IEEE Access最新文献

{"title":"Adaptive Federated Learning With Reinforcement Learning-Based Client Selection for Heterogeneous Environments","authors":"Shamim Ahmed;M. Shamim Kaiser;Sudipto Chaki;A. B. M. Shawkat Ali","doi":"10.1109/ACCESS.2025.3591699","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591699","url":null,"abstract":"This study introduces an Adaptive Federated Learning (AFL) framework designed to address the challenges of data heterogeneity, resource imbalance, and communication constraints in decentralized learning environments. The framework integrates reinforcement learning (RL) based client selection using both Tabular Q-Learning and Deep Q-Network (DQN) strategies to dynamically identify clients that most positively impact global model performance. A multi-objective reward function, combining model accuracy and execution time, guides the RL agent toward performance- and efficiency-aware client selection. For local model training, Random Forest (RF) classifiers are employed to ensure robustness to noise, class imbalance, and limited computational resources, particularly in privacy-sensitive healthcare settings. The AFL framework is evaluated on two real-world healthcare datasets BRFSS2015 and Diabetes Prediction, and extended to benchmark FL datasets (CIFAR-10 and FEMNIST) to assess scalability and generalization. Experimental results demonstrate that the DQN-based AFL achieves superior global accuracy (up to 91.3%) compared to Tabular Q-Learning and baseline methods such as FedAvg, while also reducing execution time by up to 15%. Client-level accuracy remains stable across rounds, with reward progression confirming effective RL policy convergence. These findings underscore the AFL framework’s capability to adaptively balance performance and efficiency, offering a practical and scalable solution for federated learning in heterogeneous, resource-constrained environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"131671-131695"},"PeriodicalIF":3.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725252","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":"Explainable Graph Neural Networks for Power Grid Fault Detection","authors":"Richard Bosso;Corey Chang;Mahdi Zarif;Yufei Tang","doi":"10.1109/ACCESS.2025.3591604","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591604","url":null,"abstract":"This paper proposes the application of explanation methods to enhance the interpretability of graph neural network (GNN) models in fault location for power grids. GNN models have exhibited remarkable precision in utilizing phasor data from various locations around the grid and integrating the system’s topology, an advantage rarely harnessed by alternative machine learning techniques. This capability makes GNNs highly effective in identifying fault occurrences in power grids. Despite their greater performance, these models can encounter criticism for their “black box” nature, which conceals the reasoning behind their predictions. Lack of transparency significantly hinders power utility operations, as interpretability is crucial to building trust, accountability, and actionable insights. This research presents a comprehensive framework that systematically evaluates state-of-the-art explanation strategies, representing the first use of such a framework for Graph Neural Network models for defect location detection. By assessing the strengths and weaknesses of different explanatory methods, it identifies and recommends the most effective strategies for clarifying the decision-making processes of GNN models. These recommendations aim to improve the transparency of fault predictions, allowing utility providers to better understand and trust the models’ output. The proposed framework not only enhances the practical usability of GNN-based systems but also contributes to advancing their adoption in critical power grid applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"129520-129533"},"PeriodicalIF":3.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725307","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":"MITSGRN: A Novel Computational Framework for Reconstructing Sleep Rhythm Gene Regulatory Networks Based on Mutual Information and Time-Series Big Data","authors":"Zhenyu Liu;Jiangqian Zuo;Qian Cao;Zheng Lu;Tao Li","doi":"10.1109/ACCESS.2025.3591304","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591304","url":null,"abstract":"Disruptions in sleep rhythms have emerged as a global health concern, posing serious risks to the physical and mental well-being of modern populations. Elucidating the molecular regulatory mechanisms underlying the periodic nature of sleep rhythms remains a critical scientific challenge. In this study, we propose an innovative computational framework for gene regulatory network (GRN) reconstruction based on mutual information and large-scale time-series data. The proposed framework leverages the temporal characteristics of gene expression profiles associated with sleep rhythms, and integrates k-means clustering, mutual information, and Pearson lag correlation analysis in a synergistic manner to support GRN reconstruction. We systematically evaluate the performance of our method using BEELINE open-source datasets of varying scales, with precision, recall, and cross-validation accuracy as evaluation metrics. Experimental results demonstrate that our approach significantly outperforms existing methods such as dynGENIE3 and transfer entropy in terms of both accuracy and generalization capability. Furthermore, we successfully applied the proposed framework to reconstruct the GRN governing sleep rhythms in rats. The resulting network exhibits topological features and identifies key regulatory components that are highly consistent with previously published findings. Our results highlight the advantages of mutual information-based GRN reconstruction in deciphering complex biological rhythm regulatory systems. This method not only provides a novel perspective for investigating the gene regulatory mechanisms underlying sleep rhythms, but also establishes a solid methodological foundation for exploring the pathogenesis of sleep-related disorders and advancing the development of targeted therapies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"130088-130097"},"PeriodicalIF":3.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716179","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-Stream Contrastive Learning for Medical Visual Representations Using Synthetic Images Generated by Latent Diffusion Model","authors":"Weitao Ye;Longfu Zhang;Xiaoben Jiang;Dawei Yang;Yu Zhu","doi":"10.1109/ACCESS.2025.3591544","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591544","url":null,"abstract":"Deep learning-based medical image processing methods can enhance diagnostic accuracy while significantly accelerating clinical decision workflows. However, in order to learn better visual representations, such approaches usually need substantial amount of expert-annotated data, which are highly costly. To address this issue, we propose a novel approach called Dual-Stream Contrastive Learning with Cross-Scale Token Projection (DCL-CsTP), which aims to enhance visual representations and transferable initializations. Specifically, a latent diffusion model (LDM) is leveraged to generate high-quality synthetic medical images in order to expand the dataset. Then we utilize the proposed dual-stream architecture that consists of a global semantic relations stream and a local detail relations stream to learn discriminative medical image representations from the dataset. Furthermore, a cross-scale token projection is designed to enable the model to capture various scales of focus in medical images. Comprehensive experiments are performed on two downstream tasks: medical image classification and segmentation. For multi-classification of pneumonia, our DCL-CsTP method achieves 95.90% accuracy. For lesions segmentation, our DCL-CsTP method attains 89.73% dice coefficient on the International Skin Imaging Collaboration 2018 (ISIC 2018) dataset and 82.50% dice coefficient on the Kvasir-SEG dataset. The performance superiority of the model pre-trained by DCL-CsTP is conclusively demonstrated through the above experiments on various dataset, which shows that DCL-CsTP can enhance diagnostic precision and alleviate radiologists’ image screening burdens.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"129648-129658"},"PeriodicalIF":3.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725256","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":"Comparative Analysis of the Proportional Distribution Method and the Random Forest Algorithm for Predicting Pedestrian Traffic Accident Risk","authors":"Hristo V. Uzunov;Plamen G. Matzinski;Vasil H. Uzunov;Silvia V. Dechkova","doi":"10.1109/ACCESS.2025.3591297","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591297","url":null,"abstract":"The risk of pedestrian-involved traffic accidents represents a significant challenge to road safety and necessitates objective methods for analyzing the contributing factors. This study presents a comparative analysis of two methodologies for predicting the risk of pedestrian traffic accidents: a methodology based on proportional risk distribution and the Random Forest algorithm. The analysis utilizes data derived from real court cases, where linguistic variables defined as risk factors are categorized and quantified based on expert evaluations. The results demonstrate that both approaches are applicable for risk assessment, with Random Forest exhibiting higher accuracy and robustness in handling complex and heterogeneous data. Correlation analysis confirms a statistically significant linear relationship between the outputs of the two methods, supporting their validity. Graphical representations derived from the results offer a visual interpretation of risk severity and facilitate comparison between the two approaches. The proposed method is intended for road safety experts, engineers, analysts, and institutions in the field of transportation safety. Its primary aim is to provide an objective and quantitative tool for evaluating the risk factors contributing to pedestrian-related incidents. The method supports informed decision-making regarding preventive measures and awareness campaigns targeting both drivers and pedestrians.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"129828-129844"},"PeriodicalIF":3.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716302","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":"Hybrid Feature and Optimized Deep Learning Model Fusion for Detecting Hateful Arabic Content","authors":"Karim Gasmi;Ibtihel Ben Ltaifa;Alameen Eltoum Abdalrahman;Omer Hamid;Mohamed Othman Altaieb;Shahzad Ali;Lassaad Ben Ammar;Manel Mrabet","doi":"10.1109/ACCESS.2025.3591673","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591673","url":null,"abstract":"Detecting hate speech in Arabic social media content is critical for ensuring safe, inclusive, and respectful online communication. However, this task remains challenging due to Arabic’s morphological richness, dialectal variations such as Levantine, and the scarcity of high-quality annotated data. This study proposes a comprehensive and language-aware approach to Arabic hate speech detection that integrates advanced preprocessing, targeted data augmentation, hybrid feature extraction, and deep ensemble learning. Our experiments are conducted on a Levantine Arabic tweet dataset labeled hateful or non-hateful. To address lexical variability and noise common in user-generated content, we apply a dedicated preprocessing pipeline that includes normalization, diacritic removal, and emoji filtering. To further enhance generalization and mitigate data imbalance, we employ two augmentation strategies: synonym replacement using a curated Arabic lexicon and semantic-preserving back-translation through English. We investigate lexical and contextual approaches for feature extraction, including TF-IDF vectors, contextualized AraBERT embeddings, and a hybrid combination of both. These features are input into multiple deep learning classifiers, including CNN-BiGRU, BiLSTM, and DNN architectures. To maximize predictive performance, we develop an ensemble framework that integrates these models. The final prediction is obtained through a weighted fusion of individual model outputs, where the optimal weights are selected using the Grey Wolf Optimizer (GWO), aiming to maximize classification accuracy. Experimental results demonstrate that our proposed hybrid and ensemble-based architecture achieves superior performance, with an accuracy of 83.33% and a ROC-AUC score of 89.5%, outperforming individual models and conventional baselines. These findings highlight the effectiveness of hybrid feature representations and nature-inspired optimization in enhancing Arabic hate speech detection. Our approach offers a scalable, linguistically informed solution for robust content moderation in Arabic digital spaces.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"131411-131431"},"PeriodicalIF":3.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725293","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":"YOLOv9-Based Human Face Detection and Counting Under Human-Animal Faces, Complex Imaging Environments, and Image Qualities","authors":"Sivaranjini Perikamana Narayanan;M. Sabarimalai Manikandan;Linga Reddy Cenkeramaddi","doi":"10.1109/ACCESS.2025.3591247","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591247","url":null,"abstract":"Automatic human face detection and counting can play a vital role in the recognition and tracking of infant and adult faces in both outdoor and indoor human surveillance applications and facial-vital sign measurement. Despite the advancements in deep learning networks, accurate and reliable detection is still a challenging task in the presence of different kinds of objects, animal faces, and image characteristics. In this paper, we study the effectiveness of the YOLOv9-based face detection and counting method under skin color variations, different face sizes and number of faces, mixture of human and animal faces, image properties (brightness, contrast, illumination, glare), image qualities (image blurring, low-light facial images, lens flared images, grainy images taken at night, different types of noises), and pareidolia effects. The proposed method is trained and validated using the Wider Face database. In addition, we created image databases with different kinds of image qualities and image characteristics with the above-mentioned challenges. The YOLOv9-based face detection model achieves a precision of 86%, a recall of 62.8%, and a mean average precision of 70.8% at an inference time of 15.2 ms on the Wider Face database. Evaluation results demonstrate that the YOLOv9-based face counting outperforms most of the state-of-the-art face detection and people counting methods with a mean absolute error (MAE) of 3.36 and root mean square error (RMSE) of 22.38. The model was also deployed on the Raspberry Pi edge computing platform to study the real-time performance. The YOLOv9-based method achieves an MAE of 0.53-5.76 on the untrained infant database with Gaussian, salt and pepper, and speckle noises and an MAE of 0.43-2.87 on faces inside vehicles. The study further highlights the effectiveness of the YOLOv9 model in achieving promising face detection and counting results under a range of illumination and skin color variations. Evaluation results on a wide variety of animal faces and pareidolia-induced faces demonstrate more false positives due to the lack of contextual intelligence in the generation of deep-face models. Further, results show that the deep-face model detects artificial faces (statues, art, paintings, posters) if the model is deployed in uncontrolled face-based application environments. The performance of the model is degraded under different kinds of noise and blurred images. The results of this study highlight that performance can be improved by using noise-specific filtering techniques with optimal filtering parameters, but this requires the automatic identification of noise types and their corresponding parameters.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"129600-129637"},"PeriodicalIF":3.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725282","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":"SHAP-Based Feature Selection for Enhanced Unsupervised Labeling","authors":"Mary Anne Walauskis;Taghi M. Khoshgoftaar","doi":"10.1109/ACCESS.2025.3591554","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591554","url":null,"abstract":"Manual dataset labeling is expensive, time-consuming, and susceptible to noise and inaccuracies, often necessitating significant financial investments with risks of inconsistencies from human annotations. These challenges are further extended in domains such as fraud detection because of privacy concerns due to manual annotations and severe class imbalance, which negatively impact machine learning models. Our unsupervised approach integrates SHapley Additive exPlanations (SHAP) for feature selection with our novel unsupervised labeling method which uses an ensemble unsupervised method in conjunction with a percentile-based threshold technique on the widely used Kaggle Credit Card Fraud Detection dataset. We create subsets with three and five features using unsupervised SHAP-based feature selection to determine the most impactful features, as well as use the full-featured dataset. To evaluate, we compare the newly generated binary class labels to the actual labels, which were only used for evaluation, and calculate Matthews Correlation Coefficient (MCC), Jaccard Index (JI), and Precision. Furthermore, we compare our method to an unsupervised baseline and show significant improvements. Our empirical results demonstrate that unsupervised SHAP-based feature selection consistently improves the quality of our labels, when compared to the baseline unsupervised method. Lastly, unsupervised SHAP-based feature selection improves label quality when comparing feature subsets to the full-feature dataset while reducing computational complexity. Our work provides an unsupervised framework capable of addressing the challenges of labeling highly imbalanced and unlabeled data while preserving data privacy concerns given the unsupervised nature of our methodology and application of unsupervised SHAP-based feature selection.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"130098-130109"},"PeriodicalIF":3.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716175","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":"Cobots Designed for Strength, Not Stiffness","authors":"Thierry Hubert;Amin Khorasani;Muhammad Usman;Hafsa Nouhi;Raphaël Furnémont;Bram Vanderborght;Greet Van De Perre;Tom Verstraten","doi":"10.1109/ACCESS.2025.3591262","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591262","url":null,"abstract":"Conventional industrial robots are designed to be as stiff as possible to ensure high positioning accuracy. The stiffness of a structure is, however, strongly related to its mass, leading to heavy structures. This paper aims to quantify the potential gain of reducing and eliminating the stiffness constraints, which is of lesser importance for collaborative robots, by investigating the effect of applying different optimization objectives. The resulting optimized designs are quantitatively compared using a set of performance measures and evaluated against the traditional stiffness-designed approach. It was concluded that significant improvements can be made, e.g. the robot’s mass can be reduced up to 74% compared to traditionally stiff-designed robots. The dependency of the payload as well as the structural/actuator mass distribution on the optimized results is investigated and proved to have a significant influence on the potential improvements when allowing reduced structural rigidity. The relationship between structural mass and actuator mass is investigated and compared to commercially available cobots.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"129787-129802"},"PeriodicalIF":3.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11087601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716142","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":"Deep Reinforcement Learning-Based Adaptive Nulling in Phased Array Under Dynamic Environments","authors":"Ying-Dar Lin;Jen-Hao Chang;Yuan-Cheng Lai","doi":"10.1109/ACCESS.2025.3591643","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3591643","url":null,"abstract":"A phased array consists of multiple antenna elements that can control the direction of the radiated signal by adjusting each antenna element’s phase and amplitude, which are encapsulated in the phased array weights. To obtain better communication quality, nulling, which can weaken the interference signal, is helpful by adjusting the phased array weights. In dynamic environments, rapid changes in the directions of both interference and desired signals demand equally rapid, continual updates of phased-array weights. Traditional heuristic optimizers—such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)—struggle to keep up because their iterative searches depend on pre-computed configurations that are unrealistic to obtain on the fly. To date, no heuristic, supervised-learning, or reinforcement-learning method simultaneously achieves all three requirements: fast adaptation, dataset-free operation, and continuous complex-weight control under highly dynamic environments. In this paper, an innovative deep reinforcement learning-based adaptive nulling, called DRLNuller, is proposed. DRLnuller adopts the Proximal Policy Optimization (PPO) algorithm, a typical reinforcement learning algorithm, to dynamically optimize phased array weights through continuous interaction with the environment without relying on pre-computed or labeled data. In experiments, DRLNuller after the training process outperforms PSO and GA in computation speed by <inline-formula> <tex-math>$2.83times 10^{5}$ </tex-math></inline-formula> times faster and maintains effective communication quality, an average Signal-to-Interference Ratio (SIR) of 25.06 dB, under different conditions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"130988-131002"},"PeriodicalIF":3.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716143","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}