High-Confidence Computing最新文献_第6页

Adversarial attacks against dynamic graph neural networks via node injection 通过节点注入对动态图神经网络的恶意攻击

High-Confidence Computing Pub Date : 2023-11-23 DOI: 10.1016/j.hcc.2023.100185

Yanan Jiang, Hui Xia

{"title":"Adversarial attacks against dynamic graph neural networks via node injection","authors":"Yanan Jiang, Hui Xia","doi":"10.1016/j.hcc.2023.100185","DOIUrl":"10.1016/j.hcc.2023.100185","url":null,"abstract":"<div><p>Dynamic graph neural networks (DGNNs) have demonstrated their extraordinary value in many practical applications. Nevertheless, the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance. At the same time, current adversarial attack schemes are implemented on static graphs, and the variability of attack models prevents these schemes from transferring to dynamic graphs. In this paper, we use the diffused attack of node injection to attack the DGNNs, and first propose the node injection attack based on structural fragility against DGNNs, named Structural Fragility-based Dynamic Graph Node Injection Attack (SFIA). SFIA firstly determines the target time based on the period weight. Then, it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject. Finally, an optimization function is designed to generate adversarial features for malicious nodes. Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches. When the graph is injected with 1% of the original total number of nodes through SFIA, the link prediction Recall and MRR of the target DGNN link decrease by 17.4% and 14.3% respectively, and the accuracy of node classification decreases by 8.7%.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000831/pdfft?md5=63dbda3afe972e585f8cc26ab595cb00&pid=1-s2.0-S2667295223000831-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A verifiable and efficient cross-chain calculation model for charging pile reputation 可验证、高效的充电桩信誉跨链计算模型

High-Confidence Computing Pub Date : 2023-11-23 DOI: 10.1016/j.hcc.2023.100180

Cui Zhang , Yunhua He , Bin Wu , Hui Yang , Ke Xiao , Hong Li

{"title":"A verifiable and efficient cross-chain calculation model for charging pile reputation","authors":"Cui Zhang , Yunhua He , Bin Wu , Hui Yang , Ke Xiao , Hong Li","doi":"10.1016/j.hcc.2023.100180","DOIUrl":"10.1016/j.hcc.2023.100180","url":null,"abstract":"<div><p>To solve the current situation of low vehicle-to-pile ratio, charging pile (CP) operators incorporate private CPs into the shared charging system. However, the introduction of private CP has brought about the problem of poor service quality. Reputation is a common service evaluation scheme, in which the third-party reputation scheme has the issue of single point of failure; although the blockchain-based reputation scheme solves the single point of failure issue, it also brings the challenges of storage and query efficiency. It is a feasible solution to classify and store information on multiple chains, and at this time, reputation needs to be calculated in a cross-chain mode. Crosschain reputation calculation faces the problems of correctness verification, integrity verification and efficiency. Therefore, this paper proposes a verifiable and efficient cross-chain calculation model for CP reputation. Specially, in this model, we propose a verifiable cross-chain contract calculation scheme that adopts polynomial commitment to solve the problems of polynomial damage and tampering that may be encountered in the crosschain process of outsourced polynomials, so as to ensure the integrity and correctness of polynomial calculations. In addition, the miner selection and incentive mechanism algorithm in this scheme ensures the correctness of extracted information when the outsourced polynomial is calculated on the blockchain. The security analysis and experimental results demonstrate that this scheme is feasible in practice.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 2","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000788/pdfft?md5=46bf08c2626df49825af43279b2072d6&pid=1-s2.0-S2667295223000788-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139296339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coreference resolution helps visual dialogs to focus 解决核心问题有助于视觉对话聚焦

High-Confidence Computing Pub Date : 2023-11-22 DOI: 10.1016/j.hcc.2023.100184

Tianwei Yue , Wenping Wang , Chen Liang, Dachi Chen, Congrui Hetang, Xuewei Wang

{"title":"Coreference resolution helps visual dialogs to focus","authors":"Tianwei Yue , Wenping Wang , Chen Liang, Dachi Chen, Congrui Hetang, Xuewei Wang","doi":"10.1016/j.hcc.2023.100184","DOIUrl":"10.1016/j.hcc.2023.100184","url":null,"abstract":"<div><p>Visual Dialog is a multi-modal task involving both computer vision and dialog systems. The goal is to answer multiple questions in conversation style, given an image as the context. Neural networks with attention modules are widely used for this task, because of their effectiveness in reasoning the relevance between the texts and images. In this work, we study how to further improve the quality of such reasoning, which is an open challenge. Our baseline is the Recursive Visual Attention (RVA) model, which refines the vision-text attention by iteratively visiting the dialog history. Building on top of that, we propose to improve the attention mechanism with contrastive learning. We train a Matching-Aware Attention Kernel (MAAK) by aligning the deep feature embeddings of an image and its caption, to provide better attention scores. Experiments show consistent improvements from MAAK. In addition, we study the effect of using Multimodal Compact Bilinear (MCB) pooling as a three-way feature fusion for the visual, textual and dialog history embeddings. We analyze the performance of both methods in the discussion section, and propose further ideas to resolve current limitations.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 2","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266729522300082X/pdfft?md5=ab949d922d5965a06641ae36f6129271&pid=1-s2.0-S266729522300082X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Light field depth estimation: A comprehensive survey from principles to future 光场深度估计：从原理到未来的全面考察

High-Confidence Computing Pub Date : 2023-11-22 DOI: 10.1016/j.hcc.2023.100187

Tun Wang , Hao Sheng , Rongshan Chen , Da Yang , Zhenglong Cui , Sizhe Wang , Ruixuan Cong , Mingyuan Zhao

{"title":"Light field depth estimation: A comprehensive survey from principles to future","authors":"Tun Wang , Hao Sheng , Rongshan Chen , Da Yang , Zhenglong Cui , Sizhe Wang , Ruixuan Cong , Mingyuan Zhao","doi":"10.1016/j.hcc.2023.100187","DOIUrl":"10.1016/j.hcc.2023.100187","url":null,"abstract":"<div><p>Light Field (LF) depth estimation is an important research direction in the area of computer vision and computational photography, which aims to infer the depth information of different objects in three-dimensional scenes by capturing LF data. Given this new era of significance, this article introduces a survey of the key concepts, methods, novel applications, and future trends in this area. We summarize the LF depth estimation methods, which are usually based on the interaction of radiance from rays in all directions of the LF data, such as epipolar-plane, multi-view geometry, focal stack, and deep learning. We analyze the many challenges facing each of these approaches, including complex algorithms, large amounts of computation, and speed requirements. In addition, this survey summarizes most of the currently available methods, conducts some comparative experiments, discusses the results, and investigates the novel directions in LF depth estimation.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000855/pdfft?md5=995254b6e9fd71f7ac04f1e9668cefdf&pid=1-s2.0-S2667295223000855-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139294487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A self-driving solution for resource-constrained autonomous vehicles in parked areas 停放区资源受限自动驾驶车辆的自动驾驶解决方案

High-Confidence Computing Pub Date : 2023-11-22 DOI: 10.1016/j.hcc.2023.100182

Jin Qian , Liang Zhang , Qiwei Huang , Xinyi Liu , Xiaoshuang Xing , Xuehan Li

{"title":"A self-driving solution for resource-constrained autonomous vehicles in parked areas","authors":"Jin Qian , Liang Zhang , Qiwei Huang , Xinyi Liu , Xiaoshuang Xing , Xuehan Li","doi":"10.1016/j.hcc.2023.100182","DOIUrl":"10.1016/j.hcc.2023.100182","url":null,"abstract":"<div><p>Autonomous vehicles in industrial parks can provide intelligent, efficient, and environmentally friendly transportation services, making them crucial tools for solving internal transportation issues. Considering the characteristics of industrial park scenarios and limited resources, designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research hotspot. This paper proposes an efficient autonomous driving solution based on path planning, target recognition, and driving decision-making as its core components. Detailed designs for path planning, lane positioning, driving decision-making, and anti-collision algorithms are presented. Performance analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial parks. This solution provides important references for enhancing the performance of autonomous vehicles in these areas.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000806/pdfft?md5=6cc55cff8f575313ade27c7955e65ccd&pid=1-s2.0-S2667295223000806-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural-based inexact graph de-anonymization 基于神经的非精确图形去匿名化

High-Confidence Computing Pub Date : 2023-11-22 DOI: 10.1016/j.hcc.2023.100186

Guangxi Lu , Kaiyang Li , Xiaotong Wang , Ziyue Liu , Zhipeng Cai , Wei Li

{"title":"Neural-based inexact graph de-anonymization","authors":"Guangxi Lu , Kaiyang Li , Xiaotong Wang , Ziyue Liu , Zhipeng Cai , Wei Li","doi":"10.1016/j.hcc.2023.100186","DOIUrl":"https://doi.org/10.1016/j.hcc.2023.100186","url":null,"abstract":"<div><p>Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs, which is crucial in detecting malicious activities, network analysis, social network analysis, and more. Despite its paramount importance, conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data. This paper introduces a neural-based inexact graph de-anonymization, which comprises an embedding phase, a comparison phase, and a matching procedure. The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs. The comparison phase uses a neural tensor network to ascertain node resemblances. The matching procedure employs a refined greedy algorithm to discern optimal node pairings. Additionally, we comprehensively evaluate its performance via well-conducted experiments on various real datasets. The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000843/pdfft?md5=0d8fc958c3885f06c72a47da20a82a5f&pid=1-s2.0-S2667295223000843-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139033378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An insider user authentication method based on improved temporal convolutional network 一种基于改进时间卷积网络的内部用户认证方法

High-Confidence Computing Pub Date : 2023-11-13 DOI: 10.1016/j.hcc.2023.100169

Xiaoling Tao, Yuelin Yu, Lianyou Fu, Jianxiang Liu, Yunhao Zhang

{"title":"An insider user authentication method based on improved temporal convolutional network","authors":"Xiaoling Tao, Yuelin Yu, Lianyou Fu, Jianxiang Liu, Yunhao Zhang","doi":"10.1016/j.hcc.2023.100169","DOIUrl":"10.1016/j.hcc.2023.100169","url":null,"abstract":"<div><p>With the rapid development of information technology, information system security and insider threat detection have become important topics for organizational management. In the current network environment, user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence. Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction. They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data, and also do not adequately reflect the personalized usage characteristics of users, leading to bottlenecks in the performance of the authentication algorithm. In order to solve the above problems, this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism (ECA-TCN) to extract user mouse dynamics features and constructs an one-class Support Vector Machine (OCSVM) for each user for authentication. Experimental results show that compared with four existing deep learning algorithms, the method retains more adequate key information and improves the classification performance of the neural network. In the final authentication, the Area Under the Curve (AUC) can reach 96%.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"3 4","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000673/pdfft?md5=e3a2018fd567973462b834311553fa94&pid=1-s2.0-S2667295223000673-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance of unique and secure routing protocol (USRP) in flying Adhoc Networks for healthcare applications 用于医疗保健应用的飞行 Adhoc 网络中独特安全路由协议 (USRP) 的性能

High-Confidence Computing Pub Date : 2023-11-11 DOI: 10.1016/j.hcc.2023.100170

J. Vijitha Ananthi, P. Subha Hency Jose

{"title":"Performance of unique and secure routing protocol (USRP) in flying Adhoc Networks for healthcare applications","authors":"J. Vijitha Ananthi, P. Subha Hency Jose","doi":"10.1016/j.hcc.2023.100170","DOIUrl":"10.1016/j.hcc.2023.100170","url":null,"abstract":"<div><p>Nowadays, Flying Adhoc Networks play a vital role due to its high efficiency in fast communication. Unmanned aerial vehicles transmit data much faster than other networks and are useful in all aspects of communication. In healthcare applications, wireless body area network transmits the data, whereas the security, which is the most important concern to be focused in a flying adhoc network is not satisfactory. Many intruders tamper the network, degrading the overall network performance. To avoid security issues, a unique and secure routing protocol that provides a single solution for five different types of attacks such as, black hole attacks, grey hole attacks, yoyo attacks, conjoint attack and jamming attacks, is proposed. The simulation results analyses the network performance by using the proposed routing table. In comparison to the other solutions rendered to resolve the affected network, this proposed routing protocol has a higher throughput, higher delivery rate, and lower delay. The Unique and Secure Routing Protocol (USRP) provides an integrated solution for an efficient and secure communication in a flying adhoc network.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000685/pdfft?md5=71d26afc95d290cd3a4efa882f8e618a&pid=1-s2.0-S2667295223000685-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135663879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Redactable consortium blockchain with access control: Leveraging chameleon hash and multi-authority attribute-based encryption 具有访问控制功能的可重删联盟区块链：利用变色龙哈希和基于属性的多授权加密技术

High-Confidence Computing Pub Date : 2023-11-07 DOI: 10.1016/j.hcc.2023.100168

Yueyan Dong, Yifang Li, Ye Cheng, Dongxiao Yu

{"title":"Redactable consortium blockchain with access control: Leveraging chameleon hash and multi-authority attribute-based encryption","authors":"Yueyan Dong, Yifang Li, Ye Cheng, Dongxiao Yu","doi":"10.1016/j.hcc.2023.100168","DOIUrl":"10.1016/j.hcc.2023.100168","url":null,"abstract":"<div><p>A redactable blockchain allows authorized individuals to remove or replace undesirable content, offering the ability to remove illegal or unwanted information. Access control is a mechanism that limits data visibility and ensures that only authorized users can decrypt and access encrypted information, playing a crucial role in addressing privacy concerns and securing the data stored on a blockchain. Redactability and access control are both essential components when implementing a regulated consortium blockchain in real-world situations to ensure the secure sharing of data while removing undesirable content. We propose a decentralized consortium blockchain system prototype that supports redactability and access control. Through the development of a prototype blockchain system, we investigate the feasibility of combining these approaches and demonstrate that it is possible to implement a redactable blockchain with access control in a consortium blockchain setting.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000661/pdfft?md5=354cdeda692d0ad1d062d3b27e03c072&pid=1-s2.0-S2667295223000661-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135510456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security defense strategy algorithm for Internet of Things based on deep reinforcement learning 基于深度强化学习的物联网安全防御策略算法

High-Confidence Computing Pub Date : 2023-10-12 DOI: 10.1016/j.hcc.2023.100167

Xuecai Feng, Jikai Han, Rui Zhang, Shuo Xu, Hui Xia

{"title":"Security defense strategy algorithm for Internet of Things based on deep reinforcement learning","authors":"Xuecai Feng, Jikai Han, Rui Zhang, Shuo Xu, Hui Xia","doi":"10.1016/j.hcc.2023.100167","DOIUrl":"10.1016/j.hcc.2023.100167","url":null,"abstract":"<div><p>Currently, important privacy data of the Internet of Things (IoT) face extremely high risks of leakage. Attackers persistently engage in continuous attacks on terminal devices to obtain private data of crucial importance. Although significant progress has been made in recent years in deep reinforcement learning defense strategies, most defense methods still face problems such as low defense resource allocation efficiency and insufficient defense coordination capabilities. To solve the above problems, this paper constructs a novel adversarial security scenario and proposes a security game model that integrates defense resource allocation and patrol inspection. Regarding the above game model, this paper designs a deep reinforcement learning algorithm named SDSA to calculate its security defense strategy. SDSA calculates the allocation strategy of the best patrolling strategy that is most suitable for the defender by searching the policy on a multi-dimensional discrete action space, and enables multiple defense agents to cooperate efficiently by training a multi-intelligent Dueling Double Deep Q-Network (D3QN) with prioritized experience replay. Finally, the experimental results show that the SDSA-learned security defense strategy can provide a feasible and effective security protection strategy for defenders against attacks compared to the MADDPG and OptGradFP methods.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 1","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266729522300065X/pdfft?md5=5ba5e4cf27f862d15547b114a55810e3&pid=1-s2.0-S266729522300065X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135707986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0