Pub Date : 2024-09-05DOI: 10.1109/TETC.2024.3449211
{"title":"IEEE Transactions on Emerging Topics in Computing Information for Authors","authors":"","doi":"10.1109/TETC.2024.3449211","DOIUrl":"https://doi.org/10.1109/TETC.2024.3449211","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143744","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 : 2024-09-05DOI: 10.1109/TETC.2024.3447428
Yuan-Hao Chang;Paloma Díaz;Yunpeng Xiao
{"title":"Special Section on Emerging Social Computing","authors":"Yuan-Hao Chang;Paloma Díaz;Yunpeng Xiao","doi":"10.1109/TETC.2024.3447428","DOIUrl":"https://doi.org/10.1109/TETC.2024.3447428","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 3","pages":"686-687"},"PeriodicalIF":5.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143745","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 : 2024-06-10DOI: 10.1109/TETC.2024.3395072
Jia Wu;Jian Yang;Philip S. Yu;Carlo Condo
Jia Wu received the PhD degree in computer science from the University of Technology Sydney, Ultimo, NSW, Australia. He is currently an ARC DECRA fellow with the Department of Computing, Macquarie University, Sydney, Australia. Prior to that, he was with the center for Artificial Intelligence, University of Technology Sydney. His current research interests include data mining and machine learning.
{"title":"Special Section on Community Detection in Time-Varying Information and Computing Networks: Theory, Models, and Applications","authors":"Jia Wu;Jian Yang;Philip S. Yu;Carlo Condo","doi":"10.1109/TETC.2024.3395072","DOIUrl":"https://doi.org/10.1109/TETC.2024.3395072","url":null,"abstract":"<bold>Jia Wu</b>\u0000 received the PhD degree in computer science from the University of Technology Sydney, Ultimo, NSW, Australia. He is currently an ARC DECRA fellow with the Department of Computing, Macquarie University, Sydney, Australia. Prior to that, he was with the center for Artificial Intelligence, University of Technology Sydney. His current research interests include data mining and machine learning.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 2","pages":"402-402"},"PeriodicalIF":5.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308671","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 : 2024-06-10DOI: 10.1109/TETC.2024.3394668
Gang Qu;Debdeep Mukhopadhyay;Nele Mentens;Weiqiang Liu
Gang Qu received the BS degree in mathematics from the University of Science and Technology of China (USTC), China, and the PhD degree in computer science from the University of California, Los Angeles (UCLA), USA. He is currently a professor with the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA, where he leads the Maryland Embedded Systems and Hardware Security Lab (MeshSec Lab) and the Wireless Sensor Laboratory. His research interests include hardware security and trust, artificial intelligence, security in vehicular systems, and the Internet of Things. He is also known for his work on wireless sensor networks, low power and energy efficient embedded system design.
{"title":"Special Section on Emerging Topics in Hardware Computing Systems Security","authors":"Gang Qu;Debdeep Mukhopadhyay;Nele Mentens;Weiqiang Liu","doi":"10.1109/TETC.2024.3394668","DOIUrl":"https://doi.org/10.1109/TETC.2024.3394668","url":null,"abstract":"<bold>Gang Qu</b>\u0000 received the BS degree in mathematics from the University of Science and Technology of China (USTC), China, and the PhD degree in computer science from the University of California, Los Angeles (UCLA), USA. He is currently a professor with the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA, where he leads the Maryland Embedded Systems and Hardware Security Lab (MeshSec Lab) and the Wireless Sensor Laboratory. His research interests include hardware security and trust, artificial intelligence, security in vehicular systems, and the Internet of Things. He is also known for his work on wireless sensor networks, low power and energy efficient embedded system design.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 2","pages":"482-482"},"PeriodicalIF":5.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308685","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 : 2024-06-10DOI: 10.1109/TETC.2024.3402450
Francesco Flammini;Cristina Alcaraz
Welcome to this special issue of the IEEE Transactions on Emerging Topics in Computing, dedicated to exploring the dynamic landscape of Cyber Security and Resilience. In an era where digital advancements are driving unprecedented connectivity and innovation, the imperative for robust cyber defenses and resilient systems has never been more pressing.
{"title":"Guest Editorial Navigating the Nexus of Cyber Security and Resilience","authors":"Francesco Flammini;Cristina Alcaraz","doi":"10.1109/TETC.2024.3402450","DOIUrl":"https://doi.org/10.1109/TETC.2024.3402450","url":null,"abstract":"Welcome to this special issue of the IEEE Transactions on Emerging Topics in Computing, dedicated to exploring the dynamic landscape of Cyber Security and Resilience. In an era where digital advancements are driving unprecedented connectivity and innovation, the imperative for robust cyber defenses and resilient systems has never been more pressing.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 2","pages":"558-558"},"PeriodicalIF":5.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308672","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 : 2024-06-10DOI: 10.1109/TETC.2024.3402764
{"title":"IEEE Transactions on Emerging Topics in Computing Information for Authors","authors":"","doi":"10.1109/TETC.2024.3402764","DOIUrl":"https://doi.org/10.1109/TETC.2024.3402764","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 2","pages":"C2-C2"},"PeriodicalIF":5.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308654","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}
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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