IEEE Transactions on Emerging Topics in Computing最新文献_第2页

A Novel RFET-Based FPGA Architecture Based on Delay-Aware Packing Algorithm 一种基于延迟感知封装算法的rfet FPGA结构

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-06-16 DOI: 10.1109/TETC.2025.3572712

Sheng Lu;Liuting Shang;Sungyong Jung;Qilian Liang;Chenyun Pan

引用次数: 0

What, When, Where to Compute-in-Memory for Efficient Matrix Multiplication During Machine Learning Inference 在机器学习推理过程中，什么、何时、何地在内存中进行有效的矩阵乘法计算

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-06-05 DOI: 10.1109/TETC.2025.3574508

Tanvi Sharma;Mustafa Ali;Indranil Chakraborty;Kaushik Roy

{"title":"What, When, Where to Compute-in-Memory for Efficient Matrix Multiplication During Machine Learning Inference","authors":"Tanvi Sharma;Mustafa Ali;Indranil Chakraborty;Kaushik Roy","doi":"10.1109/TETC.2025.3574508","DOIUrl":"https://doi.org/10.1109/TETC.2025.3574508","url":null,"abstract":"Matrix multiplication is the dominant computation during Machine Learning (ML) inference. To efficiently perform such multiplication operations, Compute-in-memory (CiM) paradigms have emerged as a highly energy efficient solution. However, integrating compute in memory poses key questions, such as 1) What type of CiM to use: Given a multitude of CiM design characteristics, determining their suitability from architecture perspective is needed. 2) When to use CiM: ML inference includes workloads with a variety of memory and compute requirements, making it difficult to identify when CiM is more beneficial than standard processing cores. 3) Where to integrate CiM: Each memory level has different bandwidth and capacity, creating different data reuse opportunities for CiM integration. To answer such questions regarding on-chip CiM integration for accelerating ML workloads, we use an analytical architecture-evaluation methodology with tailored mapping algorithm. The mapping algorithm aims to achieve highest weight reuse and reduced data movements for a given CiM prototype and workload. Our analysis considers the integration of CiM prototypes into the cache levels of a tensor-core-like architecture, and shows that CiM integrated memory improves energy efficiency by up to <inline-formula><tex-math>$3.4 times$</tex-math></inline-formula> and throughput by up to <inline-formula><tex-math>$15.6 times$</tex-math></inline-formula> compared to established baseline with INT-8 precision. We believe the proposed work provides insights into what type of CiM to use, and when and where to optimally integrate it in the cache hierarchy for efficient matrix multiplication.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1215-1229"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036894","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

The Cancelable Multimodal Template Protection Algorithm Based on Random Index 基于随机索引的可取消多模态模板保护算法

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-06-04 DOI: 10.1109/TETC.2025.3574359

Huabin Wang;Mingzhao Wang;Xinxin Liu;Yingfan Cheng;Fei Liu;Jian Zhou;Liang Tao

{"title":"The Cancelable Multimodal Template Protection Algorithm Based on Random Index","authors":"Huabin Wang;Mingzhao Wang;Xinxin Liu;Yingfan Cheng;Fei Liu;Jian Zhou;Liang Tao","doi":"10.1109/TETC.2025.3574359","DOIUrl":"https://doi.org/10.1109/TETC.2025.3574359","url":null,"abstract":"Current multimodal template protection methods typically require encryption or transformation of the original biometric features. However, these operations carry certain risks, as attackers may reverse-engineer or decrypt the protected multimodal templates to retrieve partial or complete information about the original templates, leading to the leakage of the original biometric features. To address this issue, we propose a cancelable multimodal template protection method based on random indexing. First, hash functions are used to generate integer sequences as index values, which are then employed to create single-modal cancelable templates using random binary vectors. Second, the single-modal cancelable templates are used as indices for random binary sequences, which locate the corresponding template information and are filled into the fusion cancelable template at the respective positions, achieving template fusion. The resulting template is unrelated to the original biometric features. Finally, without directly storing the binary factor sequences, an XOR operation is performed on the extended biometric feature vectors and random binary sequences to generate the encoded key. Experimental results demonstrate that the proposed method significantly enhances performance on the FVC2002DB1 fingerprint, MMCBNU_6000 finger-vein, and NUPT_FPV databases, while also satisfying the standards for cancelable biometric feature design. We also analyze four privacy and security attacks against this scheme.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1200-1214"},"PeriodicalIF":5.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057468","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

PipeDAP: An Efficient Communication Framework for Scheduling Decoupled All-Reduce Primitives in Distributed DNN Training 分布式深度神经网络训练中调度解耦全约简原语的高效通信框架

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-06-02 DOI: 10.1109/TETC.2025.3573522

Yunqi Gao;Bing Hu;Mahdi Boloursaz Mashhadi;Wei Wang;Rahim Tafazolli;Mérouane Debbah

{"title":"PipeDAP: An Efficient Communication Framework for Scheduling Decoupled All-Reduce Primitives in Distributed DNN Training","authors":"Yunqi Gao;Bing Hu;Mahdi Boloursaz Mashhadi;Wei Wang;Rahim Tafazolli;Mérouane Debbah","doi":"10.1109/TETC.2025.3573522","DOIUrl":"https://doi.org/10.1109/TETC.2025.3573522","url":null,"abstract":"Communication scheduling effectively improves the scalability of distributed deep learning by overlapping computation and communication tasks during training. However, existing communication scheduling frameworks based on tensor partitioning suffer from two fundamental issues: (1) partitioning schemes at the data volume level introduce extensive startup overheads leading to higher energy consumption, and (2) partitioning schemes at the communication primitive level do not provide optimal scheduling resulting in longer training time. In this article, we propose an efficient communication mechanism, namely PipeDAP, which schedules decoupled all-reduce operations in a near-optimal order to minimize the time and energy consumption of training DNN models. We build the mathematical model for PipeDAP and derive the near-optimal scheduling order of the reduce-scatter and all-gather operations. Meanwhile, we leverage simultaneous communication of reduce-scatter and all-gather operations to further reduce the startup overheads. We implement the PipeDAP architecture on PyTorch framework, and apply it for distributed training of benchmark DNN models. Experimental results on two GPU clusters demonstrate that PipeDAP achieves up to 1.82x speedup and saves up to 45.4% of energy consumption compared to the state-of-the-art communication scheduling frameworks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1170-1184"},"PeriodicalIF":5.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057426","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

DT-Net: Point Cloud Completion Network With Neighboring Adaptive Denoiser and Splitting-Based Upsampling Transformer DT-Net：带有相邻自适应去噪和基于分裂的上采样变压器的点云补全网络

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-06-02 DOI: 10.1109/TETC.2025.3573505

Aihua Mao;Qing Liu;Yuxuan Tang;Sheng Ye;Ran Yi;Minjing Yu;Yong-Jin Liu

{"title":"DT-Net: Point Cloud Completion Network With Neighboring Adaptive Denoiser and Splitting-Based Upsampling Transformer","authors":"Aihua Mao;Qing Liu;Yuxuan Tang;Sheng Ye;Ran Yi;Minjing Yu;Yong-Jin Liu","doi":"10.1109/TETC.2025.3573505","DOIUrl":"https://doi.org/10.1109/TETC.2025.3573505","url":null,"abstract":"Point cloud completion, which involves inferring missing regions of 3D objects from partial observations, remains a challenging problem in 3D vision and robotics. Existing learning-based frameworks typically leverage an encoder-decoder architecture to predict the complete point cloud based on the global shape representation extracted from the incomplete input, or further introduce a refinement network to optimize the obtained complete point cloud in a coarse-to-fine manner, which is unable to capture fine-grained local geometric details and filled with noisy points in the thin or complex structure. In this article, we propose a novel coarse-to-fine point cloud completion framework called DT-Net, by focusing on coarse point cloud denoising and multi-level upsampling. Specifically, we propose a Neighboring Adaptive Denoiser (NAD) to effectively denoise the coarse point cloud generated by an autoencoder, and reduce noise around the slender structures, making them clear and well represented. Moreover, a novel Splitting-based Upsampling Transformer (SUT), which effectively incorporates spatial and semantic relationships between local neighborhoods in the point cloud, is also proposed for multi-level upsampling. Extensive qualitative and quantitative experiments demonstrate that our method outperforms state-of-the-art methods under widely used benchmarks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1185-1199"},"PeriodicalIF":5.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057427","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

Hybrid Quantum ResNet for Time Series Classification 时间序列分类的混合量子ResNet

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-04-30 DOI: 10.1109/TETC.2025.3563944

Dae-Il Noh;Seon-Geun Jeong;Won-Joo Hwang

{"title":"Hybrid Quantum ResNet for Time Series Classification","authors":"Dae-Il Noh;Seon-Geun Jeong;Won-Joo Hwang","doi":"10.1109/TETC.2025.3563944","DOIUrl":"https://doi.org/10.1109/TETC.2025.3563944","url":null,"abstract":"Residual networks (ResNet) are known to be effective for image classification. However, challenges such as computational time remain because of the significant number of parameters. Quantum computing using quantum entanglement and quantum parallelism is an emerging computing paradigm that addresses this issue. Although quantum advantage is still studied in many research fields, quantum machine learning is a research area that leverages the strengths of quantum computing and machine learning. In this study, we investigated the quantum speedup with respect to the number of parameters in each model for a time-series classification task. This paper proposes a novel hybrid quantum residual network (HQResNet) inspired by the classical ResNet for time-series classification. HQResNet introduces a classical layer before a quantum convolutional neural network (QCNN), where the QCNN is used as a residual block. These structures enable shortcut connections and are particularly effective in achieving classification tasks without a data re-uploading scheme. We used ultra-wide-band (UWB) channel impulse response data to demonstrate the performance of the proposed algorithm and compared the state-of-the-art benchmarks with HQResNet using evaluation metrics. The results show that HQResNet achieved high performance with a small number of trainable parameters.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1083-1098"},"PeriodicalIF":5.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057432","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

Design and Implementation of Cost-Effective End-to-End Authentication Protocol for PUF-Enabled IoT Devices 为支持puf的物联网设备设计和实现具有成本效益的端到端认证协议

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-04-28 DOI: 10.1109/TETC.2025.3563064

Sourav Roy;Mahabub Hasan Mahalat;Bibhash Sen

{"title":"Design and Implementation of Cost-Effective End-to-End Authentication Protocol for PUF-Enabled IoT Devices","authors":"Sourav Roy;Mahabub Hasan Mahalat;Bibhash Sen","doi":"10.1109/TETC.2025.3563064","DOIUrl":"https://doi.org/10.1109/TETC.2025.3563064","url":null,"abstract":"The ubiquitous presence of Internet of Things (IoT) prospers in every aspect of human life. The low-powered sensors, actuators, and mobile devices in IoT transfer a high volume of security-sensitive data. Unmonitored IoT devices are highly susceptible to security vulnerabilities. Their operating environment, with minimal or no safeguards, allows physical invasion. The conventional end-to-end authentications protocols are inadequate because of the limited resources and ambient working environment of IoT. In this direction, a lightweight and secure end-to-end authentication protocol is proposed for the Physically Unclonability Function (PUF) embedded IoT devices by processing them in pairs. PUF promises to be a unique hardware-based security solution for resource-constrained devices. The proposed protocol exploits the coherent conduct of public and private key-based cryptosystems with PUF. The protocol integrates the concept of ECC with ECDH and the cryptographic hash function. Security of the proposed protocol is validated using authentication validation, BAN logic, Scyther tool, and against different adversarial attacks. The performance evaluation and extensive comparative study of the proposed protocol highlight its lightweight feature. The practical feasibility of the proposed protocol is verified by an empirical evaluation using an Arbiter PUF implemented on Xilinx Spartan-3E FPGA and Raspberry Pi as an IoT device.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1055-1067"},"PeriodicalIF":5.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057430","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

Balancing Graph Processing Workloads in Heterogeneous CPU-PIM Systems 异构CPU-PIM系统图处理负载均衡

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-04-28 DOI: 10.1109/TETC.2025.3563249

Sheng Xu;Chun Li;Le Luo;Ming Zheng;Liang Yan;Xingqi Zou;Xiaoming Chen

{"title":"Balancing Graph Processing Workloads in Heterogeneous CPU-PIM Systems","authors":"Sheng Xu;Chun Li;Le Luo;Ming Zheng;Liang Yan;Xingqi Zou;Xiaoming Chen","doi":"10.1109/TETC.2025.3563249","DOIUrl":"https://doi.org/10.1109/TETC.2025.3563249","url":null,"abstract":"Processing-in-Memory (PIM) offers a promising architecture to alleviate the memory wall challenge in graph processing applications. The key aspect of PIM is to incorporate logic within the memory, thereby leveraging the near-data advantages. State-of-the-art PIM-based graph processing accelerators tend to offload more to the memory in order to maximize near-data benefits, causing significant load imbalance in PIM systems. In this paper, we demonstrate that this intention is not true and that host processors still play a vital role in heterogeneous CPU-PIM systems. For this purpose, we propose CAPLBS, an online contention-aware Processing-in-Memory load-balance scheduler for graph processing applications in CPU-PIM systems. The core concept of CAPLBS is to steal workload candidates back to host processors with minimal off-chip data synchronization overhead when some host processors are idle. To model data contentions among workloads and determine the stealing decision, a measurement structure called Locality Cohesive Subgraph is proposed by deeply exploring the connectivity of the input graph and the memory access patterns of deployed graph applications. Experimental results show that CAPLBS achieved an average speed-up of 4.8× and 1.3× (up to 9.1× and 1.9×) compared with CPU-only and the upper bound of locality-aware fine-grained in-memory atomics. Moreover, CAPLBS adds no hardware overhead and works well with existing CPU-PIM graph processing accelerators.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1068-1082"},"PeriodicalIF":5.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057429","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

CLOG-CD: Curriculum Learning Based on Oscillating Granularity of Class Decomposed Medical Image Classification CLOG-CD：基于类分解医学图像分类振荡粒度的课程学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-04-25 DOI: 10.1109/TETC.2025.3562620

Asmaa Abbas;Mohamed Medhat Gaber;Mohammed M. Abdelsamea

{"title":"CLOG-CD: Curriculum Learning Based on Oscillating Granularity of Class Decomposed Medical Image Classification","authors":"Asmaa Abbas;Mohamed Medhat Gaber;Mohammed M. Abdelsamea","doi":"10.1109/TETC.2025.3562620","DOIUrl":"https://doi.org/10.1109/TETC.2025.3562620","url":null,"abstract":"Curriculum learning strategies have been proven to be effective in various applications and have gained significant interest in the field of machine learning. It has the ability to improve the final model’s performance and accelerate the training process. However, in the medical imaging domain, data irregularities can make the recognition task more challenging and usually result in misclassification between the different classes in the dataset. Class-decomposition approaches have shown promising results in solving such a problem by learning the boundaries within the classes of the data set. In this paper, we present a novel convolutional neural network (CNN) training method based on the curriculum learning strategy and the class decomposition approach, which we call <italic>CLOG-CD, to improve the performance of medical image classification. We evaluated our method on four different imbalanced medical image datasets, such as Chest X-ray (CXR), brain tumour, digital knee x-ray, and histopathology colorectal cancer (CRC). <italic>CLOG-CD utilises the learnt weights from the decomposition granularity of the classes, and the training is accomplished from descending to ascending order (i.e. anti-curriculum technique). We also investigated the classification performance of our proposed method based on different acceleration factors and pace function curricula. We used two pre-trained networks, ResNet-50 and DenseNet-121, as the backbone for <italic>CLOG-CD. The results with ResNet-50 show that <italic>CLOG-CD has the ability to improve classification performance with an accuracy of 96.08% for the CXR dataset, 96.91% for the brain tumour dataset, 79.76% for the digital knee x-ray, and 99.17% for the CRC dataset, compared to other training strategies. In addition, with DenseNet-121, <italic>CLOG-CD has achieved 94.86%, 94.63%, 76.19%, and 99.45% for CXR, brain tumour, digital knee x-ray, and CRC datasets, respectively.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1043-1054"},"PeriodicalIF":5.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057471","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

Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning 近似编码计算：走向快速、私有和安全的分布式机器学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2025-04-24 DOI: 10.1109/TETC.2025.3562192

Houming Qiu;Kun Zhu;Nguyen Cong Luong;Dusit Niyato

{"title":"Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning","authors":"Houming Qiu;Kun Zhu;Nguyen Cong Luong;Dusit Niyato","doi":"10.1109/TETC.2025.3562192","DOIUrl":"https://doi.org/10.1109/TETC.2025.3562192","url":null,"abstract":"In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. First, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Second, there are few existing works considering security issues in data transmission of distributed computing systems/or coded distributed machine learning systems. Third, the number of required results for which need to wait increases with the degree of decoding functions. In this article, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1030-1042"},"PeriodicalIF":5.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057472","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