Pub Date : 2025-03-07DOI: 10.1109/TETC.2025.3543119
{"title":"IEEE Transactions on Emerging Topics in Computing Publication Information","authors":"","doi":"10.1109/TETC.2025.3543119","DOIUrl":"https://doi.org/10.1109/TETC.2025.3543119","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570651","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 : 2025-03-07DOI: 10.1109/TETC.2025.3530016
We thank the following reviewers for the time and energy they have given to TETC:
{"title":"2024 Reviewers List*","authors":"","doi":"10.1109/TETC.2025.3530016","DOIUrl":"https://doi.org/10.1109/TETC.2025.3530016","url":null,"abstract":"We thank the following reviewers for the time and energy they have given to <italic>TETC</i>:","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"276-278"},"PeriodicalIF":5.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570757","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 : 2025-03-07DOI: 10.1109/TETC.2024.3472428
Radu Marculescu;Jorge Sá Silva
Edge Artificial Intelligence (AI) enables us to deploy distributed AI models, optimize computational and energy resources, minimize communication demands, and, most importantly, meet privacy requirements for Internet of Things (IoT) applications. Since data remains on the end-devices and only model parameters are shared with the server, it becomes possible to leverage the vast amount of data collected from smartphones and IoT devices without compromising the user's privacy. However, Federated Learning (FL) solutions also have well-known limitations. In particular, as systems that account for human behaviour become increasingly vital, future technologies need to become attuned to human behaviours. Indeed, we are already witnessing unparalleled advancements in technology that empower our tools and devices with intelligence, sensory abilities, and communication features. At the same time, continued advances in the miniaturization of computational capabilities can enable us to go far beyond the simple tagging and identification, towards integrating computational resources directly into these objects, thus making our tools “intelligent”. Yet, there is limited scientific work that considers humans as an integral part of these IoT-powered cyber-physical systems.
{"title":"Editorial Special Section on Emerging Edge AI for Human-in-the-Loop Cyber Physical Systems","authors":"Radu Marculescu;Jorge Sá Silva","doi":"10.1109/TETC.2024.3472428","DOIUrl":"https://doi.org/10.1109/TETC.2024.3472428","url":null,"abstract":"Edge Artificial Intelligence (AI) enables us to deploy distributed AI models, optimize computational and energy resources, minimize communication demands, and, most importantly, meet privacy requirements for Internet of Things (IoT) applications. Since data remains on the end-devices and only model parameters are shared with the server, it becomes possible to leverage the vast amount of data collected from smartphones and IoT devices without compromising the user's privacy. However, Federated Learning (FL) solutions also have well-known limitations. In particular, as systems that account for human behaviour become increasingly vital, future technologies need to become attuned to human behaviours. Indeed, we are already witnessing unparalleled advancements in technology that empower our tools and devices with intelligence, sensory abilities, and communication features. At the same time, continued advances in the miniaturization of computational capabilities can enable us to go far beyond the simple tagging and identification, towards integrating computational resources directly into these objects, thus making our tools “intelligent”. Yet, there is limited scientific work that considers humans as an integral part of these IoT-powered cyber-physical systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"3-4"},"PeriodicalIF":5.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570699","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 : 2024-12-05DOI: 10.1109/TETC.2024.3488452
Ke Chen;Shanshan Liu;Weiqiang Liu;Fabrizio Lombardi;Nader Bagherzadeh
{"title":"Guest Editorial: Special Section on “Approximate Data Processing: Computing, Storage and Applications”","authors":"Ke Chen;Shanshan Liu;Weiqiang Liu;Fabrizio Lombardi;Nader Bagherzadeh","doi":"10.1109/TETC.2024.3488452","DOIUrl":"https://doi.org/10.1109/TETC.2024.3488452","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 4","pages":"954-955"},"PeriodicalIF":5.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777561","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 : 2024-12-05DOI: 10.1109/TETC.2024.3499715
{"title":"IEEE Transactions on Emerging Topics in Computing Information for Authors","authors":"","doi":"10.1109/TETC.2024.3499715","DOIUrl":"https://doi.org/10.1109/TETC.2024.3499715","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 4","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777661","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}
False-positive or false-negative detection, and the resulting inappropriate treatments in cancer metastasis cases, have led to numerous fatal instances due to human errors. Traditional cancer diagnoses are often subjectively interpreted through naked-eye observation, which can vary among different medical practitioners. In this research, we propose a novel deep learning-based framework called Intelligent Tumor Analytics (ITA). ITA facilitates on-the-fly assessment of Whole Slide Imaging (WSI) at the histopathological level, primarily utilizing cellular appearance, spatial arrangement, and the relative proximities of various cell types (e.g., tumor cells, immune cells, and other objects of interest) observed within scanned WSI images of tumors. By automatically quantifying relevant indicators and estimating their scores, ITA establishes a standardized evaluation that aligns with widely recognized international tumor grading standards, including the TNM and Nottingham Grading Standards. The objective measurements and assessments offered by ITA provide informative and unbiased insights to users (i.e., pathologists) involved in determining prognosis and treatment plans. The quantified information regarding tumor risk and potential for further metastasis possibilities serves as crucial early knowledge during cancer development.
{"title":"Deep Learning Based Intelligent Tumor Analytics Framework for Quantitative Grading and Analyzing Cancer Metastasis: Case of Lymph Node Breast Cancer","authors":"Tengyue Li;Simon Fong;Yaoyang Wu;Xin Zhang;Qun Song;Huafeng Qin;Sabah Mohammed;Tian Feng;Juntao Gao;Andrea Sciarrone","doi":"10.1109/TETC.2024.3487258","DOIUrl":"https://doi.org/10.1109/TETC.2024.3487258","url":null,"abstract":"False-positive or false-negative detection, and the resulting inappropriate treatments in cancer metastasis cases, have led to numerous fatal instances due to human errors. Traditional cancer diagnoses are often subjectively interpreted through naked-eye observation, which can vary among different medical practitioners. In this research, we propose a novel deep learning-based framework called Intelligent Tumor Analytics (ITA). ITA facilitates on-the-fly assessment of Whole Slide Imaging (WSI) at the histopathological level, primarily utilizing cellular appearance, spatial arrangement, and the relative proximities of various cell types (e.g., tumor cells, immune cells, and other objects of interest) observed within scanned WSI images of tumors. By automatically quantifying relevant indicators and estimating their scores, ITA establishes a standardized evaluation that aligns with widely recognized international tumor grading standards, including the TNM and Nottingham Grading Standards. The objective measurements and assessments offered by ITA provide informative and unbiased insights to users (i.e., pathologists) involved in determining prognosis and treatment plans. The quantified information regarding tumor risk and potential for further metastasis possibilities serves as crucial early knowledge during cancer development.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"90-104"},"PeriodicalIF":5.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570604","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 : 2024-10-21DOI: 10.1109/TETC.2024.3481035
Tien Nguyen;Aengus Daly;Sergi Gomez-Quintana;Feargal O'Sullivan;Andriy Temko;Emanuel Popovici
Detecting seizures in neonates requires continuous electroencephalography (EEG) monitoring, a costly process that demands trained experts. Although recent advancements in machine learning offer promising solutions for automated seizure detection, the opaque nature of these algorithms poses significant challenges to their adoption in healthcare settings. A prior study demonstrated that integrating machine learning with sonification—an interpretation method that converts bio-signals into sound—can mitigate the black-box problem while enhancing seizure detection performance. This AI-assisted sonification algorithm can provide a valuable complementary tool in seizure monitoring besides the traditional visualization method. A low-power and affordable implementation of the algorithm is presented in this study using a microcontroller. To improve its practicality, we also introduce a real-time design that allows the sonification algorithm to function in parallel with data acquisition. The system consumes 12 mW in average, making it suitable for a battery-powered device.
{"title":"Low-Power Real-Time Seizure Monitoring Using AI-Assisted Sonification of Neonatal EEG","authors":"Tien Nguyen;Aengus Daly;Sergi Gomez-Quintana;Feargal O'Sullivan;Andriy Temko;Emanuel Popovici","doi":"10.1109/TETC.2024.3481035","DOIUrl":"https://doi.org/10.1109/TETC.2024.3481035","url":null,"abstract":"Detecting seizures in neonates requires continuous electroencephalography (EEG) monitoring, a costly process that demands trained experts. Although recent advancements in machine learning offer promising solutions for automated seizure detection, the opaque nature of these algorithms poses significant challenges to their adoption in healthcare settings. A prior study demonstrated that integrating machine learning with sonification—an interpretation method that converts bio-signals into sound—can mitigate the black-box problem while enhancing seizure detection performance. This AI-assisted sonification algorithm can provide a valuable complementary tool in seizure monitoring besides the traditional visualization method. A low-power and affordable implementation of the algorithm is presented in this study using a microcontroller. To improve its practicality, we also introduce a real-time design that allows the sonification algorithm to function in parallel with data acquisition. The system consumes 12 mW in average, making it suitable for a battery-powered device.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"80-89"},"PeriodicalIF":5.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570700","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 : 2024-10-17DOI: 10.1109/TETC.2024.3479082
Eric Sturzinger;Jan Harkes;Padmanabhan Pillai;Mahadev Satyanarayanan
We introduce survival-critical machine learning (SCML), in which a robot encounters dynamically evolving threats that it recognizes via machine learning (ML), and then neutralizes. We model survivability in SCML, and show the value of the recently developed approach of Live Learning. This edge-based ML technique embodies an iterative human-in-the-loop workflow that concurrently enlarges the training set, trains the next model in a sequence of “best-so-far” models, and performs inferencing for both threat detection and pseudo-labeling. We present experimental results using datasets from the domains of drone surveillance, planetary exploration, and underwater sensing to quantify the effectiveness of Live Learning as a mechanism for SCML.
{"title":"Edge-Based Live Learning for Robot Survival","authors":"Eric Sturzinger;Jan Harkes;Padmanabhan Pillai;Mahadev Satyanarayanan","doi":"10.1109/TETC.2024.3479082","DOIUrl":"https://doi.org/10.1109/TETC.2024.3479082","url":null,"abstract":"We introduce <italic>survival-critical machine learning (SCML),</i> in which a robot encounters dynamically evolving threats that it recognizes via machine learning (ML), and then neutralizes. We model survivability in SCML, and show the value of the recently developed approach of <italic>Live Learning.</i> This edge-based ML technique embodies an iterative human-in-the-loop workflow that concurrently enlarges the training set, trains the next model in a sequence of “best-so-far” models, and performs inferencing for both threat detection and pseudo-labeling. We present experimental results using datasets from the domains of drone surveillance, planetary exploration, and underwater sensing to quantify the effectiveness of Live Learning as a mechanism for SCML.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"34-47"},"PeriodicalIF":5.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10721342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570674","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 : 2024-10-16DOI: 10.1109/TETC.2024.3472059
Fengqi Li;Jiaheng Wang;Weilin Xie;Ning Tong;Deguang Wang
The proliferation of IoT devices, advancements in edge computing, and innovations in AI technology have created an ideal environment for the birth and growth of Edge AI. With the trend towards the Internet of Everything (IoE), the EdgeAI- Human-IoT architectural framework highlights the necessity for efficient data exchange interconnectivity. Ensuring secure data sharing and efficient data storage are pivotal challenges in achieving seamless data interconnection. Owing to its simplicity, ease of deployment, and consensus-reaching capabilities, the RAFT consensus algorithm, which is commonly used in distributed storage, faces limitations as the IoT scale expands. The computational, communication, and storage capabilities of nodes are constraints, and the security of data remains a concern. To address these complex challenges, we introduce the X-RAFT consensus algorithm, which is tailored for blockchain technology. This algorithm enhances system performance and robustness, mitigates the impact of system load, enhances system sustainability, and increases Byzantine fault tolerance. Through analysis and simulations, our proposed solution has been evidenced to provide reliable security and efficient performance.
{"title":"X-RAFT: Improve RAFT Consensus to Make Blockchain Better Secure EdgeAI-Human-IoT Data","authors":"Fengqi Li;Jiaheng Wang;Weilin Xie;Ning Tong;Deguang Wang","doi":"10.1109/TETC.2024.3472059","DOIUrl":"https://doi.org/10.1109/TETC.2024.3472059","url":null,"abstract":"The proliferation of IoT devices, advancements in edge computing, and innovations in AI technology have created an ideal environment for the birth and growth of Edge AI. With the trend towards the Internet of Everything (IoE), the EdgeAI- Human-IoT architectural framework highlights the necessity for efficient data exchange interconnectivity. Ensuring secure data sharing and efficient data storage are pivotal challenges in achieving seamless data interconnection. Owing to its simplicity, ease of deployment, and consensus-reaching capabilities, the RAFT consensus algorithm, which is commonly used in distributed storage, faces limitations as the IoT scale expands. The computational, communication, and storage capabilities of nodes are constraints, and the security of data remains a concern. To address these complex challenges, we introduce the X-RAFT consensus algorithm, which is tailored for blockchain technology. This algorithm enhances system performance and robustness, mitigates the impact of system load, enhances system sustainability, and increases Byzantine fault tolerance. Through analysis and simulations, our proposed solution has been evidenced to provide reliable security and efficient performance.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"22-33"},"PeriodicalIF":5.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570701","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 : 2024-10-10DOI: 10.1109/TETC.2024.3471458
Cláudio G. S. Capanema;Allan M. de Souza;Joahannes B. D. da Costa;Fabrício A. Silva;Leandro A. Villas;Antonio A. F. Loureiro
Researchers have studied how to improve Federated Learning (FL) in various areas, such as statistical and system heterogeneity, communication cost, and privacy. So far, most of the proposed solutions are either very tied to the application context or complex to be broadly reproduced in real-life applications involving humans. Developing modular solutions that can be leveraged by the vast majority of FL structures and are independent of the application people use is the new research direction opened by this paper. In this work, we propose a plugin (named FedPredict) to address three problems simultaneously: data heterogeneity, low performance of new/untrained and/or outdated clients, and communication cost. We do so mainly by combining global and local parameters (which brings generalization and personalization) in the inference step while adapting layer selection and matrix factorization techniques to reduce the downlink communication cost (server to client). Due to its simplicity, it can be applied to federated learning of different number of topologies. Results show that adding the proposed plugin to a given FL solution can significantly reduce the downlink communication cost by up to 83.3% and improve accuracy by up to 304% compared to the original solution.
{"title":"A Novel Prediction Technique for Federated Learning","authors":"Cláudio G. S. Capanema;Allan M. de Souza;Joahannes B. D. da Costa;Fabrício A. Silva;Leandro A. Villas;Antonio A. F. Loureiro","doi":"10.1109/TETC.2024.3471458","DOIUrl":"https://doi.org/10.1109/TETC.2024.3471458","url":null,"abstract":"Researchers have studied how to improve Federated Learning (FL) in various areas, such as statistical and system heterogeneity, communication cost, and privacy. So far, most of the proposed solutions are either very tied to the application context or complex to be broadly reproduced in real-life applications involving humans. Developing modular solutions that can be leveraged by the vast majority of FL structures and are independent of the application people use is the new research direction opened by this paper. In this work, we propose a plugin (named FedPredict) to address three problems simultaneously: data heterogeneity, low performance of new/untrained and/or outdated clients, and communication cost. We do so mainly by combining global and local parameters (which brings generalization and personalization) in the inference step while adapting layer selection and matrix factorization techniques to reduce the downlink communication cost (server to client). Due to its simplicity, it can be applied to federated learning of different number of topologies. Results show that adding the proposed plugin to a given FL solution can significantly reduce the downlink communication cost by up to 83.3% and improve accuracy by up to 304% compared to the original solution.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"5-21"},"PeriodicalIF":5.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570650","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}
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