IEEE Transactions on Emerging Topics in Computing最新文献

{"title":"FLCL: Feature-Level Contrastive Learning for Few-Shot Image Classification","authors":"Wenming Cao;Jiewen Zeng;Qifan Liu","doi":"10.1109/TETC.2025.3546366","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546366","url":null,"abstract":"Few-shot classification is the task of recognizing unseen classes using a limited number of samples. In this paper, we propose a new contrastive learning method called Feature-Level Contrastive Learning (FLCL). FLCL conducts contrastive learning at the feature level and leverages the subtle relationships between positive and negative samples to achieve more effective classification. Additionally, we address the challenges of requiring a large number of negative samples and the difficulty of selecting high-quality negative samples in traditional contrastive learning methods. For feature learning, we design a Feature Enhancement Coding (FEC) module to analyze the interactions and correlations between nonlinear features, enhancing the quality of feature representations. In the metric stage, we propose a centered hypersphere projection metric to map feature vectors onto the hypersphere, improving the comparison between the support and query sets. Experimental results on four few-shot classification benchmark datasets demonstrate that our method, while simple in design, outperforms previous methods and achieves state-of-the-art performance. A detailed ablation study further confirms the effectiveness of each component of our model.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"935-946"},"PeriodicalIF":5.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057473","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}

{"title":"MALITE: Lightweight Malware Detection and Classification for Constrained Devices","authors":"Sidharth Anand;Barsha Mitra;Soumyadeep Dey;Abhinav Rao;Rupsha Dhar;Jaideep Vaidya","doi":"10.1109/TETC.2025.3566370","DOIUrl":"https://doi.org/10.1109/TETC.2025.3566370","url":null,"abstract":"Today, malware is one of the primary cyber threats to organizations, pervading all types of computing devices, including resource constrained devices such as mobile phones, tablets and embedded devices like Internet-of-Things (IoT) devices. In recent years, researchers have leveraged machine learning based strategies for malware detection and classification. However, malware analysis approaches can only be employed in resource constrained environments if the methods are lightweight in nature. In this paper, we present MALITE, a lightweight malware analysis system, that can distinguish between benign and malicious binaries and classify various malware families. MALITE converts a binary into a grayscale or an RGB image requiring low memory and battery power consumption and uses computationally inexpensive malware analysis strategies. We have designed MALITE-MN, a lightweight neural network based architecture and MALITE-HRF, an ultra lightweight random forest based method that uses histogram features extracted by a sliding window. An extensive empirical evaluation is conducted on seven publicly available datasets (Malimg, Microsoft BIG, Dumpware10, MOTIF, Drebin, CICAndMal2017 and MalNet), and performance is compared to four state-of-the-art baselines. The results show that MALITE-MN and MALITE-HRF not only accurately identify and classify malware but also respectively consume several orders of magnitude lower resources (in terms of both memory as well as computation capabilities), making them much more suitable for resource constrained environments.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"1099-1112"},"PeriodicalIF":5.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057467","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}

{"title":"Quantum Implementation and Analysis of SHA-2 and SHA-3","authors":"Kyungbae Jang;Sejin Lim;Yujin Oh;Hyunjun Kim;Anubhab Baksi;Sumanta Chakraborty;Hwajeong Seo","doi":"10.1109/TETC.2025.3546648","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546648","url":null,"abstract":"Quantum computers have the potential to solve a number of hard problems that are believed to be almost impossible to solve by classical computers. This observation has sparked a surge of research to apply quantum algorithms against the cryptographic systems to evaluate its quantum resistance. In assessing the security strength of the cryptographic algorithms against the upcoming quantum threats, it is crucial to precisely estimate the quantum resource requirement (generally in terms of circuit depth and quantum bit count). The National Institute of Standards and Technology by the US government specified five quantum security levels so that the relative quantum strength of a given cipher can be compared to the standard ones. There have been some progress in the NIST-specified quantum security levels for the odd levels (i.e., 1, 3 and 5), following the work of Jaques et al. (Eurocrypt’20). However, levels 2 and 4, which correspond to the quantum collision finding attacks for the SHA-2 and SHA-3 hash functions, quantum attack complexities are arguably not well-studied. This is where our article fits in. In this article, we present novel techniques for optimizing the quantum circuit implementations for SHA-2 and SHA-3 algorithms in all the categories specified by NIST. After that, we evaluate the quantum circuits of target cryptographic hash functions for quantum collision search. Finally, we define the quantum attack complexity for levels 2 and 4, and comment on the security strength of the extended level. We present new concepts to optimize the quantum circuits at the component level and the architecture level.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"919-934"},"PeriodicalIF":5.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057425","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}

{"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}

{"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}

{"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}

{"title":"NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative Database","authors":"Dongdong Zhao;Zhihui Liu;Fengji Zhang;Lei Liu;Jacky Wai Keung;Xiao Yu","doi":"10.1109/TETC.2025.3546549","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546549","url":null,"abstract":"The emergence of <underline>A</u>ging-<underline>R</u>elated <underline>B</u>ug<underline>s</u> (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resource-intensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, <underline>C</u>ross-<underline>P</u>roject <underline>A</u>ging-<underline>R</u>elated <underline>B</u>ug <underline>P</u>rediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called <underline>C</u>ross-<underline>P</u>roject <underline>A</u>ging-<underline>R</u>elated <underline>B</u>ug <underline>P</u>rediction based on <underline>Neg</u>ative Database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Second, the corresponding <underline>N</u>egative <underline>D</u>ata<underline>B</u>ase (<italic>NDB</i>) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the <italic>NDB</i> to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"283-298"},"PeriodicalIF":5.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323167","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}

{"title":"Analyzing Wet-Neuromorphic Computing Using Bacterial Gene Regulatory Neural Networks","authors":"Samitha Somathilaka;Sasitharan Balasubramaniam;Daniel P. Martins","doi":"10.1109/TETC.2025.3546119","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546119","url":null,"abstract":"Biocomputing envisions the development computing paradigms using biological systems, ranging from micron-level components to collections of cells, including organoids. This paradigm shift exploits hidden natural computing properties, to develop miniaturized wet-computing devices that can be deployed in harsh environments, and to explore designs of novel energy-efficient systems. In parallel, we witness the emergence of AI hardware, including neuromorphic processors with the aim of improving computational capacity. This study brings together the concept of biocomputing and neuromorphic systems by focusing on the bacterial gene regulatory networks and their transformation into Gene Regulatory Neural Networks (GRNNs). We explore the intrinsic properties of gene regulations, map this to a gene-perceptron function, and propose an application-specific sub-GRNN search algorithm that maps the network structure to match a computing problem. Focusing on the model organism Escherichia coli, the base-GRNN is initially extracted and validated for accuracy. Subsequently, a comprehensive feasibility analysis of the derived GRNN confirms its computational prowess in classification and regression tasks. Furthermore, we discuss the possibility of performing a well-known digit classification task as a use case. Our analysis and simulation experiments show promising results in the offloading of computation tasks to GRNN in bacterial cells, advancing wet-neuromorphic computing using natural cells.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"902-918"},"PeriodicalIF":5.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057434","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}

{"title":"Exploiting Entity Information for Robust Prediction Over Event Knowledge Graphs","authors":"Han Yu;Hongming Cai;Shengtung Tsai;Mengyao Li;Pan Hu;Jiaoyan Chen;Bingqing Shen","doi":"10.1109/TETC.2025.3534243","DOIUrl":"https://doi.org/10.1109/TETC.2025.3534243","url":null,"abstract":"Script event prediction is the task of predicting the subsequent event given a sequence of events that already took place. It benefits task planning and process scheduling for event-centric systems including enterprise systems, IoT systems, etc. Sequence-based and graph-based learning models have been applied to this task. However, when learning data is limited, especially in a multiple-participant-involved enterprise environment, the performance of such models falls short of expectations as they heavily rely on large-scale training data. To take full advantage of given data, in this article we propose a new type of knowledge graph (KG) that models not just events but also entities participating in the events, and we design a collaborative event prediction model exploiting such KGs. Our model identifies semantically similar vertices as collaborators to resolve unknown events, applies gated graph neural networks to extract event-wise sequential features, and exploits a heterogeneous attention network to cope with entity-wise influence in event sequences. To verify the effectiveness of our approach, we designed multiple-choice narrative cloze tasks with inadequate knowledge. Our experimental evaluation with three datasets generated from well-known corpora shows our method can successfully defend against such incompleteness of data and outperforms the state-of-the-art approaches for event prediction.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"890-901"},"PeriodicalIF":5.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057431","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}

{"title":"Towards Label-Efficient Deep Learning-Based Aging-Related Bug Prediction With Spiking Convolutional Neural Networks","authors":"Yunzhe Tian;Yike Li;Kang Chen;Zhenguo Zhang;Endong Tong;Jiqiang Liu;Fangyun Qin;Zheng Zheng;Wenjia Niu","doi":"10.1109/TETC.2025.3531051","DOIUrl":"https://doi.org/10.1109/TETC.2025.3531051","url":null,"abstract":"Recent advances in Deep Learning (DL) have enhanced Aging-Related Bug (ARB) prediction for mitigating software aging. However, DL-based ARB prediction models face a dual challenge: overcoming overfitting to enhance generalization and managing the high labeling costs associated with extensive data requirements. To address the first issue, we utilize the sparse and binary nature of spiking communication in Spiking Neural Networks (SNNs), which inherently provides brain-inspired regularization to effectively alleviate overfitting. Therefore, we propose a Spiking Convolutional Neural Network (SCNN)-based ARB prediction model along with a training framework that handles the model’s spatial-temporal dynamics and non-differentiable nature. To reduce labeling costs, we introduce a Bio-inspired and Diversity-aware Active Learning framework (BiDAL), which prioritizes highly informative and diverse samples, enabling more efficient usage of the limited labeling budget. This framework incorporates bio-inspired uncertainty to enhance informativeness measurement along with using a diversity-aware selection strategy based on clustering to prevent redundant labeling. Experiments on three ARB datasets show that ARB-SCNN effectively reduces overfitting, improving generalization performance by 6.65% over other DL-based classifiers. Additionally, BiDAL boosts label efficiency for ARB-SCNN training, outperforming four state-of-the-art active learning methods by 4.77% within limited labeling budgets.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"314-329"},"PeriodicalIF":5.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323163","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}