Pub Date : 2024-04-16DOI: 10.1109/tetc.2024.3387119
Xiao-Yan Li, Jou-Ming Chang
{"title":"LP-Star : Embedding Longest Paths into Star Networks with Large-Scale Missing Edges under an Emerging Assessment Model","authors":"Xiao-Yan Li, Jou-Ming Chang","doi":"10.1109/tetc.2024.3387119","DOIUrl":"https://doi.org/10.1109/tetc.2024.3387119","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"19 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612684","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}
Pub Date : 2024-04-16DOI: 10.1109/tetc.2024.3387026
Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, J. P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
{"title":"A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model","authors":"Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, J. P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno","doi":"10.1109/tetc.2024.3387026","DOIUrl":"https://doi.org/10.1109/tetc.2024.3387026","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"34 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612832","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}
Pub Date : 2024-04-16DOI: 10.1109/tetc.2024.3386893
Sangwoo Hwang, Jaeha Kung
{"title":"One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization","authors":"Sangwoo Hwang, Jaeha Kung","doi":"10.1109/tetc.2024.3386893","DOIUrl":"https://doi.org/10.1109/tetc.2024.3386893","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"166 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612834","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}
Pub Date : 2024-04-15DOI: 10.1109/tetc.2024.3386803
Kun-chang Li, Peng-bo Wang, Run-hua Shi
{"title":"A Novel Privacy-Preserving Range Query Scheme with Permissioned Blockchain for Smart Grid","authors":"Kun-chang Li, Peng-bo Wang, Run-hua Shi","doi":"10.1109/tetc.2024.3386803","DOIUrl":"https://doi.org/10.1109/tetc.2024.3386803","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"2 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567824","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}
Pub Date : 2024-04-05DOI: 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":"https://doi.org/10.1109/tetc.2024.3383321","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"558 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can be effectively modeled as graphs, introducing a new representation learning paradigm. The significance of GNNs extends to several domains, including computer vision [3], [4], natural language processing [5], chemistry/biology [6], physics [7], traffic networks [8], and recommendation systems [9].
{"title":"Guest Editorial Emerging Trends and Advances in Graph-Based Methods and Applications","authors":"Alessandro D'Amelio;Jianyi Lin;Jean-Yves Ramel;Raffaella Lanzarotti","doi":"10.1109/TETC.2024.3374581","DOIUrl":"https://doi.org/10.1109/TETC.2024.3374581","url":null,"abstract":"The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can be effectively modeled as graphs, introducing a new representation learning paradigm. The significance of GNNs extends to several domains, including computer vision [3], [4], natural language processing [5], chemistry/biology [6], physics [7], traffic networks [8], and recommendation systems [9].","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"122-125"},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161144","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}
Pub Date : 2024-03-18DOI: 10.1109/TETC.2024.3369288
Alberto Bosio;Ronald F. DeMara;Deliang Fan;Nima TaheriNejad
Computer architecture stands at an important crossroad to surmount vital performance challenges. For more than four decades, the performance of general purpose computing systems has been improving by 20–50% per year [1]. In the last decade, this number has dropped to less than 7% per year. Most recently, that rate has slowed to only 3% per year. [1]. The demand for performance improvement, however, keeps increasing and diversifies within new application domains. This higher performance, however, often has to come at a lower power consumption cost too, adding to the complexity of the task of architectural design space optimization. Both today's computer architectures and device technologies (used to manufacture them) are facing major challenges to achieve the performance demands required by complex applications such as Artificial Intelligence (AI). The complexity stems from the extremely high number of operations to be computed and the involved amount of data.
{"title":"Guest Editorial IEEE Transactions on Emerging Topics in Special Section on Emerging In-Memory Computing Architectures and Applications","authors":"Alberto Bosio;Ronald F. DeMara;Deliang Fan;Nima TaheriNejad","doi":"10.1109/TETC.2024.3369288","DOIUrl":"https://doi.org/10.1109/TETC.2024.3369288","url":null,"abstract":"Computer architecture stands at an important crossroad to surmount vital performance challenges. For more than four decades, the performance of general purpose computing systems has been improving by 20–50% per year [1]. In the last decade, this number has dropped to less than 7% per year. Most recently, that rate has slowed to only 3% per year. [1]. The demand for performance improvement, however, keeps increasing and diversifies within new application domains. This higher performance, however, often has to come at a lower power consumption cost too, adding to the complexity of the task of architectural design space optimization. Both today's computer architectures and device technologies (used to manufacture them) are facing major challenges to achieve the performance demands required by complex applications such as Artificial Intelligence (AI). The complexity stems from the extremely high number of operations to be computed and the involved amount of data.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"4-6"},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161235","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}
Pub Date : 2024-03-18DOI: 10.1109/TETC.2024.3374568
Jinguang Han;Patrick Schaumont;Willy Susilo
Machine learning and cloud computing have dramatically increased the utility of data. These technologies facilitate our life and provide smart and intelligent services. Notably, machine learning algorithms need to learn from massive training data to improve accuracy. Hence, data is the core component of machine learning and plays an important role. Cloud computing is a new computing model that provides on-demand services, such as data storage, computing power, and infrastructure. Data owners are allowed to outsource their data to cloud servers, but will lose direct control of their data. The rising trend in data breach shows that privacy and security have been major issues in machine learning and cloud computing.
{"title":"Guest Editorial IEEE Transactions on Emerging Topics in Computing Special Section on Advances in Emerging Privacy-Preserving Computing","authors":"Jinguang Han;Patrick Schaumont;Willy Susilo","doi":"10.1109/TETC.2024.3374568","DOIUrl":"https://doi.org/10.1109/TETC.2024.3374568","url":null,"abstract":"Machine learning and cloud computing have dramatically increased the utility of data. These technologies facilitate our life and provide smart and intelligent services. Notably, machine learning algorithms need to learn from massive training data to improve accuracy. Hence, data is the core component of machine learning and plays an important role. Cloud computing is a new computing model that provides on-demand services, such as data storage, computing power, and infrastructure. Data owners are allowed to outsource their data to cloud servers, but will lose direct control of their data. The rising trend in data breach shows that privacy and security have been major issues in machine learning and cloud computing.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"266-268"},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164101","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}
Pub Date : 2024-03-18DOI: 10.1109/TETC.2024.3377773
{"title":"IEEE Transactions on Emerging Topics in Computing Information for Authors","authors":"","doi":"10.1109/TETC.2024.3377773","DOIUrl":"https://doi.org/10.1109/TETC.2024.3377773","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 1","pages":"C2-C2"},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161123","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}
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