ACM Computing Surveys最新文献

{"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}

{"title":"A Survey on Adversarial Contention Resolution","authors":"Ioana Banicescu, Trisha Chakraborty, Seth Gilbert, Maxwell Young","doi":"10.1145/3733594","DOIUrl":"https://doi.org/10.1145/3733594","url":null,"abstract":"Contention resolution addresses the challenge of coordinating access by multiple processes to a shared resource such as memory, disk storage, or a communication channel. Originally spurred by challenges in database systems and bus networks, contention resolution has endured as an important abstraction for resource sharing, despite decades of technological change. Here, we survey the literature on resolving worst-case contention, where the number of processes and the time at which each process may start seeking access to the resource is dictated by an adversary. We also highlight the evolution of contention resolution, where new concerns—such as security, quality of service, and energy efficiency—are motivated by modern systems. These efforts have yielded insights into the limits of randomized and deterministic approaches, as well as the impact of different model assumptions such as global clock synchronization, knowledge of the number of processors, feedback from access attempts, and attacks on the availability of the shared resource.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"35 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143893303","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":"Keystroke Dynamics: Concepts, Techniques, and Applications","authors":"Rashik Shadman, Ahmed Anu Wahab, Michael Manno, Matthew Lukaszewski, Daqing Hou, Faraz Hussain","doi":"10.1145/3733103","DOIUrl":"https://doi.org/10.1145/3733103","url":null,"abstract":"Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques, such as biometric authentication systems, have been developed in recent years. This paper provides a detailed review of keystroke-based authentication systems and their applications. Keystroke dynamics is a behavioral biometric that is emerging as an important tool for cybersecurity as it promises to be nonintrusive and cost-effective. In addition, no additional hardware is required, making it convenient to deploy. This survey covers novel keystroke datasets, state-of-the-art keystroke authentication algorithms, keystroke authentication on touch screen and mobile devices, and various prominent applications of such techniques beyond authentication. The paper covers all the significant aspects of keystroke dynamics and can be considered a reference for future researchers in this domain. The paper includes a discussion of the latest keystroke datasets, providing researchers with an up-to-date resource for analysis and experimentation. In addition, this survey covers the state-of-the-art algorithms adopted within this domain, offering insights into the cutting-edge techniques utilized for keystroke analysis. Moreover, this paper explains the diverse applications of keystroke dynamics, particularly focusing on security, verification, and identification uses. Beyond these crucial areas, we mention additional applications where keystroke dynamics can be applied, broadening the scope of understanding regarding its potential impact across various domains. Unlike previous survey papers, which typically concentrate on specific aspects of keystroke dynamics, our comprehensive analysis presents all relevant areas within this field. By introducing discussions on the latest advances, we provide readers with a thorough understanding of the current landscape and emerging trends in keystroke dynamics research. Furthermore, this paper presents a summary of future research opportunities, highlighting potential areas for exploration and development within the realm of keystroke dynamics. This forward-looking perspective aims to inspire further inquiry and innovation, guiding the trajectory of future studies in this dynamic field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"86 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889355","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":"IPv6 Routing Protocol for Low-Power and Lossy Networks Security Vulnerabilities and Mitigation Techniques: A Survey","authors":"Aviram Zilberman, Amit Dvir, Ariel Stulman","doi":"10.1145/3732776","DOIUrl":"https://doi.org/10.1145/3732776","url":null,"abstract":"The proliferation of the Internet of Things (IoT) has reshaped the way we interact with technology, propelling the Routing Protocol for Low-Power and Lossy Networks (RPL) into a critical role as a communication framework. Amid this transformative landscape, security vulnerabilities within RPL-based IoT networks emerge as a substantial concern. This survey delves into these vulnerabilities, offering insights into their intricacies, potential consequences, and robust mitigation strategies. Commencing with a foundational understanding of IoT networks and their real-world applications, the survey sets the stage for comprehending the significance of Routing Protocol for Low-Power and Lossy Networks (RPL). It unravels the unique characteristics of RPL networks, their Destination-Oriented Directed Acyclic Graph (DODAG) topologies, and their pivotal role in enabling seamless device communication. The survey then delves into the heart of RPL security vulnerabilities. It navigates through diverse attack vectors, such as rank attacks and version number attacks. Each vulnerability is scrutinized, unraveling its technical mechanisms and implications for network stability. Transitioning from vulnerabilities to resilience, the survey offers a panoramic view of mitigation strategies. It dissects the nuances of intrusion detection systems (IDS), exploring trust models, location-based approaches, and hybrid systems. Signature-based, anomaly-based, and specification-based detection mechanisms are evaluated for their potential to mitigate threats within RPL networks. As standards shape the IoT landscape, the survey underscores the pivotal role of RPL within this framework. It emphasizes the necessity of secure standards in mitigating vulnerabilities across interconnected IoT devices.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875759","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":"Data Augmentation on Graphs: A Technical Survey","authors":"Jiajun Zhou, Chenxuan Xie, Shengbo Gong, Zhenyu Wen, Xiangyu Zhao, Qi Xuan, Xiaoniu Yang","doi":"10.1145/3732282","DOIUrl":"https://doi.org/10.1145/3732282","url":null,"abstract":"In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques. Specifically, this survey first provides an overview of various feasible taxonomies and categorizes existing GDAug studies based on multi-scale graph elements. Subsequently, for each type of GDAug technique, this survey formalizes standardized technical definition, discuss the technical details, and provide schematic illustration. The survey also reviews domain-specific graph data augmentation techniques, including those for heterogeneous graphs, temporal graphs, spatio-temporal graphs, and hypergraphs. In addition, this survey provides a summary of available evaluation metrics and design guidelines for graph data augmentation. Lastly, it outlines the applications of GDAug at both the data and model levels, discusses open issues in the field, and looks forward to future directions. The latest advances in GDAug are summarized in GitHub.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"1 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872928","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":"Intrusion Detection Based on Federated Learning: A Systematic Review","authors":"Jose Hernandez-Ramos, Georgios Karopoulos, Efstratios Chatzoglou, Vasileios Kouliaridis, Enrique Marmol, Aurora Gonzalez-Vidal, Georgios Kambourakis","doi":"10.1145/3731596","DOIUrl":"https://doi.org/10.1145/3731596","url":null,"abstract":"The evolution of cybersecurity is closelynked to the development and improvement of artificial intelligence (AI). As a key tool for realizing more cybersecure ecosystems, Intrusion Detection Systems (IDSs) have evolved tremendously in recent years by integrating machine learning (ML) techniques to detect increasingly sophisticated cybersecurity attacks hidden in big data. However, traditional approaches rely on centralized learning, in which data from end nodes are shared with data centers for analysis. Recently, the application of federated learning (FL) in this context has attracted great interest to come up with collaborative intrusion detection approaches where data does not need to be shared. Due to the recent rise of this field, this work presents a complete, contemporary taxonomy for FL-enabled IDS approaches that stems from a comprehensive survey of the literature from 2018 to 2022. Precisely, our discussion includes an analysis of the main ML models, datasets, aggregation functions, as well as implementation libraries employed by the proposed FL-enabled IDS approaches. On top of everything else, we provide a critical view of the current state of the research around this topic, and describe the main challenges and future directions based on the analysis of the literature and our own experience in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"91 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866400","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 Comprehensive Survey on Machine Learning Driven Material Defect Detection","authors":"Jun Bai, Di Wu, Tristan Shelley, Peter Schubel, David Twine, John Russell, Xuesen Zeng, Ji Zhang","doi":"10.1145/3730576","DOIUrl":"https://doi.org/10.1145/3730576","url":null,"abstract":"Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and localization of MD constitute crucial research endeavors in addressing contemporary challenges associated with MD. In recent years, propelled by the swift advancement of machine learning (ML) technologies, particularly exemplified by deep learning, ML has swiftly emerged as the core technology and a prominent research direction for material defect detection (MDD). Through a comprehensive review of the latest literature, we systematically survey the ML techniques applied in MDD into five categories: unsupervised learning, supervised learning, semi-supervised learning, reinforcement learning, and generative learning. We provide a detailed analysis of the main principles and techniques used, together with the advantages and potential challenges associated with these techniques. Furthermore, the survey focuses on the techniques for defect detection in composite materials, which are important types of materials enjoying increasingly wide application in various industries such as aerospace, automotive, construction, and renewable energy. Finally, the survey explores potential future directions in MDD utilizing ML technologies. This survey consolidates ML-based MDD literature and provides a foundation for future research and practice.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"42 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858049","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":"Quantum Key Distribution Networks - Key Management: A Survey","authors":"Emir Dervisevic, Amina Tankovic, Ehsan Fazel, Ramana Kompella, Peppino Fazio, Miroslav Voznak, Miralem Mehic","doi":"10.1145/3730575","DOIUrl":"https://doi.org/10.1145/3730575","url":null,"abstract":"Secure communication makes the widespread use of telecommunication networks and services possible. With the constant progress of computing and mathematics, new cryptographic methods are being diligently developed. Quantum Key Distribution (QKD) is a promising technology that provides an Information-Theoretically Secure (ITS) solution to the secret-key agreement problem between two remote parties. QKD networks based on trusted relay nodes are built to provide service to a larger number of parties at arbitrary distances. They function as an add-on technology to traditional networks, generating, managing, distributing, and supplying ITS cryptographic keys. Since key resources are limited, integrating QKD network services into critical infrastructures necessitates effective key management. As a result, this paper provides a comprehensive review of key management approaches for trusted–relay QKD networks. They are analyzed to facilitate the identification of potential strategies and accelerate the future development of QKD networks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"108 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849782","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}