2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)最新文献_第2页

Another Software for Designing Electric Transformers 另一个设计变压器的软件

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197812

Jangwu Jo, Junhyun Park, Ayoung Jang, Youngsu Cho, Byeongdo Kang

引用次数: 0

SERA 2023 Cover Page 《时代》2023封面页

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/sera57763.2023.10197714

引用次数: 0

Construction and Application of Knowledge Graph for Food Therapy 食疗知识图谱的构建与应用

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197781

Qianzhong Chen, Xianghao Meng, Feng Lin, Dongsheng Shi, Y. Lin, Dongmei Li, Hao Gu, Xiaoping Zhang

引用次数: 0

Determining the Most Significant Metadata Features to Indicate Defective Software Commits 确定最重要的元数据特征以指示有缺陷的软件提交

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197721

Rupam Dey, Anahita Khojandi, K. Perumalla

引用次数: 0

Scientific Organization of Blood Donation Camp Through Lexicographic Optimization and Taxicab Path Computation 基于词典优化和出租车路径计算的献血营科学组织

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197789

P. Ghosh, Takaaki Goto, Leena Jana Ghosh, S. Sen

引用次数: 0

Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors 我们意识到了吗?智能手机传感器隐私与安全意识的实证研究

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197713

Arifa I. Champa, Md. Fazle Rabbi, Farjana Z. Eishita, M. Zibran

引用次数: 1

Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels 基于部分标注标签的非平衡大规模多标签分类研究

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197667

Xin Zhang, Yuqi Song, Fei Zuo, Xiaofeng Wang

{"title":"Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels","authors":"Xin Zhang, Yuqi Song, Fei Zuo, Xiaofeng Wang","doi":"10.1109/SERA57763.2023.10197667","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197667","url":null,"abstract":"Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In theory, this is a supervised learning method that requires a large amount of labeling. However, annotating data is time-consuming and may be infeasible for huge labeling spaces. In addition, label imbalance can limit the performance of multi-label classifiers, especially when some labels are missing. Therefore, it is meaningful to study how to train neural networks using partial labels. In this work, we address the issue of label imbalance and investigate how to train classifiers using partial labels in large labeling spaces. First, we introduce the pseudo-labeling technique, which allows commonly adopted networks to be applied in partially labeled settings without the need for additional complex structures. Then, we propose a novel loss function that leverages statistical information from existing datasets to effectively alleviate the label imbalance problem. In addition, we design a dynamic training scheme to reduce the dimension of the labeling space and further mitigate the imbalance. Finally, we conduct extensive experiments on some publicly available multi-label datasets such as COCO, NUS-WIDE, CUB, and Open Images to demonstrate the effectiveness of the proposed approach. The results show that our approach outperforms several state-of-the-art methods, and surprisingly, in some partial labeling settings, our approach even exceeds the methods trained with full labels.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124987320","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 of GAN-Based Denoising and Restoration Techniques for Adversarial Face Images 基于gan的人脸图像去噪与复原技术的性能研究

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197680

Turhan Kimbrough, Pu Tian, Weixian Liao, Wei Yu

{"title":"Performance of GAN-Based Denoising and Restoration Techniques for Adversarial Face Images","authors":"Turhan Kimbrough, Pu Tian, Weixian Liao, Wei Yu","doi":"10.1109/SERA57763.2023.10197680","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197680","url":null,"abstract":"Facial recognition (FR) systems are employed to identify and authenticate individuals. There has been a rise in privacy concerns regarding mass surveillance and unauthorized usages. As a result, one viable approach is adding adversarial noise to distort user profile images so that FR technology can be bypassed. Nonetheless, such approaches could be used by adversaries to avoid detection in surveillance footage and therefore evade identification. To combat this threat, a line of research efforts focuses on generative adversarial network (GAN)-based Denoising and Restoration to remove adversarial noise. In this paper, GAN-based methods are investigated experimentally for assessing their effectiveness. Particularly, three GAN-based approaches, i.e., Blind Face Restoration, Blur and Restore, and Image-to-image Translation, are extensively examined with several representative classification approaches. Our evaluation results show that GAN denoising schemes could improve image visual quality, but are ineffective to remove perturbations for privacy protection attached by Fawkes or Lowkey. We further discuss some future research directions on image transformation-based approaches, which can potentially improve the effectiveness.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131133833","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

Obsolescence in Operating Systems and Microprocessors 操作系统和微处理器的过时

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197809

Dheeraj N. Naraharisetti, R. Karne, J. Weymouth, A. Wijesinha

引用次数: 0

Classification of Multilingual Medical Documents using Deep Learning 使用深度学习的多语种医学文献分类

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2023-05-23 DOI: 10.1109/SERA57763.2023.10197749

W. Karaa, Dridi Kawther

{"title":"Classification of Multilingual Medical Documents using Deep Learning","authors":"W. Karaa, Dridi Kawther","doi":"10.1109/SERA57763.2023.10197749","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197749","url":null,"abstract":"Due to a large number of documents available on the web, operations such as finding a set of information contained in a document has become a difficult task, especially with multilingual documents. Hence the necessity to have performance tools for finding, organizing and classifying information. A variety of classification methods are proposed to resolve this kind of problem but these techniques suffer from limits such as the loss of information, and the loss of relations between words that affects the effectiveness and the performance of the classification process. So, this paper attempts to support the idea of multilingual document classification, especially in the biomedical domain using a new approach, based on deep learning. The key idea is to generate a new conceptual representation of textual multilingual medical documents to facilitate the classification task. In this context, a deep learning technique will be exploited for a good representation. To show the feasibility of our approach, we implemented a system related to a domain that attracts more and more attention from the data mining community: the biomedical domain. An experimental study is performed, using documents extracted from the biomedical benchmark corpus, called Oshumed, which contains documents distributed by different categories.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114743773","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