert the tacit knowledge from competent nurses' to the explicit knowledge for communication robots. The purpose of this study was to examine how to formalize the process of decisions by professional nurses using the framework of PsyNACS, which is an assessment classification system that can be used for the patients with dementia developed by our research team. We recorded the care interaction with patients of two registered nurses with more than 10 years of clinical experiences in the dementia treatment ward at the psychiatric hospital in Shikoku district in September 2020. Their voices and the responses from the patients with dementia were recorded by digital video and analyzed. This study was approved by the Tokushima University Hospital Medical Research Ethics Review Committee (approval number 3046) and the ethics committee in the implemental facility (approval number 20200902). Findings were two scenes. The first scene, the patient was repeatedly asked about nurse's name and current season by nurse. Important information for make decision at this time, patient had a significant decreased cognitive function over the past year, thus nurse needed a more detailed information about cognition of patient as charge nurse. The second scene was a patient lying in his room even though lunch time, and nurse asked if patient can eat lunch in main hall. This nurse's decision was based on his knowledge about this patient, which was temporary declined patient's consciousness level in that morning because this patient usually can eat lunch by himself in the hall. Nurses used complex empirical information like changes of patients, patient information from medical and nursing records, clinical laboratory test data, and patient's response from nurses' experience through providing their care. We record the interactions between the nurse and patient in plain text and will use it as a corpus for training the dialogue system for the communication robot. In the future, it is necessary to collect more linguistic data from the interaction between the patients and nurses and labels annotated by competent nurses to create an automatic analysis system interlocked with PsyNACS. This work was partially supported by the Research Clusters program of Tokushima University (No. 2003002).

Hirokazu Ito

Dr. Ito earned his PhD, MSN and BSN from the University of Tokushima in 2016, 2013, and in 2007 respectively. He worked as a staff nurse at the Tokushima University Hospital in Japan from 2007 to 2013. He is has been an Assistant Professor since 2013. His current research focus is on developing the Psychiatric Nursing Assessment Classification and Nursing Care Planning System (PsyNACS©), a patient database in psychiatric nursing. He has presented his research work in international conferences of nursing and engineering.

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Kazuyuki Matsumoto

He received the Ph.D degree in 2008 from Faculty of Engineering, the University of Tokushima. He is currently an associate professor at the university of Tokushima. His research interests include affective computing, Emotion Recognition and Natural Language Processing. He is a member of IPSJ.

Xin Kang Dr. Kang received his Ph.D degree from Tokushima University, Tokushima, Japan, in 2013, his M.E. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 2009, and his B.E. degree from Northeastern University, Shenyang, China, in 2006. He is currently an assistant professor in Tokushima University. His research interests include affective computing and natural language processing.

Tetsuya Tanioka

He is Professor of Nursing Outcomes Management at Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. He was selected as the first Fellow of the American Academy of Nursing (FAAN) in Japan in 2013. He earned his Ph.D. from Kochi University of Technology, Japan in 2002, his MA from Shikoku Gakuin University in 1999, a MSN from the Graduate School, St. Paul University Philippines in 2018, and the BEd from Meisei University Japan, in 1997. He was visiting scholar at the Christine E. Lynn College of Nursing, Florida Atlantic University, and is a visiting professor of St. Paul University Philippines. He is currently a board member of the Anne Boykin Institute for the Advancement of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University. He authored the Transactive Relationship Theory of Nursing (TRETON): A Nursing Engagement Model for Persons and Humanoid Nursing Robots. He was the lead editor of the book, Nursing Robots: Robotic Technology and Human Caring for the Elderly published by Fukuro Publishing, Japan, in March 2017.

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Yuko Yasuhara Yuko Yasuhara, RN; PHN, PhD, is an associate professor of nursing at the Department of Nursing Outcome Management of the Tokushima University Graduate School in Japan. She received her MSN from the Kobe City College of Nursing in 2003, and her PhD from the Kawasaki University of Medical Welfare in 2013. She worked as a stuff nurse at hospital in Kobe, Japan from 1993 to 2000. She has worked at the Tokushima University since 2001. Her research focuses on sleep and the activity of people, care for patients with ischemic heart disease, safe intramuscular injection techniques, and caring as nursing.

Rozzano Locsin Rozzano C. Locsin, RN; PhD, FAAN is Professor emeritus of Nursing at the Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan, and Professor Emeritus of Florida Atlantic University, Christine E. Lynn College of Nursing in Boca Raton. Florida. He holds Visiting Professorial positions at colleges of nursing in Thailand, Uganda, and the Philippines. As a nurse theorist and scholar of caring science in nursing, he authored the middle-range theory, Technological Competency as Caring in Nursing has edited and co-authored books, such as Advancing Technology,

Caring and Nursing Technology and Nursing Practice A Contemporary Nursing Practice.

Fuji Ren Fuji Ren received his Ph. D. degree in 1991 from the Faculty of Engineering, Hokkaido University, Japan. From 1991 to1994, he worked at CSK as a chief researcher. In 1994, he joined the Faculty of Information Sciences, Hiroshima City University, as an Associate Professor. Since 2001, he has been a Professor of the Faculty of Engineering, Tokushima University. His current research interests include Natural Language Processing, Artificial Intelligence, Affective Computing, Emotional Robot. He is the Academician of The Engineering Academy of Japan and EU Academy of Sciences. He is a senior member of IEEE, Editor-in-Chief of International Journal of Advanced Intelligence, a vice president of CAAI, and a Fellow of The Japan Federation of Engineering Societies，a Fellow of IEICE, a Fellow of CAAI. He is the President of International Advanced Information Institute, Japan.

International Journal of Advanced Intelligence Volume x, Number 0, pp.XXX-YYY, MMM, 2009. © AIA International Advanced Information Institute

Construction of Annotated TOBYO Blog Corpus for Lifestyle Disease Analysis of Diabetic PATIENT

Mopuaa Ryu Faculty of Engineering, Tokushima University, 2-1, Minamijosanjima-cho, Tokushima city, 7708506, Japan [email protected]

Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita Graduate School of Technology, Industrial and Social Sciences, Tokushima University, 2-1, Minamijosanjima-cho, Tokushima city, 7708506, Japan {matumoto;mino;kita}@is.tokushima-u.ac.jp

In this study, we target to analyze weblogs authored by diabetes patients describing their struggle against their disease. In order to build a corpus for analysis, we annotate tags to the important keywords/key phrases such as blood glucose level, diet and emotions in collected weblog data. We aim at constructing a labeled lifestyle disease weblog corpus (called as TOBYO weblog corpus) to extract useful information from patients' weblogs of against illness. For example, to improve our lifestyle, it would be very useful to know how we should live every day, what kind of diet we should eat, how much exercise is appropriate, and so on. In our study, we organize the tagged data and take statistics on part-of-speech combinations. We set the top N thresholds in the rank of the frequency of the part-of-speech and create a system to exclusively pre-process the key phrases that are ranked as more frequent part of speech combinations. The extracted key phrase information is vectorized by a model of BERT (bidirectional encoder representation from a transformer), then the key phrases are sorted out by making a model for category prediction that uses two inputs (BERT vector and character frequency vector). To evaluate our proposed method, we conduct cross validation test and open test by using several machine learning algorithms. In comparison with the baseline classification method, which is a simple and ordinal neural network method, our method can achieve higher performance.

Figure 1 Construction and Analysis of TOBYO weblog corpus

63 The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) Oct ober 10-12, 2020, Online © AIA International Advanced Information Institute

Towards Automatic Dental Diagnosis System Based on Deep Learning

Kohei Torii Graduate School of Advanced Technology and Science, Tokushima University, Minami Josanjima, Tokushima, 770-8502, Japan [email protected]

Minoru Yoshida Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Minami Josanjima, Tokushima, 770-8502, Japan [email protected]

Kazuyuki Matsumoto Graduate School of Technology, Industrial and Social Sciences, Tokushima University Minami Josanjima, Tokushima, 770-8502, Japan [email protected]

Jiro Tsuruki Tsuruki Clinic Medical Dental, Yawata, Ichikawa, Chiba, 272-0021, Japan [email protected]

Kenichiro Kobayashi Kobayashi Dental Clinic Chuo-ku, Edogawa, Tokyo, 132-0021, Japan [email protected]

Eiichi Honda Graduate School of Biomedical Sciences, Tokushima University, Kuramoto-cho, Tokushima, 770-8503, Japan [email protected]

Kenji Kita Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Minami Josanjima, Tokushima, 770-8502, Japan [email protected]

The development of technologies based on deep learning which make machines possible to recognize objects in images as much as humans is remarkable, and those technologies have been applied in the medical field. As a dental radiographic image has a lot of information, used for the diagnosis of tooth conditions including dental caries, periodontitis, cysts, and so on, researchers are trying to make machines do what dentists do on the diagnosis utilizing the images. The automatic dental diagnosis system can reduce the burden of dentists, and give an environment where dentists can double-check their diagnosis results themselves.

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However, conventional systems provide limited diagnosis results, so we expand those, and improve versatility. Specifically, our system will recognize tooth numbers including deciduous teeth, more than 30 tooth conditions and implant information. But now, there are not enough annotation data for training, so we firstly developed a software on Python, which can create and manage polygonal annotation data for segmentation including some additional information, and has functions handling DICOM format images and creating polygonal annotation automatically using Mask R-CNN model pre-trained by existing data. The credibility of the created annotation data is ensured by dentists and experts of dental radiology checking these with the data management environment.

This session focuses the application of deep learning technologies to dental radiographic images and the solution to improve efficiency creating and managing huge amount of data, which can be useful for the development of automatic medical diagnostic systems using radiographic images in the future.

References

1. Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick. Mask R-CNN, IEEE International Conference on Computer Vision (ICCV), 2017. 2. Gil Jader ; Jefferson Fontineli ; Marco Ruiz ; Kalyf Abdalla ; Matheus Pithon ; Luciano Oliveira. Deep Instance Segmentation of Teeth in Panoramic X-Ray Images, 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2018.

The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE’20) October 10-12, 2020, Online © AIA International Advanced Information Institute

Towards Analyzing Relations Between Sleeping Time and SNS Texts: Prediction of Tweet Time Span Using The Last Tweet of the Day

Minoru Yoshida Faculty of Science and Technology, Tokushima University, 2-1, Minami-Josanjima-cho, Tokushima-shi, Tokushima 770-8506, Japan [email protected]

Takumi Kojima Faculty of Science and Technology, Tokushima University, 2-1, Minami-Josanjima-cho, Tokushima-shi, Tokushima 770-8506, Japan [email protected]

Kazuyuki Matsumoto Faculty of Science and Technology, Tokushima University, 2-1, Minami-Josanjima-cho, Tokushima-shi, Tokushima 770-8506, Japan [email protected]

Kenji Kita Faculty of Science and Technology, Tokushima University, 2-1, Minami-Josanjima-cho, Tokushima-shi, Tokushima 770-8506, Japan [email protected]

Sleeping habits are one of major issues in today's healthcare. In this paper, we consider the problem of analyzing sleeping habits of people by using SNS texts. As the first step towards predicting user's sleeping time using SNS texts, we assume that the time span between the user's last post in one day and the first post in the next day can be used as a pseudo-indicator for the user's sleeping time if the user posts the text sufficiently frequently. We call such tweet time spans “pseudo-sleeping time” if the first tweet of the next day include “good morning” or similar words. We try to predict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., the last tweet of the day). Preliminary experiments show that the tweet text contains some useful information to predict the user's pseudo-sleeping time.

1. Introduction

In this paper, we discuss the problem of predicting the sleeping time of person given the SNS texts (e.g., tweets) of that person. Today, sleeping habits are one of main issues of healthcare. It will contribute greatly to our QOL if we can predict or obtain some insights about our sleeping habits from the SNS texts. However, it takes too much cost to collect accurate sleeping time data, resulting in too small size of obtained data to be useful for meaningful analysis or applicable to machine-learning algorithms.

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To solve these issues, we instead propose a concept of pseudo-sleeping time, which can be easily collected using time stamps of SNS posts. Here, pseudo-sleeping time is defined as tweet time spans between the last tweet of one day and the first tweet of the next day. Note that here “day” is not the span from 0:00 to 23:59, but the span of waking hours of a person. In other words, we regard the tweet saying “good morning” or similar things as the first tweet of one day (which we call good-morning tweets) and the tweet previous to the good-morning tweet as the last tweet of the previous day (which we call good-night tweets.) That is, pseudo-sleeping time is the time span between a good-night tweet and the next good-morning tweet.

2. Task, Data, and Experiments

We collected tweets via Twitter API and searching for “good morning” or other similar phrases. If the tweet m that contain such phrases (which we call good-morning tweets) are found, we obtain the previous tweet n (which we call good-night tweets), and calculate the time span t between m and n. In this paper, we define our prediction problem as the binary classification problem. We give label l=+1 to the tweet n if t>8 (i.e., pseudo-sleeping time is over 8 hours) and label -1 otherwise. Therefore, resulting (n, l) pairs (i.e., pairs of good-morning tweet and time-classification label) are used as our dataset and therefore the task is to predict l given n. We collected over 1,500 (n,l) pairs. Each tweet is converted to the word list by using morphological analyzer MeCab. By applying SVMs to the obtained word list, we observed that we can predict the label l with over 50% accuracy. This result suggests that the last tweet of a day contains some useful information for predicting the user's sleeping time.

Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers JP18K11549, JP20K12027.