Multimedia Systems最新文献_第2页

C2IENet: Multi-branch medical image fusion based on contrastive constraint features and information exchange C2IENet：基于对比约束特征和信息交换的多分支医学影像融合

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-09-09 DOI: 10.1007/s00530-024-01473-y

Jing Di, Chan Liang, Li Ren, Wenqing Guo, Jizhao Liu, Jing Lian

{"title":"C2IENet: Multi-branch medical image fusion based on contrastive constraint features and information exchange","authors":"Jing Di, Chan Liang, Li Ren, Wenqing Guo, Jizhao Liu, Jing Lian","doi":"10.1007/s00530-024-01473-y","DOIUrl":"https://doi.org/10.1007/s00530-024-01473-y","url":null,"abstract":"In the field of medical image fusion, traditional approaches often fail to differentiate between the unique characteristics of each raw image, leading to fused images with compromised texture and structural clarity. Addressing this, we introduce an advanced multi-branch fusion method characterized by contrast-enhanced features and interactive information exchange. This method integrates a multi-scale residual module and a gradient-dense module within a private branch to precisely extract and enrich texture details from individual raw images. In parallel, a common feature extraction branch, equipped with an information interaction module, processes paired raw images to synergistically capture complementary and shared functional information across modalities. Additionally, we implement a sophisticated attention mechanism tailored for both the private and public branches to enhance global feature extraction, thereby significantly improving the contrast and contour definition of the fused image. A novel correlation consistency loss function further refines the fusion process by optimizing the information sharing between modalities, promoting the correlation among basic cross-modal features while minimizing the correlation of high-frequency details across different modalities. Objective evaluations demonstrate substantial improvements in indices such as EN, MI, QMI, SSIM, AG, SF, and (text {Q}^{text {AB/F}}), with average increases of 23.67%, 12.35%, 4.22%, 20.81%, 8.96%, 6.38%, and 25.36%, respectively. These results underscore our method’s superiority in achieving enhanced texture detail and contrast in fused images compared to conventional algorithms, as validated by both subjective assessments and objective performance metrics.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"19 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLR-MVSNet: a lightweight network for low-texture scene reconstruction LLR-MVSNet：用于低纹理场景重建的轻量级网络

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-09-09 DOI: 10.1007/s00530-024-01464-z

Lina Wang, Jiangfeng She, Qiang Zhao, Xiang Wen, Qifeng Wan, Shuangpin Wu

{"title":"LLR-MVSNet: a lightweight network for low-texture scene reconstruction","authors":"Lina Wang, Jiangfeng She, Qiang Zhao, Xiang Wen, Qifeng Wan, Shuangpin Wu","doi":"10.1007/s00530-024-01464-z","DOIUrl":"https://doi.org/10.1007/s00530-024-01464-z","url":null,"abstract":"In recent years, learning-based MVS methods have achieved excellent performance compared with traditional methods. However, these methods still have notable shortcomings, such as the low efficiency of traditional convolutional networks and simple feature fusion, which lead to incomplete reconstruction. In this research, we propose a lightweight network for low-texture scene reconstruction (LLR-MVSNet). To improve accuracy and efficiency, a lightweight network is proposed, including a multi-scale feature extraction module and a weighted feature fusion module. The multi-scale feature extraction module uses depth-separable convolution and point-wise convolution to replace traditional convolution, which can reduce network parameters and improve the model efficiency. In order to improve the fusion accuracy, a weighted feature fusion module is proposed, which can selectively emphasize features, suppress useless information and improve the fusion accuracy. With rapid computational speed and high performance, our method surpasses the state-of-the-art benchmarks and performs well on the DTU and the Tanks & Temples datasets. The code of our method will be made available at https://github.com/wln19/LLR-MVSNet.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"114 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual convolutional neural network with attention for image blind denoising 用于图像盲去噪的具有注意力的双卷积神经网络

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-09-08 DOI: 10.1007/s00530-024-01469-8

Wencong Wu, Guannan Lv, Yingying Duan, Peng Liang, Yungang Zhang, Yuelong Xia

{"title":"Dual convolutional neural network with attention for image blind denoising","authors":"Wencong Wu, Guannan Lv, Yingying Duan, Peng Liang, Yungang Zhang, Yuelong Xia","doi":"10.1007/s00530-024-01469-8","DOIUrl":"https://doi.org/10.1007/s00530-024-01469-8","url":null,"abstract":"Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a novel dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a dual CNN. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"13 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physical-prior-guided single image dehazing network via unpaired contrastive learning 通过非配对对比学习实现物理先导的单一图像去毛刺网络

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-09-06 DOI: 10.1007/s00530-024-01462-1

Mawei Wu, Aiwen Jiang, Hourong Chen, Jihua Ye

{"title":"Physical-prior-guided single image dehazing network via unpaired contrastive learning","authors":"Mawei Wu, Aiwen Jiang, Hourong Chen, Jihua Ye","doi":"10.1007/s00530-024-01462-1","DOIUrl":"https://doi.org/10.1007/s00530-024-01462-1","url":null,"abstract":"Image dehazing aims to restore high fidelity clear images from hazy ones. It has wide applications on many intelligent image analysis systems in computer vision area. Many prior-based and learning-based methods have already made significant progress in this field. However, the domain gap between synthetic and real hazy images still negatively impacts model’s generalization performance in real-world scenarios. In this paper, we have proposed an effective physical-prior-guided single image dehazing network via unpaired contrastive learning (PDUNet). The learning process of PDUNet consists of pre-training stage on synthetic data and fine-tuning stage on real data. Mixed-prior modules, controllable zero-convolution modules, and unpaired contrastive regularization with hybrid transmission maps have been proposed to fully utilize complementary advantages of both prior-based and learning-based strategies. Specifically, mixed-prior module provides precise haze distributions. Zero-convolution modules serving as controllable bypass supplement pre-trained model with additional real-world haze information, as well as mitigate the risk of catastrophic forgetting during fine-tuning. Hybrid prior-generated transmission maps are employed for unpaired contrastive regularization. Through leveraging physical prior statistics and vast of unlabel real-data, the proposed PDUNet exhibits excellent generalization and adaptability on handling real-world hazy scenarios. Extensive experiments on public dataset have demonstrated that the proposed method improves PSNR,NIQE and BRISQUE values by an average of 0.33, 0.69 and 2.3, respectively, with comparable model efficiency compared to SOTA. Related codes and model parameters will be publicly available on Github https://github.com/Jotra9872/PDU-Net.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"44 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring coherence from heterogeneous representations for OCR image captioning 从异构表征中探索连贯性，为 OCR 图像添加标题

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-09-06 DOI: 10.1007/s00530-024-01470-1

Yao Zhang, Zijie Song, Zhenzhen Hu

{"title":"Exploring coherence from heterogeneous representations for OCR image captioning","authors":"Yao Zhang, Zijie Song, Zhenzhen Hu","doi":"10.1007/s00530-024-01470-1","DOIUrl":"https://doi.org/10.1007/s00530-024-01470-1","url":null,"abstract":"Text-based image captioning is an important task, aiming to generate descriptions based on reading and reasoning the scene texts in images. Text-based image contains both textual and visual information, which is difficult to be described comprehensively. Recent works fail to adequately model the relationship between features of different modalities and fine-grained alignment. Due to the multimodal characteristics of scene texts, the representations of text usually come from multiple encoders of visual and textual, leading to heterogeneous features. Though lots of works have paid attention to fuse features from different sources, they ignore the direct correlation between heterogeneous features, and the coherence in scene text has not been fully exploited. In this paper, we propose Heterogeneous Attention Module (HAM) to enhance the cross-modal representations of OCR tokens and devote it to text-based image captioning. The HAM is designed to capture the coherence between different modalities of OCR tokens and provide context-aware scene text representations to generate accurate image captions. To the best of our knowledge, we are the first to apply the heterogeneous attention mechanism to explore the coherence in OCR tokens for text-based image captioning. By calculating the heterogeneous similarity, we interactively enhance the alignment between visual and textual information in OCR. We conduct the experiments on the TextCaps dataset. Under the same setting, the results show that our model achieves competitive performances compared with the advanced methods and ablation study demonstrates that our framework enhances the original model in all metrics.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"49 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades 使用改进的麻雀搜索算法，以最小控制点进行自适应 B 样条曲线拟合，用于航空发动机叶片的几何建模

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-08-30 DOI: 10.1007/s00530-024-01452-3

Chang Su, Yong Han, Suihao Lu, Dongsheng Jiang

{"title":"Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades","authors":"Chang Su, Yong Han, Suihao Lu, Dongsheng Jiang","doi":"10.1007/s00530-024-01452-3","DOIUrl":"https://doi.org/10.1007/s00530-024-01452-3","url":null,"abstract":"In Industry 4.0 and advanced manufacturing, producing high-precision, complex products such as aero-engine blades involves sophisticated processes. Digital twin technology enables the creation of high-precision, real-time 3D models, optimizing manufacturing processes and improving product qualification rates. Establishing geometric models is crucial for effective digital twins. Traditional methods often fail to meet precision and efficiency demands. This paper proposes a fitting method based on an improved sparrow search algorithm (SSA) for high-precision curve fitting with minimal control points. This enhances modeling precision and efficiency, creating models suitable for digital twin environments and improving machining qualification rates. The SSA’s position update function is enhanced, and an internal node vector update range prevents premature convergence and improves global search capabilities. Through automatic iterations, optimal control points are calculated using the least squares method. Fitness values, based on local and global errors, are iteratively calculated to achieve target accuracy. Validation with aero-engine blade data showed fitting accuracies of 1e−3 mm and 1e−5 mm. Efficiency in searching for minimal control points improved by 34.7%–49.6% compared to traditional methods. This SSA-based fitting method significantly advances geometric modeling precision and efficiency, addressing modern manufacturing challenges with high-quality, real-time production capabilities.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"11 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HNQA: histogram-based descriptors for fast night-time image quality assessment HNQA：基于直方图的描述符，用于快速夜间图像质量评估

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-08-30 DOI: 10.1007/s00530-024-01440-7

Maryam Karimi, Mansour Nejati

{"title":"HNQA: histogram-based descriptors for fast night-time image quality assessment","authors":"Maryam Karimi, Mansour Nejati","doi":"10.1007/s00530-024-01440-7","DOIUrl":"https://doi.org/10.1007/s00530-024-01440-7","url":null,"abstract":"Taking high quality images at night is a challenging issue for many applications. Therefore, assessing the quality of night-time images (NTIs) is a significant area of research. Since there is no reference image for such images, night-time image quality assessment (NTQA) must be performed blindly. Although the field of blind quality assessment of natural images has gained significant popularity over the past decade, the quality assessment of NTIs usually confront complex distortions such as contrast loss, chroma noise, color desaturation, and detail blur, that have been less investigated. In this paper, a blind night-time image quality evaluation model is proposed by generating innovative quality-aware local feature maps, including detail exposedness, color saturation, sharpness, contrast, and naturalness. In the next step, these maps are compressed and converted into global feature representations using histograms. These feature histograms are used to learn a Gaussian process regression (GPR) quality prediction model. The suggested BIQA approach for night images undergoes a comprehensive evaluation on a standard night image database. The results of the experiments demonstrate the superior prediction performance of the proposed BIQA method for night images compared to other advanced BIQA methods despite its simplicity of implementation and execution speed. Hence, it is readily applicable in real-time scenarios.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"2 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wacml: based on graph neural network for imbalanced node classification algorithm Wacml：基于图神经网络的不平衡节点分类算法

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-08-30 DOI: 10.1007/s00530-024-01454-1

Junfeng Wang, Jiayue Yang, Lidun

{"title":"Wacml: based on graph neural network for imbalanced node classification algorithm","authors":"Junfeng Wang, Jiayue Yang, Lidun","doi":"10.1007/s00530-024-01454-1","DOIUrl":"https://doi.org/10.1007/s00530-024-01454-1","url":null,"abstract":"The presence of a large number of robot accounts on social media has led to negative social impacts. In most cases, the distribution of robot accounts and real human accounts is imbalanced, resulting in insufficient representativeness and poor performance of a few types of samples. Graph neural networks can effectively utilize user interaction and are widely used to process graph structure data, achieving good performance in robot detection. However, previous robot detection methods based on GNN mostly considered the impact of class imbalance. However, in graph-structured data, the imbalance caused by differences in the position and structure of labeled nodes makes the processing results of GNN prone to bias toward larger categories. Due to the lack of consideration for the unique connectivity issues of the graph structure, the classification performance of nodes is not ideal. Therefore, in response to the shortcomings of existing schemes, this paper proposes a class imbalanced node classification algorithm based on minority weighting and abnormal connectivity margin loss, which extends the traditional imbalanced classification idea in the field of machine learning to graph-structured data and jointly handles the problem of quantity imbalance and graph-structured abnormal connectivity to improve GNN’s perception of connection anomalies. In the node feature aggregation stage, weighted aggregation is applied to minority classes. In the oversampling stage, the SMOTE algorithm is used to process imbalanced data, while considering node representation and topology structure. Simultaneously training an edge generator to model relationship information, combined with abnormal connectivity margin loss, to enhance the model’s learning of connectivity information, greatly improving the quality of the edge generator. Finally, we evaluated a publicly available dataset, and the experimental results showed that it achieved good results in classifying imbalanced nodes.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"128 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contour-assistance-based video matting localization 基于轮廓辅助的视频消隐定位

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-08-30 DOI: 10.1007/s00530-024-01456-z

Zhu Wenyi, Ding Xiangling, Zhang Chao, Deng Yingqian, Zhao Yulin

{"title":"Contour-assistance-based video matting localization","authors":"Zhu Wenyi, Ding Xiangling, Zhang Chao, Deng Yingqian, Zhao Yulin","doi":"10.1007/s00530-024-01456-z","DOIUrl":"https://doi.org/10.1007/s00530-024-01456-z","url":null,"abstract":"Video matting is a technique used to replace foreground objects in video frames by predicting their alpha matte. Originally developed for film special effects, advertisements, and live streaming, video matting can also be exploited for malicious tampering, leaving imperceptible traces. This highlights the need for effective forensic techniques to detect such tampering. Current research in video matting forensics is limited, largely focusing on frame-by-frame analysis, which fails to account for the temporal characteristics of videos and thus falls short in accurately localizing tampered regions. In this paper, we address this gap by leveraging the entire video sequence to improve tampering detection. We propose a two-branch network that integrates contour information of tampered objects into the forgery localization process, enhancing the extraction of tampering traces and contour features. Additionally, we introduce a tamper contour detection module and a feature enhancement module to refine tampered region identification. Extensive experiments conducted on both overt and synthetic tampering datasets demonstrate that our method effectively locates tampered regions, outperforming existing video forensics techniques.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"58 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weakly-supervised temporal action localization using multi-branch attention weighting 利用多分支注意力加权进行弱监督时间动作定位

IF 3.9 3区计算机科学

Multimedia Systems Pub Date : 2024-08-30 DOI: 10.1007/s00530-024-01445-2

Mengxue Liu, Wenjing Li, Fangzhen Ge, Xiangjun Gao

{"title":"Weakly-supervised temporal action localization using multi-branch attention weighting","authors":"Mengxue Liu, Wenjing Li, Fangzhen Ge, Xiangjun Gao","doi":"10.1007/s00530-024-01445-2","DOIUrl":"https://doi.org/10.1007/s00530-024-01445-2","url":null,"abstract":"Weakly-supervised temporal action localization aims to train an accurate and robust localization model using only video-level labels. Due to the lack of frame-level temporal annotations, existing weakly-supervised temporal action localization methods typically rely on multiple instance learning mechanisms to localize and classify all action instances in an untrimmed video. However, these methods focus only on the most discriminative regions that contribute to the classification task, neglecting a large number of ambiguous background and context snippets in the video. We believe that these controversial snippets have a significant impact on the localization results. To mitigate this issue, we propose a multi-branch attention weighting network (MAW-Net), which introduces an additional non-action class and integrates a multi-branch attention module to generate action and background attention, respectively. In addition, considering the correlation among context, action, and background, we use the difference of action and background attention to construct context attention. Finally, based on these three types of attention values, we obtain three new class activation sequences that distinguish action, background, and context. This enables our model to effectively remove background and context snippets in the localization results. Extensive experiments were performed on the THUMOS-14 and Activitynet1.3 datasets. The experimental results show that our method is superior to other state-of-the-art methods, and its performance is comparable to those of fully-supervised approaches.","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"62 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0