2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)最新文献_第4页

Text generation by probabilistic suffix tree language model 基于概率后缀树语言模型的文本生成

2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2021-12-21 DOI: 10.1109/iSAI-NLP54397.2021.9678167

S. Marukatat

引用次数: 0

Named Entity Recognition of Thai Documents using CRF with a Simple Data Masking Technique 使用CRF和简单数据掩蔽技术的泰文文档命名实体识别

2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2021-12-21 DOI: 10.1109/iSAI-NLP54397.2021.9678156

引用次数: 0

iSAI-NLP Committee iSAI-NLP委员会

2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2021-12-21 DOI: 10.1109/isai-nlp54397.2021.9678191

引用次数: 0

Feasibility of Prediction Model for Internal Tumor Target Volume from 4-D Computed Tomography of Lung cancer 肺癌四维计算机断层扫描内部肿瘤靶体积预测模型的可行性

2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2021-12-21 DOI: 10.1109/iSAI-NLP54397.2021.9678177

U. Puangragsa, Pitchayakorn Lomvisai, P. Phasukkit, Sarut Puangragsa, J. Setakornnukul, Nongluck Houngkamhang, Petchanon Thongserm, P. Dankulchai

{"title":"Feasibility of Prediction Model for Internal Tumor Target Volume from 4-D Computed Tomography of Lung cancer","authors":"U. Puangragsa, Pitchayakorn Lomvisai, P. Phasukkit, Sarut Puangragsa, J. Setakornnukul, Nongluck Houngkamhang, Petchanon Thongserm, P. Dankulchai","doi":"10.1109/iSAI-NLP54397.2021.9678177","DOIUrl":"https://doi.org/10.1109/iSAI-NLP54397.2021.9678177","url":null,"abstract":"4-Dimensional computed tomography (4DCT) is the most common technique to determine organ movement due to breathing motion. However, the ability of 4DCT to acquire CT images as a function of the respiratory phase increases higher radiation dose. To reduce the patient’s radiation dose, this study created lung motion prediction models used to estimate tumor target movement in ten respiratory phases by detecting only external organ movement during a complete respiration cycle without radiation with Kinect. The average overall amplitude difference between RPM and Kinect signals in the phantom experiment was 0.02 ± 0.1 mm. F1 score of 100% for all most all classifications except classification 2,3,6,7 and 8 of 85%,83%,90%, 84%,85% where irregular breathing pattern. Essentially, the proposed tumor movement scheme’s total accuracy (average of F1 scores) is 92.7 %. Deep learning model can predict tumor motion range and classification zone by used detection of the external respiratory signal","PeriodicalId":339826,"journal":{"name":"2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122694083","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

Replay Attack Detection in Automatic Speaker Verification Based on ResNeWt18 with Linear Frequency Cepstral Coefficients 基于线性倒频系数ResNeWt18的说话人自动验证重播攻击检测

2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2021-12-21 DOI: 10.1109/iSAI-NLP54397.2021.9678164

Anuwat Chaiwongyen, Kanokkarn Pinkeaw, W. Kongprawechnon, Jessada Karnjana, M. Unoki

{"title":"Replay Attack Detection in Automatic Speaker Verification Based on ResNeWt18 with Linear Frequency Cepstral Coefficients","authors":"Anuwat Chaiwongyen, Kanokkarn Pinkeaw, W. Kongprawechnon, Jessada Karnjana, M. Unoki","doi":"10.1109/iSAI-NLP54397.2021.9678164","DOIUrl":"https://doi.org/10.1109/iSAI-NLP54397.2021.9678164","url":null,"abstract":"This paper proposes, effective method for replay attack detection used in an automatic speaker verification system. The replay attack is of interest because it is the most straightforward and effective attack and is challenging to detect. It is a playback of the recording of the voice of a target speaker. From the literature, no speech features work well with all classifiers, and there is no investigation of using ResNet-based model, called ResNeWt, with linear frequency cepstral coefficient (LFCC). Therefore, a replay attack detection model based on 18-layer ResNeWt that takes LFCCs as the input, was constructed in this paper. The proposes method was tested on a dataset provided by ASVspoof 2019 competition. In terms of the equal error rate (EER), the proposed method is the best in all existing methods, with an EER of 0.29%. The comparison in terms of replay attack detection was also made in detail. The performance of the proposed method in terms of the balanced accuracy, precision, recall, and F1-score was considerably better than existing methods.","PeriodicalId":339826,"journal":{"name":"2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887132","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

DataCat: Attention-based Open Government Data (OGD) Category Recommendation Framework DataCat:基于关注的开放政府数据(OGD)类别推荐框架

2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) Pub Date : 2021-12-21 DOI: 10.1109/iSAI-NLP54397.2021.9678174

Natnaree Sornkongdang, Nuttapong Sanglerdsinlapachai, Chutiporn Anutariya

{"title":"DataCat: Attention-based Open Government Data (OGD) Category Recommendation Framework","authors":"Natnaree Sornkongdang, Nuttapong Sanglerdsinlapachai, Chutiporn Anutariya","doi":"10.1109/iSAI-NLP54397.2021.9678174","DOIUrl":"https://doi.org/10.1109/iSAI-NLP54397.2021.9678174","url":null,"abstract":"A data category recommendation framework for Thailand’s open government data portal (ThOGD) is proposed to assist data providers when publishing and registering a new dataset into the portal’s data catalog. However, existing approaches such as a multi-label classification problem, have not adopted the semantic features of data categories sufficiently. Deep learning model for Natural Language Processing has recently demonstrated to achieve high potential in learning the different degrees of semantic feature abstraction because all layers of multi-head attention blocks are provided with different fragments of metadata descriptions and corresponding tags. To obtain a robust recommendation result, this paper proposes DataCat: a Category Recommendation Framework using the attention-based framework through the ThOGD portal. Within this framework, the integrated multi-layers with particular semantic information are directly attached to the output layer of a network to enhance the effectiveness of information retrieval. The results point out that the attention-based framework has a weighted effect on loss of optimization. The performance when looking at the macro average of precision and F1-score improves by 0.664% and 0.557%, respectively. The micro average of those improves by 0.806%, and 0.698%, respectively.","PeriodicalId":339826,"journal":{"name":"2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)","volume":"720 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133181170","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