Neural Networks最新文献_第8页

UNAGI: Unified neighbor-aware graph neural network for multi-view clustering

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-23 DOI: 10.1016/j.neunet.2025.107193

Zheming Xu , Congyan Lang , Lili Wei , Liqian Liang , Tao Wang , Yidong Li , Michael C. Kampffmeyer

{"title":"UNAGI: Unified neighbor-aware graph neural network for multi-view clustering","authors":"Zheming Xu , Congyan Lang , Lili Wei , Liqian Liang , Tao Wang , Yidong Li , Michael C. Kampffmeyer","doi":"10.1016/j.neunet.2025.107193","DOIUrl":"10.1016/j.neunet.2025.107193","url":null,"abstract":"<div><div>Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optimal graphs that reflect the underlying topology of the data. However, current MGRC approaches are limited by their disjoint two-stage process, where the graph structure is learned in the first stage before the GNN messages are propagated in the subsequent stage. Additionally, current approaches neglect the importance of cross-view structural consistency and semantic-level information and only consider intra-view embeddings. To address these issues, we propose a <strong>U</strong>nified <strong>N</strong>eighbor-<strong>A</strong>ware <strong>G</strong>raph neural network for multi-v<strong>I</strong>ew clustering (UNAGI). Specifically, we develop a novel framework that seamlessly merges the optimization of the graph topology and sample representations through a differentiable graph adapter, which enables a unified training paradigm. In addition, we propose a unique regularization to learn robust graphs and align the inter-view graph topology with the guidance of neighbor-aware pseudo-labels. Extensive experimental evaluation across seven datasets demonstrates UNAGI’s ability to achieve superior clustering performance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107193"},"PeriodicalIF":6.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-23 DOI: 10.1016/j.neunet.2025.107191

Chenglong Shi , Surong Yan , Shuai Zhang , Haosen Wang , Kwei-Jay Lin

{"title":"Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation","authors":"Chenglong Shi , Surong Yan , Shuai Zhang , Haosen Wang , Kwei-Jay Lin","doi":"10.1016/j.neunet.2025.107191","DOIUrl":"10.1016/j.neunet.2025.107191","url":null,"abstract":"<div><div>Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsity problems. However, caused by random augmentations (e.g., crop, mask, and reorder), existing methods may produce positive views with inconsistent semantics, which degrades performance. Although some efforts have been made by providing new operations (e.g., insert and substitute), challenges have not been well addressed due to information scarcity. Inspired by the massive semantic relationships in the Item Knowledge Graph (IKG), we propose a Knowledge-Guided Semantically consistent Contrastive Learning model for sequential recommendation (KGSCL). Specifically, we introduce two knowledge-guided augmentation operations, KG-substitute and KG-insert, to create semantically consistent and meaningful views. These operations add knowledge-related items from the neighbors in the IKG to augment the sequence, aligning real-world associations to retain original semantics. Meanwhile, we design a co-occurrence-based sampling strategy to complement knowledge-guided augmentations for selecting more correlated neighbors. Moreover, we introduce a view-target CL to model the correlation between semantically consistent views and target items since they exhibit similar user preferences. Experimental results on six widely used datasets demonstrate the effectiveness of our KGSCL in recommendation performance, robustness, and model convergence compared with 14 state-of-the-art competitors. Our code is available at: <span><span>https://github.com/LFM-bot/KGSCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107191"},"PeriodicalIF":6.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

SAR remote sensing image segmentation based on feature enhancement

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-23 DOI: 10.1016/j.neunet.2025.107190

Wei Wei, Yanyu Ye, Guochao Chen, Yuming Zhao, Xin Yang, Lei Zhang, Yanning Zhang

{"title":"SAR remote sensing image segmentation based on feature enhancement","authors":"Wei Wei, Yanyu Ye, Guochao Chen, Yuming Zhao, Xin Yang, Lei Zhang, Yanning Zhang","doi":"10.1016/j.neunet.2025.107190","DOIUrl":"10.1016/j.neunet.2025.107190","url":null,"abstract":"<div><div>Synthetic aperture radar (SAR) images are crucial in remote sensing due to their ability to capture high-quality images regardless of environmental conditions. Though it has been studied for years, the following aspects still limit its further improvement. (1) Due to the unique imaging mechanism of SAR images, the influence of speckle noise cannot be avoided. (2) High-resolution SAR remote sensing images contain complex surface features, and the intersection of multiple targets makes boundary information unclear. To address these problems, we propose a SAR remote sensing image segmentation method based on feature enhancement. Specifically, we propose utilizing wavelet transform on the original SAR remote sensing image along with an encoder–decoder network to learn the structural features. This approach enhances the feature expression and mitigates the impact of speckle noise. Secondly, we design a post-processing refinement module that consists of a small cascaded encoder–decoder. This module refines the segmentation results, making the boundary information clearer. Finally, to further enhance the segmentation results, we incorporate a self-distillation module into the encoder. This enhances hierarchical interaction in the encoder, enabling better learning of semantic information by the shallow layer for segmentation. Two SAR image segmentation datasets demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107190"},"PeriodicalIF":6.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Online ensemble model compression for nonstationary data stream learning

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107151

Rodrigo G.F. Soares , Leandro L. Minku

{"title":"Online ensemble model compression for nonstationary data stream learning","authors":"Rodrigo G.F. Soares , Leandro L. Minku","doi":"10.1016/j.neunet.2025.107151","DOIUrl":"10.1016/j.neunet.2025.107151","url":null,"abstract":"<div><div>Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of the underlying data distributions over time (concept drifts). However, the most common type of data stream learning approach are ensemble approaches, which involve the training of multiple base learners. This can severely increase their computational cost, especially when the learners have to recover from concept drift, rendering them inadequate for applications with tight time and space constraints. In this work, we propose Online Weight Averaging (OWA) — a robust and fast online model compression method for nonstationary data streams based on stochastic weight averaging. It is the first online model compression for nonstationary data streams, which is capable of compressing an evolving ensemble of neural networks into a single model continuously over time. It combines several snapshots of a neural network over time by averaging its weights in specific time steps to find promising regions in the loss landscape with the ability to forget weights from outdated time steps when a concept drift occurs. In this way, at any point in time, a single neural network is maintained to represent a whole ensemble, leveraging the power of ensembles while being appropriate for applications with tight speed requirements. Our experiments show that this key advantage of our proposed method also translates into other advantages such as (1) significant savings in computational cost compared to state-of-the-art data stream ensemble methods while (2) delivering similar predictive performance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107151"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

SQGE: Support-query prototype guidance and enhancement for few-shot relational triple extraction

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107172

Chen Gao , Xuan Zhang , Zhi Jin , Wei Cai , Danyang Wang , Kunpeng Du , Chunlin Yin , Tong Li

{"title":"SQGE: Support-query prototype guidance and enhancement for few-shot relational triple extraction","authors":"Chen Gao , Xuan Zhang , Zhi Jin , Wei Cai , Danyang Wang , Kunpeng Du , Chunlin Yin , Tong Li","doi":"10.1016/j.neunet.2025.107172","DOIUrl":"10.1016/j.neunet.2025.107172","url":null,"abstract":"<div><div>The current few-shot relational triple extraction (FS-RTE) techniques, which rely on prototype networks, have made significant progress. Nevertheless, the scarcity of data in the support set results in both intra-class and inter-class gaps in FS-RTE. Instances with restricted support sets make capturing the various features of target instances in the query set difficult, resulting in intra-class gaps. The support set lacks discernible target category characteristics, and the distances between data from various categories are insufficient, leading to intra-class gaps. In this paper, we propose an FS-RTE method based on support-query prototype guidance and enhancement (SQGE). It includes a support-query prototype guide module, which creates query prototypes based on the support prototype and combines the two prototypes. The fusion prototype can accurately capture the fundamental feature that aligns with the query set, suitably match the query features, and reduce the intra-class gap. Furthermore, to address the inter-class gap, we employ entity-level feature enhancement to improve the feature representation of target entities belonging to the same class. On the other hand, we construct positive and negative instances of the target class through contrastive learning, which not only strengthens the representation of the same target class but also distinguishes the feature space of the target class from other classes. Extensive experimental results on three datasets demonstrate the effectiveness of our approach. All the code and data are made available in <span><span>https://github.com/gao929165733/SQGE_code</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107172"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107194

Zhen Peng , Yunfan Wang , Qika Lin , Bo Dong , Chao Shen

{"title":"When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute","authors":"Zhen Peng , Yunfan Wang , Qika Lin , Bo Dong , Chao Shen","doi":"10.1016/j.neunet.2025.107194","DOIUrl":"10.1016/j.neunet.2025.107194","url":null,"abstract":"<div><div>Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose <span>Eagle</span>, a deep framework based on bipartitE grAph learninG for anomaLy dEtection. Specifically, we disentangle instances and attributes as two disjoint and independent node sets, then formulate the input attributed network as an intra-connected bipartite graph that involves two different relations: edges across two types of nodes described by attribute values, and links between nodes of the same type recorded in the network topology. By learning a self-supervised edge-level prediction task, named affinity inference, <span>Eagle</span> has good physical sense in explaining abnormal deviations from each attribute. Experiments corroborate the effectiveness of <span>Eagle</span> under transductive and inductive task settings. Moreover, case studies illustrate that <span>Eagle</span> is more user-friendly as it opens the door for humans to understand abnormalities from the perspective of different feature combinations.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107194"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143042960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Two algorithms for improving model-based diagnosis using multiple observations and deep learning

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107185

Ran Tai, Dantong Ouyang, Liming Zhang

{"title":"Two algorithms for improving model-based diagnosis using multiple observations and deep learning","authors":"Ran Tai, Dantong Ouyang, Liming Zhang","doi":"10.1016/j.neunet.2025.107185","DOIUrl":"10.1016/j.neunet.2025.107185","url":null,"abstract":"<div><div>Model-based diagnosis (MBD) is a critical problem in artificial intelligence. Recent advancements have made it possible to address this challenge using methods like deep learning. However, current approaches that use deep learning for MBD often struggle with accuracy and computation time due to the limited diagnostic information provided by a single observation. To address this challenge, we introduce two novel algorithms, Discret2DiMO (Discret2Di with Multiple Observations) and Discret2DiMO-DC (Discret2Di with Multiple Observations and Dictionary Cache), which enhance MBD by integrating multiple observations with deep learning techniques. Experimental evaluations on a simulated three-tank model demonstrate that Discret2DiMO significantly improves diagnostic accuracy, achieving up to a 685.06% increase and an average improvement of 59.18% over Discret2Di across all test cases. To address computational overhead, Discret2DiMO-DC additionally implements a caching mechanism that eliminates redundant computations during diagnosis. Remarkably, Discret2DiMO-DC achieves comparable accuracy while reducing computation time by an average of 95.74% compared to Discret2DiMO and 89.42% compared to Discret2Di, with computation times reduced by two orders of magnitude. These results indicate that our proposed algorithms significantly enhance diagnostic accuracy and efficiency in MBD compared with the state-of-the-art algorithm, highlighting the potential of integrating multiple observations with deep learning for more accurate and efficient diagnostics in complex systems.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107185"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

SAC-BL: A hypothesis testing framework for unsupervised visual anomaly detection and location

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107147

Xinsong Ma, Jie Wu, Weiwei Liu

{"title":"SAC-BL: A hypothesis testing framework for unsupervised visual anomaly detection and location","authors":"Xinsong Ma, Jie Wu, Weiwei Liu","doi":"10.1016/j.neunet.2025.107147","DOIUrl":"10.1016/j.neunet.2025.107147","url":null,"abstract":"<div><div>Reconstruction-based methods achieve promising performance for visual anomaly detection (AD), relying on the underlying assumption that the anomalies cannot be accurately reconstructed. However, this assumption does not always hold, especially when suffering weak anomalous (a.k.a. normal-like) examples. More significantly, the existing methods primarily devote to obtaining the strong discriminative score functions, but neglecting the systematic investigation of the decision rule based on the proposed score function. Unlike previous work, this paper solves the AD issue starting from the decision rule within the statistical framework, providing a new insight for AD community. Specifically, we frame the AD task as a multiple hypothesis testing problem, Then, we propose a novel betting-like (BL) procedure with an embedding of strong anomaly constraint network (SACNet), called SAC-BL, to address this testing problem. In SAC-BL, BL procedure serves as the decision rule and SACNet is trained to capture the critical discriminative information from weak anomalies. Theoretically, our SAC-BL can control false discovery rate (FDR) at the prescribed level. Finally, we conduct extensive experiments to verify the superiority of SAC-BL over previous method.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107147"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

ShadowGAN-Former: Reweighting self-attention based on mask for shadow removal

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107175

Jianyi Hu , Shuhuan Wen , Jiaqi Li , Hamid Reza Karimi

{"title":"ShadowGAN-Former: Reweighting self-attention based on mask for shadow removal","authors":"Jianyi Hu , Shuhuan Wen , Jiaqi Li , Hamid Reza Karimi","doi":"10.1016/j.neunet.2025.107175","DOIUrl":"10.1016/j.neunet.2025.107175","url":null,"abstract":"<div><div>Shadow removal remains a challenging visual task aimed at restoring the original brightness of shadow regions in images. Many existing methods overlook the implicit clues within non-shadow regions, leading to inconsistencies in the color, texture, and illumination of the reconstructed shadow-free images. To address these issues, we propose an efficient hybrid model of Transformer and Generative Adversarial Network (GAN), named ShadowGAN-Former, which utilizes information from non-shadow regions to assist in shadow removal. We introduce the Multi-Head Transposed Attention (MHTA) and Gated Feed-Forward Network (Gated FFN), designed to enhance focus on key features while reducing computational costs. Furthermore, we propose the Shadow Attention Reweight Module (SARM) to reweight the self-attention maps based on the correlation between shadow and non-shadow regions, thereby emphasizing the contextual relevance between them. Experimental results on the ISTD and SRD datasets show that our method outperforms popular and state-of-the-art shadow removal algorithms, with the SARM module improving PSNR by 5.42% and reducing RMSE by 14.76%.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107175"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A multi-agent reinforcement learning framework for cross-domain sequential recommendation

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-01-22 DOI: 10.1016/j.neunet.2025.107192

Huiting Liu , Junyi Wei , Kaiwen Zhu , Peipei Li , Peng Zhao , Xindong Wu

{"title":"A multi-agent reinforcement learning framework for cross-domain sequential recommendation","authors":"Huiting Liu , Junyi Wei , Kaiwen Zhu , Peipei Li , Peng Zhao , Xindong Wu","doi":"10.1016/j.neunet.2025.107192","DOIUrl":"10.1016/j.neunet.2025.107192","url":null,"abstract":"<div><div>Sequential recommendation models aim to predict the next item based on the sequence of items users interact with, ordered chronologically. However, these models face the challenge of data sparsity. Recent studies have explored cross-domain sequential recommendation, where users’ interaction data across multiple source domains are leveraged to enhance recommendations in data-sparse target domains. Despite this, users’ interests in the target and source domains may not align perfectly. Additionally, current research often neglects the collaboration between different transfer strategies across source domains, leading to suboptimal performance. To address these challenges, we propose a multi-agent reinforcement learning framework for cross-domain sequential recommendation (MARL4CDSR). Unlike traditional approaches that transfer knowledge from the entire source domain sequence, MARL4CDSR uses agents to select relevant items from source domain sequences for transfer. This approach optimizes the transfer process by coordinating agents’ strategies within each source domain through a multi-agent reinforcement learning framework. Additionally, we introduce an information fusion module with a cross-attention mechanism to align the embedding representations of selected source domain items with target domain items. A reward function based on score differences for the next item optimizes the multi-agent system. We evaluate the method on three Amazon domains: Movies_and_TV, Toys_and_Games, and Books. Our proposed model MARL4CDSR outperforms all baselines on all metrics. Specifically, for the Movies&Books<span><math><mo>→</mo></math></span>Toys task, where the target domain interaction sequence is relatively sparse, MARL4CDSR improves NDCG@10 and HR@10 by 14.76% and 10.25%, respectively.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107192"},"PeriodicalIF":6.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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