Tokushima University / Educator and Researcher Directory --- KANG, XIN

Personal Web Page

https://researchmap.jp/kang-xin (researchmap)

Field of Study

○	Informatics
○	Affective Computing
○	Biomedical Engineering
○	Informatics
○	Natural Language Processing

Subject of Study

○	AI Transparency (artificial intelligence, deep learning, Causal Machine Learning, Cognitive Dual Process Imitative Neuro-Symbolic AI)
○	Multimodal Affective Computing (artificial intelligence, deep learning, Multimodal Machine Learning, Emotion Recognition)
○	AI Support for Mental Health Care (Cognitive Dual Process Imitative Neuro-Symbolic AI, Causal Machine Learning, Multimodal Machine Learning, Depression Detection Assistant AI)
○	AI Support for Financial Decision-Making (artificial intelligence, deep learning, Causal Machine Learning, Large Language Models, financial engineering)
○	Artificial General Intelligence based on Large Language Models (artificial intelligence, deep learning, cognitive psychology, optimization)

Book / Paper

Book:

1.	Hiroki Tanioka and XIN KANG : いちばんやさしいディープラーニング入門教室, Sotechsha Co., Ltd., Tokyo, Feb. 2018. (Keyword) machine learning / Deep Learning / Keras / Python
2.	Fuji Ren, Shun Nishide, Kazuyuki Matsumoto, XIN KANG, Duo Feng and Mengjia He : Artificial Intelligence with Uncertainty, NTS, Jun. 2017.

Academic Paper (Judged Full Paper):

1.	Kiuchi Keita, XIN KANG, Ryota Nishimura, Sasayama Manabu and Kazuyuki Matsumoto : Predicting Physical and Mental Health Status One Month Later through Interview-Based Stress Assessment, Industrial Health, Vol.62, No.4, 237-251, (Summary) This study conducted an interview-based stress evaluation that considered the psychosocial models of work stress and verified the evaluations predictive validity. A four-stage assessment comprising a pre-survey, pre-interview questionnaire, stress assessment interview, and post-survey after one month was conducted with 50 Japanese workers. Additionally, 16 occupational health professionals provided stress evaluations based on recorded interview videos. Variables based on intraclass correlation coefficients (ICCs) were computed in multiple ways to compare the agreement among the evaluators. The generalized estimating equation (GEE) was conducted to evaluate the prediction models. The overall ICC among the evaluators was 0.58. The GEE revealed that the mean score of the evaluators in the interview-based stress evaluation significantly predicted psychological symptoms (β =2.02, p=0.019), burnout (β =0.77, p<0.001), and well-being (β =0.64, p=0.007) one month later, even after adjusting for the self-reported stress levels measured in the pre-survey. The predictive validity of the proposed interview-based stress evaluation was confirmed. Although there are several challenges in standardizing this evaluation, semi-structured interviews are an effective tool for understanding work stress. (Keyword) burnout / chronic stress / interview-based assessment / job stress / stress assessment / well-being (Link to Publication Site) ● Publication site (DOI): 10.2486/indhealth.2023-0144 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.2486/indhealth.2023-0144 (DOI: 10.2486/indhealth.2023-0144)
2.	Keita Kiuchi, Hidehiro Umehara, Koushi Irizawa, XIN KANG, Masahito Nakataki, Minoru Yoshida, Shusuke Numata and Kazuyuki Matsumoto : An Exploratory Study of the Potential of Online Counseling for University Students by a Human-Operated Avatar Counselor, Healthcare, Vol.12, No.1287, 2024. (Summary) Recently, the use of digital technologies, such as avatars and virtual reality, has been increasingly explored to address university students mental health issues. However, there is limited research on the advantages and disadvantages of counselors using avatars in online video counseling. Herein, 25 university students were enrolled in a pilot online counseling session with a human counselor-controlled avatar, and asked about their emotional experiences and impressions of the avatar and to provide qualitative feedback on their communication experience. Positive emotions during the session were associated with impressions of the avatars intelligence and likeability. The anthropomorphism, animacy, likeability, and intelligent impressions of the avatar were interrelated, indicating that the avatars smile and the counselors expertise in empathy and approval may have contributed to these impressions. However, no associations were observed between participant experiences and their prior communication with avatars, or between participant experiences and their gender or the perceived gender of the avatar. Accordingly, recommendations for future practice and research are provided. Accumulating practical and empirical findings on the effectiveness of human-operated avatar counselors is crucial for addressing university students mental health issues. (Keyword) avatar counseling / human-operated avatars / university students / digital mental health / empathy / communication / anthropomorphism / animacy / likeability / perceived intelligence (Link to Publication Site) ● Publication site (DOI): 10.3390/healthcare12131287 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38998822 ● Summary page in Scopus @ Elsevier: 2-s2.0-85198398651 (DOI: 10.3390/healthcare12131287, PubMed: 38998822, Elsevier: Scopus)
3.	XIN KANG : Transparency in the dimABSA Task with Neuro-Symbolic and Generative AI, IEICE Technical Report, Vol.124, No.90, 74-79, 2024. (Keyword) Generative AI / Neuro-Symbolic AI / Transparent AI / Affective Computing / Multi-Agent Collaboration
4.	Linhuang Wang, XIN KANG, Fei Ding, Satoshi Nakagawa and Fuji Ren : A Joint Local Spatial and Global Temporal CNN-Transformer for Dynamic Facial Expression Recognition, Applied Soft Computing, Vol.161, 1-13, 2024. (Keyword) Dynamic facial expression recognition / Affective computing / Transformer / Convolution neural network (Tokushima University Institutional Repository) ● Metadata: 119348 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.asoc.2024.111680 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.asoc.2024.111680 (Tokushima University Institutional Repository: 119348, DOI: 10.1016/j.asoc.2024.111680)
5.	Fei Ding, XIN KANG, Linhuang Wang, Yunong Wu, Satoshi Nakagawa and Fuji Ren : Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis, Electronics, Vol.13, No.9, 1-16, 2024. (Keyword) Generative Learning / Financial Argument Analysis / Prompt Engineering / causal inference (Link to Publication Site) ● Publication site (DOI): 10.3390/electronics13091746 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/electronics13091746 (DOI: 10.3390/electronics13091746)
6.	Keita Kiuchi, XIN KANG, Ryota Nishimura, Manabu Sasayama and Kazuyuki Matsumoto : Causal Effects of High Stress Assessed via Interviews on Mental and Physical Health: Toward Computer Agent-Driven Stress Assessment, Journal of Occupational and Environmental Medicine, 2024. (Summary) This study investigated the causal effect of high stress assessment via an interview on the mental and physical health of workers one month later. Stress assessment interviews and feedback were conducted with 50 Japanese workers. In addition to the interviewer, two occupational health professionals assessed participants' stress based on recordings. The average treatment effect was estimated by propensity score matching. High stress, according to the interview-based assessment, had a significant negative causal effect on self-reported well-being one month later (95% confidence interval: -3.02, -1.10). In addition, no effect of high stress on stress load, mental and physical symptoms, or burnout was observed. This study provides important insights into the prognosis of individuals who were assessed through interviews to have high stress. The findings are expected to help automate stress assessments using computer agents. (Link to Publication Site) ● Publication site (DOI): 10.1097/JOM.0000000000003117 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38603579 ● Search Scopus @ Elsevier (PMID): 38603579 ● Search Scopus @ Elsevier (DOI): 10.1097/JOM.0000000000003117 (DOI: 10.1097/JOM.0000000000003117, PubMed: 38603579)
7.	Fuji Ren, Yangyang Zhou, Jiawen Deng, Kazuyuki Matsumoto, Duo Feng, Tianhao She, Ziyun Jiao, Zheng Liu, Taihao Li, Satoshi Nakagawa and XIN KANG : Tracking Emotions using an Evolutionary Model of Mental State Transitions: Introducing a New Paradigm, Intelligent Computing, 1-24, 2024. (Link to Publication Site) ● Publication site (DOI): 10.34133/icomputing.0075 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.34133/icomputing.0075 (DOI: 10.34133/icomputing.0075)
8.	Rongyu Dou and XIN KANG : TAM-SenticNet: A Neuro-Symbolic AI approach for early depression detection via social media analysis, Computers & Electrical Engineering, Vol.114, 1-10, 2024. (Keyword) Neuro-Symbolic AI / Depression detection / Social media analysis / Early intervention / Sentiment analysis (Tokushima University Institutional Repository) ● Metadata: 118923 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.compeleceng.2023.109071 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85183150959 (Tokushima University Institutional Repository: 118923, DOI: 10.1016/j.compeleceng.2023.109071, Elsevier: Scopus)
9.	Yuxiang Zhou, XIN KANG, Fuji Ren, Huimin Lu, Satoshi Nakagawa and Xiao Shan : A Multi-attention and Depthwise Separable Convolution Network for Medical Image Segmentation, Neurocomputing, Vol.564, No.126970, 2023. (Keyword) U-Net / dual attention / attention gate / depthwise separable convolution / medical image segmentation (Tokushima University Institutional Repository) ● Metadata: 118693 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.neucom.2023.126970 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.neucom.2023.126970 (Tokushima University Institutional Repository: 118693, DOI: 10.1016/j.neucom.2023.126970)
10.	LIU ZHENG, XIN KANG and Fuji Ren : Dual-TBNet: Improving the Robustness of Speech Features via Dual-Transformer-BiLSTM for Speech Emotion Recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol.31, 2193-2203, 2023. (Keyword) speech emotion recognition / affective computing / speech representation learning / feature fusion Transformer (Tokushima University Institutional Repository) ● Metadata: 118515 (Link to Publication Site) ● Publication site (DOI): 10.1109/TASLP.2023.3282092 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/TASLP.2023.3282092 (Tokushima University Institutional Repository: 118515, DOI: 10.1109/TASLP.2023.3282092)
11.	Fei Ding, XIN KANG and Fuji Ren : Neuro or Symbolic? Fine-tuned Transformer with Unsupervised LDA Topic Clustering for Text Sentiment Analysis, IEEE Transactions on Affective Computing, 1-15, 2023. (Tokushima University Institutional Repository) ● Metadata: 118516 (Link to Publication Site) ● Publication site (DOI): 10.1109/TAFFC.2023.3279318 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/TAFFC.2023.3279318 (Tokushima University Institutional Repository: 118516, DOI: 10.1109/TAFFC.2023.3279318)
12.	Yangyang Zhou, XIN KANG and Fuji Ren : Prompt Consistency for Multi-label Textual Emotion Detection, IEEE Transactions on Affective Computing, 1-10, 2023. (Tokushima University Institutional Repository) ● Metadata: 118259 (Link to Publication Site) ● Publication site (DOI): 10.1109/TAFFC.2023.3254883 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85149879267 (Tokushima University Institutional Repository: 118259, DOI: 10.1109/TAFFC.2023.3254883, Elsevier: Scopus)
13.	XIN KANG, Shi Xuefeng, Yunong Wu and Fuji Ren : Active learning with complementary sampling for instructing class-biased multi-label text emotion classification, IEEE Transactions on Affective Computing, Vol.14, No.1, 523-536, 2023. (Keyword) Active Learning / Complementary Sampling / Class Biased Multi Label Classification / Text Emotion (Tokushima University Institutional Repository) ● Metadata: 118256 (Link to Publication Site) ● Publication site (DOI): 10.1109/TAFFC.2020.3038401 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/TAFFC.2020.3038401 (Tokushima University Institutional Repository: 118256, DOI: 10.1109/TAFFC.2020.3038401)
14.	Fuji Ren, Zheng Liu and XIN KANG : An efficient framework for constructing speech emotion corpus based on integrated active learning strategies, IEEE Transactions on Affective Computing, Vol.13, No.4, 1929-1940, 2022. (Tokushima University Institutional Repository) ● Metadata: 118257 (Link to Publication Site) ● Publication site (DOI): 10.1109/TAFFC.2022.3192899 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/TAFFC.2022.3192899 (Tokushima University Institutional Repository: 118257, DOI: 10.1109/TAFFC.2022.3192899)
15.	Li Zhiyang, Li Bin, Ni Hongjun, Fuji Ren, Lv Shuaishuai and XIN KANG : An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles, Metals, Vol.12, No.11, 1-16, 2022. (Tokushima University Institutional Repository) ● Metadata: 118180 (Link to Publication Site) ● Publication site (DOI): 10.3390/met12111809 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85141827896 (Tokushima University Institutional Repository: 118180, DOI: 10.3390/met12111809, Elsevier: Scopus)
16.	Hirokazu Ito, Kazuyuki Matsumoto, XIN KANG, Tetsuya Tanioka, Yuko Yasuhara, Rozzano De Castro Locsin and Fuji Ren : The AI Development Through Transforming Tacit Knowledge to Explicit Knowledge of Nurses' Dialogue for Patients with Dementia, International Journal of Advanced Intelligence (IJAI), Vol.12, No.1, 11-21, 2021. (Keyword) natural language processing / artificial intelligence / dementia care / PsyNACS / explicit and tacit knowledge / nurses' dialogue / humanoid robot (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1010576118604664832 (CiNii: 1010576118604664832)
17.	wenjie Liu, Wu Guoqing, Fuji Ren and XIN KANG : DFF-ResNet: An Insect Pest Recognition Model based on Residual Networks, Big Data Mining and Analytics, Vol.3, No.4, 300-310, 2020. (Tokushima University Institutional Repository) ● Metadata: 115879 (Link to Publication Site) ● Publication site (DOI): 10.26599/BDMA.2020.9020021 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.26599/BDMA.2020.9020021 (Tokushima University Institutional Repository: 115879, DOI: 10.26599/BDMA.2020.9020021)
18.	Jiaqiao Zhang, XIN KANG, Hongjun Ni and Fuji Ren : Surface defect detection of steel strips based on classification priority YOLOv3-dense network, Ironmaking and Steelmaking, Vol.8, 1-12, 2020. (Summary) The steel strip is an essential raw material in the machinery industry. Besides, the surface defects of the steel strip directly determine its performance. To achieve rapid and effective detection of the defects, a CP-YOLOv3-dense (classification priority YOLOv3 DenseNet) neural network was proposed in the present study. The model used YOLOv3 as basic network, implemented priority classification on the images, and then replaced the two residual network modules with two dense network modules. Therefore, the network can receive the multi-layer convolution features output by the dense connection block before making predictions, consequently enhancing the reuse and fusion of features. Finally, the six kinds of surface defects were detected by the improved network. According to the results, the detection precision of the CP-YOLOv3-dense network is 85.7%, the recall rate is 82.3%, the mean average precision is 82.73%, and the detection time of each image is 9.68 ms. (Keyword) Steel strip / defect detection / deep learning / neural network model / surface technology (Tokushima University Institutional Repository) ● Metadata: 115375 (Link to Publication Site) ● Publication site (DOI): 10.1080/03019233.2020.1816806 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85091091649 (Tokushima University Institutional Repository: 115375, DOI: 10.1080/03019233.2020.1816806, Elsevier: Scopus)
19.	Zheng Liu, XIN KANG, Shun Nishide and Fuji Ren : Vowel priority lip matching scheme and similarity evaluation model based on humanoid robot Ren-Xin, Journal of Ambient Intelligence and Humanized Computing, 2020. (Tokushima University Institutional Repository) ● Metadata: 118255 (Link to Publication Site) ● Publication site (DOI): 10.1007/s12652-020-02175-9 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85086366472 (Tokushima University Institutional Repository: 118255, DOI: 10.1007/s12652-020-02175-9, Elsevier: Scopus)
20.	XIN KANG, Yunong Wu and Fuji Ren : Toward action comprehension for searching: Mining actionable intents in query entities, Journal of the Association for Information Science and Technology, 2019. (Keyword) action mining / actionable knowledge / actionable intent ranking (Tokushima University Institutional Repository) ● Metadata: 118254 (Link to Publication Site) ● Publication site (DOI): 10.1002/asi.24220 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85064660402 (Tokushima University Institutional Repository: 118254, DOI: 10.1002/asi.24220, Elsevier: Scopus)
21.	XIN KANG, Fuji Ren and Yunong Wu : Exploring latent semantic information for textual emotion recognition in Blog articles, IEEE/CAA Journal of Automatica Sinica, Vol.5, No.1, 204-216, 2018. (Keyword) emotion recognition / natural language understanding / emotion-topic model / Bayesian inference / multi-label classification (Tokushima University Institutional Repository) ● Metadata: 115378 (Link to Publication Site) ● Publication site (DOI): 10.1109/JAS.2017.7510421 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/JAS.2017.7510421 (Tokushima University Institutional Repository: 115378, DOI: 10.1109/JAS.2017.7510421)
22.	Yunong Wu, Kenji Kita, Kazuyuki Matsumoto, XIN KANG and Fuji Ren : Constructing A Short Text Conversation system Based on the Relations Between Posts and Comments, International Journal of Advanced Intelligence (IJAI), Vol.9, No.3, 369-380, 2017.
23.	Shun Nishide, Hidenobu Shibasaki, XIN KANG and Fuji Ren : Generation of Humanlike Facial Expression for Natural Human-Robot Interaction System, International Journal of Advanced Intelligence (IJAI), Vol.9, No.1, 77-65, 2017. (Summary) Recognition and expression of emotions are indispensible functions for robots intended to play a role in the human society. Among various modalities, facial expression is said to be one of the most important factors for represent emotions. In this paper, we present our work on creating humanlike facial expressions for the humanoid robot Actroid, and adapting it to actual human-robot interaction scenario. Facial expressions were created manually using the software Wten source code, by adjusting parameters of facial features. The created expressions were implemented in a xed dialogue between a human and robot. Experiments were conducted with human subjects talking with the robot. Evaluations were done using questionaires on naturalness and e ectiveness of facial expressions during the dialogue. (Keyword) Human robot interaction / Facial expression / Humanoid robot
24.	XIN KANG, Yunong Wu and Fuji Ren : Disambiguating Users' Temporal Intent in Search Queries with Deep Neural Networks, International Journal of Advanced Intelligence (IJAI), Vol.9, No.1, 11-28, 2017.
25.	Chao Li, Fuji Ren and XIN KANG : Selectional Preferences Based on Distributional Semantic Model, WSEAS Transactions on Computers, Vol.15, 258-264, 2016. (Summary) In this paper, we propose a approach based on distributional semantic model to the selectional preference in the verb & dobj (direct object) relationship. The distributional representations of words are employed as the semantic feature by using theWord2Vec algorithm. The machine learning method is used to build the discrimination model. Experimental results show that the proposed approach is effective to discriminate the compatibility of the object words and the performance could be improved by increasing the number of training data. By comparing the previous method, the proposed method obtain the promising results with obvious improvement. Moreover, the results demonstrate that the semantics is an universal, effective and stable feature in this task, which is consistent with our awareness of using words. (Tokushima University Institutional Repository) ● Metadata: 118263 (Tokushima University Institutional Repository: 118263)
26.	Sun Yan, Fuji Ren, XIN KANG and Changqin Quan : Developing a Japanese Adverb-Emotion Corpus to Investigate the Effect of Adverbs in Japanese Sentence Emotion Classification, International Journal of Advanced Intelligence (IJAI), Vol.8, No.1, 99-116, 2016. (Keyword) adverb emotion / emotion feature / emotion corpus / emotion classification
27.	XIN KANG, Fuji Ren and Yunong Wu : Semi-Supervised Learning of Author-Specific Emotions in Micro-Blogs, IEEJ Transactions on Electrical and Electronic Engineering (TEEE), Vol.11, No.6, 768-775, 2016. (Summary) Learning emotions from texts has been an active research topic in affective computing. However, the lack of reliable connection between emotions and language features has caused severely biased emotion predictions. Moreover, the author-specific patterns in emotion expression could potentially affect emotion predictions, which has never been studied. In this paper, we propose a semisupervised learning algorithm to learn emotional features from large-scaled micro-blog documents with a Bayesian network, and introduce an emotion transition factor to generate the author-specific emotion predictions. We infer the author-specific emotions in micro-blog streams through belief propagation, and learn the emotional features through an expectation maximization estimation procedure. We report results of single-label and multilabel emotion predictions on a micro-blog stream corpus, and analyze the improvements achieved by the semisupervised feature learning strategy and the incorporation of emotion transition patterns. Finally, we perform personality analysis based on the authors' emotion distribution and examine emotion distributions in the learned features. (Link to Publication Site) ● Publication site (DOI): 10.1002/tee.22302 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1002/tee.22302 (DOI: 10.1002/tee.22302)
28.	Yan Sun, Changqin Quan, XIN KANG, Zuopeng Zhang and Fuji Ren : Customer emotion detection by emotion expression analysis on adverbs, Information Technology and Management, Vol.16, No.4, 303-311, 2015. (Summary) The growing interests of affective computing(AC) and its implementation in e-business demandresearchers to explore the applications of AC techniques indetecting customer emotions. Recent studies have shownthat language is powerful in conveying emotions, and agood understanding of major language features bears greatimplications for customer relationship management.However, the effect of adverbs in text emotion predictionhas only been briefly mentioned in a few related works, butnever thoroughly studied. This paper addresses the gap byinvestigating how to detect customer emotions throughanalyzing the adverbs in emotion expressions. In particular,we develop a Japanese adverb emotion corpus, analyzeemotion usage of adverbs, and further derive an adverbemotionlexicon and its rule base. Utilizing these resources,we design and perform experiments to classify emotions in sentences so as to evaluate the effectiveness of differentadverb features including adverbs, adverb emotions, andadverb emotion rules. Our experiments and analysis showthe great impact of adverbs on emotion expressions, whichcan be applied to assist e-businesses in improving customerrelated processes. (Keyword) Affective computing / E-business / Customer emotions / Adverb analysis (Link to Publication Site) ● Publication site (DOI): 10.1007/s10799-014-0204-2 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1007/s10799-014-0204-2 (DOI: 10.1007/s10799-014-0204-2)
29.	Fuji Ren, XIN KANG and Changqin Quan : Examining Accumulated Emotional Traits in Suicide Blogs with an Emotion Topic Model, IEEE Journal of Biomedical and Health Informatics, Vol.20, No.5, 1384-1396, 2015. (Summary) Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people's daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors. (Tokushima University Institutional Repository) ● Metadata: 118262 (Link to Publication Site) ● Publication site (DOI): 10.1109/JBHI.2015.2459683 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 26208372 ● Search Scopus @ Elsevier (PMID): 26208372 ● Search Scopus @ Elsevier (DOI): 10.1109/JBHI.2015.2459683 (Tokushima University Institutional Repository: 118262, DOI: 10.1109/JBHI.2015.2459683, PubMed: 26208372)
30.	Fuji Ren and XIN KANG : Employing Hierarchical Bayesian Networks in Simple and Complex Emotion Topic Analysis, Computer Speech & Language, Vol.27, No.4, 943-968, 2013. (Summary) Traditional emotion models, when tagging single emotions in documents, often ignore the fact that most documents convey complex human emotions. In this paper, we join emotion analysis with topic models to find complex emotions in documents, as well as the intensity of the emotions, and study how the document emotions vary with topics. Hierarchical Bayesian networks are employed to generate the latent topic variables and emotion variables. On average, our model on single emotion classification outperforms the traditional supervised machine learning models such as SVM and Naive Bayes. The other model on the complex emotion classification also achieves promising results. We thoroughly analyze the impact of vocabulary quality and topic quantity to emotion and intensity prediction in our experiments. The distribution of topics such as Friend and Job are found to be sensitive to the documents' emotions, which we call emotion topic variation in this paper. This reveals the deeper relationship between topics and emotions. (Keyword) Emotion classification / Emotion topic model / Complex emotion / Hierarchical Bayesian network (Link to Publication Site) ● Publication site (DOI): 10.1016/j.csl.2012.07.012 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84875925315 (DOI: 10.1016/j.csl.2012.07.012, Elsevier: Scopus)
31.	XIN KANG and Fuji Ren : Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network, China Communications, Vol.9, No.3, 99-109, 2012. (Summary) In this paper, we provide a Word Emotion Topic (WET) model to predict the complex word e-motion information from text, and discover the dis-tribution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories: joy, love, expectation, surprise, anxiety, sorrow, anger and hate. We use a hierarchical Bayesian network to model the emo-tions and topics in the text. Both the complex emo-tions and topics are drawn from raw texts, without considering any complicated language features. Our experiment shows promising results of word emo-tion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text. We also explore the topic distribution by ex-amining the emotion topic variation in an emotion topic diagram. (Keyword) word emotion classification / complex emotion / emotion intensity prediction / emotion-top-ic variation / hierarchical Bayesian network (Tokushima University Institutional Repository) ● Metadata: 118261 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84861773080 (Tokushima University Institutional Repository: 118261, Elsevier: Scopus)
32.	Yunong Wu, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition, International Journal of Intelligent Engineering and Systems, Vol.4, No.4, 19-26, 2011. (Summary) In this study, we make a scheme to explore the importance of modification relation for the emotional keywords annotation and emotion types recognition. We extract three modification features which are degree words,negative words and conjunctions from the Chinese emotion corpus named Ren-CECps. Beside word and part-of speech,three modification relations are adopted as feature in this study. We have carried out eight experiments with different feature sets for emotional keywords annotation and emotion types recognition in sentence level. Eight basic emotion types have been selected and Conditional Random Fields have been employed as the algorithm. In the part of evaluation, we demonstrate the importance of the modification features and our experiment results show the effectiveness of the modification features for improving the performance of emotional keywords annotation and emotion types recognition. (Keyword) Emotional Keywords Annotation / Emotion types recognition / Modification relation / CRFs (Link to Publication Site) ● Publication site (DOI): 10.22266/ijies2011.1231.03 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84859614924 (DOI: 10.22266/ijies2011.1231.03, Elsevier: Scopus)

Proceeding of International Conference:

1.	Mori Daiki, Kazuyuki Matsumoto, XIN KANG, Manabu Sasayama and Keita Kiuchi : SMACS: Stress Management AI Chat System, Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024), Vol.2, 167-174, Porto, Nov. 2024. (Summary) The purpose of this study is to develop a stress management AI chat system that can connect users who want mental health care with counselors. By means of this chat system, a conversational AI based on a large language model (LLM) will collect data on the user's stressors through text chats with the user. The system is personalized to the user based on the collected data. This paper describes the nature of the data collected in the preliminary experiment conducted in March 2024 and the results of its analysis, and discusses considerations for the main experiment to be conducted after July 2024. The preliminary experiment was conducted with 11 students over a 3-week period. Discuss the distribution of the data collected and the issues involved in building a model for predicting stress levels. (Keyword) conversational AI / mental health / mental healthcare / stress detection / Large Language Models / chat applications / natural language processing
2.	XIN KANG, Zhang Zhifei, Jiazheng Zhou, Yunong Wu, Shi Xuefeng and Kazuyuki Matsumoto : TMAK-Plus at SIGHAN-2024 dimABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis, The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 1-8, Bangkok, Aug. 2024.
3.	Wang Linhuang, XIN KANG, Ding Fei, Haitao Yu, Yunong Wu, Kazuyuki Matsumoto, Nakagawa Satoshi and Fuji Ren : Self Decoupling-Reconstruction Network for Facial Expression Recognition, International Joint Conference on Neural Networks (IJCNN 2024), 1-8, Yokohama, Jul. 2024.
4.	Linhuang Wang, XIN KANG, Satoshi Nakagawa and Fuji Ren : Msstnet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition, The 49th IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP 2024), 1-5, Apr. 2024. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICASSP48485.2024.10446699 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85201179213 (DOI: 10.1109/ICASSP48485.2024.10446699, Elsevier: Scopus)
5.	Yamane Daichi and XIN KANG : TUA1 at NTCIR-17 FinArg-1 Task, Proceedings of the 17th NTCIR Conference on Evaluation of Information Access Technologies, 1-5, Tokyo, Dec. 2023.
6.	Yuxiang Zhou, XIN KANG, Fuji Ren, Satoshi Nakagawa and Xiao Shan : DEU-Net: Dual Encoder U-Net for 3D Medical Image Segmentation, The 22nd International Conference on Computer and Information Technology, 1-7, Nov. 2023. (Link to Publication Site) ● Publication site (DOI): 10.1109/TrustCom60117.2023.00382 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85195458711 (DOI: 10.1109/TrustCom60117.2023.00382, Elsevier: Scopus)
7.	Fujita Ryo and XIN KANG : Research on Creating Buzzing Tweets Through Large Language Models, The 8th International Symposium on Artificial Intelligence and Robotics (ISAIR 2023), 1-8, Oct. 2023.
8.	Tomomatsu Toyohiko and XIN KANG : Facial Expression Recognition Using Self-Supervised Learning, The 8th International Symposium on Artificial Intelligence and Robotics (ISAIR 2023), 1-16, Oct. 2023.
9.	Linhuang Wang, XIN KANG, Satoshi Nakagawa and Fuji Ren : CenterMatch: A Center Matching Method for Semi-supervised Facial Expression Recognition, The 6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2023), 1-12, Oct. 2023. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-99-8537-1_30 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85181773525 (DOI: 10.1007/978-981-99-8537-1_30, Elsevier: Scopus)
10.	Ochi Arata and XIN KANG : Learning a Parallel Network for Emotion Recognition Based on Small Training Data, The 8th International Conference on Systems and Informatics (ICSAI 2022), 1-5, Jan. 2023. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSAI57119.2022.10005394 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85146951828 (DOI: 10.1109/ICSAI57119.2022.10005394, Elsevier: Scopus)
11.	Shingo Hanahira and XIN KANG : Resolving Context Contradictions in the Neural Dialogue System based on Sentiment Information, The 7th International Conference on Machine Learning and Soft Computing (ICMLSC2023), 1-8, Jan. 2023. (Link to Publication Site) ● Publication site (DOI): 10.1145/3583788.3583816 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85162696400 (DOI: 10.1145/3583788.3583816, Elsevier: Scopus)
12.	Hagio Shohei and XIN KANG : A Study of Response Sentence Generation Considering Topic in Neural Dialogue Model, The 1st International Conference on Communication, Security and Artificial Intelligence (ICCSAI-2022), 1-6, Dec. 2022.
13.	Li Xiudong, XIN KANG and Fuji Ren : Use Active Learning to Construct Japanese Emoji Emotion Database, Communications in Computer and Information Science, Vol.1701, 332-340, Dec. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-19-7943-9_29 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85145262078 (DOI: 10.1007/978-981-19-7943-9_29, Elsevier: Scopus)
14.	He Jiaxin, XIN KANG and Fuji Ren : Gait Recognition for Laboratory Safety Management Based on Human Body Pose Model, Communications in Computer and Information Science, Vol.1701, 323-331, Dec. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-19-7943-9_28 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85145261599 (DOI: 10.1007/978-981-19-7943-9_28, Elsevier: Scopus)
15.	Haoyu Wang, XIN KANG and Fuji Ren : Emotion-Sentence-DistilBERT: A Sentence-BERT-Based Distillation Model for Text Emotion Classification, Communications in Computer and Information Science, Vol.1701, 313-322, Dec. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-19-7943-9_27 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85145253184 (DOI: 10.1007/978-981-19-7943-9_27, Elsevier: Scopus)
16.	Junya Furutani, XIN KANG, Keita Kiuchi, Ryota Nishimura, Manabu Sasayama and Kazuyuki Matsumoto : Learning a Bimodal Emotion Recognition System Based on Small Amount of Speech Data, 2022 8th International Conference on Systems and Informatics (ICSAI), 160-164, Kunming, China, Dec. 2022. (Summary) This paper presents a bimodal emotion recognition system based on the voice and text information using small amount of speech data. Specifically, speech is divided into voice and text to learn emotion classifiers for each modal. The probabilities obtained from these emotion classifiers are weighted based on Mehrabians rule and summed up for each emotion to calculate the final score for bimodal emotion recognition. To create a highly accurate system while solving the problem that there are few Japanese speech data with emotion labels, we propose a novel data augmentation method and employ a transfer learning approach based on a pre-trained VGG16 model and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model on the tweets. In order to prove the effectiveness of the proposed method, we revealed the recognition results for 7 emotional states, i.e., anger, sadness, joy, fear, surprise, disgust, and neutral. The experiment result suggested that our novel data augmentation method improved the accuracy and that bimodal predictions based on voice and text-based outperformed the single-model predictions. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSAI57119.2022.10005454 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85146960025 (DOI: 10.1109/ICSAI57119.2022.10005454, Elsevier: Scopus)
17.	Tomoki Kusunose, XIN KANG, Keita Kiuchi, Ryota Nishimura, Manabu Sasayama and Kazuyuki Matsumoto : Facial Expression Emotion Recognition Based on Transfer Learning and Generative Model, 2022 8th International Conference on Systems and Informatics (ICSAI), 107-112, Kunming, China, Dec. 2022. (Summary) Facial expression emotion recognition has been a popular research topic, which played an important role in assisting the natural human-machine conversation. The conventional method for emotion estimation from facial expressions is to learn a CNN-based image classification model from scratch, However, learning such model requires a large number of labeled facial expression images, which is still a limited resource until now. To solve this problem, we propose a data augmentation method based on StyleGAN2 to generate artificial expression images with respect to seven emotions and use them as the additional training data. We further train an expression emotion recognition model based on a VGG16 network through transfer learning. In this research, we proposed a method using transfer learning and augmented images of facial expressions using trained VGG16 and StyleGAN2 and conducted experiments to achieve higher recognition accuracy for racial expression emotion recognition. Our experiment based on the CFEE dataset suggested that an emotion recognition accuracy of 75.10% could be obtained through transfer learning and the accuracy could further improved to 82.04% with the augmented expression images. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSAI57119.2022.10005478 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85146960739 (DOI: 10.1109/ICSAI57119.2022.10005478, Elsevier: Scopus)
18.	Zhou yuxiang, XIN KANG and Fuji Ren : MDSU-Net: A Multi-attention and Depthwise Separable Convolution Network for Stroke Lesion Segmentation, 2022 9th International Conference on Biomedical and Bioinformatics Engineering (ICBBE 2022), 1-14, Nov. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1145/3574198.3574200 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85150363787 (DOI: 10.1145/3574198.3574200, Elsevier: Scopus)
19.	Fuji Ren, Jiao Ziyun and XIN KANG : Improved Transformer-based Implicit Latent GAN with Multi-headed Self-attention for Unconditional Text Generation, The Fifth International Conference on Intelligence Science (ICIS2022), Vol.659, 166-173, Oct. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-031-14903-0_18 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85144543119 (DOI: 10.1007/978-3-031-14903-0_18, Elsevier: Scopus)
20.	XIN KANG, Rongyu Dou and Haitao Yu : TUA1 at eRisk 2022: Exploring Affective Memories for Early Detection of Depression, Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022), Vol.13390, 1-12, Sep. 2022. (Keyword) Time-aware affective memory / affective state / latency penalty / Early Detection of Depression (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85136934660 (Elsevier: Scopus)
21.	Li Bin, Fuji Ren, Ni Hongjun, XIN KANG, Lv Shuaishuai and Hao Zhuangzhuang : Classification Method of Surface Defects of Aluminum Profile Based on Transfer Learning, International Conference on Machine Learning and Intelligent Systems Engineering (MLISE 2022), 1-5, Aug. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1109/MLISE57402.2022.00008 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85143074519 (DOI: 10.1109/MLISE57402.2022.00008, Elsevier: Scopus)
22.	Ding Fei, XIN KANG, Yunong Wu and Fuji Ren : TUA1 at the NTCIR-16 DialEval-2 Task, Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies, 1-5, Jun. 2022.
23.	Zhou Yangyang, XIN KANG and Fuji Ren : Employing Contrastive Strategies for Multi-label Textual Emotion Recognition, 12th International Conference on Intelligent Information Processing (IIP2022), 13-24, May 2022. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-031-03948-5_24 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85132047240 (DOI: 10.1007/978-3-031-03948-5_24, Elsevier: Scopus)
24.	Liu Zheng, XIN KANG and Fuji Ren : Improving speech emotion recognition by fusing pre-trained and acoustic features using Transformer and BiLSTM, 12th International Conference on Intelligent Information Processing (IIP2022), 1-12, May 2022. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-031-03948-5_28 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85132037268 (DOI: 10.1007/978-3-031-03948-5_28, Elsevier: Scopus)
25.	Yuma Komoto, XIN KANG and Fuji Ren : Creating a Japanese dialogue corpus with multi-level topic analysis, 2022 4th International Conference on Natural Language Processing, Mar. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICNLP55136.2022.00065 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85139450830 (DOI: 10.1109/ICNLP55136.2022.00065, Elsevier: Scopus)
26.	Tatsuya Ikegami, XIN KANG and Fuji Ren : Improvement of Japanese Text Emotion Analysis by Active Learning Using Transformers Language Model, Proceeding of 2022 IEEE 14th International Conference on Computer Research and Development(ICCRD), 171-177, Jan. 2022. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICCRD54409.2022.9730387 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85127793016 (DOI: 10.1109/ICCRD54409.2022.9730387, Elsevier: Scopus)
27.	Zhuangzhuang Hao, Fuji Ren, XIN KANG, Ni Hongjun, Lv Shuaishuai and Wang Hui : Classification of Steel Strip Surface Defects Based on Optimized ResNet18, 2021 IEEE International Conference on Agents (ICA), 61-62, Dec. 2021. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICA54137.2021.00018 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85127619007 (DOI: 10.1109/ICA54137.2021.00018, Elsevier: Scopus)
28.	KAIXUAN WANG, Fuji Ren, XIN KANG, Shuaishuai Lv, Hongjun Ni and Yuan Haifeng : Thermal Defect Detection and Location for Power Equipment based on Improved VGG16, Proceedings of the 5th IEEE International Conference on Agent, Dec. 2021. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICA54137.2021.00014 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85127674002 (DOI: 10.1109/ICA54137.2021.00014, Elsevier: Scopus)
29.	Kubono Yusuke, Shun Nishide, XIN KANG and Fuji Ren : Prediction and Generation of Multiple Complex Drawing Figures From Partial Drawing Sequences, 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, 1-5, Nov. 2021. (Link to Publication Site) ● Publication site (DOI): 10.1109/HNICEM54116.2021.9732036 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85127538609 (DOI: 10.1109/HNICEM54116.2021.9732036, Elsevier: Scopus)
30.	XIN KANG, Yunong Wu and Fuji Ren : TUA1 at the NTCIR-15 DialEval-1 Task, Proceedings of the 15th NTCIR Conference on Evaluation of Information Access Technologies, 53-56, Tokyo, Dec. 2020.
31.	Shi Xuefeng, XIN KANG, Liao Ping and Fuji Ren : Building Label-Balanced Emotion Corpus Based On Active Learning for Text Emotion Classification, In Proceedings of 2019 4th International Symposium on Artificial Intelligence and Robotics (ISAIR2019), 13-24, Nov. 2020. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85097405906 (Elsevier: Scopus)
32.	Shota Inui, Fuji Ren, Shun Nishide and XIN KANG : Gait recognition in real environment using gait energy image generated by Mask R-CNN, 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China, Oct. 2020. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICMA49215.2020.9233582 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/ICMA49215.2020.9233582 (DOI: 10.1109/ICMA49215.2020.9233582)
33.	Kyoya Abe, Fuji Ren, Shun Nishide and XIN KANG : Construction of emergency medical support system based on understanding dialect intention, The 15th International Conference on Natural Language Processing and Knowledge Engineering, Xian, China and Web, Oct. 2020.
34.	Fei Ding, XIN KANG, Shun Nishide, Guan Zhijin and Fuji Ren : A fusion model for multi-label emotion classification based on BERT and topic clustering, In International Symposium on Artificial Intelligence and Robotics 2020, Vol.11574, 98-111, Oct. 2020. (Link to Publication Site) ● Publication site (DOI): 10.1117/12.2579255 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1117/12.2579255 (DOI: 10.1117/12.2579255)
35.	LI Qianqian, Fuji Ren, Shen Xiaoyan and XIN KANG : Speech emotion recognition based on data enhancement in time-frequency domain, International Symposium on Artificial Intelligence and Robotics 2020, Vol.115740R, 234-241, Oct. 2020. (Link to Publication Site) ● Publication site (DOI): 10.1117/12.2579205 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85096352430 (DOI: 10.1117/12.2579205, Elsevier: Scopus)
36.	Liu Jia, XIN KANG, Shun Nishide and Fuji Ren : Collaborative filtering recommendation algorithm based on bisecting K-means clustering, In International Symposium on Artificial Intelligence and Robotics 2020, Vol.11574, 311-318, Oct. 2020. (Link to Publication Site) ● Publication site (DOI): 10.1117/12.2580026 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85096355579 (DOI: 10.1117/12.2580026, Elsevier: Scopus)
37.	Hirokazu Ito, Kazuyuki Matsumoto, XIN KANG, Tetsuya Tanioka, Yuko Yasuhara, Rozzano De Castro Locsin and Fuji Ren : Examination to develop the artificial intelligence through transforming tacit knowledge of nurses' dialogue for patients with dementia towards explicit knowledge, The 15th NLPKE (NLPKE 2020), WEB, Oct. 2020.
38.	jiaqiao zhang, XIN KANG, Hongjun Ni and Fuji Ren : Surface Defects Classification of Steel Strips based on Transfer Learning and Naive Bayes Model, Proceedings of The 15 th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE20), 22-23, Oct. 2020.
39.	Masago Koji, Amo Mizuho, Shun Nishide, XIN KANG and Fuji Ren : Generation of Musical Scores from Chord Sequences Using Neurodynamic Model, International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 638-648, Sep. 2020. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-55789-8_55 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85091292688 (DOI: 10.1007/978-3-030-55789-8_55, Elsevier: Scopus)
40.	zheng liu, XIN KANG, Shun Nishide and Fuji Ren : A Chinese-Japanese Vowel Priority Lip Matching Scheme Based on Humanoid Robot Ren-Xin, In Proceedings of 2020 5th International Symposium on Artificial Intelligence and Robotics (ISAIR2020), Jun. 2020.
41.	Sumida Hiroaki, Fuji Ren, Shun Nishide and XIN KANG : Environment Recognition Using Robot Camera, 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), 282-286, May 2020. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICBDA49040.2020.9101205 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/ICBDA49040.2020.9101205 (DOI: 10.1109/ICBDA49040.2020.9101205)
42.	Chen Haiyan, Liu Zheng, XIN KANG, Shun Nishide and Fuji Ren : Investigating voice features for Speech emotion recognition based on four kinds of machine learning methods, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), 195-199, Singapore, Dec. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073725 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CCIS48116.2019.9073725 (DOI: 10.1109/CCIS48116.2019.9073725)
43.	Yasumura Ryosuke, Shun Nishide, XIN KANG and Fuji Ren : Constructing a Fundamental DevelopmentalDrawing Learning Model Using an Arm Robot, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), 185-189, Singapore, Dec. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073689 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CCIS48116.2019.9073689 (DOI: 10.1109/CCIS48116.2019.9073689)
44.	Li Jiaen, Fuji Ren, Shun Nishide and XIN KANG : Character action recognition based on deep convolutional neural network and action sequence, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), 149-153, Singapore, Dec. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073735 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CCIS48116.2019.9073735 (DOI: 10.1109/CCIS48116.2019.9073735)
45.	Lu Xin, XIN KANG, Shun Nishide and Fuji Ren : Object detection based on SSD-ResNet, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), 89-92, Singapore, Dec. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073753 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CCIS48116.2019.9073753 (DOI: 10.1109/CCIS48116.2019.9073753)
46.	Dou Rongyu, Shun Nishide, Fuji Ren and XIN KANG : ExploringUncertain Samples through Active Learning To Enhance Text Emotion Classification, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), 26-30, Singapore, Dec. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073729 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CCIS48116.2019.9073729 (DOI: 10.1109/CCIS48116.2019.9073729)
47.	Hu Junlin, XIN KANG, Shun Nishide and Fuji Ren : Text multi-label sentiment analysis based on Bi-LSTM, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), 16-20, Singapore, Dec. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073727 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CCIS48116.2019.9073727 (DOI: 10.1109/CCIS48116.2019.9073727)
48.	Zhou Zheng, XIN KANG, Shun Nishide and Fuji Ren : Capture Important Information for Reading andReasoning by A Two-Pass Attention Mechanism in Dynamic Memory Network, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2019), Dec. 2019.
49.	Yun Gao, Haitao Yu, XIN KANG and Fuji Ren : TUA1 at the TREC 2019: Deep Learning Track, Proceedings of 28th Text Retrieval Conference (TREC 2019), Vol.1250, 1-3, Nov. 2019. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85180130017 (Elsevier: Scopus)
50.	Zhou Yitao, Fuji Ren, Shun Nishide and XIN KANG : Facial Sentiment Classification Based on Resnet-18 Model, Proceedings of 2019 International Conference on Electronic Engineering and Informatics (EEI), 463-466, Sep. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/EEI48997.2019.00106 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/EEI48997.2019.00106 (DOI: 10.1109/EEI48997.2019.00106)
51.	Zhou Yangyang, XIN KANG and Fuji Ren : TUA1 at ImageCLEF 2019 VQA-Med: a Classification and Generation Model based on Transfer Learning, Working Notes of CLEF (2019), Vol.2380, 1-7, Lugano, Sep. 2019. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85070500176 (Elsevier: Scopus)
52.	Yuxiang Zhou, Ni Hongjun, Fuji Ren and XIN KANG : Face and Gender Recognition System Based on Convolutional Neural networks, Proceedings of 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 1091-1095, Tianjin, Aug. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICMA.2019.8816192 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/ICMA.2019.8816192 (DOI: 10.1109/ICMA.2019.8816192)
53.	Fu Kangwei, Sun Ling, XIN KANG and Fuji Ren : Text Detection for Natural Scene based on MobileNet V2 and U-Net, Proceedings of 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 1560-1564, Tianjin, Aug. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICMA.2019.8816384 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85072394301 (DOI: 10.1109/ICMA.2019.8816384, Elsevier: Scopus)
54.	Tang Jingwei, Yao Xingtian, XIN KANG, Shun Nishide and Fuji Ren : Position-Free Hand Gesture Recognition Using Single Shot MultiBox Detector Based Neural Network, Proceedings of 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 2251-2256, Tianjin, Aug. 2019. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICMA.2019.8816203 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85072403779 (DOI: 10.1109/ICMA.2019.8816203, Elsevier: Scopus)
55.	Zhou Yangyang, Liu Zheng, XIN KANG, Yunong Wu and Fuji Ren : TUA1 at the NTCIR-14 STC-3 Task, Proceedings of the 14th NTCIR Conference, 338-345, Jun. 2019.
56.	Lu Qingqing, Zhang Xiaomei, XIN KANG and Fuji Ren : Feature Fusion Based on Neural Image Captioning with Spatial Attention, Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering (WCSE 2019), 195-200, Hong Kong, Jun. 2019. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081102148 (Elsevier: Scopus)
57.	Lu Yitian, Jiang Ping, Shun Nishide, XIN KANG and Fuji Ren : Traffic Sign Recognition Based on Up-sampling Convolution, Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering (WCSE 2019), 136-142, Hong Kong, Jun. 2019. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081101925 (Elsevier: Scopus)
58.	Liu Zheng, Fuji Ren and XIN KANG : Research on the Effect of Different Speech Segment Lengths on Speech Emotion Recognition Based on LSTM, Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering (WCSE 2019), 491-499, Hong Kong, Jun. 2019. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85081110971 (Elsevier: Scopus)
59.	Yoshie Setsu, XIN KANG, Shun Nishide and Fuji Ren : Emotion recognition for Twitter language based on lingual and expressional information, Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2018), 730-734, Nov. 2018. (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS.2018.8691222 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85064982962 (DOI: 10.1109/CCIS.2018.8691222, Elsevier: Scopus)
60.	Tianhao She, XIN KANG, Shun Nishide and Fuji Ren : Improving LEO Robot ConversationalAbility via Deep Learning Algorithms for 416 Children with Autism, Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS2018), 416-420, Nov. 2018.
61.	XIN KANG, Yunong Wu and Fuji Ren : Progressively improving supervised emotion classification through active learning, Lecture Notes in Computer Science, Vol.11248, 49-57, Oct. 2018. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-03014-8_4 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85057096653 (DOI: 10.1007/978-3-030-03014-8_4, Elsevier: Scopus)
62.	Yunong Wu, XIN KANG, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emoticon-based emotion analysis for Weibo articles in sentence level, Lecture Notes in Computer Science, Vol.11248, 104-112, Oct. 2018. (Keyword) Emotion Analysis / Emoticon (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-03014-8_9 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85057082718 (DOI: 10.1007/978-3-030-03014-8_9, Elsevier: Scopus)
63.	Liu Ning, Zhou Zheng, XIN KANG and Fuji Ren : TUA1 at eRisk 2018, CEUR Workshop Proceedings (CEUR-WS.org), Vol.2125, 1-7, Sep. 2018. (Keyword) early risk detection / feature representation / CNN / LSTM
64.	Zhou Yangyang, XIN KANG and Fuji Ren : Employing Inception-Resnet-v2 and Bi-LSTM for Medical Domain Visual Question Answering, CEUR Workshop Proceedings (CEUR-WS.org), Vol.2125, 1-11, Sep. 2018. (Keyword) VQA-Med / Inception-Resnet-v2 / Bi-LSTM / Attention (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85051091321 (Elsevier: Scopus)
65.	Yunong Wu, XIN KANG, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Sentence Emotion Classification for Intelligent Robotics Based on Word Lexicon and Emoticon Emotions, Proceedings of the International Conference of Intelligent Robotic and Control Engineering, 38-41, Aug. 2018. (Keyword) robotics / natural language processing (Link to Publication Site) ● Publication site (DOI): 10.1109/IRCE.2018.8492969 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85056756985 (DOI: 10.1109/IRCE.2018.8492969, Elsevier: Scopus)
66.	XIN KANG, Yunong Wu and Fuji Ren : Mining Actionable Intents to Facilitate Search Engine Users' Actions, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 343-351, Chengdu, Dec. 2017.
67.	Qianlu Huang, Fuji Ren, XIN KANG, Shun Nishide and Qiang Wang : Recognizing facial emotions in the video, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 316-327, Chengdu, Dec. 2017.
68.	Yuming Xu, Fuji Ren, XIN KANG, Shun Nishide and Ping Jiang : Human Pose Recognition in Robots Based on Angle of Joint Vector, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 304-315, Chengdu, Dec. 2017.
69.	Qian Zhang, Fuji Ren, XIN KANG, Shun Nishide, Shibing Zhang and Xiaoge Zhang : Building Emotional Corpus for Microblogging Emoticons, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 289-303, Chengdu, Dec. 2017.
70.	Jie Shen, Ziyun Jiao, Fuji Ren, XIN KANG, Shun Nishide, Suichun Qu and Yi Yang : Realization and Improvement of Robot Object Recognition, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 251-263, Chengdu, Dec. 2017.
71.	Yunong Wu, Kenji Kita, Kazuyuki Matsumoto, XIN KANG and Fuji Ren : Exploring the Relations Between Posts and Comments for Short Text Conversation, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 228-238, Chengdu, Dec. 2017.
72.	Ruixue Xia, Fuji Ren, XIN KANG and Shun Nishide : RESEARCH ON CONSTRUCTION AND ANALYSIS OF JAPANESE-CHINESE EMOTIONAL EXPRESSION CORPUS, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 216-223, Chengdu, Dec. 2017.
73.	Yasuda Tadayoshi, Yamashita Maki, Shun Nishide, XIN KANG and Fuji Ren : Learning Efficient Drawing Sequence Through Training of Recurrent Neural Network Model, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 65-76, Chengdu, Dec. 2017.
74.	Yunong Wu, XIN KANG, Kenji Kita and Fuji Ren : TUA1 at NTCIR-13 Short Text Conversation 2 Task, Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies, 211-214, Tokyo, Dec. 2017.
75.	Mengjia He, XIN KANG and Fuji Ren : TUA1 at the NTCIR-13 OpenLiveQ Task, Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies, 105-107, Tokyo, Dec. 2017.
76.	Chao Li, XIN KANG and Fuji Ren : Medweb Task: Identify Multi-Symptoms from Tweets Based on Active Learning and Semantic Information, Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies, 75-80, Tokyo, Dec. 2017.
77.	XIN KANG, Yunong Wu and Fuji Ren : TUA1 at the NTCIR-13 Actionable Knowledge Graph Task: Sampling Related Actions from Online Searching, Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies, 346-353, Tokyo, Dec. 2017.
78.	Ma Lei, Jiang Bitao and XIN KANG : Shorthand Character Recognition based on Leap Motion Sensor, The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16), Vol.0, No.0, 1, Okinawa, Dec. 2016.
79.	Shun Nishide, Hidenobu Shibasaki, XIN KANG and Fuji Ren : Generation of Humanlike Facial Expression for Natural Human-Robot Interaction System, The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16), Okinawa, Dec. 2016.
80.	XIN KANG, Yunong Wu and Fuji Ren : Disambiguating Users' Temporal Intent in Search Queries with Deep Neural Networks, The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16), 1, Okinawa, Dec. 2016.
81.	Ning Liu, Mengjia He, Chao Li, XIN KANG and Fuji Ren : TUTA1 at the NTCIR-12 Temporalia Task, Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies, 258-261, Tokyo, Jun. 2016. (Keyword) temporal search / relation extraction / similarity computing
82.	XIN KANG, Yunong Wu and Fuji Ren : KGO at the NTCIR-12 Temporalia Task: Exploring Temporal Information in Search Queries, Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies, 247-252, Tokyo, Jun. 2016.
83.	XIN KANG and Fuji Ren : Understanding Blog author's emotions with hierarchical Bayesian models, 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC), 1-6, Mexico City, Mexico, May 2016. (Summary) The research of inferring emotions in natural language is confronted with two major challenges: the lack of basic knowledge in emotion expressions and the co-occurrence of separate emotions through all language unites. In this paper, we propose a Bayesian inference method to explore the basic knowledge with respect to emotion expression in different semantic dimensions, and to infer the co-occurrence of multiple emotion labels through words to the document. Specifically, we incorporate emotions and semantic dimensions as the latent factors in determining the distribution of observed words in a corpus of Blog articles. For each Blog article, we further generalize emotions from words to the document by incorporating a document specific hierarchy in the emotion distributions. The basic knowledge and co-occurred emotion labels in words and documents are obtained through a Gibbs sampling inference. Our experiment is performed on the well-developed Chinese emotion corpus, i.e. Ren-CECps, which indicates both higher accuracy and better robustness in our word and document emotion predictions compared with those generated by the state-of-the-art emotion prediction algorithms, and demonstrates a distribution of emotions in different word semantic dimensions. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICNSC.2016.7479037 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84978085764 (DOI: 10.1109/ICNSC.2016.7479037, Elsevier: Scopus)
84.	Chao Li, Fuji Ren and XIN KANG : Verb-object Selectional Preferences in Chinese Based on Distributional Semantic Model, 18th International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems (MAMECTIS '16), 59-62, Venice, Italy, Jan. 2016. (Summary) In this paper, we propose a approach based on distributional semantic model on selectional preference task in the verb & dobj (direct object) relationship. The distributional representations of words are employed as the semantic feature by using the Word2Vec algorithm. Experimental results show that the proposed approach is effective to discriminate the compatibility of the object words and the performance could be improved by increasing the number of training data. Moreover, the results demonstrate that the semantics is an universal, effective and stable feature in this task, which is consistent with our awareness of using words.
85.	XIN KANG, Yunong Wu and Zhang Zhifei : Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification, Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing, 68-73, Beijing, Jul. 2015. (Link to Publication Site) ● Publication site (DOI): 10.18653/v1/W15-3112 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.18653/v1/W15-3112 (DOI: 10.18653/v1/W15-3112)
86.	Yu Haitao, XIN KANG and Fuji Ren : TUTA1 at the NTCIR-11 Temporalia Task, Proceedings of the 11th NTCIR Conference, 461-467, Dec. 2014.
87.	Yunong Wu, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Exploring Emotional Words for Chinese Document Chief Emotion Analysis, The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25), 597-606, Dec. 2011.
88.	Wu Yunong, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Exploring Emotional Words for Chinese Document Chief Emotion Analysis, The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25), 597-606, Dec. 2011.
89.	Yunong Wu, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Modification Relations based Emotional Keywords Annotation using Conditional Random Fields, The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011), 81-88, Nov. 2011.
90.	Wu Yunong, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Modification Relations based Emotional Keywords Annotation using Conditional Random Fields, The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011), 81-88, Nov. 2011.
91.	XIN KANG, Fuji Ren and Yunong Wu : Bottom Up: Exploring Word Emotions for Chinese Sentence Chief Sentiment Classification, Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering, 422-426, Beijing, Aug. 2010. (Summary) In this paper we demonstrate the effectiveness of employing basic sentiment components for analyzing the chief sentiment of Chinese sentence among nine categories of sentiments (including No emotion). Compared to traditional lexicon based methods, our research explores emotion intensities of words and phrases in an eight dimensional sentiment space as features. An emotion matrix kernel is designed to evaluate inner product of these sentiment features for SVM classification with O(n) time complexity. Experimental result shows our method significantly improves performance of sentiment classification. (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2010.5587793 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78649273536 (DOI: 10.1109/NLPKE.2010.5587793, Elsevier: Scopus)
92.	XIN KANG, Wang Xiaojie, Tan Yongmei and Fuji Ren : A Simple Method Improving Precision of Cross Domain Bilingual Sentence Alignment, International Conference on Advanced Intelligence (ICAI-08), 204-210, Oct. 2008.
93.	XIN KANG, Wang Xiaojie and Fuji Ren : Exploiting Syntactic and Semantic Information in Coarse Chinese Question Classification, The 4th IEEE International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), 1-7, Oct. 2008. (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2008.4906803 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-67650361209 (DOI: 10.1109/NLPKE.2008.4906803, Elsevier: Scopus)

Proceeding of Domestic Conference:

1.	Daiki Mori, Kazuyuki Matsumoto, XIN KANG, Manabu Sasayama and Keita Kiuchi : Development of an individually adaptable stress detection model using a stress management AI chat system, 第 23 回情報科学技術フォーラム(FIT2024)講演論文集, Vol.3, 357-360, Sep. 2024. (Summary) The aim of this research is to develop a stress management AI chat system that can connect users and counsellors. By developing this system and conducting user evaluations, we aim to confirm the effectiveness of the proposed method and contribute to the research and development of mental health care AI that can handle stress in an engineering way. This paper presents the results of preliminary experiments focusing on the construction of a chat system. The preliminary experiment was conducted for about three weeks during March 2024, and the results of the analysis of the data accumulated in the system during the experiment are discussed for the main experiment scheduled to be conducted after July 2024. (Keyword) stress management system / AI chatbot / stress detection model / personalization
2.	Daiki Mori, Kazuyuki Matsumoto, XIN KANG, Manabu Sasayama and Keita Kiuchi : Development of a Stress Management AI Chat System to Reduce Social Isolation, IT healthcare, Vol.19, No.1, 100-105, Aug. 2024. (Summary) This study aims to develop an AI chat system that can manage stress in order to support people who are isolated. The system aims to improve the efficiency of collecting data on stress factors and prevent the accumulation of stress through AI chat, as well as to support stress management by detecting stress and guiding the user to a specialized institution. This paper discusses the results of the analysis based on the data accumulated in the preliminary experiment conducted in March 2024, and discusses issues to be considered for the main experiment to be conducted in and after July 2024. The preliminary experiment was conducted on about 11 students in a laboratory for three weeks. The AI chat system changes the dialogue model every week, and the differences in the nature of the data collected in each case are compared to select the most appropriate dialogue model and to improve the model for this experiment. In particular, this time, the AI chat system is evaluated based on the average value of stress reduction in the post-chat questionnaire. (Keyword) Conversational AI / Mental Health Care / Stress Detection / LLM / Counseling Support
3.	Kotaro Kashihara, Toshiki Takanabe, Keita Kiuchi, Hidehiro Umehara, Koshi Irizawa, Masahito Nakataki, Shusuke Numata, XIN KANG, Minoru Yoshida and Kazuyuki Matsumoto : Constructing multimodal counseling dataset for depression detection and feature analysis, IT healthcare, Vol.19, No.1, 94-99, Aug. 2024. (Summary) In recent years, depression caused by stress and anxiety has been on the rise worldwide, and the number of people taking leave due to depression and other mental illnesses has been increasing in Japan as well. However, there is a serious shortage of counselors and psychiatrists, making early detection difficult. In this study, for early detection of depression, 25 Japanese university students were interviewed by a professional counselor for about 30 minutes, and a multimodal dataset consisting of verbal, audio, video, and heart rate data was constructed during the interview. Before and after the interviews, a questionnaire about the subjects' current psychological state was also administered. The constructed dataset was analyzed by extracting features using GiNZA, OpenSMILE, and OpenFace, and comparing them with the results of the questionnaire. In the future, we plan to conduct interviews with mentally ill subjects and collect data from them. (Keyword) multimodal dataset / depression detection / dataset
4.	TAKANABE Toshiki, Kazuyuki Matsumoto, 木内敬太, XIN KANG, Ryota Nishimura and 篠山学 : 感情分析のためのカウンセリングマルチモーダルデータセットの構築および評価, 情報処理学会全国大会講演論文集, 4-83-4-84, Mar. 2024. (Summary) コロナ禍をきっかけとして，オンラインカウンセリングが急激に普及した．オンラインカウンセリングは，細かな表情や声色の変化の認識が難しいことが問題点として挙げられる．カウンセラーの判断を助け，業務効率化のために，クライアントの感情の自動分析が有効である．人間は相手の感情を読み取るとき，声色，表情，発話内容など複数の要素から総合的に判断を行っている．AIによる感情推定も同様に，複数の異なるモダリティを組み合わせて用いるマルチモーダル感情推定により，高精度な感情分析が可能になると考えられる．マルチモーダル感情推定には，学習用データとして，感情のラベルが付与されたマルチモーダルデータセットが必要となる．本研究では，オンラインカウンセリング中のクライアントの感情分析のためのマルチモーダルデータセットを構築し，その評価を行う．本データセットには，カウンセラーによる労務者を対象としたオンラインカウンセリングにおける労務者の様子を収録した動画と，第三者により付与された客観感情ラベルが含まれている． (Keyword) マルチモーダル感情分析 / カウンセリング面談 / フライスのカッパ係数 / ラッセルの円環モデル / オンラインカウンセリング
5.	Manabu Sasayama, Keita Kiuchi, XIN KANG, Ryota Nishimura and Kazuyuki Matsumoto : インタビュー対話における問い返し文の生成に関する検討, 言語処理学会第29回年次大会発表論文集, 405-407, Mar. 2023. (Summary) 遠隔勤務をしているとき，遠隔労働者が自分自身のストレスに気づけないことも多い．精神面の健康状態が深刻になる前に自分自身で気づくことができれば，健康を維持することにつながる．本研究では，遠隔労働者が健康状態をセルフチェックするためのバーチャルエージェント対話システムの構築を目的としている．本稿では，対話時に必要となる「問い返し」の生成手法について考察する．
6.	Kazuyuki Matsumoto, 木内敬太, XIN KANG, Ryota Nishimura and 篠山学 : Relationships Between Client Stress and Multimodal Emotion Estimation in Counseling Situations, JSKE 第18回日本感性工学会春季大会講演論文集, Mar. 2023. (Summary) This study aims to develop a technology to detect the degree of stress of remote workers, which has been increasing in recent years, from multimodal data such as video and audio data during interviews. In this paper, we use interview data obtained in preliminary experiments to analyze the relationship between emotions estimated from facial, audio, and verbal information and stress evaluation by interview. Using the emotion estimation results for each modal based on machine learning algorithm as input, we train a model to predict stress evaluation results, evaluate errors, and confirm the validity of stress evaluation using the emotion estimation results. In addition, by clustering the time-series change patterns of the emotion estimation results, we will analyze whether or not there is a relationship between each type of emotion and stress evaluation. (Keyword) automatic stress detection / multimodal emotion estimation / remote worker
7.	Tatsuya Oda and XIN KANG : HOG特徴量を用いた歩容認識に関する研究, 情報処理学会第85回全国大会, 1-2, Mar. 2023.
8.	Ikegami Tatsuya, Fuji Ren, Shun Nishide and XIN KANG : Tweet日本語感情コーパスに対するBERTの効果の検証, 情報処理学会全国大会講演論文集, 527-528, Mar. 2021.
9.	Shogo Terao, Shun Nishide, XIN KANG and Fuji Ren : ロボット教師における特定人物の音声の自動生成, 情報処理学会第83回全国大会講演論文集, 225-226, Mar. 2021.
10.	Naoyuki Kadota, Shun Nishide, XIN KANG and Fuji Ren : ロボット教師における特定分野のQAシステムの構築, 情報処理学会第83回全国大会講演論文集, 563-564, Mar. 2021.
11.	Koji Masago, Shun Nishide, XIN KANG and Fuji Ren : 神経力学モデルを用いた楽譜の学習とコード系列からの和音生成, 第83回情報処理学会全国大会, Vol.2021, No.2, 451-452, Mar. 2021.
12.	Yusuke Kubono, Shun Nishide, XIN KANG and Fuji Ren : 神経力学モデルによる複雑図形の学習と生成, 情報処理学会第83回全国大会, Vol.2021, No.2, 449-450, Mar. 2021.
13.	Naoto Yamasaki, XIN KANG, Shun Nishide and Fuji Ren : ロボット教師による特定分野の記述式問題の判定について, 電子情報通信学会総合大会講演論文集, Mar. 2020.
14.	Jumpei Fusanobori, XIN KANG, Shun Nishide and Fuji Ren : 単語の感情属性を活かしたロボット教師の表情生成, 電子情報通信学会総合大会講演論文集, Mar. 2020.
15.	Shogo Terao, XIN KANG, Shun Nishide and Fuji Ren : ロボット教師における特定人物の音声の自動生成, 電子情報通信学会総合大会講演論文集, Mar. 2020.
16.	Naoyuki Kadota, XIN KANG, Shun Nishide and Fuji Ren : ロボット教師における特定分野のQAシステムの構築, 電子情報通信学会総合大会講演論文集, Mar. 2020.
17.	Mako Kawano, Shun Nishide, XIN KANG and Fuji Ren : 深層モデルを用いた画像系列の特徴量自己組織化と映像生成, 情報処理学会全国大会講演論文集, 2-289-2-290, Mar. 2020.
18.	Fusei Nomoto, Shun Nishide, XIN KANG and Fuji Ren : 構造に注目した神経力学モデルを用いた漢字の筆跡ダイナミクスの学習, 情報処理学会全国大会講演論文集, 2-281-2-282, Mar. 2020.
19.	Yusuke Kubono, Shun Nishide, XIN KANG and Fuji Ren : ペンの持ち上げを考慮した神経力学モデルによる描画図形予測と生成, 情報処理学会全国大会講演論文集, 2-283-2-284, Mar. 2020.
20.	Koji Masago, Shun Nishide, XIN KANG and Fuji Ren : リカレントニューラルネットワークを用いた未学習のコードからの楽譜生成, 情報処理学会全国大会講演論文集, 2-279-2-280, Mar. 2020.
21.	Ryosuke Yasumura, Shun Nishide, XIN KANG and Fuji Ren : アームロボットを用いた発達的描画モデルの構築, 日本ロボット学会学術講演会, Sep. 2019.
22.	Kensuke Kondoh, Shun Nishide, XIN KANG and Fuji Ren : Kinectを用いた神経力学モデルによる人の身体動作のクラスタリング, 情報処理学会全国大会講演論文集, 2-519-2-520, Mar. 2019.
23.	Shota Inui, Shun Nishide, XIN KANG and Fuji Ren : 深層学習を用いた人の行動認識と感情生成, 情報処理学会全国大会講演論文集, 2-517-2-518, Mar. 2019.
24.	Ryosuke Yasumura, Shun Nishide, XIN KANG and Fuji Ren : アームロボットのための発達的な描画学習モデルの基盤システム構築, 情報処理学会全国大会講演論文集, 2-313-2-314, Mar. 2019.
25.	Mizuho Amoh, Shun Nishide, XIN KANG and Fuji Ren : 音符・コード系列に注目した神経力学モデルによる音楽情報の階層的学習, 情報処理学会全国大会講演論文集, 2-375-2-376, Mar. 2019.
26.	Misaki Mori, Shun Nishide, XIN KANG and Fuji Ren : 神経力学モデルを用いた部分描画系列からの描画図形予測, 情報処理学会全国大会講演論文集, 2-285-2-286, Mar. 2019.
27.	Yuzuki Nonaka, Shun Nishide, XIN KANG and Fuji Ren : ペンの持ち上げを考慮した神経力学モデルによる手書き漢字の学習, 情報処理学会全国大会講演論文集, 2-255-2-256, Mar. 2019.
28.	Hiroki Urakami, Shun Nishide, XIN KANG and Fuji Ren : Human Robot Interaction System Based on Emotion State Transition in Continuous Emotion Space, 情報処理学会全国大会講演論文集, 2-399-2-400, Mar. 2018.
29.	Tadayoshi Yasuda, Shun Nishide, XIN KANG and Fuji Ren : Hierarchical Learning of Primitives Using Neural Network Model, 情報処理学会全国大会講演論文集, 2-219-2-220, Mar. 2018.
30.	Yuki Sugimoto, Fuji Ren, Shun Nishide and XIN KANG : 心的状態遷移ネットワークに基づく感情会話システムの構築, 言語処理学会年次大会, 658-661, Mar. 2018.
31.	Yuko Takata, Shun Nishide, XIN KANG and Fuji Ren : Learning Drawing Sequences Using Recurrent Neural Network Model, 情報処理学会全国大会講演論文集, 2-489-2-490, Mar. 2018.
32.	Setsu Yoshie, XIN KANG, Shun Nishide and Fuji Ren : Extraction emotion from spoken language based on the language information, The 31st Annual Conference of the Japanese Society for Artificial Intelligence, Vol.0, No.0, 1-2, May 2017.
33.	Yoshie Setsu, XIN KANG, Shun Nishide and Fuji Ren : Extraction emotion from spoken language based on the language information, The 31st Annual Conference of the Japanese Society for Artificial Intelligence, 1-2, May 2017.
34.	XIN KANG : 言語処理に基づく感情認識, エンジニアリングフェスティバル 2016, Sep. 2016.

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

Learning conversation agents for long-vision response generation in multi-round communications (Project/Area Number: 19K20345 )
A scalable privacy-preserving information retrieval system based on federated optimization, on-device intelligence and semantic matching (Project/Area Number: 19H04215 )
Search by Researcher Number (80777350)

Associate Professor : KANG, XIN

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Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)