2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第4页

Comparison of Search Behaviors in Chess, Shogi, and the game of Go 国际象棋、棋棋和围棋中搜索行为的比较

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00041

Shogo Takeuchi

{"title":"Comparison of Search Behaviors in Chess, Shogi, and the game of Go","authors":"Shogo Takeuchi","doi":"10.1109/TAAI57707.2022.00041","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00041","url":null,"abstract":"Searching is important in games, and the relationship between depth and the best-changes has been investigated in chess programs using alpha-beta search. The combination of Deep Neural Network and Monte-Carlo Tree Search is a successful method in chess, shogi, and go, and it is important to investigate this method. Our purpose in this work is to find the differences in games and in search methods. If programs using the same method behaves differently in the different games, it may be possible to find the differences in the games. In this paper, we focus on an increase or decrease in evaluation value with increasing search depth reported in previous research in chess and the problem of fortress. We obtained the different results from the previous work when using Leela Chess Zero, a chess program with Deep Neural Network and Monte-Carlo Tree Search, although the results for other game programs with Deep Neural Network and Monte-Carlo Tree Search and other chess programs are the same from the previous work. The combination of a large number of draws and pUCT is a possible cause. We believe that there is room to allocate more resources to the best moves and that improvements can be made through ingenuity.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"49 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":"125451123","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

Bi-Sep: A Multi-Resolution Cross-Domain Monaural Speech Separation Framework 一种多分辨率跨域单音语音分离框架

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00022

Kuan-Hsun Ho, J. Hung, Berlin Chen

{"title":"Bi-Sep: A Multi-Resolution Cross-Domain Monaural Speech Separation Framework","authors":"Kuan-Hsun Ho, J. Hung, Berlin Chen","doi":"10.1109/TAAI57707.2022.00022","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00022","url":null,"abstract":"In recent years, deep neural network (DNN)-based time-domain methods for monaural speech separation have substantially improved under an anechoic condition. However, the performance of these methods degrades when facing harsher conditions, such as noise or reverberation. Although adopting Short-Time Fourier Transform (STFT) for feature extraction of these neural methods helps stabilize the performance in non-anechoic situations, it inherently loses the fine-grained vision, which is one of the particularities of time-domain methods. Therefore, this study explores incorporating time and STFT-domain features to retain their beneficial characteristics. Furthermore, we leverage a Bi-Projection Fusion (BPF) mechanism to merge the information between two domains. To evaluate the effectiveness of our proposed method, we conduct experiments in an anechoic setting on the WSJ0-2mix dataset and noisy/reverberant settings on WHAM!/WHAMR! dataset. The experiment shows that with a cost of ignorable degradation on anechoic dataset, the proposed method manages to promote the performance of existing neural models when facing more complicated environments.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"120 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":"127285440","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: 1

Emergent Generation of Camouflage Patterns by Artificial Ants 人工蚂蚁伪装图案的紧急生成

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00040

Koichiro Sato, Kazunori Mizuno

引用次数: 0

Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation? 院内元信息对抽象出院摘要生成有用吗?

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00034

Kenichiro Ando, Mamoru Komachi, T. Okumura, Hiromasa Horiguchi, Yuji Matsumoto

{"title":"Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?","authors":"Kenichiro Ando, Mamoru Komachi, T. Okumura, Hiromasa Horiguchi, Yuji Matsumoto","doi":"10.1109/TAAI57707.2022.00034","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00034","url":null,"abstract":"During the patient's hospitalization, the physician must record daily observations of the patient and summarize them into a brief document called “discharge summary” when the patient is discharged. Automated generation of discharge summary can greatly relieve the physicians' burden, and has been addressed recently in the research community. Most previous studies of discharge summary generation using the sequence-to-sequence architecture focus on only inpatient notes for input. However, electric health records (EHR) also have rich structured metadata (e.g., hospital, physician, disease, length of stay, etc.) that might be useful. This paper investigates the effectiveness of medical meta-information for summarization tasks. We obtain four types of meta-information from the EHR systems and encode each meta-information into a sequence-to-sequence model. Using Japanese EHRs, meta-information encoded models increased ROUGE-1 by up to 4.45 points and BERTScore by 3.77 points over the vanilla Longformer. Also, we found that the encoded meta-information improves the precisions of its related terms in the outputs. Our results showed the benefit of the use of medical meta-information.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"23 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":"130194497","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

Enhancement of CNN-based 2048 Player with Monte-Carlo Tree Search 用蒙特卡罗树搜索增强基于cnn的2048播放器

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00018

Shota Watanabe, Kiminori Matsuzaki

引用次数: 0

Interactive Hashtag Recommendation System 交互式标签推荐系统

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00038

Chun-Ting Lin, Tsai-Yen Li

引用次数: 1

Robustness Analysis of Neural Network Designs for ReLU Family and Batch Normalization ReLU族与批归一化神经网络设计的鲁棒性分析

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00010

Hang Chen, Yi-Pei Su, Yean-Ru Chen, Chi-Chieh Chiu, Sao-Jie Chen

{"title":"Robustness Analysis of Neural Network Designs for ReLU Family and Batch Normalization","authors":"Hang Chen, Yi-Pei Su, Yean-Ru Chen, Chi-Chieh Chiu, Sao-Jie Chen","doi":"10.1109/TAAI57707.2022.00010","DOIUrl":"https://doi.org/10.1109/TAAI57707.2022.00010","url":null,"abstract":"The so-called neural network (NN) robustness problem, its original definition is that if an image is perturbed, the classification result of the image can still maintain the original correct category. It means that at the end of the entire NN operation, the lower bound of the target category value must be greater than the upper bound value of all other categories. There are multiple design techniques which can either bring the neural networks higher accuracy or maintain accuracy while reducing the computation effort at the same time. However, very few work focus on giving an efficient and reliable estimation of the trend of robustness changing directly with respect to the design factors. Lacking such information would either damage on designing a robust NN for critical systems or postpone the robustness analysis after the design completed and then results in paying more cost on NN design modifications. In this paper, we not only provide numerous experimental results but also propose three extended lemmas based on the related work which analyzes robustness with Lipschitz constant, to discuss how the two commonly used design factors, the ReLU based activation functions and batch normalization technique, bring the effectiveness to the robustness changing trend, under the condition of that the NN can still retain acceptable accuracy. We can conclude that we encourage to adopt ReLU than its other family activation functions (e.g. Leaky-ReLU and ELU) but discourage to use batch normalization compared with adopting it in the same NN design if we expect for higher robustness.","PeriodicalId":111620,"journal":{"name":"2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"84 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":"134279167","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

Removement of MisLeading Data in Transfer Learning Using BERT 利用BERT去除迁移学习中的误导数据

2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2022-12-01 DOI: 10.1109/TAAI57707.2022.00043

S. Iwamoto, Hiroyuki Shinnou

引用次数: 0