IEEE Transactions on Emerging Topics in Computing最新文献_第10页

Distributed Time Synchronization in NOMA-Assisted Ultra-Dense Networks noma辅助超密集网络中的分布式时间同步

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2026-01-28 DOI: 10.1109/TETC.2026.3656238

Debjani Goswami;Indrakshi Dey;Nicola Marchetti;Suvra Sekhar Das

{"title":"Distributed Time Synchronization in NOMA-Assisted Ultra-Dense Networks","authors":"Debjani Goswami;Indrakshi Dey;Nicola Marchetti;Suvra Sekhar Das","doi":"10.1109/TETC.2026.3656238","DOIUrl":"https://doi.org/10.1109/TETC.2026.3656238","url":null,"abstract":"Ultra-dense networks (UDNs) represent a transformative access architecture for upcoming sixth generation (6G) systems, poised to meet the surging demand for high data rates. Achieving precise synchronization across diverse base stations (BSs) is critical in these networks to mitigate inter-cell interference (ICI). However, traditional centralized synchronization approaches face substantial challenges in dense urban, including limited access to Global Positioning System (GPS), dependence on reliable backhaul, and high signaling overhead demands. This study advances a low-complexity distributed synchronization solution. A primary focus is on assessing the algorithm’s accuracy incorporating the effects of information exchange delays, which are pronounced in large-networks. Recognizing the pivotal role of neighbor-gathered information in the proposed approach, this research employs downlink Non-Orthogonal Multiple Access (NOMA) to reduce message-gathering delays between transmitters (TXs) and receivers (RXs). The proposed algorithm is evaluated to assess effectiveness under message exchange delays, analyzing impact of system parameters like network connectivity, size, sub-bands, etc., on synchronization speed. The findings demonstrate that the NOMA-based information-gathering technique significantly accelerates network synchronization compared to orthogonal access schemes. This advancement is crucial for meeting the low-latency requirements of beyond fifth generation (5G) systems, underscoring the potential of distributed synchronization as a cornerstone for next-generation UDN deployments.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"222-239"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511538","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

Pixel2Voxel: 3D Reconstruction and Visualization From Limited Number of X-Rays With 3D-Aware Diffusion Models and Iterative Refinement Pixel2Voxel: 3D重建和可视化从有限数量的x射线与3D感知扩散模型和迭代细化

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-01-01 Epub Date: 2025-11-25 DOI: 10.1109/TETC.2025.3635000

Gaofeng Deng;Sijie Ding;Arie E. Kaufman

{"title":"Pixel2Voxel: 3D Reconstruction and Visualization From Limited Number of X-Rays With 3D-Aware Diffusion Models and Iterative Refinement","authors":"Gaofeng Deng;Sijie Ding;Arie E. Kaufman","doi":"10.1109/TETC.2025.3635000","DOIUrl":"https://doi.org/10.1109/TETC.2025.3635000","url":null,"abstract":"2D X-ray radiography is a widely used medical imaging technique due to its low cost, rapid acquisition, and low radiation exposure. However, X-ray images are limited to the 2D plane, making it difficult for physicians to visualize and assess the 3D anatomical information. We present Pixel2Voxel, a method to reconstruct 3D volume from a limited number (1 or 2) of 2D X-ray images for the visualization of 3D anatomical structures. Our approach backprojects the local image features onto their corresponding voxels and aggregates the 2D features from the images to get the 3D geometry-aware volume features. To address the inherent ambiguity stemming from sparse input data, we introduce a novel conditional diffusion model that seamlessly integrates with the 3D volume features. This integration significantly enhances our model’s ability to generate high-fidelity and geometrically consistent 3D volumes. Furthermore, we propose an iterative refinement method that substantially improves the reconstruction quality. We evaluate our method through extensive experiments on real patient datasets, demonstrating its superior performance in 3D reconstruction compared to recent methods. Besides, we evaluate our method’s capabilities in 3D organ shape reconstruction and volume measurement. Our promising results showcase its great potential across various medical applications, augmenting 2D X-rays by enabling assessment and visualization of 3D anatomical information, such as organ shape, size, and location, with lower cost and lower radiation exposure of X-rays.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 1","pages":"42-53"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510399","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

IEEE Transactions on Emerging Topics in Computing Publication Information IEEE计算出版信息新兴主题汇刊

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-12-05 DOI: 10.1109/TETC.2025.3633547

引用次数: 0

Multi-View Partial Multi-Label Learning via Class Activation Specific Features Collaborative Learning 基于类激活的多视图部分多标签学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-12 DOI: 10.1109/TETC.2025.3629677

Anhui Tan;Jianhang Xu;Weiping Ding;Jiye Liang;Witold Pedrycz

{"title":"Multi-View Partial Multi-Label Learning via Class Activation Specific Features Collaborative Learning","authors":"Anhui Tan;Jianhang Xu;Weiping Ding;Jiye Liang;Witold Pedrycz","doi":"10.1109/TETC.2025.3629677","DOIUrl":"https://doi.org/10.1109/TETC.2025.3629677","url":null,"abstract":"Multi-view partial multi-label learning deals with scenarios where samples contain heterogeneous features and are associated with both relevant and corrupted labels. Existing methods struggle to effectively capture label-related features through adequate feature interaction while simultaneously integrating inter- and intra-view features. To address these challenges, we propose a robust and scalable framework, Class Activation Specific Features Collaborative Network, designed to handle feature heterogeneity and facilitate comprehensive feature fusion in multi-view partial multi-label learning. The framework integrates label-specific feature extraction with collaborative information propagation through two key components: 1) View-Specific Class Activation Map, which transforms multi-view features into compact class label representations and 2) Class Information Propagation Correction, which refines and propagates accurate class label information by leveraging graph convolutional networks and transformers. Additionally, we introduce a multi-faceted loss function that promotes robust feature learning and architectural stability via consistency-based structural loss, while improving generalization through knowledge distillation. Extensive experiments on benchmark datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods in multi-view partial multi-label learning tasks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1522-1535"},"PeriodicalIF":5.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729447","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

HIFLA: Hilbert-Inspired Federated Learning via Action Principles HIFLA：希尔伯特启发的基于行动原则的联邦学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-11 DOI: 10.1109/TETC.2025.3629528

Koffka Khan

{"title":"HIFLA: Hilbert-Inspired Federated Learning via Action Principles","authors":"Koffka Khan","doi":"10.1109/TETC.2025.3629528","DOIUrl":"https://doi.org/10.1109/TETC.2025.3629528","url":null,"abstract":"Federated learning (FL) often suffers from client heterogeneity – differences in data distributions and learning behavior across clients that can degrade the global model’s performance. This paper addresses this challenge with HIFLA (Hilbert-Inspired Federated Learning via Action Principles), a novel approach that leverages variational mechanics. HIFLA formulates the federated training process as the minimization of a global action functional, yielding entropy- regularized Euler–Lagrange dynamics for client and server updates. A key innovation is the introduction of an <italic>interaction potential among client models, which mitigates divergence caused by non-i.i.d. data by coupling their updates in the action formulation. Empirically, HIFLA improves model accuracy on heterogeneous FL benchmarks, outperforming standard methods in the presence of statistical heterogeneity. It also demonstrates enhanced robustness against adversarial clients: even when a fraction of participants behave maliciously or send corrupted updates, the HIFLA-based model converges reliably with minimal performance loss. Overall, our results indicate that an action-principle-driven paradigm can effectively tackle client heterogeneity and adversarial robustness in federated learning, paving the way for more resilient and generalizable FL systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1536-1552"},"PeriodicalIF":5.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729326","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

A Novel Proportional-Integral-Parameter Zeroing Neural Network and Its Application to the Quaternion-Valued Time-Varying Linear Matrix Inequality 一种新型比例-积分-参数归零神经网络及其在四元数值时变线性矩阵不等式中的应用

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-11 DOI: 10.1109/TETC.2025.3629357

Jiajie Luo;Jiguang Li;Lin Xiao;Jichun Li;Wenxing Ji;William Holderbaum;Peng Qi

{"title":"A Novel Proportional-Integral-Parameter Zeroing Neural Network and Its Application to the Quaternion-Valued Time-Varying Linear Matrix Inequality","authors":"Jiajie Luo;Jiguang Li;Lin Xiao;Jichun Li;Wenxing Ji;William Holderbaum;Peng Qi","doi":"10.1109/TETC.2025.3629357","DOIUrl":"https://doi.org/10.1109/TETC.2025.3629357","url":null,"abstract":"Zeroing neural network (ZNN) with variable convergence parameter has been a research hotspot recently, and it can be divided into two categories: the varying-parameter ZNN (VP-ZNN) model, and the fuzzy-parameter ZNN (FP-ZNN) model. Both of the two models have their own advantages and disadvantages. VP-ZNN models are usually efficient but not intelligent, while FP-ZNN models are usually intelligent but not efficient. Inspired by the classic proportional–integral–derivative (PID) control technology, we proposed a novel proportional-integral-parameter ZNN (PIP-ZNN) model, which is both efficient and intelligent, to solve the quaternion-valued time-varying linear matrix inequalities (QVTV-LMI) problem. Integrated with adaptive convergence parameters (ACP), the PIP-ZNN model dynamically adjusts its convergence in response to error changes and then achieves optimized performance. Compared with FP-ZNN models which are based on fuzzy logic systems, the PID-based PIP-ZNN models are simpler and more efficient. With the incorporation of a robust activation function (RAF), the PIP-ZNN model demonstrates fixed-time stability and robustness in the presence of both attenuated and constant disturbances. Theoretical analyses in this paper establish the fixed-time stability and robustness of the PIP-ZNN model, including an estimation of the upper bound of the settling-time function. Numerical experiments here validate these advanced features further, emphasizing the efficacy and excellent performance of the proposed PIP-ZNN model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1565-1576"},"PeriodicalIF":5.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729402","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

GraphMamba: Graph Tokenization Mamba for Hyperspectral Image Classification 用于高光谱图像分类的图标记化曼巴

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-11-05 DOI: 10.1109/TETC.2025.3626943

Muhammad Ahmad;Manuel Mazzara;Salvatore Distefano;Adil Mehmood Khan;Muhammad Hassaan Farooq Butt;Muhammad Usama;Danfeng Hong

{"title":"GraphMamba: Graph Tokenization Mamba for Hyperspectral Image Classification","authors":"Muhammad Ahmad;Manuel Mazzara;Salvatore Distefano;Adil Mehmood Khan;Muhammad Hassaan Farooq Butt;Muhammad Usama;Danfeng Hong","doi":"10.1109/TETC.2025.3626943","DOIUrl":"https://doi.org/10.1109/TETC.2025.3626943","url":null,"abstract":"Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. Traditional methods, including conventional machine learning and convolutional neural networks (CNNs), often struggle to effectively capture intricate spectral-spatial features and global contextual information. Transformer-based models, while powerful in capturing long-range dependencies, often demand substantial computational resources, posing challenges in scenarios where labeled datasets are limited, as in HSI applications. To overcome such challenges, this work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms. The model introduces a novel hybridization of state-space modeling and Gated Recurrent Units (GRU), capturing both linear and nonlinear spatial-spectral dynamics. This approach enhances the ability to model complex spatial-spectral relationships while maintaining scalability and computational efficiency across diverse HSI datasets. Through comprehensive experiments, we demonstrate that GraphMamba outperforms existing state-of-the-art models, offering a scalable and robust solution for complex HSI classification tasks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1510-1521"},"PeriodicalIF":5.4,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729314","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

Investigating and Mitigating Critical Faults in Floating-Point and Posit Arithmetic Hardware 浮点和正算术硬件关键故障的调查和缓解

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-10-06 DOI: 10.1109/TETC.2025.3615827

Josie E. Rodriguez Condia;Juan-David Guerrero-Balaguera;Robert Limas Sierra;Matteo Sonza Reorda

{"title":"Investigating and Mitigating Critical Faults in Floating-Point and Posit Arithmetic Hardware","authors":"Josie E. Rodriguez Condia;Juan-David Guerrero-Balaguera;Robert Limas Sierra;Matteo Sonza Reorda","doi":"10.1109/TETC.2025.3615827","DOIUrl":"https://doi.org/10.1109/TETC.2025.3615827","url":null,"abstract":"Mature computing formats, such as Floating-Point (FP), provide optimal accuracy to process real values and are essential in most scientific domains. However, the massive market adoption of highly parallel systems, with advanced technology nodes, in several domains exacerbates the need for highly reliable systems. Formerly, most reliability evaluations targeted FP hardware. Unfortunately, fine-grain assessments on cores with recent arithmetic format alternatives, such as Posit (particularly suited for Artificial Intelligence), have remained partially unexplored. Similarly, the effects of corruption on operations due to faulty hardware are not well-known, which may prevent the proposal of effective mitigation mechanisms. This work exhaustively evaluates the fine-grain effects of permanent faults in the hardware of arithmetic cores for the three most extensively used operations in modern applications (<italic>Add, <italic>Multiply, and <italic>Multiply and Add), including machine learning, implemented in Posit and FP. Our results indicate that Posit cores are less fault-vulnerable than FP ones. However, Posit cores are more prone to induce significant operational corruption than FP ones (5.2% to 7.5%). We also found that absolute errors in faulty FP cores are higher by up to 2 orders of magnitude than in Posit ones. Finally, we applied and evaluated three mitigation mechanisms (<italic>Self-Check and repair, <italic>Dual Modular Redundancy, and <italic>Triple Modular Redundancy), effectively reducing the most critical errors with moderate area costs (20% to 110%).","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1605-1617"},"PeriodicalIF":5.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674752","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

Security-Driven Task Scheduling Under Deadline Constraints for MPSoCs With Untrusted 3PIP Cores 具有不可信3PIP内核的mpsoc在截止日期约束下的安全驱动任务调度

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-10-02 DOI: 10.1109/TETC.2025.3614659

Nan Wang;Lijun Lu;Songping Liu;Hongqing Zhu;Yu Zhu

{"title":"Security-Driven Task Scheduling Under Deadline Constraints for MPSoCs With Untrusted 3PIP Cores","authors":"Nan Wang;Lijun Lu;Songping Liu;Hongqing Zhu;Yu Zhu","doi":"10.1109/TETC.2025.3614659","DOIUrl":"https://doi.org/10.1109/TETC.2025.3614659","url":null,"abstract":"The high penetration of third-party intellectual property in MPSoCs gives rise to security concerns, and a set of security-driven constraints is imposed into the task scheduling step of the design process to protect MPSoCs against hardware Trojan attacks. Due to the significant performance and area overheads incurred, designers start to selectively apply security-driven constraints to achieve the design targets, but they often ignore that parts of a design may be more vulnerable to hardware Trojan attacks. In this study, the differences in vulnerability to hardware Trojan attacks are also considered in the MPSoC design process, and a security-driven task scheduling method is proposed to minimize both the design vulnerability and chip area under deadline constraints. First, the schedule length is iteratively optimized by a maximum weight independent set-based method that minimizes the vulnerability increment. Second, tasks are assigned to IP vendors with a minimized number of cores required by maximizing the core sharing of tasks. Finally, tasks are scheduled to time periods using the force-directed scheduling method. Experimental results demonstrate the effectiveness of the proposed method in reducing the number of cores while maintaining system security under deadline constraints.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1577-1590"},"PeriodicalIF":5.4,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674811","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

Asyn2F: An Asynchronous Federated Learning Framework With Bidirectional Model Aggregation Asyn2F：具有双向模型聚合的异步联邦学习框架

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-09-22 DOI: 10.1109/TETC.2025.3609004

Tien-Dung Cao;Nguyen T. Vuong;Thai Q. Le;Hoang V. N. Dao;Tram Truong-Huu

{"title":"Asyn2F: An Asynchronous Federated Learning Framework With Bidirectional Model Aggregation","authors":"Tien-Dung Cao;Nguyen T. Vuong;Thai Q. Le;Hoang V. N. Dao;Tram Truong-Huu","doi":"10.1109/TETC.2025.3609004","DOIUrl":"https://doi.org/10.1109/TETC.2025.3609004","url":null,"abstract":"In federated learning, the models can be trained synchronously or asynchronously. Many existing works have focused on developing an aggregation method for the server to aggregate multiple local models into the global model with improved performance. They ignore the heterogeneity of the training workers, which causes the delay in the training of the local models, leading to the obsolete information issue. In this paper, we design and develop <sc>Asyn2F, an <sc>Asynchronous <sc>Federated learning <sc>Framework with bidirectional model aggregation. By bidirectional aggregation, <sc>Asyn2F, on one hand, allows the server to asynchronously aggregate multiple local models and generate a new global model. On the other hand, it allows the training workers to aggregate the new version of the global model into a local model, which is being optimized even in the middle of a training epoch. We develop <sc>Asyn2F considering various practical implementation requirements with geographically distributed and heterogeneous training workers. Extensive experiments with different datasets show that the models trained by <sc>Asyn2F achieve higher performance compared to the state-of-the-art techniques. The experiments also demonstrate the effectiveness, practicality, and scalability of <sc>Asyn2F, making it ready for practical deployment.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 4","pages":"1618-1632"},"PeriodicalIF":5.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729449","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