Knowledge-Based Systems最新文献

Sample efficient reinforcement learning via low-rank regularization 基于低秩正则化的样本高效强化学习

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-27 DOI: 10.1016/j.knosys.2025.114176

Jiamin Liu , Heng Lian

引用次数: 0

Multiscale welding defect detection method based on image adaptive enhancement 基于图像自适应增强的多尺度焊接缺陷检测方法

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-25 DOI: 10.1016/j.knosys.2025.114174

Huyue Cheng , Hongquan Jiang , Deqiang Jing , Lei Huang , Jianmin Gao , Yong Zhang , Bo Meng

{"title":"Multiscale welding defect detection method based on image adaptive enhancement","authors":"Huyue Cheng , Hongquan Jiang , Deqiang Jing , Lei Huang , Jianmin Gao , Yong Zhang , Bo Meng","doi":"10.1016/j.knosys.2025.114174","DOIUrl":"10.1016/j.knosys.2025.114174","url":null,"abstract":"<div><div>The automatic detection of welding internal defects using radiographic images is an important technique for improving the efficiency and consistency of weld fault diagnosis. However, accurate defect detection is challenging due to the low contrast of radiographic images and the large difference in the sizes of different welding defects. In existing methods, the ray image enhancement and defect detection processes are isolated, and the enhancements that are beneficial to defect detection need to be obtained by manual parameter adjustment, which cannot adapt to large-scale detection tasks. Moreover, the adjustment strategy of the methods to the input image is not conducive to detecting multiscale welding defects. Therefore, this paper proposes a multiscale welding defect detection method based on image adaptive enhancement to address these problems. The method comprises two modules: image adaptive adjustment (IAA) and defect detection based on global and local semantic fusion (DD-GLF). In the IAA module, the parameter prediction network is trained to adaptively predict the parameters of the differentiable image processing function to improve the detection accuracy, and in the DD-GLF module, a defect detection model that accepts global and local window images of welds as inputs is designed to detect multiscale welding defects. Experiments on actual inspection data show that the proposed method achieves enhancement results that are consistent with those of human experts and performs well for dense and large defects.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114174"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724933","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

LPGOH: Label-prototype guided online hashing for efficient cross-modal retrieval LPGOH：标签原型引导在线哈希高效跨模态检索

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-25 DOI: 10.1016/j.knosys.2025.114159

Shu-Juan Peng , Xueting Jiang , Xin Liu , Ji-Xiang Du , Jianjia Cao

{"title":"LPGOH: Label-prototype guided online hashing for efficient cross-modal retrieval","authors":"Shu-Juan Peng , Xueting Jiang , Xin Liu , Ji-Xiang Du , Jianjia Cao","doi":"10.1016/j.knosys.2025.114159","DOIUrl":"10.1016/j.knosys.2025.114159","url":null,"abstract":"<div><div>Cross-modal hashing has recently received widespread attention due to its fast query speed, and existing batch-based methods are generally inefficient in an online scenario, i.e., multi-modal data points appear in a streaming manner. Although some online cross-modal hashing methods have been explored, they often neglect the semantic interdependency among the label categories and potentially suffer from the limited semantic preservations between newly coming data and existing data. To alleviate this concern, this paper proposes an efficient label-prototype guided online hashing (LPGOH) for cross-modal retrieval, which can incrementally learn the discriminative hash codes of streaming data while adaptively optimizing the hash function in a streaming manner. To be specific, the proposed framework first innovates a group of label-prototype codes to exploit the semantic interdependency between the label categories, and then combine the semantic similarity regularization to jointly learn the semantic-preserving hash codes. Meanwhile, <span><math><mrow><mi>ε</mi></mrow></math></span>-dragging operation is seamlessly utilized to provide provable large semantic margins, which can further promote the discrimination power of the learnt hash code and speed up the learning process. Besides, an online discrete optimization algorithm is efficiently designed to parse the semantic interdependency between the label categories, learn the compact hash codes for the current arriving data, and optimize the hash functions adaptively. Accordingly, the hash codes of streaming data are discriminatively learned to benefit various online cross-modal retrieval tasks. Extensive experiments evaluated on benchmark datasets verify the advantages of the proposed LPGOH framework, by achieving the competitive and mostly improved retrieval performance over the state-of-the-arts.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114159"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724932","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

Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild 跨域多步骤思考：零采样细粒度交通标志识别

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-25 DOI: 10.1016/j.knosys.2025.114172

Yaozong Gan , Guang Li , Ren Togo , Keisuke Maeda , Takahiro Ogawa , Miki Haseyama

{"title":"Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild","authors":"Yaozong Gan , Guang Li , Ren Togo , Keisuke Maeda , Takahiro Ogawa , Miki Haseyama","doi":"10.1016/j.knosys.2025.114172","DOIUrl":"10.1016/j.knosys.2025.114172","url":null,"abstract":"<div><div>In this study, we propose <strong>C</strong>ross-<strong>d</strong>omain <strong>M</strong>ulti-step <strong>T</strong>hinking (<strong>CdMT</strong>) to improve zero-shot fine-grained traffic sign recognition (TSR) performance in the wild. Zero-shot fine-grained TSR in the wild is challenging due to the cross-domain problem between clean template traffic signs and real-world counterparts, and existing approaches particularly struggle with cross-country TSR scenarios, where traffic signs typically differ between countries. The proposed CdMT framework tackles these challenges by leveraging the multi-step reasoning capabilities of large multimodal models (LMMs). We introduce context, characteristic, and differential descriptions to design multiple thinking processes for LMMs. Context descriptions, which are enhanced by center coordinate prompt optimization, enable the precise localization of target traffic signs in complex road images and filter irrelevant responses via novel prior traffic sign hypotheses. Characteristic descriptions, which are derived from in-context learning with template traffic signs, bridge cross-domain gaps and enhance fine-grained TSR. Differential descriptions refine the multimodal reasoning ability of LMMs by distinguishing subtle differences among similar signs. CdMT is independent of training data and requires only simple and uniform instructions, enabling it to achieve cross-country TSR. We conducted extensive experiments on three benchmark datasets and two real-world datasets from different countries. The proposed CdMT framework achieved superior performance compared with other state-of-the-art methods on all five datasets, with recognition accuracies of 0.93, 0.89, 0.97, 0.89, and 0.85 on the GTSRB, BTSD, TT-100K, Sapporo, and Yokohama datasets, respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114172"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724988","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

Cluster-graph convolution networks for robust multi-view clustering 鲁棒多视图聚类的聚类图卷积网络

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-25 DOI: 10.1016/j.knosys.2025.114163

Wei Zheng , Xiao-Yuan Jing , Wei Liu , Fei Wu , Changhui Hu , Bo Du

{"title":"Cluster-graph convolution networks for robust multi-view clustering","authors":"Wei Zheng , Xiao-Yuan Jing , Wei Liu , Fei Wu , Changhui Hu , Bo Du","doi":"10.1016/j.knosys.2025.114163","DOIUrl":"10.1016/j.knosys.2025.114163","url":null,"abstract":"<div><div>Existing deep contrastive representation learning methods for unlabeled multi-view data have shown impressive performance by shrinking the cross-view discrepancy. However, most of these methods primarily focus on the procedure of common semantics extraction from multiple views, which is just one of the factors affecting the performance of unsupervised multi-view representation learning. Two additional factors are often overlooked: i) how to improve the discriminative ability of final representations. Existing unsupervised-based approaches normally perform worse on clustering as the number of categories increases. ii) how to balance the contribution of multiple views (specifically in data with more than two views). We observe that the quality of the learned representation is also influenced by certain views, i.e., the model precision may be decreased when some views are involved in the training. To address these factors, we propose a novel contrastive learning-based method, called Cluster-Graph Convolution networks for Robust Multi-view Clustering (CGC-RMC), for unlabeled multi-view data. Specifically, we design a specialized spatial-based cluster-graph convolution and a new adaptive sample-weighted strategy in a contrastive-based basic framework for the above two factors. Additionally, the proposed method adopts a communication fusion module to relieve the influence of view-private information in final view representations. Extensive experiments demonstrate that the proposed method outperforms eleven competitive unsupervised representation learning methods on six multi-view datasets based on the performance of the learned representation on the clustering task.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114163"},"PeriodicalIF":7.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724935","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

KMANet: A spatio-temporal enhancement network for micro-action recognition KMANet：用于微动作识别的时空增强网络

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-24 DOI: 10.1016/j.knosys.2025.114139

Jian Zhou , Jingchao Yao , Nan Su , Jingchen Lu , Qingyang Yu , Yichi Zhang , Wenqiang Hu

{"title":"KMANet: A spatio-temporal enhancement network for micro-action recognition","authors":"Jian Zhou , Jingchao Yao , Nan Su , Jingchen Lu , Qingyang Yu , Yichi Zhang , Wenqiang Hu","doi":"10.1016/j.knosys.2025.114139","DOIUrl":"10.1016/j.knosys.2025.114139","url":null,"abstract":"<div><div>Action recognition technology has gained widespread application due to its ability to capture and process fine-grained motion details. Recent research has increasingly focused on analyzing individual emotions and intentions, bringing greater attention to micro-action recognition (MAR), which involves subtle and low-intensity movements. However, MAR faces several challenges, such as subtle variations in motion amplitude and highly similar visual features. These factors limit the effectiveness of traditional action recognition methods in achieving high detection accuracy. To address these limitations, we drew inspiration from the MAR benchmark MANet and focused on temporal feature modeling and effectively discriminative regions of micro-actions. Accordingly, we propose a two-stage MAR framework with a collaborative mechanism, termed KMANet, which adopts a two-stage spatiotemporal feature enhancement strategy. Specifically, in the temporal dimension, we design a Key Frame Attention Mechanism (KFAM) to automatically focus on key-frame sequences of micro-actions and capture inter-frame dynamic relationships, thereby reducing the interference of non-essential frames. This approach effectively addresses the issue of insignificant motion amplitude changes. The integration of the Micro-Action Focus Module (MAFM) on top of KFAM serves to further enhance local spatial features and reinforce detailed representation in core motion regions. The integration of these two modules achieves a substantial improvement in recognition accuracy at a minor computational expense. Extensive experimentation on the MAR dataset MA-52 and BBSI demonstrates that, in comparison to state-of-the-art methods, KMANet fulfills the requirements of fine-grained scenario detection and attains superior recognition accuracy and performance in micro-action recognition tasks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114139"},"PeriodicalIF":7.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724989","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

Pyramidal structure-correlated refinement for robust face alignment 稳健面对齐的金字塔结构相关精化

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-24 DOI: 10.1016/j.knosys.2025.114098

Qiyuan Dai, Qiang Ling

{"title":"Pyramidal structure-correlated refinement for robust face alignment","authors":"Qiyuan Dai, Qiang Ling","doi":"10.1016/j.knosys.2025.114098","DOIUrl":"10.1016/j.knosys.2025.114098","url":null,"abstract":"<div><div>Recent face alignment methods attempt to capture representations of facial landmarks and learn the correlation between them. However, they often ignore the consistency between local landmarks and the overall face shape, which may lead to the low-efficiency correlation learning between long-distance landmarks. Besides, due to the uncertain localization, these methods may capture invalid local cues of landmark representations. To resolve these issues, we propose a pyramidal structure-correlated refinement method that integrates a novel fusion interactor into a pyramidal refinement framework. Specifically we introduce a fusion interactor to aggregate local regression cues of landmark representations into a global representation and encode the facial structure information. The facial structure information is then allocated to local representations to compensate for missing contexts of landmarks, such as occluded parts. Unlike vanilla attention mechanisms, our fusion interactor performs indirect interaction to avoid inconsistent landmark contexts, and incurs tiny computational complexity burdens. Additionally, to obtain valid local cues of landmarks, we further introduce a pyramidal refinement framework with multi-scale feature maps, which can sample landmark representations from the feature maps of specific scales according to the uncertainty of sampling positions. It can also gradually regularize the global representation with correct multi-scale spatial contexts to constrain the overall face shape. Experiments on some popular benchmarks demonstrate the effectiveness and robustness of our proposed method, especially its notably low failure rates in challenging scenarios.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114098"},"PeriodicalIF":7.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713828","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

Actminer: Applying causality tracking and increment aligning for graph-based threat hunting Actminer：将因果关系跟踪和增量对齐应用于基于图的威胁搜索

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-24 DOI: 10.1016/j.knosys.2025.114169

Mingjun Ma , Tiantian Zhu , Shuang Li , Tieming Chen , Mingqi Lv , Zhengqiu Weng , Guolang Chen

{"title":"Actminer: Applying causality tracking and increment aligning for graph-based threat hunting","authors":"Mingjun Ma , Tiantian Zhu , Shuang Li , Tieming Chen , Mingqi Lv , Zhengqiu Weng , Guolang Chen","doi":"10.1016/j.knosys.2025.114169","DOIUrl":"10.1016/j.knosys.2025.114169","url":null,"abstract":"<div><div>To defend against advanced persistent threats on the endpoint, threat hunting employs security knowledge, such as cyber threat intelligence (CTI), to continuously analyze system audit logs through retrospective scanning, querying, or pattern matching, aiming to uncover attack patterns/graphs that traditional detection methods (e.g., recognition for point of interest) fail to capture. However, existing threat hunting systems based on provenance graphs face challenges of high false negatives (FNs), high false positives (FPs), and low efficiency when confronted with diverse attack tactics and voluminous audit logs. To address these issues, we propose a system called <span>Actminer</span>, which constructs query graphs from descriptive relationships in CTI reports for precise threat hunting (i.e., graph alignment) on provenance graphs. First, we present a heuristic search strategy based on equivalent semantic transfer to reduce FNs. Second, we establish a filtering mechanism based on causal relationships of attack behaviors to mitigate FPs. Finally, we design a tree structure to incrementally update the alignment results, significantly improving hunting efficiency. Evaluation on the DARPA Engagement dataset demonstrates that compared with the SOTA POIROT, <span>Actminer</span> reduces FPs by 39.1 %, eliminates all FNs, and effectively counters adversarial attacks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114169"},"PeriodicalIF":7.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714328","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 segmented motion synthesis method for robotic task-oriented locomotion imitation system 面向任务的机器人运动模仿系统的分段运动综合方法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-23 DOI: 10.1016/j.knosys.2025.114152

Haobin Shi , Ziming He , Jianning Zhan , Kao-Shing Hwang

{"title":"A segmented motion synthesis method for robotic task-oriented locomotion imitation system","authors":"Haobin Shi , Ziming He , Jianning Zhan , Kao-Shing Hwang","doi":"10.1016/j.knosys.2025.114152","DOIUrl":"10.1016/j.knosys.2025.114152","url":null,"abstract":"<div><div>Recent research highlights the potential of learning agile robotic locomotion by imitating segmented motion data from humans. However, using single-mode motion data for imitation learning is inefficient for task-specific actions, and motion capture and retargeting processes can be time-consuming. To address these challenges, we propose a motion synthesis framework that combines segmented motions to produce task-specific behaviors characterized by natural movement. Our approach involves three main components: the State Variational Autoencoder (SVAE), the Control Network of Synthesized Motion (SMC-Net), and Critical Joint Constraints (CJC). The SVAE learns motion dynamics from segmented movements and encodes them into a latent space, enabling efficient combination of diverse motions during reinforcement learning. The SMC-Net selects optimal postures from segmented data using Deep Reinforcement Learning (DRL), and its integration with the SVAE’s latent space enhances motion realism. Critical joint constraints are incorporated into the reward to further improve motion quality. Testing on two reach-target-and-reaction tasks with three types of motions demonstrated a 2.6-fold increase in mean rewards and a 1.1-fold reduction in task completion time compared to state-of-the-art baselines using single-mode motions.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114152"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711213","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

Spatio-temporal meta-learning for trajectory representation learning 轨迹表征学习的时空元学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-07-23 DOI: 10.1016/j.knosys.2025.114141

Zhouzheng Xu, Yuxing Wu, Hang Zhou, Chaofan Fan, Bingyi Li, Kaiyue Liu, Yaqin Ye, Shunping Zhou, Shengwen Li

{"title":"Spatio-temporal meta-learning for trajectory representation learning","authors":"Zhouzheng Xu, Yuxing Wu, Hang Zhou, Chaofan Fan, Bingyi Li, Kaiyue Liu, Yaqin Ye, Shunping Zhou, Shengwen Li","doi":"10.1016/j.knosys.2025.114141","DOIUrl":"10.1016/j.knosys.2025.114141","url":null,"abstract":"<div><div>Trajectory representation learning translates sequences into low-dimensional vectors that are convenient for computer processing and analysis. Trajectory representation learning is widely used by various intelligent applications and is notable for its ability to enhance application performance. However, previous methods assume that trajectories are independently and identically distributed in time and space. In practice, trajectories exhibit significant heterogeneity in time and space sources due to the uncertainty of individual activities and the diversity of activity patterns. This leads to bias in the generated representation vectors that fail to effectively support various geographic applications. To address this issue, this study proposes a spatio-temporal meta-learning method for trajectory representation learning, namely STMetaT, which aims to generate accurate trajectory representation vectors. STMetaT designs a spatio-temporal constraint sampling module that divides trajectory sets into subsets based on the frequency and density of trajectories, which constructs training task samples with diverse spatio-temporal semantics. And, STMetaT uses a multi-view local encoder to generate representation vectors for each subset by fusing the diversity of trajectory semantics. Finally, STMetaT learns a generalization process from local to global promotability to optimize the trajectory representation vectors. Extensive experiments on two urban trajectory datasets show that STMetaT outperforms baseline methods in three classical evaluation tasks, thereby improving the performance of trajectory representation. The proposed method provides an approach for learning trajectory representation by combining meta-learning, and also provides a methodological reference for various intelligent applications.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"327 ","pages":"Article 114141"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714326","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