ACM Computing Surveys最新文献

{"title":"Generative AI for Intelligent Transportation Systems: Road Transportation Perspective","authors":"Huan Yan, Yong Li","doi":"10.1145/3719290","DOIUrl":"https://doi.org/10.1145/3719290","url":null,"abstract":"Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in areas like image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems (ITS), such as data sparsity, difficulty in observing abnormal scenarios, and in modeling data uncertainty. In this review, we systematically investigate the relevant literature on generative AI techniques in addressing key issues in different types of tasks in ITS tailored specifically for road transportation. First, we introduce the principles of different generative AI techniques. Then, we classify tasks in ITS into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We systematically illustrate how generative AI techniques addresses key issues in these four different types of tasks. Finally, we summarize the challenges faced in applying generative AI to intelligent transportation systems, and discuss future research directions based on different application scenarios.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"48 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920278","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}

{"title":"Unveiling the Covert Vulnerabilities in Multi-Factor Authentication Protocols: A Systematic Review and Security Analysis","authors":"Kok Wee Ang, Eyasu Getahun Chekole, Jianying Zhou","doi":"10.1145/3734864","DOIUrl":"https://doi.org/10.1145/3734864","url":null,"abstract":"Nowadays, cyberattacks are growing at an alarming rate, causing widespread havoc to the digital community. In particular, authentication attacks have become a dominant attack vector, allowing intruders to impersonate legitimate users and maliciously access resources. Traditional single-factor authentication (SFA) protocols, which rely on a single authentication factor are often insufficient to address the growing sophistication of modern cyberattacks. To address the shortcomings in SFA, multi-factor authentication (MFA) protocols have been widely adopted in recent years, raising the security bar against impostors and restricting unauthorized accesses. MFA enhances security by incorporating multiple authentication factors, such as knowledge-based (e.g., passwords), possession-based (e.g., tokens), and inherent-based factors (e.g., biometrics), among others. However, while MFA is generally considered more secure than SFA, it is not foolproof. Because, critical vulnerabilities may still arise due to design or implementation flaws in MFA protocols. These vulnerabilities are often overlooked by designers or users and remain undetected until exploited by attackers, potentially resulting in catastrophic consequences. Unfortunately, existing works failed to adequately analyze and identify most of such critical security flaws in MFA protocols. In this work, we systematically analyze the intricate design and construction of MFA protocols to uncover potential design-level security flaws. To this end, we first define eight security evaluation criteria that are essential to critically evaluate design-level security flaws of MFA protocols. These criteria are primarily derived from existing and newly introduced MFA security requirements. We then review a range of MFA protocols across various domains. Using our established evaluation criteria, we perform a systematic security analysis and evaluation of these protocols, particularly focusing on their design and construction. Ultimately, we uncover several security flaws in most of the MFA protocols evaluated. Due to space limitation, we select ten of those protocols for deeper security analysis and provide a detailed discussion of the respective flaws identified. Additionally, we devised relevant mitigation strategies for each of the flaws identified. We believe that our findings provide valuable insights to cybersecurity researchers and practitioners to help them addressing a wide range of security flaws in MFA protocols.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"24 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920390","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}

{"title":"A Study on the Prevalence of Privacy in Software Engineering","authors":"Pattaraporn Sangaroonsilp, Hoa Khanh Dam","doi":"10.1145/3734216","DOIUrl":"https://doi.org/10.1145/3734216","url":null,"abstract":"The continuous growth and widespread use of digital technologies has made the protection of personal data and individual rights become one of the major concerns when developing software systems and applications. Furthermore, data protection regulations and privacy standards have imposed additional responsibilities on software engineers. Failure to achieve these privacy and regulatory requirements can lead to user dissatisfaction, damage to organisations’ reputation, and potential legal non-compliance. Software engineering (SE) researchers have been exploring various aspects to address privacy concerns in software development. To provide the SE community a comprehensive overview of the prevalence of privacy considerations in SE research, we conducted a systematic mapping study following well-established guidelines for collecting and categorising primary studies. Our study identified 187 primary studies, and categorised their contributions based on various perspectives. Our results reveal the following key findings: (a) privacy considerations are prevalent in 12.14% of all possible primary studies; (b) the most common type of problems, contributions and validation methods employed; and (c) the distribution of focus and scope in primary studies as well as the potential directions for future research. These findings offer both quantitative and qualitative insights of trends and opportunities that can benefit both SE researchers, academics and practitioners.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"226 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909817","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}

{"title":"Driving Healthcare Monitoring with IoT and Wearable Devices: A Systematic Review","authors":"João Baiense, Eftim Zdravevski, Paulo Coelho, Ivan Miguel Pires, Fernando Velez","doi":"10.1145/3731595","DOIUrl":"https://doi.org/10.1145/3731595","url":null,"abstract":"Wearable technologies have become a significant part of the healthcare industry, collecting personal health data and extracting valuable information for real-time assistance. This review paper analyzes thirty-five scientific publications on driving healthcare monitoring with IoT and wearable device applications. These papers were considered in a quantitative and qualitative analysis using the Natural Language Processing framework and the PRISMA methodology to filter the search results. The selected papers were published between January 2010 and May 2024 in one of the following scientific databases: IEEE Xplore, Springer, ScienceDirect (i.e., Elsevier), Association for Computing Machinery (ACM), Multidisciplinary Digital Publishing Institute (MDPI), or PubMed Central. The analysis considers population, methods, hardware, features, and communications. The research highlights that data collected from one or numerous sensors is processed and accessible in a database server for various uses, such as informing professional careers or assisting users. The review suggests that robust and efficient driving healthcare monitoring with IoT and wearable devices applications can be designed considering the valuable principles presented in this review.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"95 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909821","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}

{"title":"When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey","authors":"Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo","doi":"10.1145/3734217","DOIUrl":"https://doi.org/10.1145/3734217","url":null,"abstract":"Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"18 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909816","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}

{"title":"A Survey of Side-Channel Attacks on Branch Prediction Units","authors":"Jihoon Kim, Hyerean Jang, Youngjoo Shin","doi":"10.1145/3734218","DOIUrl":"https://doi.org/10.1145/3734218","url":null,"abstract":"The CPU architecture landscape is constantly evolving to optimize performance. However, this has inadvertently exposed vulnerabilities such as microarchitectural traces that can be exploited in side-channel attacks. The Branch Prediction Unit (BPU) plays a critical role in improving processor performance, but also introduces vulnerabilities to microarchitectural side-channel attacks. Despite ongoing efforts to develop defensive techniques, the continued emergence of new attack methods underscores the need for comprehensive analysis. This paper aims to address this research gap by conducting a thorough investigation of BPU-based side-channel attacks. This survey presents a novel taxonomy for the systematic classification of BPU-based side-channel attacks and defenses. The attacks and defenses are categorized based on three components: manipulated unit, core technique, and disclosure method. The analysis provides a structured evaluation of the effectiveness of defense techniques against each attack technique. This study not only enhances the understanding of BPU exploitation, but also provides valuable insights for software developers and CPU designers to help them protect against evolving side-channel threats.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"18 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909819","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}

{"title":"Two Decades of Automated AI Planning Methods in Construction and Fabrication: a Systematic Review","authors":"Shermin Sherkat, Thomas Wortmann, Andreas Wortmann","doi":"10.1145/3729529","DOIUrl":"https://doi.org/10.1145/3729529","url":null,"abstract":"Task planning and scheduling are crucial for construction or fabrication (CF) processes. Automating them is necessary for more efficient plans in terms of time and resources. However, most construction planning processes are still performed manually despite the existence of various AI methods. Symbolic AI automated task planning (ATP) techniques offer a variety of features to tackle task planning problems, but their application to CF has not been researched yet. This study identifies the current state of research and gaps in the literature regarding these AI techniques while providing directions for future research. We conduct a systematic review that evaluates existing literature on ATP in terms of environmental characteristics, modeling languages, ATP techniques, and results. We searched the ACM, IEEE, Scopus, WOS, and SpringerLink databases for papers published in the last 20 years (2002-2022) that discuss symbolic AI methods used in task planning within the CF fields. Our findings indicate that research on automated planning is currently limited regarding the characteristics of CF environments. Only a few papers have utilized symbolic languages, AI planners, and ATP techniques. No paper has evaluated their planning system in an on-site CF process. As a result, many symbolic languages, planners, and ATP techniques remain unexplored.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"9 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902947","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}

{"title":"The Evolution of Reinforcement Learning in Quantitative Finance: A Survey","authors":"Nikolaos Pippas, Elliot Ludvig, Cagatay Turkay","doi":"10.1145/3733714","DOIUrl":"https://doi.org/10.1145/3733714","url":null,"abstract":"Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance. This survey critically evaluates 167 publications, exploring diverse RL applications and frameworks in finance. Financial markets, marked by their complexity, multi-agent nature, information asymmetry, and inherent randomness, serve as an intriguing test-bed for RL. Traditional finance offers certain solutions, and RL advances these with a more dynamic approach, incorporating machine learning methods, including transfer learning, meta-learning, and multi-agent solutions. This survey dissects key RL components through the lens of Quantitative Finance. We uncover emerging themes, propose areas for future research, and critique the strengths and weaknesses of existing methods.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"34 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901186","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}

{"title":"A Survey on Error-Bounded Lossy Compression for Scientific Datasets","authors":"Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang, Robert Underwood, Zhaorui Zhang, Milan Shah, Yafan Huang, Jiajun Huang, Xiaodong Yu, Congrong Ren, Hanqi Guo, Grant Wilkins, Dingwen Tao, Jiannan Tian, Sian Jin, Zizhe Jian, Daoce Wang, Md Hasanur Rahman, Boyuan Zhang, Shihui Song, Jon Calhoun, Guanpeng Li, Kazutomo Yoshii, Khalid Alharthi, Franck Cappello","doi":"10.1145/3733104","DOIUrl":"https://doi.org/10.1145/3733104","url":null,"abstract":"Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular pros and cons. In this paper we provide a comprehensive survey of emerging error-bounded lossy compression techniques. The key contribution is fourfold. (1) We summarize a novel taxonomy of lossy compression into 6 classic models. (2) We provide a comprehensive survey of 10 commonly used compression components/modules. (3) We summarized pros and cons of 47 state-of-the-art lossy compressors and present how state-of-the-art compressors are designed based on different compression techniques. (4) We discuss how customized compressors are designed for specific scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901205","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}

{"title":"Prerequisite Relation Learning: A Survey and Outlook","authors":"Youheng Bai, Zitao Liu, Teng Guo, Mingliang Hou, Kui Xiao","doi":"10.1145/3733593","DOIUrl":"https://doi.org/10.1145/3733593","url":null,"abstract":"Prerequisite relation (PR) learning is a fundamental task in educational technology that identifies dependencies between learning resources to facilitate personalized learning experiences and optimize educational content organization. This survey provides a systematic review of prerequisite relation learning, emphasizing both methodological advances and practical applications. We first explore two distinct granularities of learning resources: knowledge concepts (KCs) and learning objects (LOs), establishing their definitions and relationships. We then introduce a novel classification framework for prerequisite relation learning methods based on both feature types and enhancement relationships, categorizing existing approaches into four types: (1) multi-source knowledge features for KCs’ prerequisite relation learning; (2) semantic knowledge features for LOs’ prerequisite relation learning; (3) LOs-enhanced learning for KCs’ prerequisite relation learning; and (4) KCs-enhanced learning for LOs’ prerequisite relation learning. The survey highlights recent developments in modeling KCs’ prerequisite relations. We provide a comprehensive analysis of evaluation methodologies, including both intrinsic metrics and extrinsic evaluation. Furthermore, we analyze the practical impact of prerequisite relations in educational applications, from adaptive learning path generation to curriculum design. Finally, we discuss current challenges and future opportunities for prerequisite relation learning.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"69 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143893305","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}