Tsinghua Science and Technology最新文献_第6页

Betweenness Approximation for Edge Computing with Hypergraph Neural Networks 利用超图神经网络进行边缘计算的间隔近似法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010106

Yaguang Guo;Wenxin Xie;Qingren Wang;Dengcheng Yan;Yiwen Zhang

{"title":"Betweenness Approximation for Edge Computing with Hypergraph Neural Networks","authors":"Yaguang Guo;Wenxin Xie;Qingren Wang;Dengcheng Yan;Yiwen Zhang","doi":"10.26599/TST.2023.9010106","DOIUrl":"https://doi.org/10.26599/TST.2023.9010106","url":null,"abstract":"Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things (IoTs), given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications. Network Dismantling (ND), which is a basic problem, attempts to find an optimal set of nodes that will maximize the connectivity degradation in a network. However, current approaches mainly focus on simple networks that model only pairwise interactions between two nodes, whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world, which can be better modeled as hypernetwork. The structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a hypernetwork. Although some hypernetwork centrality measures (e.g., betweenness) can be used for hypernetwork dismantling, they face the problem of balancing effectiveness and efficiency. Therefore, we propose a betweenness approximation-based hypernetwork dismantling method with a Hypergraph Neural Network (HNN). The proposed approach, called “HND”, trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way, utilizing the well-trained model to approximate the betweenness of the nodes. Extensive experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"331-344"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CamDroid: Context-Aware Model-Based Automated GUI Testing for Android Apps CamDroid：基于上下文感知模型的 Android 应用程序图形用户界面自动测试

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2024.9010038

Hongyi Wang;Yang Li;Jing Yang;Daqiang Hu;Zhi Liao

引用次数: 0

GaitFFDA: Feature Fusion and Dual Attention Gait Recognition Model GaitFFDA：特征融合与双重注意力步态识别模型

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010089

Zhixiong Wu;Yong Cui

{"title":"GaitFFDA: Feature Fusion and Dual Attention Gait Recognition Model","authors":"Zhixiong Wu;Yong Cui","doi":"10.26599/TST.2023.9010089","DOIUrl":"https://doi.org/10.26599/TST.2023.9010089","url":null,"abstract":"Gait recognition has a wide range of application scenarios in the fields of intelligent security and transportation. Gait recognition currently faces challenges: inadequate feature methods for environmental interferences and insufficient local-global information correlation. To address these issues, we propose a gait recognition model based on feature fusion and dual attention. Our model utilizes the ResNet architecture as the backbone network for fundamental gait features extraction. Subsequently, the features from different network layers are passed through the feature pyramid for feature fusion, so that multi-scale local information can be fused into global information, providing a more complete feature representation. The dual attention module enhances the fused features in multiple dimensions, enabling the model to capture information from different semantics and scale information. Our model proves effective and competitive results on CASIA-B (NM: 95.6%, BG: 90.9%, CL: 73.7%) and OU-MVLP (88.1%). The results of related ablation experiments show that the model design is effective and has strong competitiveness.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"345-356"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676356","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed Strategy Nash Equilibrium for Scheduling Games on Batching-Machines with Activation Cost

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-08-20 DOI: 10.26599/TST.2024.9010056

Long Zhang;Zhiwen Wang;Jingwen Wang;Donglei Du;Chuanwen Luo

引用次数: 0

Software Reliability Assessment: An Architectural and Component Impact Analysis

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-28 DOI: 10.26599/TST.2024.9010101

Saleh Alyahyan;Mohammed Naif Alatawi;Mrim M. Alnfiai;Shoayee Dlaim Alotaibi;Abdullah Alshammari;Zaid Alzaid;Hathal Salamah Alwageed

引用次数: 0

Maximizing Overall Service Profit: Multi-Edge Service Pricing as a Stochastic Game Model 整体服务利润最大化：作为随机博弈模型的多边缘服务定价

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2024.9010050

Shengye Pang;Xinkui Zhao;Jiayin Luo;Jintao Chen;Fan Wang;Jianwei Yin

{"title":"Maximizing Overall Service Profit: Multi-Edge Service Pricing as a Stochastic Game Model","authors":"Shengye Pang;Xinkui Zhao;Jiayin Luo;Jintao Chen;Fan Wang;Jianwei Yin","doi":"10.26599/TST.2024.9010050","DOIUrl":"https://doi.org/10.26599/TST.2024.9010050","url":null,"abstract":"The diversified development of the service ecosystem, particularly the rapid growth of services like cloud and edge computing, has propelled the flourishing expansion of the service trading market. However, in the absence of appropriate pricing guidance, service providers often devise pricing strategies solely based on their own interests, potentially hindering the maximization of overall market profits. This challenge is even more severe in edge computing scenarios, as different edge service providers are dispersed across various regions and influenced by multiple factors, making it challenging to establish a unified pricing model. This paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge services. Subsequently, an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction, achieving optimal profits. Finally, an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three stages. This optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary mechanisms. Experimental results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations, while also demonstrating the effectiveness of our algorithm in resolving game problems.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1872-1889"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Time-Aware Cross-Blockchain Data Migration 关于时间感知的跨区块链数据迁移

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010136

Mengqiu Zhang;Qiang Qu;Li Ning;Jianping Fan

{"title":"On Time-Aware Cross-Blockchain Data Migration","authors":"Mengqiu Zhang;Qiang Qu;Li Ning;Jianping Fan","doi":"10.26599/TST.2023.9010136","DOIUrl":"https://doi.org/10.26599/TST.2023.9010136","url":null,"abstract":"With the widespread adoption of blockchain applications, the imperative for seamless data migration among decentralized applications has intensified. This necessity arises from various factors, including the depletion of blockchain disk space, transitions between blockchain systems, and specific requirements such as temporal data analysis. To meet these challenges and ensure the sustained functionality of applications, it is imperative to conduct time-aware cross-blockchain data migration. This process is designed to facilitate the smooth iteration of decentralized applications and the construction of a temporal index for historical data, all while preserving the integrity of the original data. In various application scenarios, this migration task may encompass the transfer of data between multiple blockchains, involving movements from one chain to another, from one chain to several chains, or from multiple chains to a single chain. However, the success of data migration hinges on the careful consideration of factors such as the reliability of the data source, data consistency, and migration efficiency. This paper introduces a time-aware cross-blockchain data migration approach tailored to accommodate diverse application scenarios, including migration between multiple chains. The proposed solution integrates a collective mechanism for controlling, executing, and storing procedures to address the complexities of data migration, incorporating elements such as transaction classification and matching. Extensive experiments have been conducted to validate the efficacy of the proposed approach.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1810-1820"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution 利用混合生成式对抗网络和 YOLO3 解决方案加强电力线绝缘体健康监测

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010137

Ramakrishna Akella;Sravan Kumar Gunturi;Dipu Sarkar

{"title":"Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution","authors":"Ramakrishna Akella;Sravan Kumar Gunturi;Dipu Sarkar","doi":"10.26599/TST.2023.9010137","DOIUrl":"https://doi.org/10.26599/TST.2023.9010137","url":null,"abstract":"In the critical field of electrical grid maintenance, ensuring the integrity of power line insulators is a primary concern. This study introduces an innovative approach for monitoring the condition of insulators using aerial surveillance via drone-mounted cameras. The proposed method is a composite deep learning framework that integrates the “You Only Look Once” version 3 (YOLO3) model with deep convolutional generative adversarial networks (DCGAN) and super-resolution generative adversarial networks (SRGAN). The YOLO3 model excels in rapidly and accurately detecting insulators, a vital step in assessing their health. Its effectiveness in distinguishing insulators against complex backgrounds enables prompt detection of defects, essential for proactive maintenance. This rapid detection is enhanced by DCGAN's precise classification and SRGAN's image quality improvement, addressing challenges posed by low-resolution drone imagery. The framework's performance was evaluated using metrics such as sensitivity, specificity, accuracy, localization accuracy, damage sensitivity, and false alarm rate. Results show that the SRGAN+DCGAN+YOLO3 model significantly outperforms existing methods, with a sensitivity of 98%, specificity of 94%, an overall accuracy of 95.6%, localization accuracy of 90%, damage sensitivity of 92%, and a reduced false alarm rate of 8%. This advanced hybrid approach not only improves the detection and classification of insulator conditions but also contributes substantially to the maintenance and health of power line insulators, thus ensuring the reliability of the electrical power grid.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1796-1809"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Streaming Histogram Publication Over Weighted Sliding Windows Under Differential Privacy 在差异隐私条件下通过加权滑动窗口进行流式直方图发布

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20 DOI: 10.26599/TST.2023.9010083

Xiujun Wang;Lei Mo;Xiao Zheng;Zhe Dang

{"title":"Streaming Histogram Publication Over Weighted Sliding Windows Under Differential Privacy","authors":"Xiujun Wang;Lei Mo;Xiao Zheng;Zhe Dang","doi":"10.26599/TST.2023.9010083","DOIUrl":"https://doi.org/10.26599/TST.2023.9010083","url":null,"abstract":"Continuously publishing histograms in data streams is crucial to many real-time applications, as it provides not only critical statistical information, but also reduces privacy leaking risk. As the importance of elements usually decreases over time in data streams, in this paper we model a data stream by a sequence of weighted sliding windows, and then study how to publish histograms over these windows continuously. The existing literature can hardly solve this problem in a real-time way, because they need to buffer all elements in each sliding window, resulting in high computational overhead and prohibitive storage burden. In this paper, we overcome this drawback by proposing an online algorithm denoted by Efficient Streaming Histogram Publishing (ESHP) to continuously publish histograms over weighted sliding windows. Specifically, our method first creates a novel sketching structure, called Approximate-Estimate Sketch (AESketch), to maintain the counting information of each histogram interval at every time instance; then, it creates histograms that satisfy the differential privacy requirement by smartly adding appropriate noise values into the sketching structure. Extensive experimental results and rigorous theoretical analysis demonstrate that the ESHP method can offer equivalent data utility with significantly lower computational overhead and storage costs when compared to other existing methods.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"29 6","pages":"1674-1693"},"PeriodicalIF":6.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10566026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Total Contents 总内容

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2024-06-20

引用次数: 0