2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering最新文献

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“Do you mean I was wrong?” A Preliminary Approach on a Graph-based Framework for Suggesting Alternate Interpretations on Japanese Conversations “你是说我错了?”基于图表的日语会话交替解释建议框架初探

2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering Pub Date : 2021-10-13 DOI: 10.1109/QIR54354.2021.9716192

Takaaki Kawai, Naoki Fukuta

{"title":"“Do you mean I was wrong?” A Preliminary Approach on a Graph-based Framework for Suggesting Alternate Interpretations on Japanese Conversations","authors":"Takaaki Kawai, Naoki Fukuta","doi":"10.1109/QIR54354.2021.9716192","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716192","url":null,"abstract":"When a person cannot predict how his or her speech will be interpreted by others, communication problems will happen in person-to-person communications. In the case of communication at workplaces, junior staff may receive his or her supervisor’s words as verbal violence even if the supervisor spoke no offense words. This research aims to achieve the method that shows the candidates of other person’s interpretations in advance. If the interpretations were shown in advance, we can avoid speaking the words eliciting misunderstanding. As a concrete application, this research focuses on the conversation on text chat software. The text chat software shows the candidates of text interpretation which the other person will feel. An opinion mining research has reported that building a semantic tree is effective for text meaning recognition. The research of misinformation detection also has reported the effectiveness of graph data use. In this study, we construct a semantic tree to recognize Japanese text conversations. We also implement the function that transforms the text based on the grammar to show malicious meaning the receiver may perceive. The evaluation showed that the proposed method can transform texts into other texts that clearly express malicious meanings. A translation process was done in practical time, which was 0.32 seconds on average.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133496502","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

Performance Evaluation of Machine Learning Classifiers for Face Recognition 人脸识别中机器学习分类器的性能评价

2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering Pub Date : 2021-10-13 DOI: 10.1109/QIR54354.2021.9716171

D. Sudiana, M. Rizkinia, Fahri Alamsyah

{"title":"Performance Evaluation of Machine Learning Classifiers for Face Recognition","authors":"D. Sudiana, M. Rizkinia, Fahri Alamsyah","doi":"10.1109/QIR54354.2021.9716171","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716171","url":null,"abstract":"The digital world, especially image processing, has been evolving due to the needs of society and the importance of digital-based system security. One of the rapidly progressing technologies is the face recognition system using artificial intelligence. It recognizes a person’s face registered in the database for verification purposes. In this study, we evaluate the face recognition systems based on machine learning classifier algorithms and Principal Component Analysis (PCA) for feature extraction. Seven machine learning algorithms were considered, i.e., Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, Naïve Bayes, Multi-Layer Perceptron (MLP), and Convolutional Neural network (CNN). In the CNN scenario, PCA was not used since it has its feature extraction capability. The first six classifiers were set to the most optimal settings. At the same time, CNN used the LeNet-5 architecture trained with a dropout rate of 0.25, 60 epochs, batch size of 20, Adam optimizer, and cross-categorical entropy for the loss function. The input image size was 64×64×1 obtained from the Olivetti faces database. CNN, SVM, and LR achieved the three highest accuracies, i.e., 98.75%, 98.75%, and 97.50%, respectively.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122940","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

Mismatch Reduction using 2-D Inductance Map for Robust Vector Control of IPM Motor 基于二维电感映射的IPM电机鲁棒矢量控制失配减少

2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering Pub Date : 2021-10-13 DOI: 10.1109/QIR54354.2021.9716174

F. Husnayain, T. Noguchi, Kiyohiro Iwama, F. Yusivar

引用次数: 1

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