IEEE Access最新文献

{"title":"Corrections to “New Power Interface Based on Multi-Dimensional Golden Section Search Algorithm for Power-Hardware-in-the-Loop Applications”","authors":"Juan Constantine;Kuo Lung Lian;You Fang Fan;Chu Ying Xiao;Zhao-Peng He","doi":"10.1109/ACCESS.2025.3553774","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553774","url":null,"abstract":"Presents corrections to the paper, Corrections to “New Power Interface Based on Multi-Dimensional Golden Section Search Algorithm for Power-Hardware-in-the-Loop Applications”","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53546-53546"},"PeriodicalIF":3.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945926","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrections to “Smarter World Living Lab as an Integrated Approach: Learning How to Improve Quality of Life”","authors":"Suhono Harso Supangkat;Hendra Sandhi Firmansyah;Rezky Kinanda;Irma Rizkia","doi":"10.1109/ACCESS.2025.3551100","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3551100","url":null,"abstract":"Presents corrections to the paper, (Corrections to “Smarter World Living Lab as an Integrated Approach: Learning How to Improve Quality of Life”).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52725-52725"},"PeriodicalIF":3.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”","authors":"Oriane Pierrès;Markus Christen;Felix M. Schmitt-Koopmann;Alireza Darvishy","doi":"10.1109/ACCESS.2025.3534338","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534338","url":null,"abstract":"Presents corrections to the paper, (Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"50556-50558"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-Enriched Recommendations: Bridging the Gap in Alloy Material Selection With Large Language Models","authors":"Tongwei Wu;Shiyu Du;Yiming Zhang;Honggang Li","doi":"10.1109/ACCESS.2025.3554125","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554125","url":null,"abstract":"Navigating materials performance databases efficiently is a persistent challenge in materials science and engineering, particularly in the selection of alloy materials. While recommendation systems address information overload, traditional approaches relying on historical user data face limitations such as data sparsity and cold-start issues. This study presents a novel recommendation model that integrates domain-specific knowledge graphs with large language models (LLMs) to enhance recommendation accuracy in alloy material selection. A knowledge graph for alloys is developed, encapsulating technical material data and relationships to improve retrieval and recommendation outcomes. LLMs are employed for label clustering and natural language-based instruction-following to craft user profiles and enhance data representation. Two graph enhancement strategies, integrated with attention mechanisms, effectively capture user preferences. Experimental results on a ferroalloy dataset demonstrate the model’s superior performance compared to baseline methods, significantly addressing data sparsity while offering personalized, accurate recommendations. This research bridges the gap between knowledge graphs and LLMs in recommendation systems, contributing a flexible, intelligent solution to streamline material selection processes.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53124-53139"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modality-Guided Refinement Learning for Multimodal Emotion Recognition","authors":"Sunyoung Cho","doi":"10.1109/ACCESS.2025.3554708","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554708","url":null,"abstract":"Multimodal emotion recognition (MER) aims to understand human emotions by leveraging multiple modalities. Previous MER methods have focused on learning enhanced multimodal representations through various interaction and fusion mechanisms, utilizing different types of features from individual modalities. However, these methods often fail to account for the varying contributions of each modality to emotion, leading to suboptimal representations. To address this, we propose a modality-guided refinement learning framework that enhances multimodal representations by incorporating modality information. Specifically, we decouple multimodal representations into modality-invariant and modality-specific components by introducing shared and private encoders, which are learned by leveraging the distributional properties of the representations in their latent subspaces, guided by a modality classifier. Our method introduces margin constraints to further refine these decoupled representations, adaptively considering the contribution of each modality during the decoupling and multimodal learning processes. This optimization reduces information loss and corruption, resulting in more robust and discriminative multimodal representation learning. We evaluate our proposed method through experiments on two benchmark MER datasets: the CMU Multimodal Corpus of Sentiment Intensity (CMU-MOSI) and the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI). Comprehensive experiments demonstrate that our method outperforms several baseline models in multimodal emotion recognition.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53558-53567"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EF-StrongSORT: An Enhanced Feature StrongSORT Model for Multi-Object Tracking","authors":"Miar Mamdouh Khalil;Sherine Nagy Saleh;Noha S. Tawfik;Mazen Nabil Elagamy","doi":"10.1109/ACCESS.2025.3554706","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554706","url":null,"abstract":"Multi-object tracking (MOT) faces persistent challenges owing to the complexities introduced by occlusions, dynamic appearance variations, and the rapid motion of objects within a scene. These issues are further complicated by the need for robust identity management and consistent object re-identification across frames. To improve the performance of multi-object tracking, this study introduces EF-StrongSORT, which extends the StrongSORT model, incorporating advanced object detection, efficient feature extraction, and identity management techniques. The EF-StrongSORT demonstrates an improvement over conventional tracking methods by achieving higher accuracy and robustness in challenging scenarios. The experimental results show that the EF-StrongSORT enhances the performance of multi-object tracking techniques and is better than existing approaches on the MOT17, MOT20 and DanceTrack benchmarks. On the MOT17 dataset, EF-StrongSORT outperformed StrongSORT with improvements of +5.5 in HOTA, +7.7 in MOTA, +4 in IDF1, and a decrease of 828 in IDS. On the MOT20 dataset, EF-StrongSORT showed improvements of +2 in HOTA, +6.3 in MOTA, +1.1 in IDF1, and a reduction of 16 in IDS compared to StrongSORT. On the DanceTrack dataset, EF-StrongSORT achieved improvements of +4.3 in HOTA, +2.2 in MOTA, and +1.4 in AssA compared to the latest state-of-the-art model, LQTTrack. These results emphasize the contributions of the proposed model to the improvement of quality and efficiency of multi-object tracking systems targeted for specific problems, including object appearance changes and occlusions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53608-53620"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Security and Privacy-Preserving Consortium Blockchain-Based Accessing Control in Mobile Crowdsensing","authors":"Abdulrahman Alamer","doi":"10.1109/ACCESS.2025.3554600","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554600","url":null,"abstract":"In current mobile crowdsensing (MCS) systems, there is limited attention given to the security threats associated with access to the profile records (PR) of participating mobile devices. For example, most existing studies consider stakeholders of MCS applications as fully trusted entities, which granting them unlimited authorization to access the Participated Mobile Devices’ PR for the purpose of collecting sensing data. From this point, hackers may exploit this trusted point to gain unlimited authorization access to a particular participated mobile devices. They can achieve this by launching attacks, such as creating counterfeit applications as a trusted MCS applications and then posing as legitimate stakeholders to request access to the targeted devices. Thus, the hacker will gain full authorized access to the participating mobile devices’ PR, in which will result in the disclosure of security and privacy-related information of their participated devices. Therefore, the blockchain paradigm is recommended as the optimal solution for ensuring data access, owing to its advantages of immutability. However, because the blockchain is a decentralized database, a malicious MCS-server will be able to disclose the privacy of participating mobile devices by linking multiple blocks generated for the same device while it performs different tasks. Based on the aforementioned issue, this work designs a consortium blockchain-based access control system to protect the privacy rights of participating mobile devices in MCS. Furthermore, an efficient searchable keyword encryption methodology is proposed to link between the consortium blockchain and the privacy blockchain, thereby enhancing system security and access control. Finally, a security analysis and performance evaluation are conducted to demonstrate the efficiency of the proposed protocol.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53815-53834"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XRGuard: A Model-Agnostic Approach to Ransomware Detection Using Dynamic Analysis and Explainable AI","authors":"M. Adnan Alvi;Zunera Jalil","doi":"10.1109/ACCESS.2025.3553562","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553562","url":null,"abstract":"Ransomware remains a persistent and evolving cybersecurity threat, demanding advanced and adaptable detection strategies. Traditional methods often fall short as signature-based systems are easily circumvented by emerging ransomware variants, while techniques like obfuscation and polymorphism add complexity to the detection process. Although machine learning and deep learning techniques present viable solutions, the opacity of complex black-box models can hinder their application in critical security environments. This paper introduces XRGuard, a novel ransomware detection framework that utilizes machine learning techniques to analyze Event Tracing for Windows (ETW) logs, identifying critical file I/O patterns indicative of ransomware attacks. By incorporating XAI techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), XRGuard bridges the trust gap associated with complex machine learning models by providing transparent and interpretable explanations for its decisions. Experimental results demonstrate that XRGuard achieves a 99.69% accuracy rate with an exceptionally low false positive rate of 0.5%. By enhancing detection accuracy and offering clear explanations of its operations, XRGuard not only improves security but also fosters trust and a deeper understanding of ransomware behaviors.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53159-53170"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational and Planning Perspectives on Battery Swapping and Wireless Charging Technologies: A Multidisciplinary Review","authors":"Sarah M. Kandil;Akmal Abdelfatah;Maher A. Azzouz","doi":"10.1109/ACCESS.2025.3554336","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554336","url":null,"abstract":"The electrification of the transportation system is considered one of the most viable solutions to address the pressing need to shift towards sustainable development. However, one of the major challenges to the rapid adoption of Electric Vehicles (EVs) is the lack of the right charging infrastructure where and when it is needed. Motivated by the necessity for a multidisciplinary approach, this study addresses the complex operational and planning challenges involved in integrating transportation and electrical networks for effective EV charging. Thus, this research aims to survey the literature to identify the adopted technologies, and the application and allocation of the right mix of the technologies to better serve the seamless adoption of EVs. That problem is multifaced where transportation network requirements and the electrical grid are important to support the charging loads at the needed time. The literature survey adopts the PRISMA methodology, contributing to the existing literature by highlighting the following shortcomings: 1) the problem of allocating the charging technology is either purely viewed from a transportation or an electrical perspective, 2) there is a gap in adopting both networks’ requirements comprehensively, 3) the literature predominately focuses on a single technology either battery swapping or wireless charging, 4) introducing new technologies highlights the impact of each on both networks in terms of return of investment, traffic flows, and power grids operational conditions, 5) research direction is focused more towards operation and routing while service allocation is relatively new and has not yet been extensively explored, and 6) transportation network research focuses on a static representation of the transportation network and EV demand. This study’s main contribution, besides identifying critical gaps in the existing literature and possible future research directions, is the proposal of a novel framework that integrates multiple charging technologies. This framework is designed to optimize infrastructure deployment, enhancing the efficiency and economic viability of EV charging systems across both transportation and electrical networks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52775-52806"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Channel Dynamic Gated Spatio-Temporal Graph for Traffic Flow Forecasting","authors":"Chao Wang;Jun-Feng Hao;He Huang;Wang Zou;Xia Sun;Ting Peng","doi":"10.1109/ACCESS.2025.3553535","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553535","url":null,"abstract":"Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficiency of transportation networks. However, current research neglects the relationships between the local and global traffic flow data. Additionally, the predefined static graph structure fails to adequately capture the dynamic spatial features of traffic flow. To address the these challenges, this paper proposes a Dual-Channel Dynamic Gated Spatio-Temporal graph network (DC-DGST) for traffic flow prediction. We consider hourly slices as the local feature and daily slices to be the global feature of traffic flow. The DC-DGST framework employs a dual-channel structure to capture spatiotemporal dependencies between global and local features. It transforms the predefined static graph into a dynamic graph, enabling the establishment of connections between input data and historical information. Furthermore, we design gated spatio-temporal blocks based on residual structures within the spatio-temporal module. Specifically, we utilize Graph Gated Neural Networks (GGNNs) to learn and integrate both static and dynamic graphs, while Transformer encoders are used to capture long-range dependencies in the temporal sequence. We conducted a series of experiments on four publicly available benchmark datasets: PEMS03, PEMS04, PEMS07, and PEMS08. The results demonstrate that our model significantly outperforms baseline models. Moreover, the dual-channel structure effectively captures the correlation between local and global traffic flow features, while the dynamic graph enhances the model’s overall performance.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52995-53006"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}