ACM Computing Surveys最新文献_第8页

Semantic Models of Performance Indicators: A Systematic Survey 绩效指标的语义模型：系统调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-22 DOI: 10.1145/3719291

Claudia Diamantini, Tarique Khan, Domenico Potena, Emanuele Storti

引用次数: 0

A Survey on Hypergraph Mining: Patterns, Tools, and Generators

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-20 DOI: 10.1145/3719002

Geon Lee, Fanchen Bu, Tina Eliassi-Rad, Kijung Shin

{"title":"A Survey on Hypergraph Mining: Patterns, Tools, and Generators","authors":"Geon Lee, Fanchen Bu, Tina Eliassi-Rad, Kijung Shin","doi":"10.1145/3719002","DOIUrl":"https://doi.org/10.1145/3719002","url":null,"abstract":"Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling collaboration networks, which may involve not just two but three or more people, the use of hypergraphs allows us to explore beyond pairwise (dyadic) patterns and capture groupwise (polyadic) patterns. The mathematical complexity of hypergraphs offers both opportunities and challenges for hypergraph mining. The goal of hypergraph mining is to find structural properties recurring in real-world hypergraphs across different domains, which we call patterns. To find patterns, we need tools. We divide hypergraph mining tools into three categories: (1) null models (which help test the significance of observed patterns), (2) structural elements (i.e., substructures in a hypergraph such as open and closed triangles), and (3) structural quantities (i.e., numerical tools for computing hypergraph patterns such as transitivity). There are also hypergraph generators, whose objective is to produce synthetic hypergraphs that are a faithful representation of real-world hypergraphs. In this survey, we provide a comprehensive overview of the current landscape of hypergraph mining, covering patterns, tools, and generators. We provide comprehensive taxonomies for each and offer in-depth discussions for future research on hypergraph mining.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462205","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 Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular Networks

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-20 DOI: 10.1145/3718736

Leila Benarous, Sherali Zeadally, Saadi Boudjit, Abdelhamid Mellouk

{"title":"A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular Networks","authors":"Leila Benarous, Sherali Zeadally, Saadi Boudjit, Abdelhamid Mellouk","doi":"10.1145/3718736","DOIUrl":"https://doi.org/10.1145/3718736","url":null,"abstract":"Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users’ location privacy may be perilous beyond simple curiosity as it may scale to cyber-stalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies which have been considered as the most appropriate solution that maintains the trade-off between ease of application, good protection level and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"2 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462201","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

Security Approaches for Data Provenance in the Internet of Things: A Systematic Literature Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-19 DOI: 10.1145/3718735

Omair Faraj, David Megias, Joaquin Garcia-Alfaro

{"title":"Security Approaches for Data Provenance in the Internet of Things: A Systematic Literature Review","authors":"Omair Faraj, David Megias, Joaquin Garcia-Alfaro","doi":"10.1145/3718735","DOIUrl":"https://doi.org/10.1145/3718735","url":null,"abstract":"The Internet of Things (IoT) relies on resource-constrained devices deployed in unprotected environments. Given their constrained nature, IoT systems are vulnerable to security attacks. Data provenance, which tracks the origin and flow of data, provides a potential solution to guarantee data security, including trustworthiness, confidentiality, integrity, and availability in IoT systems. Different types of risks may be faced during data transmission in single-hop and multi-hop scenarios, particularly due to the interconnectivity of IoT systems, which introduces security and privacy concerns. Attackers can inject malicious data or manipulate data without notice, compromising data integrity and trustworthiness. Data provenance offers a way to record the origin, history, and handling of data to address these vulnerabilities. A systematic literature review of data provenance in IoT is presented, exploring existing techniques, practical implementations, security requirements, and performance metrics. Respective contributions and shortcomings are compared. A taxonomy related to the development of data provenance in IoT is proposed. Open issues are identified, and future research directions are presented, providing useful insights for the evolution of data provenance research in the context of the IoT.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462204","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

Class-Imbalanced Learning on Graphs: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-19 DOI: 10.1145/3718734

Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla

引用次数: 0

Automated Program Repair: Emerging Trends Pose and Expose Problems for Benchmarks

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-19 DOI: 10.1145/3704997

Joseph Renzullo, Pemma Reiter, Westley Weimer, Stephanie Forrest

{"title":"Automated Program Repair: Emerging Trends Pose and Expose Problems for Benchmarks","authors":"Joseph Renzullo, Pemma Reiter, Westley Weimer, Stephanie Forrest","doi":"10.1145/3704997","DOIUrl":"https://doi.org/10.1145/3704997","url":null,"abstract":"Machine learning (ML) pervades the field of Automated Program Repair (APR). Algorithms deploy neural machine translation and large language models (LLMs) to generate software patches, among other tasks. But, there are important differences between these applications of ML and earlier work, which complicates the task of ensuring that results are valid and likely to generalize. A challenge is that the most popular APR evaluation benchmarks were not designed with ML techniques in mind. This is especially true for LLMs, whose large and often poorly-disclosed training datasets may include problems on which they are evaluated. This paper reviews work in APR published in the field’s top five venues since 2018, emphasizing emerging trends in the field, including the dramatic rise of ML models, including LLMs. ML-based papers are categorized along structural and functional dimensions, and a variety of issues are identified that these new methods raise. Importantly, data leakage and contamination concerns arise from the challenge of validating ML-based APR using existing benchmarks, which were designed before these techniques were popular. We discuss inconsistencies in evaluation design and performance reporting and offer pointers to solutions where they are available. Finally, we highlight promising new directions that the field is already taking.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"87 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462203","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 for Cross-Domain Few-Shot Visual Recognition: A Survey 用于跨域少镜头视觉识别的深度学习：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-17 DOI: 10.1145/3718362

Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal Patel, Li Liu

{"title":"Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey","authors":"Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal Patel, Li Liu","doi":"10.1145/3718362","DOIUrl":"https://doi.org/10.1145/3718362","url":null,"abstract":"While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This paper presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim to provide both a position paper and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427140","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

From Perception to Computation: Revisiting Delay Optimization for Connected Autonomous Vehicles

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-17 DOI: 10.1145/3718361

Tianen Liu, Shuai Wang, Zheng Dong, Borui Li, Tian He

引用次数: 0

Green Federated Learning: A New Era of Green Aware AI

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-15 DOI: 10.1145/3718363

Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Francesco Piccialli

{"title":"Green Federated Learning: A New Era of Green Aware AI","authors":"Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Francesco Piccialli","doi":"10.1145/3718363","DOIUrl":"https://doi.org/10.1145/3718363","url":null,"abstract":"The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today’s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it’s imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it’s crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417493","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 Big Data Analytics: Characteristics, Tools and Techniques

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-15 DOI: 10.1145/3718364

Mohammad Shahnawaz, Manish Kumar

{"title":"A Comprehensive Survey on Big Data Analytics: Characteristics, Tools and Techniques","authors":"Mohammad Shahnawaz, Manish Kumar","doi":"10.1145/3718364","DOIUrl":"https://doi.org/10.1145/3718364","url":null,"abstract":"Modern computing devices generate vast amounts of diverse data. It means that a fast transition through various computing devices leads to big data production. Big data with high velocity, volume, and variety presents challenges like data inconsistency, scalability, real-time analysis, and tool selection. Although numerous solutions have been proposed for big data processing, they are often limited in scope and effectiveness. This survey aims to address the lack of comprehensive analysis of big data challenges in relation to machine learning (ML) and the Internet of Things (IoT) environments, particularly concerning the 7Vs of big data. It emphasizes the significance of selecting suitable tools to address each unique big data characteristic, providing a structured approach to manage these challenges effectively. The article systematically reviews big data characteristics and associated techniques, with a detailed discussion of various tools and their applications. Additionally, it analyzes existing ML methods and techniques for IoT data analytics in big data contexts. Through a systematic literature review (SLR), we examine key aspects, including core concepts, benefits, limitations, and the impact of big data on ML algorithms and IoT data analytics. We highlight groundbreaking studies addressing big data challenges to impact future research and enhance big data-driven applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"208 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417495","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