2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第4页

A Transportation Analytic Solution for Predicting Flight Cancellations 预测航班取消的运输解析解

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00050

Shawn J. Lanting, C. Leung, Khush Bhrugesh Patel, Sanskar Raval, Liza Yashin

引用次数: 0

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00014

Radhwane Gherbaoui, Nacéra Benamrane, Mohammed Ouali

引用次数: 0

Multiple Objective optimization of Stock Portfolio Using Evolutionary Computation under COVID-19 COVID-19下基于进化计算的股票投资组合多目标优化

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00020

Asuka Tai, Koki Yoshioka, H. Dozono

引用次数: 0

On the Improvements of Mouse Dynamics Based Continuous User Authentication 基于连续用户认证的鼠标动力学改进研究

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00010

Hayri Durmaz, Mehmet Keskinöz

{"title":"On the Improvements of Mouse Dynamics Based Continuous User Authentication","authors":"Hayri Durmaz, Mehmet Keskinöz","doi":"10.1109/IRI58017.2023.00010","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00010","url":null,"abstract":"Traditional authentication methods are vulnerable when users leave their devices unattended or if their credentials are compromised. In contrast, continuous authentication offers a perpetual strategy for user validation, ensuring that only authorized users access critical information throughout their entire usage. The problem of continuous authentication boils down to a binary classification task: determining whether the usage is legal or illegal. Deep learning presents a promising solution for this problem, although the use of convolutional neural networks (CNNs) in continuous authentication still has room for improvement. In this study, we employ residual learning to train and test a user authentication model. To further enhance the accuracy of the results, we implement a realistic augmentation method and employ a superior image mapping technique compared to existing literature. As a result, we achieve significantly more accurate results than those reported in the referenced studies. On average, our tests yield a False Accept Rate of 0.45 and a False Reject Rate of 0.34, which are 6.5 times better than the referenced studies. These findings demonstrate a substantial improvement in the usability and effectiveness of real-world cybersecurity applications.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116695644","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

Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique 通过一种新的集成特征选择技术增强信用卡欺诈检测

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00028

Huanjing Wang, Qianxin Liang, John T. Hancock, T. Khoshgoftaar

{"title":"Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique","authors":"Huanjing Wang, Qianxin Liang, John T. Hancock, T. Khoshgoftaar","doi":"10.1109/IRI58017.2023.00028","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00028","url":null,"abstract":"Identifying fraudulent activities in credit card transactions is an inherent component of financial computing. The focus of our research is on the Credit Card Fraud Detection Dataset, which is widely used due to its authentic transaction data. In numerous machine learning applications, feature selection has become a crucial step. To improve the chance of discovering the globally optimal feature set, we employ ensembles of feature ranking methods. These ensemble methods merge multiple feature ranking lists through a median approach. We conduct a comprehensive empirical study that examines two different ensembles of feature ranking techniques, including an ensemble of twelve threshold-based feature selection (TBFS) techniques and an ensemble of five supervised feature selection (SFS) techniques. Additionally, we present results where all features are used. We construct classification models using two Decision Tree-based classifiers, CatBoost and XGBoost, and evaluate them using two different performance metrics, the Area Under the Receiver Operating Characteristic Curve (AUC) and the Area under the Precision-Recall Curve (AUPRC). Since AUPRC provides a more accurate representation of the number of false positives, especially for highly imbalanced datasets, evaluating models for AUPRC is a wise choice. The experimental results demonstrate that the ensemble of SFS and all features performs similarly or better than the ensemble of TBFS. Moreover, we find that XGBoost outperforms CatBoost in terms of AUPRC.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129703656","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

Ensemble Learning for UAV Detection: Developing a Multi-Class Multimodal Dataset 无人机检测的集成学习:多类多模态数据集的开发

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00025

J. McCoy, A. Rawal, D. Rawat

引用次数: 0

Resource-Centric Goal Model Slicing for Detecting Feature Interactions 以资源为中心的目标模型切片检测特征交互

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00018

Zedong Peng, Mahima Dahiya, Tessneem Khalil, Nan Niu, Tanmay Bhowmik, Yilong Yang

引用次数: 0

Using BERT to Understand TikTok Users’ ADHD Discussion 使用BERT理解TikTok用户对ADHD的讨论

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00043

Kayla Pineda, A. M. Perrotti, Faryaneh Poursardar, Danielle Graber, S. Jayarathna

引用次数: 0

Ballot Tabulation Using Deep Learning 使用深度学习的选票制表

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00026

Fei Zhao, Chengcui Zhang, Nitesh Saxena, D. Wallach, AKM SHAHARIAR AZAD RABBY

{"title":"Ballot Tabulation Using Deep Learning","authors":"Fei Zhao, Chengcui Zhang, Nitesh Saxena, D. Wallach, AKM SHAHARIAR AZAD RABBY","doi":"10.1109/IRI58017.2023.00026","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00026","url":null,"abstract":"Currently deployed election systems that scan and process hand-marked ballots are not sophisticated enough to handle marks insufficiently filled in (e.g., partially filled-in), improper marks (e.g., using check marks or crosses instead of filling in bubbles), or marks outside of bubbles, other than setting a threshold to detect whether the pixels inside bubbles are dark and dense enough to be counted as a vote. The current works along this line are still largely limited by their degree of automation and require substantial manpower for annotation and adjudication. In this study, we propose a highly automated deep learning (DL) mark segmentation model-based ballot tabulation assistant able to accurately identify legitimate ballot marks. For comparison purposes, a highly customized traditional computer vision (T-CV) mark segmentation-based method has also been developed to compare with the DL-based tabulator, with a detailed discussion included. Our experiments conducted on two real election datasets achieved the highest accuracy of 99.984% on ballot tabulation. In order to further enhance our DL model’s capability of detecting the marks that are underrepresented in training datasets, e.g., insufficiently or improperly filled marks, we propose a Siamese network architecture that enables our DL model to exploit the contrasting features between a hand-marked ballot image and its corresponding blank template image to detect marks. Without the need for extra data collection, by incorporating this novel network architecture, our DL model-based tabulation method not only achieved a higher accuracy score but also substantially reduced the overall false negative rate.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126585149","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

“What You See Is What You Test”: Recommending Features from GUIs for Requirements-Based Testing “所见即所测”:为基于需求的测试推荐gui特性

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00057

Zedong Peng, J. Savolainen, Jianzhang Zhang, Nan Niu

{"title":"“What You See Is What You Test”: Recommending Features from GUIs for Requirements-Based Testing","authors":"Zedong Peng, J. Savolainen, Jianzhang Zhang, Nan Niu","doi":"10.1109/IRI58017.2023.00057","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00057","url":null,"abstract":"Requirements-based testing (RBT) advocates the design of test cases in order to adequately exercise the behavior of a software system without regard to the internal details of the implementation. To address the challenge that requirements descriptions may be inaccurate in practice, we align requirements engineering and software testing in a novel way by not counting on a complete and up-to-date requirements documentation. Rather, we maintain the black-box nature of RBT to recommend features as the units of testing from software’s graphical user interfaces (GUIs). In particular, we exploit optical character recognition (OCR) to identify the textual information from GUIs, and further build the GUI-feature correspondences based on software’s user-centric documentation which may exhibit partial correctness. Such correspondences from multiple software systems in the same domain serve as a foundation for our recommendation engine, which suggests the to-be-tested features related to a given GUI. We demonstrate our recommender’s feasibility with a study of five products in the web conferencing domain, and the results show the more complete set of features against which a GUI needs to be tested.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131394677","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