IEEE Transactions on Emerging Topics in Computing最新文献_第5页

A Novel Privacy-Preserving Range Query Scheme With Permissioned Blockchain for Smart Grid 针对智能电网的新型隐私保护范围查询方案与许可区块链

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-15 DOI: 10.1109/TETC.2024.3386803

Kun-Chang Li;Peng-Bo Wang;Run-Hua Shi

引用次数: 0

On the Privacy of the Count-Min Sketch: Extracting the Top-K Elements 论计数-最小草图的隐私性：提取前 K 元素

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-04-04 DOI: 10.1109/TETC.2024.3383321

Alfonso Sánchez-Macián;Jorge Martínez;Pedro Reviriego;Shanshan Liu;Fabrizio Lombardi

{"title":"On the Privacy of the Count-Min Sketch: Extracting the Top-K Elements","authors":"Alfonso Sánchez-Macián;Jorge Martínez;Pedro Reviriego;Shanshan Liu;Fabrizio Lombardi","doi":"10.1109/TETC.2024.3383321","DOIUrl":"10.1109/TETC.2024.3383321","url":null,"abstract":"Estimating the frequency of elements in a data stream and identifying the elements that appear many times (also known as heavy hitters) are needed in many applications such as traffic monitoring in networks or popularity estimate in web and social networks. The Count-Min Sketch (CMS) is probably one of the most widely used algorithms for frequency estimate. The CMS uses a sub-linear space to provide queries for data streams and retrieve an approximate value for the frequency of events. It has been used in many different applications and scenarios, making its security and privacy a matter of interest. This paper considers the privacy of the CMS and presents an algorithm to extract the most frequent elements (also known as top-K) and their estimate from a CMS. This is possible for universes of a limited size; when the attacker has access to the sketch, its hash functions and the counters at a specific point of time. The algorithm is tested using CAIDA traces showing that it is able to retrieve the group of top-K elements with an acceptable percentage of false positives and negatives. The results improve with the size of the sketch and for smaller values of K, indicating that in some practical settings an attacker can extract substantial information about the top-K elements from the sketch. The code used in the simulation is provided in a public open-source repository to facilitate reproducing our results and extending the ideas presented in this paper.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 4","pages":"1056-1065"},"PeriodicalIF":5.1,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10492661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Online Resource Provisioning and Batch Scheduling for AIoT Inference Serving in an XPU Edge Cloud 在 XPU 边缘云中为人工智能物联网推理服务进行在线资源调配和批量调度

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-30 DOI: 10.1109/TETC.2024.3403874

Rongkai Liu;Yuting Wu;Kongyange Zhao;Zhi Zhou;Xiang Gao;Xianchen Lin;Xiaoxi Zhang;Xu Chen;Gang Lu

{"title":"Online Resource Provisioning and Batch Scheduling for AIoT Inference Serving in an XPU Edge Cloud","authors":"Rongkai Liu;Yuting Wu;Kongyange Zhao;Zhi Zhou;Xiang Gao;Xianchen Lin;Xiaoxi Zhang;Xu Chen;Gang Lu","doi":"10.1109/TETC.2024.3403874","DOIUrl":"10.1109/TETC.2024.3403874","url":null,"abstract":"Driven by the accelerated convergence of artificial intelligence (AI) and the Internet of Things (IoT), the recent years have witnessed the booming of <italic>Artificial Intelligence of Things</i> (AIoT). Edge clouds place computing and service capabilities at the network edges to reduce network transmission overhead, which has been widely recognized as the critical infrastructure for AIoT applications. Meanwhile, to accelerate computation-intensive edge cloud AI operations, specialized AI accelerators such as GPU, NPU, and TPU have been increasingly integrated into edge clouds. For such emerging XPU edge clouds, utilizing costly XPUs more efficiently has become a significant challenge. In this paper, we present an online optimization framework for joint resource provisioning and batch scheduling for more cost-efficient AIoT inference serving in an XPU edge cloud. The essential optimization process for the online framework is to first adaptively batch inference tasks to increase the system throughput without compromising the service level agreement (SLA). Next, heterogeneous XPU resources are provisioned for the batches. Finally, the resource instance is consolidated to a minimum of physical servers. Via extensive trace-driven simulations, we verify the performance of the presented online optimization framework.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"234-249"},"PeriodicalIF":5.1,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188462","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

Linearizing Binary Optimization Problems Using Variable Posets for Ising Machines 利用伊辛机的可变 Posets 实现二元优化问题的线性化

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-30 DOI: 10.1109/TETC.2024.3403871

Kentaro Ohno;Nozomu Togawa

{"title":"Linearizing Binary Optimization Problems Using Variable Posets for Ising Machines","authors":"Kentaro Ohno;Nozomu Togawa","doi":"10.1109/TETC.2024.3403871","DOIUrl":"10.1109/TETC.2024.3403871","url":null,"abstract":"Ising machines are next-generation computers expected to efficiently sample near-optimal solutions of combinatorial optimization problems. Combinatorial optimization problems are modeled as quadratic unconstrained binary optimization (QUBO) problems to apply an Ising machine. However, current state-of-the-art Ising machines still often fail to output near-optimal solutions due to the complicated energy landscape of QUBO problems. Furthermore, the physical implementation of Ising machines severely restricts the size of QUBO problems to be input as a result of limited hardware graph structures. In this study, we take a new approach to these challenges by injecting auxiliary penalties preserving the optimum, which reduces quadratic terms in QUBO objective functions. The process simultaneously simplifies the energy landscape of QUBO problems, allowing the search for near-optimal solutions, and makes QUBO problems sparser, facilitating encoding into Ising machines with restriction on the hardware graph structure. We propose linearization of QUBO problems using variable posets as an outcome of the approach. By applying the proposed method to synthetic QUBO instances and to multi-dimensional knapsack problems, we empirically validate the effects on enhancing minor-embedding of QUBO problems and the performance of Ising machines.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"250-261"},"PeriodicalIF":5.1,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fair Influence Maximization in Social Networks: A Community-Based Evolutionary Algorithm 社交网络中的公平影响力最大化：基于社群的进化算法

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-30 DOI: 10.1109/TETC.2024.3403891

Kaicong Ma;Xinxiang Xu;Haipeng Yang;Renzhi Cao;Lei Zhang

{"title":"Fair Influence Maximization in Social Networks: A Community-Based Evolutionary Algorithm","authors":"Kaicong Ma;Xinxiang Xu;Haipeng Yang;Renzhi Cao;Lei Zhang","doi":"10.1109/TETC.2024.3403891","DOIUrl":"10.1109/TETC.2024.3403891","url":null,"abstract":"Influence maximization (IM) has been extensively studied in network science, which attempts to find a subset of users to maximize the influence spread. A new variant of IM, fair IM (FIM), which primarily enhances the fair propagation of information, has attracted increasing attention in academia. However, existing algorithms for FIM suffer from a trade-off between fairness and running time, as it is difficult to ensure that users are fairly influenced in terms of sensitive attributes, such as race or gender, while maintaining a high influence spread. To tackle this problem, herein, we propose an effective and efficient community-based evolutionary algorithm for FIM (named CEA-FIM). In CEA-FIM, a community-based node selection strategy is proposed to identify potential nodes, which not only considers the size of the community but also the attributes of the nodes in the community. Subsequently, we designed an evolutionary algorithm based on the proposed node selection strategy to hasten the solution search, including the novel initialization, crossover, and mutation strategies. We validated the proposed algorithm by performing experiments on real-world and synthetic networks. The experimental results show that the proposed CEA-FIM achieves a better balance between effectiveness and efficiency than state-of-the-art methods do.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"262-275"},"PeriodicalIF":5.1,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188537","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

Dynamically Activated De-Glaring and Detail- Recovery for Low-Light Image Enhancement Directly on Smart Cameras 直接在智能相机上动态激活去光晕和细节恢复功能，以增强弱光下的图像效果

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-27 DOI: 10.1109/TETC.2024.3403935

Shao-Wei Dong;Ching-Hu Lu

{"title":"Dynamically Activated De-Glaring and Detail- Recovery for Low-Light Image Enhancement Directly on Smart Cameras","authors":"Shao-Wei Dong;Ching-Hu Lu","doi":"10.1109/TETC.2024.3403935","DOIUrl":"10.1109/TETC.2024.3403935","url":null,"abstract":"Low-light conditions often significantly affect the stability of a computer-vision system. Existing studies of unpaired-learning-based low-light image enhancement do not consider glare that occurs during the night, which can lead to significant degradation of image quality. To improve image quality, our study proposes an additional enhancement module that can be applied to existing methods. That is, our proposed “lightweight low-light image de-glaring network” can remove glare from low-light images. We also propose a “low-light image-detail-recovery network” to enhance the boundary details of low-light images after removing glare to further improve image quality. The experimental results show that our proposed approaches can effectively improve low-light image quality. In addition, we propose “dynamically activated de-glaring” to assess the quality of input images first to determine whether de-glaring should be undertaken in order to effectively utilize the computational resources of a smart camera and avoid unnecessary image enhancement. The experimental results show that running time and frames per second can be greatly improved when applied to real-world scenarios.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"222-233"},"PeriodicalIF":5.1,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170261","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

Guest Editorial Emerging Trends and Advances in Graph-Based Methods and Applications 特约编辑基于图形的方法和应用的新趋势和新进展

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-18 DOI: 10.1109/TETC.2024.3374581

Alessandro D'Amelio;Jianyi Lin;Jean-Yves Ramel;Raffaella Lanzarotti

引用次数: 0

Guest Editorial IEEE Transactions on Emerging Topics in Special Section on Emerging In-Memory Computing Architectures and Applications 客座编辑 IEEE Transactions on Emerging Topics 的新兴内存计算体系结构与应用专栏

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-18 DOI: 10.1109/TETC.2024.3369288

Alberto Bosio;Ronald F. DeMara;Deliang Fan;Nima TaheriNejad

引用次数: 0

Guest Editorial IEEE Transactions on Emerging Topics in Computing Special Section on Advances in Emerging Privacy-Preserving Computing 客座编辑 IEEE《计算领域新兴课题论文集》"新兴隐私保护计算的进展 "专栏

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-18 DOI: 10.1109/TETC.2024.3374568

Jinguang Han;Patrick Schaumont;Willy Susilo

引用次数: 0

IEEE Transactions on Emerging Topics in Computing Information for Authors 电气和电子工程师学会（IEEE）《计算领域新兴专题论文》（IEEE Transactions on Emerging Topics in Computing）供作者参考的信息

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-03-18 DOI: 10.1109/TETC.2024.3377773

引用次数: 0