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Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network. 利用优化的变异自动编码器 Wasserstein 生成对抗网络识别物联网设备类型,增强物联网安全性。
IF 1.1 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-31 DOI: 10.1080/0954898X.2024.2304214
Jothi Shri Sankar, Saravanan Dhatchnamurthy, Anitha Mary X, Keerat Kumar Gupta
{"title":"Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network.","authors":"Jothi Shri Sankar, Saravanan Dhatchnamurthy, Anitha Mary X, Keerat Kumar Gupta","doi":"10.1080/0954898X.2024.2304214","DOIUrl":"10.1080/0954898X.2024.2304214","url":null,"abstract":"<p><p>Due to the massive growth in Internet of Things (IoT) devices, it is necessary to properly identify, authorize, and protect against attacks the devices connected to the particular network. In this manuscript, IoT Device Type Identification based on Variational Auto Encoder Wasserstein Generative Adversarial Network optimized with Pelican Optimization Algorithm (IoT-DTI-VAWGAN-POA) is proposed for Prolonging IoT Security. The proposed technique comprises three phases, such as data collection, feature extraction, and IoT device type detection. Initially, real network traffic dataset is gathered by distinct IoT device types, like baby monitor, security camera, etc. For feature extraction phase, the network traffic feature vector comprises packet sizes, Mean, Variance, Kurtosis derived by Adaptive and concise empirical wavelet transforms. Then, the extracting features are supplied to VAWGAN is used to identify the IoT devices as known or unknown. Then Pelican Optimization Algorithm (POA) is considered to optimize the weight factors of VAWGAN for better IoT device type identification. The proposed IoT-DTI-VAWGAN-POA method is implemented in Python and proficiency is examined under the performance metrics, like accuracy, precision, f-measure, sensitivity, Error rate, computational complexity, and RoC. It provides 33.41%, 32.01%, and 31.65% higher accuracy, and 44.78%, 43.24%, and 48.98% lower error rate compared to the existing methods.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643428","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
Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach. 用于估算芒果质量指标和分级的改进型深度信念网络:基于计算机视觉的中性方法。
IF 1.1 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-15 DOI: 10.1080/0954898X.2023.2299851
Mukesh Kumar Tripathi, Shivendra
{"title":"Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach.","authors":"Mukesh Kumar Tripathi, Shivendra","doi":"10.1080/0954898X.2023.2299851","DOIUrl":"10.1080/0954898X.2023.2299851","url":null,"abstract":"<p><p>This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic model transformation, Feature Extraction, and Grading. The raw images are first pre-processed by following five major stages: read, resize, noise removal, contrast enhancement via CLAHE, and Smoothing via filtering. The pre-processed images are then converted into a neutrosophic domain for more effective mango grading. The image is processed under a new Geometric Mean based neutrosophic approach to transforming it into the neutrosophic domain. Finally, the prediction of TSS for the different chilling conditions is done by Improved Deep Belief Network (IDBN) and based on this; the grading of mango is done automatically as the model is already trained with it. Here, the prediction of TSS is carried out under the consideration of SSC, firmness, and TAC. A comparison between the proposed and traditional methods is carried out to confirm the efficacy of various metrics.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467171","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
M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images. M2AI-CVD:使用眼底图像的多模态人工智能心血管风险预测系统。
IF 1.1 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-27 DOI: 10.1080/0954898X.2024.2306988
Premalatha Gurumurthy, Manjunathan Alagarsamy, Sangeetha Kuppusamy, Niranjana Chitra Ponnusamy
{"title":"M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.","authors":"Premalatha Gurumurthy, Manjunathan Alagarsamy, Sangeetha Kuppusamy, Niranjana Chitra Ponnusamy","doi":"10.1080/0954898X.2024.2306988","DOIUrl":"10.1080/0954898X.2024.2306988","url":null,"abstract":"<p><p>Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572032","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
State identification for a class of uncertain switched systems by differential neural networks. 用微分神经网络识别一类不确定开关系统的状态。
IF 1.1 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-11 DOI: 10.1080/0954898X.2023.2296115
Isaac Chairez, Alejandro Garcia-Gonzalez, Alberto Luviano-Juarez
{"title":"State identification for a class of uncertain switched systems by differential neural networks.","authors":"Isaac Chairez, Alejandro Garcia-Gonzalez, Alberto Luviano-Juarez","doi":"10.1080/0954898X.2023.2296115","DOIUrl":"10.1080/0954898X.2023.2296115","url":null,"abstract":"<p><p>This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139418624","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
Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet. 使用基于区块链的轻量级深度实例检测网络,在生物识别多模态多融合中提高云用户身份验证的安全性和隐私性。
IF 1.1 3区 计算机科学
Network-Computation in Neural Systems Pub Date : 2024-08-01 Epub Date: 2024-01-31 DOI: 10.1080/0954898X.2024.2304707
Selvarani Poomalai, Keerthika Venkatesan, Surendran Subbaraj, Sundar Radha
{"title":"Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet.","authors":"Selvarani Poomalai, Keerthika Venkatesan, Surendran Subbaraj, Sundar Radha","doi":"10.1080/0954898X.2024.2304707","DOIUrl":"10.1080/0954898X.2024.2304707","url":null,"abstract":"<p><p>This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet-(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139643429","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
CEO social media celebrity status and credit rating assessment 首席执行官社交媒体名人地位和信用等级评估
IF 5.9 3区 管理学
Internet Research Pub Date : 2024-07-15 DOI: 10.1108/intr-02-2023-0084
Yue Fang, Xin Bao, Baiqing Sun, Raymond Yiu Keung Lau
{"title":"CEO social media celebrity status and credit rating assessment","authors":"Yue Fang, Xin Bao, Baiqing Sun, Raymond Yiu Keung Lau","doi":"10.1108/intr-02-2023-0084","DOIUrl":"https://doi.org/10.1108/intr-02-2023-0084","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This paper aims to investigate the effect of CEO social media celebrity status on credit ratings and to determine whether potential threats on the CEO celebrity status negatively moderate the above association.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The authors collected tweets for 874 CEOs from 513 unique S&amp;P 1500 firms. A panel data analysis was conducted on a panel with 4,235 observations from 2009 to 2020. We then tested the hypothesis with the ordinal logit model.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The empirical findings confirmed that CEO social media celebrity status is positively associated with corporate credit rating outcomes. Our path analyses revealed that CEOs with higher social media celebrity status have less incentive to conduct risk-taking behaviors and thus benefit credit ratings. When the rating agencies perceive potential threats to CEO celebrity status, including CEO myopia and CEO overconfidence, the association between CEO social media celebrity status and credit rating is weakened.</p><!--/ Abstract__block -->\u0000<h3>Practical implications</h3>\u0000<p>This study provides an in-depth understanding of CEO social media perception on credit ratings for firms' managers and capital market participants. Findings can help managers and firms improve their strategies for leveraging social media to release credit constraints. The debt market participants could adopt the CEO social media celebrity status and its concerned threats to setting debt contracts with an adequate price.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This is likely to be the first study that examines the effect of CEO social media celebrity status on credit ratings. The findings of this study also reveal that social media certificated celebrity CEOs tend to be capable of enhancing firm revenue and have lower risk-taking incentives, unlike mass media certificated celebrity CEOs.</p><!--/ Abstract__block -->","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ARIMA-SVR-based risk aggregation modeling in the financial behavior 基于 ARIMA-SVR 的金融行为风险聚合模型
IF 2.5 4区 计算机科学
Kybernetes Pub Date : 2024-07-15 DOI: 10.1108/k-01-2024-0249
Zhangong Huang, Huwei Li
{"title":"ARIMA-SVR-based risk aggregation modeling in the financial behavior","authors":"Zhangong Huang, Huwei Li","doi":"10.1108/k-01-2024-0249","DOIUrl":"https://doi.org/10.1108/k-01-2024-0249","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>Once regional financial risks erupt, they not only affect the stability and security of the financial system in the region, but also trigger a comprehensive financial crisis, damage the national economy, and affect social stability. Therefore, it is necessary to regulate regional financial risks through artificial intelligence methods.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>In this manuscript, we scrutinize the loan data pertaining to aggregated regional financial risks and proffer an ARIMA-SVR loan data regression model, amalgamating traditional statistical regression methods with a machine learning framework. This model initially employs the ARIMA model to accomplish historical data fitting and subsequently utilizes the resultant error as input for SVR to refine the non-linear error. Building upon this, it integrates with the original data to derive optimized prediction results.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The experimental findings reveal that the ARIMA-SVR (Autoregress Integrated Moving Average Model-Support Vector Regression) method advanced in this discourse surpasses individual methods in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) indices, exhibiting superiority to the deep learning LSTM method.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>An ARIMA-SVR framework for the financial risk recognition is proposed. This presentation furnishes a benchmark for future financial risk prediction and the forecasting of associated time series data.</p><!--/ Abstract__block -->","PeriodicalId":49930,"journal":{"name":"Kybernetes","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Fast Global Image Smoothing via Quasi Weighted Least Squares 通过准加权最小二乘法快速平滑全局图像
IF 19.5 2区 计算机科学
International Journal of Computer Vision Pub Date : 2024-07-13 DOI: 10.1007/s11263-024-02105-8
Wei Liu, Pingping Zhang, Hongxing Qin, Xiaolin Huang, Jie Yang, Michael Ng
{"title":"Fast Global Image Smoothing via Quasi Weighted Least Squares","authors":"Wei Liu, Pingping Zhang, Hongxing Qin, Xiaolin Huang, Jie Yang, Michael Ng","doi":"10.1007/s11263-024-02105-8","DOIUrl":"https://doi.org/10.1007/s11263-024-02105-8","url":null,"abstract":"<p>Image smoothing is a long-studied research area with tremendous approaches proposed. However, how to perform high-quality image smoothing with less computational cost still remains a challenging problem. In this paper, we try to solve this problem with a newly proposed global optimization based method named quasi weighted least squares. In our method, the 2D image is first re-ordered into a 1D vector via a newly proposed 2D-to-1D transformation. We then properly remove some original 2D neighborhood connections. The remaining neighboring pixels can simply form 1D neighborhood connections in the transformed 1D vector while they still contain the 2D neighborhood information in the original 2D image space. These together result in a quite compact linear system that can be easily and efficiently solved, which makes our method a fast global image smoothing approach. Our method is on par with the fastest approaches in terms of processing speed, however, it is able to yield comparable performance with the state-of-the-art ones in terms of smoothing quality. Our method can also work as a solver to approximate the weighted least squares problem in complex systems, and it can achieve similar results but runs much faster. The efficiency and effectiveness of our method are validated through comprehensive experiments in several tasks. Our code is publicly available at: https://github.com/wliusjtu/Q-WLS.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":null,"pages":null},"PeriodicalIF":19.5,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Modeling and Analysis of Signal Integrity of High-Frequency Transmission Channel With Degraded Fuzz Button Connectors 带有降级模糊按钮连接器的高频传输通道信号完整性建模与分析
IF 2.1 3区 计算机科学
IEEE Transactions on Electromagnetic Compatibility Pub Date : 2024-07-12 DOI: 10.1109/temc.2024.3422076
Wenjia Wang, Jinchun Gao, Ziren Wang, Tianmeng Zhang, Chaoyi Wang, Hafiz Muhammad Bilal
{"title":"Modeling and Analysis of Signal Integrity of High-Frequency Transmission Channel With Degraded Fuzz Button Connectors","authors":"Wenjia Wang, Jinchun Gao, Ziren Wang, Tianmeng Zhang, Chaoyi Wang, Hafiz Muhammad Bilal","doi":"10.1109/temc.2024.3422076","DOIUrl":"https://doi.org/10.1109/temc.2024.3422076","url":null,"abstract":"","PeriodicalId":55012,"journal":{"name":"IEEE Transactions on Electromagnetic Compatibility","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602639","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
Fuzzy Deduplication: Color-Aware Deduplication for Multi-Media Data 模糊重复数据删除:针对多媒体数据的颜色感知重复数据删除
IF 8.1 2区 计算机科学
IEEE Transactions on Services Computing Pub Date : 2024-07-12 DOI: 10.1109/tsc.2024.3418351
Zehui Tang, Shengke Zeng, Song Han, Yawen Feng, Tao Li, Mingxing He
{"title":"Fuzzy Deduplication: Color-Aware Deduplication for Multi-Media Data","authors":"Zehui Tang, Shengke Zeng, Song Han, Yawen Feng, Tao Li, Mingxing He","doi":"10.1109/tsc.2024.3418351","DOIUrl":"https://doi.org/10.1109/tsc.2024.3418351","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":null,"pages":null},"PeriodicalIF":8.1,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
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