IEEE Open Journal of the Computer Society最新文献

Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection

IEEE Open Journal of the Computer Society Pub Date : 2024-12-20 DOI: 10.1109/OJCS.2024.3521217

Xu Wang;Qisheng Xu;Kele Xu;Ting Yu;Bo Ding;Dawei Feng;Yong Dou

{"title":"Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection","authors":"Xu Wang;Qisheng Xu;Kele Xu;Ting Yu;Bo Ding;Dawei Feng;Yong Dou","doi":"10.1109/OJCS.2024.3521217","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3521217","url":null,"abstract":"In the realm of Key Performance Indicator (KPI) anomaly detection, deep learning has emerged as a pivotal technology. Yet, the development of effective deep learning models is hindered by several challenges: scarce and complex labeled data, noise interference from data handling, the necessity to capture temporal dependencies in time series KPI data, and the complexity of multivariate data analysis. Despite recent progress in large models that show potential for handling complex, multidimensional tasks, the lack of extensive, high-quality datasets presents a significant barrier for directly training these models in KPI anomaly detection. This scarcity limits the models' ability to learn and generalize effectively within this specific domain. To overcome this, we propose an innovative approach to adapt fully pretrained large models from other domains to KPI anomaly detection, thereby mitigating data constraints and enhancing detection precision. Our approach involves adapting large models to anomaly detection tasks using patch operations and fine-tuning techniques, which significantly enhances the model's temporal dependency capture capabilities. Furthermore, to address the multivariate challenge, we introduce a novel feature extraction method based on channel independence to optimize information processing across multidimensional features. Additionally, we leverage frequency domain information to design a feature enhancement method, further boosting the model's detection accuracy. By integrating these innovative techniques, we have developed a large-scale KPI anomaly detection model named ViTSD. Empirical evidence from experiments on five benchmark datasets and two additional datasets demonstrates ViTSD's superior performance, outperforming existing models across various evaluation metrics.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"176-187"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10811835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Open Journal of the Computer Society Publication Information

IEEE Open Journal of the Computer Society Pub Date : 2024-12-18 DOI: 10.1109/OJCS.2023.3338872

引用次数: 0

IEEE Open Journal of the Computer Society Information for Authors

IEEE Open Journal of the Computer Society Pub Date : 2024-12-18 DOI: 10.1109/OJCS.2023.3338889

引用次数: 0

Reducing Data Volume in News Topic Classification: Deep Learning Framework and Dataset

IEEE Open Journal of the Computer Society Pub Date : 2024-12-18 DOI: 10.1109/OJCS.2024.3519747

Luigi Serreli;Claudio Marche;Michele Nitti

引用次数: 0

Improving Accuracy and Calibration of Deep Image Classifiers With Agreement-Driven Dynamic Ensemble

IEEE Open Journal of the Computer Society Pub Date : 2024-12-18 DOI: 10.1109/OJCS.2024.3519984

Pedro Conde;Rui L. Lopes;Cristiano Premebida

{"title":"Improving Accuracy and Calibration of Deep Image Classifiers With Agreement-Driven Dynamic Ensemble","authors":"Pedro Conde;Rui L. Lopes;Cristiano Premebida","doi":"10.1109/OJCS.2024.3519984","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3519984","url":null,"abstract":"One of the biggest challenges when considering the applicability of Deep Learning systems to real-world problems is the possibility of failure in \u0000<italic>critical\u0000 situations. Possible strategies to tackle this problem are two-fold: (i) models need to be highly accurate, consequently reducing this risk of failure; (ii) facing the impossibility of completely eliminating the risk of error, the models should be able to inform the level of uncertainty at the prediction level. As such, state-of-the-art DL models should be \u0000<italic>accurate\u0000 and also \u0000<italic>calibrated\u0000, meaning that each prediction has to codify its confidence/uncertainty in a way that approximates the true likelihood of correctness. Nonetheless, relevant literature shows that improvements in \u0000<italic>accuracy\u0000 and \u0000<italic>calibration\u0000 are not usually related. This motivates the development of Agreement-Driven Dynamic Ensemble, a deep ensemble method that - by dynamically combining the advantages of two different ensemble strategies - is capable of achieving the highest possible accuracy values while obtaining also substantial improvements in calibration. The merits of the proposed algorithm are shown through a series of representative experiments, leveraging two different neural network architectures and three different datasets against multiple state-of-the-art baselines.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"164-175"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10806808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revolutionizing User Authentication Exploiting Explainable AI and CTGAN-Based Keystroke Dynamics

IEEE Open Journal of the Computer Society Pub Date : 2024-12-09 DOI: 10.1109/OJCS.2024.3513895

Hussien Abdel Raouf;Mostafa M. Fouda;Mohamed I. Ibrahem

{"title":"Revolutionizing User Authentication Exploiting Explainable AI and CTGAN-Based Keystroke Dynamics","authors":"Hussien Abdel Raouf;Mostafa M. Fouda;Mohamed I. Ibrahem","doi":"10.1109/OJCS.2024.3513895","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3513895","url":null,"abstract":"Due to the reliability and efficiency of keystroke dynamics, enterprises have adopted it widely in multi-factor authentication systems, effectively strengthening user authentication and thereby boosting the security of online and offline services. The existing works that detect imposter users suffer from performance and robustness degradation. Therefore, this article introduces a novel methodology to enhance user authentication and identify imposter users who attempt to have unauthorized access. We first use quantile transformation (QT) to mitigate outliers in the user's typing behavior that affects the authentication process and then employ conditional tabular generative adversarial networks (CTGAN) for data augmentation to learn the users' typing patterns better. Next, five accurate transfer learning models (VGG19, EfficientNetB0, Resnet50, MobileNetV2, and DenseNet121) are utilized for extracting effective features within the typing patterns, so our methodology can detect imposter users accurately and hence make precise decisions to enhance the user authentication process. Finally, we ensure transparency and trust in our user authentication methodology by incorporating explainable artificial intelligence (XAI), utilizing local interpretable model-agnostic explanations (LIME). Extensive experiments using a publicly available keystroke dynamics benchmark dataset from Carnegie Mellon University (CMU) showcase superior security performance and robustness using the proposed methodology compared to the state-of-the-art approaches.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"97-108"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum Long Short-Term Memory-Assisted Optimization for Efficient Vehicle Platooning in Connected and Autonomous Systems

IEEE Open Journal of the Computer Society Pub Date : 2024-12-09 DOI: 10.1109/OJCS.2024.3513237

Mahzabeen Emu;Taufiq Rahman;Salimur Choudhury;Kai Salomaa

{"title":"Quantum Long Short-Term Memory-Assisted Optimization for Efficient Vehicle Platooning in Connected and Autonomous Systems","authors":"Mahzabeen Emu;Taufiq Rahman;Salimur Choudhury;Kai Salomaa","doi":"10.1109/OJCS.2024.3513237","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3513237","url":null,"abstract":"Vehicle platooning, especially when dedicated to carrying goods, represents a forward-looking approach to optimizing logistics and freight transportation using autonomous vehicles. In this study, we propose to employ Quantum Long Short Term Memory (QLSTM) models to predict the vehicle dynamics of a leading vehicle of the platoon. This predictive capability allows the following vehicles to adjust their behaviours dynamically. By doing so, we aim to optimize control strategies and maintain string stability within vehicle platoons. This approach leverages the unique computational advantages of quantum computing, particularly in processing complex temporal data, potentially leading to more accurate and efficient dynamic systems in vehicular platoon infrastructure. The simulation results indicate that the QLSTM model is highly efficient by learning more information in fewer epochs compared to traditional Long Short Term Memory (LSTM) models. This efficiency contributes to minimizing control errors, enhancing the precision and reliability of vehicle dynamics in the context of autonomous vehicle platooning. This research not only enhances the predictability of autonomous vehicle platoons but also opens pathways for research into how quantum computing can be integrated into real-time dynamic systems analysis and control.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"119-128"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10783047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation

IEEE Open Journal of the Computer Society Pub Date : 2024-12-02 DOI: 10.1109/OJCS.2024.3510555

Xinguang Zhang;Shiyang Chen;Zhouhang Shao;Yongjie Niu;Li Fan

{"title":"Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation","authors":"Xinguang Zhang;Shiyang Chen;Zhouhang Shao;Yongjie Niu;Li Fan","doi":"10.1109/OJCS.2024.3510555","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3510555","url":null,"abstract":"Lithographic hotspot detection is crucial for ensuring manufacturability and yield in advanced integrated circuit (IC) designs. While machine learning approaches have shown promise, they often struggle with detecting truly-never-seen-before (TNSB) hotspots and reducing false alarms on hard-to-classify (HTC) patterns. This article presents a novel multi-task deep learning framework for lithographic hotspot detection that addresses these challenges. Our key contributions include: (1) A synthetic pattern generation method based on early design space exploration (EDSE) to augment training data and improve TNSB hotspot detection; (2) A multi-task convolutional neural network architecture that jointly performs hotspot classification and localization; and (3) An adaptive loss function that balances hotspot detection accuracy and false alarm reduction. Experimental results on the ICCAD-2019 benchmark dataset demonstrate that our approach achieves 98.5% accuracy in hotspot detection with only 1.2% false alarm rate, significantly outperforming state-of-the-art methods. Furthermore, we show a 22% improvement in TNSB hotspot detection and a 5X reduction in false alarms on HTC patterns compared to previous techniques. The proposed framework provides a robust solution for lithographic hotspot detection in early stages of IC design, enabling more efficient design-for-manufacturability optimization.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"140-151"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CD-LLMCARS: Cross Domain Fine-Tuned Large Language Model for Context-Aware Recommender Systems

IEEE Open Journal of the Computer Society Pub Date : 2024-11-28 DOI: 10.1109/OJCS.2024.3509221

Adeel Ashraf Cheema;Muhammad Shahzad Sarfraz;Usman Habib;Qamar Uz Zaman;Ekkarat Boonchieng

{"title":"CD-LLMCARS: Cross Domain Fine-Tuned Large Language Model for Context-Aware Recommender Systems","authors":"Adeel Ashraf Cheema;Muhammad Shahzad Sarfraz;Usman Habib;Qamar Uz Zaman;Ekkarat Boonchieng","doi":"10.1109/OJCS.2024.3509221","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3509221","url":null,"abstract":"Recommender systems are essential for providing personalized content across various platforms. However, traditional systems often struggle with limited information, known as the cold start problem, and with accurately interpreting a user's comprehensive preferences, referred to as context. The proposed study, CD-LLMCARS (Cross-Domain fine-tuned Large Language Model for Context-Aware Recommender Systems), presents a novel approach to addressing these issues. CD-LLMCARS leverages the substantial capabilities of the Large Language Model (LLM) Llama 2. Fine-tuning Llama 2 with information from multiple domains can enhance the generation of contextually relevant recommendations that align with a user's preferences in areas such as movies, music, books, and CDs. Techniques such as Low-Rank Adaptation (LoRA) and Half Precision Training (FP16) are both effective and resource-efficient, allowing CD-LLMCARS to perform optimally in cold start scenarios. Extensive testing of CD-LLMCARS indicates outstanding accuracy, particularly in challenging scenarios characterized by limited user history data relevant to the cold start problem. CD-LLMCARS offers precise and pertinent recommendations to users, effectively mitigating the limitations of traditional recommender systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"49-59"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10771726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ECMO: An Efficient and Confidential Outsourcing Protocol for Medical Data

IEEE Open Journal of the Computer Society Pub Date : 2024-11-25 DOI: 10.1109/OJCS.2024.3506114

Xiangyi Meng;Yuefeng Du;Cong Wang

引用次数: 0