ACM Computing Surveys最新文献_第3页

A Survey of AIOps in the Era of Large Language Models 大型语言模型时代的AIOps概述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746635

Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip Yu, Ying Li

{"title":"A Survey of AIOps in the Era of Large Language Models","authors":"Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip Yu, Ying Li","doi":"10.1145/3746635","DOIUrl":"https://doi.org/10.1145/3746635","url":null,"abstract":"As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research papers published between January 2020 and December 2024 to answer four key research questions (RQs). In RQ1, we examine the diverse failure data sources utilized, including advanced LLM-based processing techniques for legacy data and the incorporation of new data sources enabled by LLMs. RQ2 explores the evolution of AIOps tasks, highlighting the emergence of novel tasks and the publication trends across these tasks. RQ3 investigates the various LLM-based methods applied to address AIOps challenges. Finally, RQ4 reviews evaluation methodologies tailored to assess LLM-integrated AIOps approaches. Based on our findings, we discuss the state-of-the-art advancements and trends, identify gaps in existing research, and propose promising directions for future exploration.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A Comprehensive Survey on Self-Supervised Learning for Recommendation 基于推荐的自监督学习研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746280

Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang

{"title":"A Comprehensive Survey on Self-Supervised Learning for Recommendation","authors":"Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang","doi":"10.1145/3746280","DOIUrl":"https://doi.org/10.1145/3746280","url":null,"abstract":"Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user behaviors and preferences. However, supervised learning methods encounter challenges in real-life scenarios due to data sparsity, resulting in limitations in their ability to learn representations effectively. To address this, self-supervised learning (SSL) techniques have emerged as a solution, leveraging inherent data structures to generate supervision signals without relying solely on labeled data. By leveraging unlabeled data and extracting meaningful representations, recommender systems utilizing SSL can make accurate predictions and recommendations even when confronted with data sparsity. In this paper, we provide a comprehensive review of self-supervised learning frameworks designed for recommender systems, encompassing a thorough analysis of over 170 papers. We conduct an exploration of nine distinct scenarios, enabling a comprehensive understanding of SSL-enhanced recommenders in different contexts. For each domain, we elaborate on different self-supervised learning paradigms, namely contrastive learning, generative learning, and adversarial learning, so as to present technical details of how SSL enhances recommender systems in various contexts. We consistently maintain the related open-source materials at https://github.com/HKUDS/Awesome-SSLRec-Papers.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"36 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study 基于少镜头学习的医学图像分割：综述与比较研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746224

Theekshana Dissanayake, Yasmeen George, Dwarikanath Mahapatra, Shridha Sridharan, Clinton Fookes, Zongyuan Ge

{"title":"Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study","authors":"Theekshana Dissanayake, Yasmeen George, Dwarikanath Mahapatra, Shridha Sridharan, Clinton Fookes, Zongyuan Ge","doi":"10.1145/3746224","DOIUrl":"https://doi.org/10.1145/3746224","url":null,"abstract":"Medical image segmentation plays a crucial role in assisting clinicians with diagnosing critical medical conditions. In deep learning, few-shot learning methods aim to replicate human learning by leveraging fewer examples for determining a prediction for a novel class. Researchers in the medical imaging community have also explored novel methods for few-shot medical image segmentation, leveraging meta-learning, foundation models and self-supervised learning (SSL). Acknowledging this growing interest, we review the literature on few-shot medical image segmentation from 2020 to early 2025, focusing on architectural modifications, loss-inspired learning strategies, and meta-learning frameworks. We further divide each category into fine-grained deep learning-oriented solutions, including self-supervised learning, contrastive learning, regularization, and foundation models providing in-depth discussions on architectural improvements and representation learning strategies. Additionally, we present preliminary results from several few-shot segmentation models across both medical and computer vision domains, evaluating their strengths and limitations for medical image applications. Finally, based on the limitations observed, advancements from the natural image domain, and empirical findings, we outline future research directions, providing specific insights into data-efficient learning, rapid adaptation of foundation models and generalization. The code is available here.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"7 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Prediction of Certification in MOOCs: A Systematic Literature Review mooc认证预测：系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-20 DOI: 10.1145/3743671

Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea

{"title":"Prediction of Certification in MOOCs: A Systematic Literature Review","authors":"Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea","doi":"10.1145/3743671","DOIUrl":"https://doi.org/10.1145/3743671","url":null,"abstract":"Massive Open Online Courses (MOOCs) have been proliferating, offering free or low-cost content for learners. Nevertheless, the certification rate of both free and paid courses has been low (between 4.5% - 13% and 1% - 3%, respectively). Thus, this study aims to survey MOOCs certification predictive models, synthesise results for a comprehensive and deep understanding of this field and explore how these models contributed to addressing the very low certification level. We adopted the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) for transparently conducting the present review and reporting the results from the works reviewed. Additionally, this SLR highlights several trends and limitations within the present predictive models, including some methodological concerns: the extent to which the present models are generalisable, the excessive filtration of the experimental population, the incompatibility of some experiments with real-time scenarios (nonrealistic modelling), and the shallow reporting of model performances. We have also discussed the replicability of the present models and ongoing efforts towards building a state-of-the-art predictive model. Finally, we highlight future research opportunities in the field of MOOC certification prediction that either deal with the limitations of the present models or address unanswered questions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"24 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges 不为人知修辞格检测的计算方法：系统调查和研究挑战

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-19 DOI: 10.1145/3744554

Ramona Kühn, Jelena Mitrović, Michael Granitzer

引用次数: 0

Causality in Bandits: A Survey 《强盗》中的因果关系：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-19 DOI: 10.1145/3744917

Chandrasekar Subramanian, Balaraman Ravindran

引用次数: 0

Static Code Analysis for IoT Security: A Systematic Literature Review 物联网安全静态代码分析：系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-19 DOI: 10.1145/3745019

Diego Gomes, Eduardo Felix, Fernando Aires, Marco Vieira

{"title":"Static Code Analysis for IoT Security: A Systematic Literature Review","authors":"Diego Gomes, Eduardo Felix, Fernando Aires, Marco Vieira","doi":"10.1145/3745019","DOIUrl":"https://doi.org/10.1145/3745019","url":null,"abstract":"The growth of the Internet of Things (IoT) has provided significant advances in several areas of the industry, but security concerns have also increased due to this expansion. Many IoT devices are the target of cyber attacks due to various firmware, source code, and software vulnerabilities. In this context, static code analysis, leveraging various techniques, has emerged as an effective approach to examine and identify security vulnerabilities, including insecure functions, buffer overflows, and code injection. However, recent research has shown several challenges associated with this approach, such as limited understanding of vulnerabilities, inadequate threat detection, and insufficient semantic analysis of IoT device source code. Consequently, several IoT security research studies integrate static analysis with other methods, such as dynamic analysis, machine learning, and natural language processing, to enhance vulnerability analysis and detection. To provide a comprehensive understanding of the current state of static analysis in IoT security, this systematic literature review explores existing vulnerabilities, techniques, and methods while highlighting the challenges that hinder the extraction of meaningful insights from such analyses.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"36 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144319556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Advancing 5G Security and Privacy with AI: A Survey 用人工智能推进5G安全和隐私：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-16 DOI: 10.1145/3744555

Haoxin He, Shufan Fei, Zheng Yan

{"title":"Advancing 5G Security and Privacy with AI: A Survey","authors":"Haoxin He, Shufan Fei, Zheng Yan","doi":"10.1145/3744555","DOIUrl":"https://doi.org/10.1145/3744555","url":null,"abstract":"With the global deployment of the fifth-generation (5G) mobile technology, a new era characterized by ultra-high data speeds, ultra-low latency, and massive connectivity has emerged. However, these advancements also introduce new security and privacy challenges. The integration of new technologies in 5G has fundamentally altered the network structure, rendering traditional security methods inadequate. Artificial intelligence (AI), with its advanced data analysis and pattern recognition capabilities, is a promising solution to enhance security and privacy in 5G networks. While existing surveys discuss AI applications for 5G, there is a lack of a comprehensive survey on the performance of various AI-based solutions for 5G security and privacy. This paper aims to fill this gap by providing an in-depth review of the latest advancements in AI for 5G security and privacy. We begin with an overview of the security and privacy challenges in 5G networks, including potential vulnerabilities, attack vectors, and privacy issues. We then propose a set of evaluation criteria for assessing various AI-based solutions. Following this, we present a taxonomy of AI-based security and privacy solutions and review the latest advancements. Finally, we identify open issues and propose future directions for utilizing AI to enhance 5G security and privacy.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"45 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 in Stance Detection: A Survey 深度学习在姿态检测中的应用综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-13 DOI: 10.1145/3744641

Parush Gera, Tempestt Neal

引用次数: 0

Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey 知识增强图机器学习用于药物发现：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-12 DOI: 10.1145/3744237

Zhiqiang Zhong, Anastasia Barkova, Davide Mottin

{"title":"Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey","authors":"Zhiqiang Zhong, Anastasia Barkova, Davide Mottin","doi":"10.1145/3744237","DOIUrl":"https://doi.org/10.1145/3744237","url":null,"abstract":"Artificial Intelligence has become integral to intelligent drug discovery, with Graph Machine Learning (GML) emerging as a powerful structure-based method for modelling graph-structured biomedical data and investigating their properties. However, GML faces challenges such as limited interpretability and heavy dependency on abundant high-quality training data. On the other hand, knowledge-based methods leverage biomedical knowledge databases, e.g. , Knowledge Graphs (KGs), to explore unknown knowledge. Nevertheless, KG construction is resource-intensive and often neglects crucial structural information in biomedical data. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with scarce training data. Nevertheless, a systematic definition for this burgeoning research direction is yet to be established. This survey formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug discovery and organises collected KaGML works into four categories following a novel-defined taxonomy. We also present a comprehensive overview of long-standing drug discovery principles and provide the foundational concepts and cutting-edge techniques for graph-structured data and knowledge databases. To facilitate research in this promptly emerging field, we share collected practical resources that are valuable for intelligent drug discovery and provide an in-depth discussion of the potential avenues for future advancements.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"26 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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