IEEE Transactions on Emerging Topics in Computing最新文献_第2页

Performability of Service Chains With Rejuvenation: A Multidimensional Universal Generating Function Approach 具有再生的服务链的可执行性：一种多维通用生成函数方法

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-11-18 DOI: 10.1109/TETC.2024.3496195

Luigi De Simone;Mario Di Mauro;Roberto Natella;Fabio Postiglione

{"title":"Performability of Service Chains With Rejuvenation: A Multidimensional Universal Generating Function Approach","authors":"Luigi De Simone;Mario Di Mauro;Roberto Natella;Fabio Postiglione","doi":"10.1109/TETC.2024.3496195","DOIUrl":"https://doi.org/10.1109/TETC.2024.3496195","url":null,"abstract":"Network Function Virtualization (NFV) converts legacy telecommunication systems into modular software appliances, known as service chains, running on the cloud. To address potential software aging-related issues, rejuvenation is often employed to clean up their state and maximize performance and availability. In this work, we propose a framework to model the performability of service chains with rejuvenation. Performance modeling uses queueing theory, specifically adopting an <inline-formula><tex-math>$M/G/m$</tex-math></inline-formula> model with the Allen-Cunneen approximation, to capture real-world aspects related to service times. Availability modeling is addressed through the Multidimensional Universal Generating Function (MUGF), a recent technique that achieves computational efficiency when dealing with systems with many sub-elements, particularly useful for multi-provider service chains. Additionally, we deploy an experimental testbed based on the Open5GS service chain, to estimate key performance and availability parameters. Supported by experimental results, we evaluate the impact of rejuvenation on the performability of the Open5GS service chain. The numerical analysis shows that i) the configuration of replicas across nodes is important to meet availability goals; ii) rejuvenation can bring one additional “nine” of availability, depending on the time to recovery; and iii) MUGF can significantly reduce computational complexity through straightforward algebraic manipulations.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"341-353"},"PeriodicalIF":5.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323051","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

Deep Learning Based Intelligent Tumor Analytics Framework for Quantitative Grading and Analyzing Cancer Metastasis: Case of Lymph Node Breast Cancer 基于深度学习的智能肿瘤分析框架，用于定量分级和分析肿瘤转移：淋巴结性乳腺癌病例

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-11-12 DOI: 10.1109/TETC.2024.3487258

Tengyue Li;Simon Fong;Yaoyang Wu;Xin Zhang;Qun Song;Huafeng Qin;Sabah Mohammed;Tian Feng;Juntao Gao;Andrea Sciarrone

{"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}

引用次数: 0

Open and Closed-Loop Predictive Control Strategies for Software Rejuvenation 软件复兴的开闭环预测控制策略

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-22 DOI: 10.1109/TETC.2024.3481997

Teresa Arauz;José M. Maestre;Paula Chanfreut;Daniel E. Quevedo;Eduardo F. Camacho

引用次数: 0

Low-Power Real-Time Seizure Monitoring Using AI-Assisted Sonification of Neonatal EEG 人工智能辅助新生儿脑电图超声低功率实时监测癫痫发作

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-21 DOI: 10.1109/TETC.2024.3481035

Tien Nguyen;Aengus Daly;Sergi Gomez-Quintana;Feargal O'Sullivan;Andriy Temko;Emanuel Popovici

{"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}

引用次数: 0

Edge-Based Live Learning for Robot Survival 基于边缘的机器人生存实时学习

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-17 DOI: 10.1109/TETC.2024.3479082

Eric Sturzinger;Jan Harkes;Padmanabhan Pillai;Mahadev Satyanarayanan

引用次数: 0

X-RAFT: Improve RAFT Consensus to Make Blockchain Better Secure EdgeAI-Human-IoT Data X-RAFT：改进RAFT共识，使区块链更安全的edge - ai - human - iot数据

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-16 DOI: 10.1109/TETC.2024.3472059

Fengqi Li;Jiaheng Wang;Weilin Xie;Ning Tong;Deguang Wang

{"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}

引用次数: 0

Cost-Effective Software Rejuvenation Combining Time-Based and Inspection-Based Policies 结合基于时间和基于检查策略的高性价比软件复兴

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-11 DOI: 10.1109/TETC.2024.3475214

Laura Carnevali;Marco Paolieri;Riccardo Reali;Leonardo Scommegna;Enrico Vicario

{"title":"Cost-Effective Software Rejuvenation Combining Time-Based and Inspection-Based Policies","authors":"Laura Carnevali;Marco Paolieri;Riccardo Reali;Leonardo Scommegna;Enrico Vicario","doi":"10.1109/TETC.2024.3475214","DOIUrl":"https://doi.org/10.1109/TETC.2024.3475214","url":null,"abstract":"Software rejuvenation is a proactive maintenance technique that counteracts software aging by restarting a system, making selection of rejuvenation times critical to improve reliability without incurring excessive downtime costs. Various stochastic models of Software Aging and Rejuvenation (SAR) have been developed, mostly having an underlying stochastic process in the class of Continuous Time Markov Chains (CTMCs), Semi-Markov Processes (SMPs), and Markov Regenerative Processes (MRGPs) under the enabling restriction, requiring that at most one general (GEN), i.e., non-Exponential, timer be enabled in each state. We present a SAR model with an underlying MRGP under the bounded regeneration restriction, allowing for multiple GEN timers to be concurrently enabled in each state. This expressivity gain not only supports more accurate fitting of duration distributions from observed statistics, but also enables the definition of mixed rejuvenation strategies combining time-based and inspection-based policies, where the time to the next inspection or rejuvenation depends on the outcomes of diagnostic tests. Experimental results show that replacing GEN timers with Exponential timers with the same mean (to satisfy the enabling restriction) yields inaccurate rejuvenation policies, and that mixed rejuvenation outperforms time-based rejuvenation in maximizing reliability, though at the cost of an acceptable decrease in availability.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"354-369"},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323147","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

A Novel Prediction Technique for Federated Learning 一种新的联邦学习预测技术

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-10 DOI: 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

{"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}

引用次数: 0

FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning FedRDF：联盟学习中抵御中毒攻击的稳健动态聚合函数

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-10 DOI: 10.1109/TETC.2024.3474484

Enrique Mármol Campos;Aurora Gonzalez-Vidal;José L. Hernández-Ramos;Antonio Skarmeta

{"title":"FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning","authors":"Enrique Mármol Campos;Aurora Gonzalez-Vidal;José L. Hernández-Ramos;Antonio Skarmeta","doi":"10.1109/TETC.2024.3474484","DOIUrl":"https://doi.org/10.1109/TETC.2024.3474484","url":null,"abstract":"Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handle sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients’ weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"48-67"},"PeriodicalIF":5.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570755","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

Federated Learning Approach for Collaborative and Secure Smart Healthcare Applications 用于协作和安全智能医疗保健应用程序的联邦学习方法

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-10 DOI: 10.1109/TETC.2024.3473911

Quy Vu Khanh;Abdellah Chehri;Van Anh Dang;Quy Nguyen Minh

{"title":"Federated Learning Approach for Collaborative and Secure Smart Healthcare Applications","authors":"Quy Vu Khanh;Abdellah Chehri;Van Anh Dang;Quy Nguyen Minh","doi":"10.1109/TETC.2024.3473911","DOIUrl":"https://doi.org/10.1109/TETC.2024.3473911","url":null,"abstract":"Across all periods of human history, the importance attributed to health has remained a fundamental and significant facet. This statement holds greater validity within the present context. The pressing demand for healthcare solutions with real-time capabilities, affordability, and high precision is crucial in medical research and technology progress. In recent times, there has been a significant advancement in emerging technologies such as AI, IoT, blockchain, and edge computing. These breakthrough developments have led to the creation of various intelligent applications. Smart healthcare applications can be realized by combining robust AI detection and prediction capabilities with edge computing architecture, which offers low computing costs and latency. In this paper, we begin by conducting a literature review of AI-assisted EC-based smart healthcare applications from the past three years. Our goal is to identify gaps and barriers in this field. We propose a smart healthcare architecture model that integrates AI technology into the edge. Finally, we summarize the challenges and research directions associated with the proposed model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"68-79"},"PeriodicalIF":5.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570756","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