ACM Computing Surveys最新文献_第6页

Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey 大型语言模型的独特安全和隐私威胁：综合调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-11 DOI: 10.1145/3764113

Shang Wang, Tianqing Zhu, Bo Liu, Ming Ding, Dayong Ye, Wanlei Zhou, Philip Yu

引用次数: 0

Software Fairness: An Analysis and Survey 软件公平：分析与调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-08 DOI: 10.1145/3762170

Ezekiel Soremekun, Mike Papadakis, Maxime Cordy, Yves Le Traon

{"title":"Software Fairness: An Analysis and Survey","authors":"Ezekiel Soremekun, Mike Papadakis, Maxime Cordy, Yves Le Traon","doi":"10.1145/3762170","DOIUrl":"https://doi.org/10.1145/3762170","url":null,"abstract":"In the last decade, researchers have studied fairness as a software property. In particular, how to engineer fair software systems. This includes specifying, designing, and validating fairness properties. However, the landscape of works addressing bias as a software engineering concern is unclear, i.e., techniques and studies that analyze the fairness properties of learning-based software. In this work, we provide a clear view of the state-of-the-art in software fairness analysis. To this end, we collect, categorize and conduct in-depth analysis of 164 publications investigating the fairness of learning-based software systems. Specifically, we study the evaluated fairness measure, the studied tasks, the type of fairness analysis, the main idea of the proposed approaches and the access level (e.g., black, white or grey box). Our findings include the following: (1) Fairness concerns (such as fairness specification and requirements engineering) are under-studied; (2) Fairness measures such as conditional, sequential and intersectional fairness are under-explored; (3) Semi-structured datasets (e.g., audio, image, code and text) are barely studied for fairness analysis in the SE community; and (4) Software fairness analysis techniques hardly employ white-box, in-processing machine learning (ML) analysis methods. In summary, we observed several open challenges including the need to study intersectional/sequential bias, policy-based bias handling and human-in-the-loop, socio-technical bias mitigation.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"69 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017300","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 Tutorial on Discriminative Clustering and Mutual Information 判别聚类与互信息教程

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-05 DOI: 10.1145/3748255

Louis Ohl, Pierre-Alexandre Mattei, Frederic Precioso

{"title":"A Tutorial on Discriminative Clustering and Mutual Information","authors":"Louis Ohl, Pierre-Alexandre Mattei, Frederic Precioso","doi":"10.1145/3748255","DOIUrl":"https://doi.org/10.1145/3748255","url":null,"abstract":"To cluster data is to separate samples into distinctive groups that should ideally have some cohesive properties. Today, numerous clustering algorithms exist, and their differences lie essentially in what can be perceived as “cohesive properties”. Therefore, hypotheses on the nature of clusters must be set: they can be either generative or discriminative. As the last decade witnessed the impressive growth of deep clustering methods that involve neural networks to handle high-dimensional data often in a discriminative manner; we concentrate mainly on the discriminative hypotheses. In this paper, our aim is to provide an accessible historical perspective on the evolution of discriminative clustering methods and notably how the nature of assumptions of the discriminative models changed over time: from decision boundaries to invariance critics. We notably highlight how mutual information has been a historical cornerstone of the progress of (deep) discriminative clustering methods. We also show some known limitations of mutual information and how discriminative clustering methods tried to circumvent those. We then discuss the challenges that discriminative clustering faces with respect to the selection of the number of clusters. Finally, we showcase these techniques using the dedicated Python package, GemClus , that we have developed for discriminative clustering.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"304 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002955","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

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey 领域专门化是使大型语言模型具有颠覆性的关键：一个综合综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-03 DOI: 10.1145/3764579

Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, Liang Zhao

{"title":"Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey","authors":"Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, Liang Zhao","doi":"10.1145/3764579","DOIUrl":"https://doi.org/10.1145/3764579","url":null,"abstract":"Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to making large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to summarize better and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"63 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930904","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

SoK: Bitcoin Layer Two (L2) SoK：比特币第二层（L2）

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-01 DOI: 10.1145/3763232

Minfeng Qi, Qin Wang, Zhipeng Wang, Manvir Schneider, Tianqing Zhu, Shiping Chen, William Knottenbelt, Thomas Hardjono

{"title":"SoK: Bitcoin Layer Two (L2)","authors":"Minfeng Qi, Qin Wang, Zhipeng Wang, Manvir Schneider, Tianqing Zhu, Shiping Chen, William Knottenbelt, Thomas Hardjono","doi":"10.1145/3763232","DOIUrl":"https://doi.org/10.1145/3763232","url":null,"abstract":"In this paper, we present the first Systematization of Knowledge (SoK) on constructing Layer Two (L2) solutions for Bitcoin. We carefully examine a representative subset of ongoing Bitcoin L2 solutions (40 out of 335 extensively investigated cases) and provide a concise yet impactful identification of six classic design patterns through two approaches (i.e., modifying transactions & creating proofs). Notably, we are the first to incorporate the inscription technology (emerged in mid-2023), along with a series of related innovations. We further establish a reference framework that serves as a baseline criterion ideally suited for evaluating the security aspects of Bitcoin L2 solutions, and which can also be extended to broader L2 applications. We apply this framework to evaluate each of the projects we investigated. We find that the inscription-based approaches introduce new functionality (i.e., programability) to Bitcoin systems, whereas existing proof-based solutions primarily address scalability challenges. Our security analysis reveals new attack vectors targeting data/state (availability, verification), assets (withdrawal, recovery), and users (disputes, censorship).","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"38 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924225","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

The Role of the Internet of Things (IoT) in Achieving the United Nations (UN) Sustainable Development Goals (SDGs) - A Systematic Review 物联网（IoT）在实现联合国（UN）可持续发展目标（sdg）中的作用-系统回顾

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-30 DOI: 10.1145/3765516

Abdullahi Kutiriko Abubakar, Lee Gillam, Nishanth Sastry

{"title":"The Role of the Internet of Things (IoT) in Achieving the United Nations (UN) Sustainable Development Goals (SDGs) - A Systematic Review","authors":"Abdullahi Kutiriko Abubakar, Lee Gillam, Nishanth Sastry","doi":"10.1145/3765516","DOIUrl":"https://doi.org/10.1145/3765516","url":null,"abstract":"As the 2030 deadline for achieving the Sustainable Development Goals (SDGs) approaches, the Internet of Things (IoT) has become a key enabler of sustainable development. This paper presents a systematic review of IoT-SDG research from 2015–2024, mapping its applications across seven macro-sectors: health, food and agriculture, energy and environment, education and employment, industry and innovation, governance and human rights, and smart cities and smart spaces. Our analysis identifies three major trends: (i) a shift from conceptual designs to real-world deployments, including grassroots innovations in developing economies tailored to local priorities; (ii) increasing reliance on enabling technologies such as cloud, edge, and machine learning, which together enhance scalability and responsiveness; and (iii) the growing use of IoT data not only for operational efficiency, but to quantify the impact of SDG interventions and identify areas for refinement. Despite this progress, barriers remain, including limited connectivity, dependence on centralised infrastructures, and challenges of interoperability, particularly in low-resource settings. These findings underscore the need for context-specific, edge-driven architectures and scalable mobile applications that can bridge digital divides. By synthesising achievements, gaps, and future opportunities, this review offers actionable insights for policymakers, technologists, and researchers seeking to harness IoT more effectively in support of an inclusive and sustainable SDG Vision 2030.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"25 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919143","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

Memory Analysis for Malware Detection: A Comprehensive Survey Using the OSCAR Methodology 恶意软件检测的内存分析：使用OSCAR方法的综合调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-28 DOI: 10.1145/3764580

Yasin Dehfouli, Arash Habibi Lashkari

{"title":"Memory Analysis for Malware Detection: A Comprehensive Survey Using the OSCAR Methodology","authors":"Yasin Dehfouli, Arash Habibi Lashkari","doi":"10.1145/3764580","DOIUrl":"https://doi.org/10.1145/3764580","url":null,"abstract":"The steady growth of malware over the years has now sharply escalated, with a 30% surge in global cyberattacks in 2024. This rise demands advanced detection, as traditional methods often miss sophisticated or fileless malware. Memory analysis detects traces left by any malware in volatile memory, revealing runtime behaviors, privilege escalation attempts, and active processes. An examination of prior research shows that existing surveys on memory analysis have significant gaps, as none provide a comprehensive overview of the field. To address these gaps, this survey systematically proposes key research questions and addresses them using the OSCAR (Obtain, Strategize, Collect, Analyze, Report) methodology. Memory acquisition techniques and tools have been discussed with the most diverse taxonomy provided to the best of our knowledge. Furthermore, forensic methods, tools, and studies are categorized into four distinct approaches, with a comprehensive taxonomy at the end. We also evaluated and ranked memory dump datasets using our proposed scoring system. Finally, the survey covers malware detection methods, examining both machine learning and traditional approaches and their accuracy, benefits, drawbacks, and challenges. This survey aims to provide a comprehensive and up-to-date overview of the field of memory analysis, with a focus on detecting malicious activities.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"29 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915647","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

Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches 早期痴呆症诊断的新趋势：对先进机器学习方法的分析

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-28 DOI: 10.1145/3764578

Badal Gami, Manav Agrawal, Rahul Katarya

{"title":"Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches","authors":"Badal Gami, Manav Agrawal, Rahul Katarya","doi":"10.1145/3764578","DOIUrl":"https://doi.org/10.1145/3764578","url":null,"abstract":"Dementia is the waning of cognitive abilities, which is typically seen with the natural aging process and includes issues with memory, language, and problem-solving abilities. Artificial Intelligence (AI) techniques are one viable method for the diagnosis of dementia. Despite recent advances in dementia informatics research and AI, accurate early diagnoses are still far from ideal. This study focuses on showcasing a comprehensive analysis of emerging AI approaches applied to early dementia diagnosis, highlighting trends across neuroimaging, speech, EEG, and clinical data. The proposed work’s main contributions include a summary of the potential challenges and vulnerabilities with dementia informatics research, a wide range of diagnostic issues in dementia care, a descriptive comparison of the elementary manuscripts judged on evaluation parameters such as precision, responsiveness, and definiteness and an offering of a descriptive set of data for developing Machine Learning (ML) and Deep Learning (DL) models. The manuscript also provides a valuable overview of new avenues for informatics research on dementia and advanced ML. The main objective is to fill a gap in the literature by offering an in-depth analysis and overview of the application of AI in dementia research, providing a foundational roadmap for accelerating impactful, data-driven dementia care solutions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"29 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916110","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

Slicing of Probabilistic Programs: A Review of Existing Approaches 概率程序的切片：对现有方法的回顾

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-28 DOI: 10.1145/3764581

Federico Olmedo

引用次数: 0

A Comprehensive Review of Real-Time Multi-View Multi-Person Markerless Motion Capture 实时多视角多人无标记运动捕捉技术综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-27 DOI: 10.1145/3757733

Pierre Nagorny, Bart Kevelham, Sylvain Chagué, Caecilia Charbonnier

{"title":"A Comprehensive Review of Real-Time Multi-View Multi-Person Markerless Motion Capture","authors":"Pierre Nagorny, Bart Kevelham, Sylvain Chagué, Caecilia Charbonnier","doi":"10.1145/3757733","DOIUrl":"https://doi.org/10.1145/3757733","url":null,"abstract":"Markerless human body motion capture promises to remove markers from capture studios, thus simplifying its diverse application fields, from life science to virtual reality. This comprehensive review examines recent advances in real-time markerless motion capture systems from 2020 to 2024, focusing on real-time multi-view, multi-person tracking solutions. Recent advancements, particularly driven by neural network-based pose estimation, have enabled real-time tracking with minimal latency, achieving at least 25 frames per second. Through systematic analysis, we evaluate these methods based on three key metrics: accuracy in pose reconstruction, end-to-end latency, and computational efficiency. Special attention is given to how architectural decisions impact system scalability regarding the number of camera viewpoints and tracked individuals. While current methods show promise for applications like sports analysis and virtual reality, challenges remain in achieving optimal performance across all metrics. Through systematic analysis of leading real-time pipelines, we identify key technical advances and persistent challenges. This synthesis provides critical insights for researchers and practitioners working to develop more robust markerless motion capture systems, while outlining important directions for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"32 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906146","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