IEEE Access最新文献

{"title":"Community-Based Memetic Algorithm for Influence Maximization in Large-Scale Networks","authors":"Mithun Roy;Indrajit Pan","doi":"10.1109/ACCESS.2025.3563308","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563308","url":null,"abstract":"Effective information diffusion across large-scale network is key for influence maximization. Recent research has shown a significant surge in interest in modeling, performance estimation, and seed identification across various networked systems. Moreover, a simulation of useful interactions among many significant groups within networks was developed to simulate real-world marketing and spreading information more accurately. A good diffusion model identifies the minimum number of effective seeds capable of achieving maximum diffusion effects across the network. Limited focus has been placed on measuring the strength of seeds in competitive spreading situations. There is a research gap in determining effective strategy for this purpose. This study proposes a memetic algorithm based on a community for large-scale social networks. The proposed algorithm optimizes the influence spread by identifying the most influential nodes among the communities, depending on their inter- or intra-community propagation dynamics. This algorithm combines the concept of genetic algorithm with a reachability-based local search method to accelerate the convergence process. This approach offers a robust method for maximizing the influence of network structure and interactions. An experimental evaluation on real-world social network datasets shows the performance superiority of this community-based memetic algorithm (CBMA-IM) over existing algorithms.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72754-72768"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892506","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":"Scattering Medium Removal Using Adaptive Masks for Scatter in the Spatial Frequency Domain","authors":"Jongpil Jeong;Min-Chul Lee","doi":"10.1109/ACCESS.2025.3563369","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563369","url":null,"abstract":"To address this issue, this paper presents an adaptive method for removing scattering media using a mask based on wireless communication fading models. We hypothesize a similarity between light propagation and wireless communication systems, which incorporates scattering estimates through models such as the Rayleigh and Rician fading models, which are applied to process the captured images and mitigate scattering effects. Our proposed method incorporates two systems: the Scattered Image Model and the Scattering Media Model. The conventional dehazing method requires processing sequences’ approximated depth map or specific background. However, the proposed method functions regardless of the image’s depth and specific background colors. To validate the proposed method, we conducted optical experiments and tested outdoor images. The results were compared with conventional haze-removal methods, such as dark channel prior and Peplography, using various image quality metrics, e.g., the Peak Signal-to-Noise ratio, Structural Similarity Index Measurement, Tone Mapped Image Quality, and Feature Similarity Index Measurement extended to color imagery. The experimental results demonstrated significant improvements over the conventional methods across all metrics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72769-72777"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892459","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":"Toward Precise Quantification of Energy Expenditure in Wheelchair Users","authors":"Iftikhar Alam;Zulfiqar Ali;Wassauf Khalid;Gauhar Ali;Mohammed ELAffendi","doi":"10.1109/ACCESS.2025.3563385","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563385","url":null,"abstract":"Modern electronic devices like smart bands, smartwatches, smartphones, and treadmills are widely used to track exertion metrics, also called energy expenditure, such as step counts, running, time, and distance. However, these devices often fail to meet the needs of individuals with mobility impairments, such as wheelchair users, for whom such metrics are hard to evaluate. This research introduces a tailored model to track and quantify exertion data for manual wheelchair users. The existing Heart Intensity Metric (HIM), which relies on parameters such as heart rate, weight, age, and time (exercise duration), is adapted with a revised Activity Intensity Assessor (AIA). The model incorporates critical factors for wheelchair users, including heart rate, adjusted movement status (1 for movement and zero for no movement), and inclination status, with new parameters, such as Metabolic Equivalent of Task (MET), and wheelchair speed. The revised AIA is then adapted for the energy expenditure formula to calculate calorie-burning estimation specifically for manual wheelchair users. The revised approach minimizes false positives commonly produced by existing approaches for manual wheelchair users, especially in scenarios involving non-movement exercises like upper limb activities. Unlike prior models, the proposed AIA ensures precise energy expenditure calculations, even during stationary activities, and reflects a zero-calorie expenditure when no exercise occurs. Results are statistically verified and demonstrate that traditional formulas yield inaccurate calorie estimations for wheelchair users, while the revised model aligns better with physiological realities. This work provides a practical framework for designing electronic tools that effectively track energy expenditure/total energy (ET), also known as exertion efforts, and estimate calories burnt by manual wheelchair users. The scope of this study is limited to examining energy expenditure exclusively for manual wheelchairs. The electric wheelchairs are beyond the scope of this study.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72189-72201"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902693","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":"FPGA Acceleration With Hessian-Based Comprehensive Intra-Layer Mixed-Precision Quantization for Transformer Models","authors":"Woohong Byun;Jongseok Woo;Saibal Mukhopadhyay","doi":"10.1109/ACCESS.2025.3563196","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563196","url":null,"abstract":"Recent advancements in using FPGAs as co-processors for language model acceleration, particularly for energy efficiency and flexibility, face challenges due to limited memory capacity. This limitation hinders the deployment of transformer-based language models. To address this challenge, we propose a novel software-hardware co-optimization framework that integrates Hessian-based intra-layer mixed-precision quantization with a runtime bit-configurable FPGA accelerator. Our proposed Hessian-based row-wise weight quantization addresses hardware inefficiencies in traditional parameter-wise and channel-wise approaches by enabling mixed-precision weight matrices to be split into two uniform-precision matrices, thereby simplifying hardware requirements. Additionally, our Query-Key coupled attention activation quantization optimally aligns precision within each outer product pair in attention calculations, reducing hardware complexity and memory management overhead. Our concurrent quantization method balances the benefits of row-wise weight quantization and Query-Key coupled activation quantization while maximizing energy efficiency through multi-precision optimization. To support this algorithm, we design a multi-precision FPGA accelerator capable of handling both 2n-based and non-2n mixed-precision operations. It is implemented on a single Xilinx ZCU102 FPGA board, operating at 200MHz with a power consumption of 15.08W during inference on the 110-million-parameter BERT-Base and 345-million-parameter GPT-2 Medium transformer models. Coupled with the proposed algorithm and dataflow optimization, it enables on-chip storage of all necessary parameters, minimizing off-chip memory access. Experimental results demonstrate that our FPGA accelerator significantly outperforms existing solutions, achieving energy efficiency improvements ranging from <inline-formula> <tex-math>$2.22times $ </tex-math></inline-formula> to <inline-formula> <tex-math>$17.23times $ </tex-math></inline-formula> over state-of-the-art FPGA accelerators.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"70282-70297"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883349","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":"Impact of Elevation Angle on Multi-Beam LEO Satellite Communication Systems","authors":"Agnes Fastenbauer;Megumi Kaneko;Philipp Svoboda;Markus Rupp","doi":"10.1109/ACCESS.2025.3563252","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563252","url":null,"abstract":"Compared to well-established geosynchronous equatorial orbit (GEO) satellite networks, low Earth orbit (LEO) satellites bring new challenges to overcome, such as the distortion of the satellite footprint with varying elevation angle. The impact of the elevation angle on system behavior is not sufficiently studied in literature, and guidelines to parameterize LEO systems are lacking. This paper addresses these gaps by providing a framework to analyze the satellite footprint behavior of arbitrary multi-beam satellite systems with large antenna arrays and analyzing the system behavior of a LEO satellite operating in the Ka-band (30GHz) for varying elevation angle and serving area size. The analysis considers the directivity and antenna array steering of the antenna array and the curvature of the Earth. The provided framework allows repeatable analysis and offers a means to parameterize systems in terms of serving area size, beam design, and operating elevation angles. Analysis over elevation angles confirms the strong influence of the satellite elevation angle on the system performance and indicates that elevation angle dependence of LEO systems needs to be considered in the evaluation of future technologies. It is shown that the system drifts from a noise-limited regime at high elevation angles to an interference-limited regime with decreasing elevation angle. The findings suggest a minimum elevation angle of 30° for practical systems, as lower elevation angles show excessive propagation loss and severe interference due to beam distortion. Link budget analysis further indicates that systems require highly directional antennas with large gain to serve handheld user devices.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71723-71737"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883464","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":"Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems","authors":"Andrea Pozzi;Alessandro Incremona;Daniele Toti","doi":"10.1109/ACCESS.2025.3563300","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563300","url":null,"abstract":"Lithium-ion batteries play a pivotal role in enabling eco-friendly mobility, particularly in electric vehicles, but optimizing their charging process to improve battery lifespan, safety, and overall efficiency remains a significant challenge. Traditional predictive control methods are limited by their reliance on precise models, which are often hindered by uncertainties in battery parameters due to aging, production variability, and operational conditions. While stochastic predictive control policies can address these uncertainties by incorporating them directly into the optimization process, they typically introduce considerable computational complexity. In response to this challenge, this paper presents a novel approach that adapts imitation learning to efficiently approximate stochastic predictive control strategies, thus significantly reducing the computational burden through offline training. Specifically, the proposed method leverages the Dataset Aggregation algorithm to overcome the issue of distributional shift, a common limitation in imitation learning frameworks. Simulations based on a detailed electrochemical model demonstrate the effectiveness of the method, adhering to probabilistic constraints while offering a scalable and computationally efficient solution for advanced battery management systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71041-71052"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883482","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":"Inversion of Magnetic Anomaly Based on Cross Attention Transformer","authors":"Juntao Lei;Jieru Chi;Shandong Li","doi":"10.1109/ACCESS.2025.3563187","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563187","url":null,"abstract":"Three-dimensional magnetic anomaly inversion is regarded as one of the most effective methods for accurately retrieving subsurface magnetization distributions. However, existing deep learning methods for magnetic anomaly inversion suffer from issues such as the lack of accuracy in some model structures, poor boundary details, and the skin effect. To address this technical challenge, we propose a magnetic anomaly inversion method based on Transformer architectures, with constraints from magnetic anomaly measurement data. Our method employs a hierarchical encoder-decoder network constructed with Transformer Blocks and introduces three key innovations: 1) We propose a Transformer Block based on cross-attention mechanism. Leveraging this mechanism, the Transformer Block can extract features from both magnetic anomaly and magnetic gradient anomaly data, thereby significantly enhancing the accuracy of boundary detection. 2) We propose a learnable Multi-Scale Feature Fusion Module. This module is devised to integrate the multi-scale features from each stage of the encoder, facilitating the decoder to achieve high-precision inversion. 3) We propose a forward constraint loss function. During network training, this loss function ensures that the inversion results adhere to geophysical principles. This methodology not only elevates the inversion accuracy but also effectively alleviates the skin effect. Experimental results show that, compared to other methods, our approach can accurately reconstruct the shape and location of the magnetization model, improve structural accuracy, enhance boundary details, and reduce the skin effect. Furthermore, the method was applied to magnetic anomaly data from a region in Tianjin, China, successfully predicting the distribution of magnetically related pipeline. This demonstrates its potential as a valuable tool for magnetic anomaly inversion.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71984-71994"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883494","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 GAN Guided NCCT to CECT Synthesis With an Advanced CNN-Transformer Aggregated Generator","authors":"Haozhe Wang;Dawei Gong;Rongzhen Zhou;Junbo Liang;Ruili Zhang;Wenbin Ji;Sailing He","doi":"10.1109/ACCESS.2025.3563375","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563375","url":null,"abstract":"Computed tomography (CT) is essential for diagnosing and managing various diseases, with contrast-enhanced CT (CECT) offering higher contrast images following contrast agent injection. Nevertheless, the usage of contrast agents may cause side effects. Therefore, achieving high-contrast CT images without the need for contrast agent injection is highly desirable. The main contributions of this paper are as follows: 1) We designed a GAN-guided CNN-Transformer aggregation network called GCTANet for the CECT image synthesis task. We propose a CNN-Transformer Selective Fusion Module (CTSFM) to fully exploit the interaction between local and global information for CECT image synthesis. 2) We propose a two-stage training strategy. We first train a non-contrast CT (NCCT) image synthesis model to deal with the misalignment between NCCT and CECT images. Then we trained GCTANet to predict real CECT images using synthetic NCCT images. 3) A multi-scale Patch hybrid attention block (MSPHAB) was proposed to obtain enhanced feature representations. MSPHAB consists of spatial self-attention and channel self-attention in parallel. We also propose a spatial channel information interaction module (SCIM) to fully fuse the two kinds of self-attention information to obtain a strong representation ability. We evaluated GCTANet on two private datasets and one public dataset. On the neck dataset, the PSNR and SSIM achieved were <inline-formula> <tex-math>$35.46pm 2.783$ </tex-math></inline-formula> dB and <inline-formula> <tex-math>$0.970pm 0.020$ </tex-math></inline-formula>, respectively; on the abdominal dataset, <inline-formula> <tex-math>$25.75pm 5.153$ </tex-math></inline-formula> dB and <inline-formula> <tex-math>$0.827pm 0.073$ </tex-math></inline-formula>, respectively; and on the MRI-CT dataset, <inline-formula> <tex-math>$29.61pm 1.789$ </tex-math></inline-formula> dB and <inline-formula> <tex-math>$0.917pm 0.032$ </tex-math></inline-formula>, respectively. In particular, in the area around the heart, where obvious movements and disturbances were unavoidable due to the heartbeat and breathing, GCTANet still successfully synthesized high-contrast coronary arteries, demonstrating its potential for assisting in coronary artery disease diagnosis. The results demonstrate that GCTANet outperforms existing methods.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72202-72220"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902589","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 Symbiosis of Multi-Criteria Decision Making and Electroencephalography: A Review of Techniques, Applications, and Future Directions","authors":"Aylin Adem;Erman Çakıt;Metin Dağdeviren;Anna Szopa;Waldemar Karwowski","doi":"10.1109/ACCESS.2025.3562099","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3562099","url":null,"abstract":"Multi-criteria decision-making (MCDM) problems are generally oriented toward strategic decisions and have a significant economic aspect, holding a special place in decision-making processes. To address these problems, specialized techniques have been developed and utilized. Electroencephalography (EEG) is a technique that measures brain activity, and the results can be used both to diagnose neurological disorders and to create more productive and healthier working conditions in a wide variety of areas. This paper discusses the collaborative nature between MCDM and EEG, focusing on how EEG and MCDM processes can enhance each other. Based on the database search results, thirty-five out of 149 papers were selected for the review process. By examining the literature on both macro and micro scales, the following were mainly retrieved: a) the MCDM techniques applied and their use in analyzing EEG data, b) EEG devices and wave types used in the MCDM process, c) the attributes of MCDM or EEG that were focused on, and d) current trends in knowledge and research opportunities. The results of this study will help identify potential future research areas, as well as provide a comprehensive overview of the existing literature. To summarize the findings, it can be concluded that EEG measurements of decision-makers’ cognitive states during the application of MCDM techniques improve the MCDM process and the presentation of these techniques, or they enhance the results obtained by MCDM based on the cognitive states of decision-makers when evaluating alternatives. Additionally, MCDM techniques contribute to improvements in the classification or feature extraction stages of data obtained through EEG.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"75067-75084"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902669","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":"RPEE-Heads Benchmark: A Dataset and Empirical Comparison of Deep Learning Algorithms for Pedestrian Head Detection in Crowds","authors":"Mohamad Abubaker;Zubayda Alsadder;Hamed Abdelhaq;Maik Boltes;Ahmed Alia","doi":"10.1109/ACCESS.2025.3563311","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563311","url":null,"abstract":"The automatic detection of pedestrian heads in crowded environments is essential for crowd analysis and management tasks, particularly in high-risk settings such as high dense railway platforms and event entrances. These environments, characterized by dense crowds and dynamic movements, are underrepresented in public datasets, posing challenges for existing deep learning models. To address this gap, we introduce the Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset, a novel, diverse, high-resolution, and accurately annotated resource. It includes 109,913 annotated pedestrian heads across 1,886 images from 66 video recordings, with an average of 56.2 heads per image. Annotations include bounding boxes for visible head regions. In addition to introducing the RPEE-Heads dataset, this paper evaluates eight state-of-the-art object detection algorithms using the dataset and analyzes the impact of head size on detection accuracy. The experimental results show that You Only Look Once v9 and Real-Time Detection Transformer outperform the other algorithms, achieving mean average precisions of 90.7% and 90.8%, with inference times of 11 and 14 milliseconds, respectively. Moreover, the findings underscore the need for specialized datasets like RPEE-Heads for training and evaluating accurate models for head detection in railway platforms and event entrances. The dataset and pretrained models are available at <uri>https://doi.org/10.34735/ped.2024.2</uri>.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73451-73467"},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902700","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}