2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第2页

Automatic Evaluation of Discussion Quality Using Topic Relevance and Participants' Performance 基于话题相关性和参与者表现的讨论质量自动评估

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00023

Yoko Nishihara, W. Sunayama, Shiho Imashiro

{"title":"Automatic Evaluation of Discussion Quality Using Topic Relevance and Participants' Performance","authors":"Yoko Nishihara, W. Sunayama, Shiho Imashiro","doi":"10.1109/TAAI57707.2022.00023","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00023","url":null,"abstract":"People often have discussions with others in working cooperatively. Discussions are conducted with given topics. Participants in discussions give their opinions to have an agreement on an issue. They listen to others' opinions and give their views. The duration of a discussion is limited, so a discussion can not be held for a long time. Since the participants have to get an agreement in a limited duration, the quality of discussion must keep high. However, it is difficult for the participants to assess the quality of the discussion. An automatic evaluation method for the quality of discussion is required. This paper proposes an automatic evaluation method of the discussion quality. We assume if a discussion has much information related to discussion topics, the quality of the discussion must be high. We also assume if all participants give their opinions related to the discussion topics, the quality of the discussion must be high. Based on the assumptions, the proposed method takes discussion texts and keywords related to discussion topics to evaluate topic relevance and participant's performance. The proposed method generates an equation of the discussion quality using multiple regression analysis. If a new discussion is given, the generated equation can automatically assess the discussion's Quality. In evaluation experiments, we used 20 discussion texts and obtained an equation for the quality of discussion. We found that the obtained equation could assess the quality of discussion with high accuracy (a multiple $R$ was 0.92).","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121460303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Reply-to Relation in Textual Group Chat using Unlabeled Dialogue Scripts and Next Sentence Prediction 使用未标记对话脚本和下一句预测识别文本组聊天中的回复关系

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00025

Junjie Shan, Yoko Nishihara, Yihong Han

{"title":"Identifying Reply-to Relation in Textual Group Chat using Unlabeled Dialogue Scripts and Next Sentence Prediction","authors":"Junjie Shan, Yoko Nishihara, Yihong Han","doi":"10.1109/TAAI57707.2022.00025","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00025","url":null,"abstract":"With instant message (IM) software becoming an important part of work and study, more and more research has begun to aim at supporting people's communication by analyzing their chat messages, such as topic provision and relationship sustainment. As the essential step for achieving these research works, the first task is to identify the relations between those large amounts of chat messages, especially in the situation of group chats. In this paper, we propose a method to identify the “reply-to” relations in IM's group chat from unlabeled textual data by using the next sentence prediction (NSP) approach. First, we proposed a method of automatically sampling two messages with and without the “reply-to” relation from unlabeled dialogue scripts to prepare the training data. Second, we built and trained three settings of the NSP model through the training data to identify the “reply-to” relations between two text chat messages. These NSP models were based on the pre-trained Japanese BERT (bidirectional encoder representation from transformers) model. Last, we evaluated the trained models through actual text group chat data with manual labels. The evaluation data contains 444 textual chat messages from three chat groups, each group has three chat members. Evaluation results showed that the models reached a max accuracy up to 69.6%, higher than past methods, and the top F1 score is 0.558.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126400046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proposal for Visualizing Sound Information in Movies 电影中声音信息可视化的建议

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00046

Y. Takama, Ikuya Sasakata, H. Shibata

引用次数: 0

Three Player Otrio will be Strongly Solved 三人游戏将被强力解决

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00015

Runa Kubota, Lucien Troillet, Kiminori Matsuzaki

引用次数: 0

Improving Clustering Uncertainty-weighted Embeddings for Active Domain Adaptation 改进聚类不确定加权嵌入的主动域自适应

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00013

Shengsen Wu, Hsuan-Tien Lin

引用次数: 0

Preliminary Design, Implementation and Observations of the Multi-player Game Tri-Othello 多人游戏《三奥赛罗》的初步设计、实现与观察

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00016

Jr-Chang Chen, Chao-Liang Weng, T. Hsu

引用次数: 0

Human Values Estimation on News Articles through BERT-extracted Opinion Expressions 基于bert提取意见表达的新闻文章人文价值评估

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00026

Yihong Han, Yoko Nishihara, Junjie Shan

{"title":"Human Values Estimation on News Articles through BERT-extracted Opinion Expressions","authors":"Yihong Han, Yoko Nishihara, Junjie Shan","doi":"10.1109/TAAI57707.2022.00026","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00026","url":null,"abstract":"This paper proposes a human values estimation method with an opinion extraction approach. We assume that opinion sentences reflect human values the most. For a given article, the proposed method extracts the opinion sentences from the texts and estimates the human values included in the opinion sentences. The opinion sentence extraction is conducted by classifying each sentence as an opinion sentence or a non-opinion sentence. The proposed method concatenates sentences from the same article to extend the input texts as an upsampling approach while estimating the human values. The upsampling approach enriches the information volume of the training data. The distribution of human values from each opinion sentence shows the article's general human values. We conducted two evaluation experiments. We used the editorial articles from Mainichi Newspaper as the corpus data. The first experiment evaluated the performance of opinion sentence extraction. The training accuracy of opinion sentence extraction was 92%. In the evaluation test, the model reached a $F_{1}$ score of 85.5%. The results showed that opinion sentences could be extracted with high accuracy. The second experiment evaluated the performance of human values estimation. There are five categories for human values estimation. The experiment was conducted with the same editorial articles from Mainichi Newspaper as the corpus data. We applied a training data enhancement approach by increasing the sentence number of training input and achieved a training accuracy up of over 50%. The results showed that the human values of opinion sentences could be estimated with high accuracy.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117069502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Food Allocation in Food Banks with Multi-agent Deep Reinforcement Learning 基于多智能体深度强化学习的食品银行食品分配优化

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00045

Tomoshi Iiyama, D. Kitakoshi, Masato Suzuki

引用次数: 0

Improving LiDAR Semantic Segmentation on Minority Classes and Generalization Capability for Autonomous Driving 改进激光雷达小类语义分割及自动驾驶泛化能力

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00032

Chiao-Hua Tseng, Yu-Ting Lin, Wen-Chieh Lin, Chieh-Chih Wang

{"title":"Improving LiDAR Semantic Segmentation on Minority Classes and Generalization Capability for Autonomous Driving","authors":"Chiao-Hua Tseng, Yu-Ting Lin, Wen-Chieh Lin, Chieh-Chih Wang","doi":"10.1109/TAAI57707.2022.00032","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00032","url":null,"abstract":"LiDARs have emerged as an important sensor in autonomous driving systems because they offer more accurate geometric measurements than cameras and radars. Therefore, LiDARs have been commonly combined with cameras or radars to tackle many perception problems in autonomous driving, such as object detection, semantic segmentation, or navigation. For semantic segmentation of LiDAR data, due to the class imbalance issue of large-scale scene, there is a performance gap between majority classes and minority classes of large-scale dataset. The minority classes usually include the crucial classes to the autonomous driving, such as “person”, “motorcyclist”, “traffic-sign”. To improve the performance of minority classes, we adopt U-Net++ as the architecture, KPConv as convolution operator, and use both dice loss and cross entropy as loss functions. We get 5.1% mIoU improvement on SemanticKITTI of all classes and 9.5% mIoU improvement of minority classes. Moreover, due to the different resolution of LiDAR sensors, we show the generalization capability of our model by training it on 64-beam dataset and testing on 32-beam and 128-beam dataset. We get 3.3% mIoU improvement on 128-beam dataset and 1.9% mIoU improvement on 32-beam dataset.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123733672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Greedy-Based Precise Expansion Algorithm for Customized Group Team Formation Problem 基于贪婪的定制群体队形问题精确展开算法

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00030

Pei-Yi Yeh, Shiou-Chi Li, Hao-Shang Ma, Jen-Wei Huang

引用次数: 0