ACM Computing Surveys最新文献

{"title":"A Survey of mmWave Backscatter: Applications, Platforms, and Technologies","authors":"Yimiao Sun, Yuan He, Yang Zou, Jiaming Gu, Xiaolei Yang, Jia Zhang, Ziheng Mao","doi":"10.1145/3723004","DOIUrl":"https://doi.org/10.1145/3723004","url":null,"abstract":"As a key enabling technology of the Internet of Things (IoT) and 5G communication networks, millimeter wave (mmWave) backscatter has undergone noteworthy advancements and brought significant improvement to prevailing sensing and communication systems. Past few years have witnessed growing efforts in innovating mmWave backscatter transmitters ( <jats:italic>e.g.,</jats:italic> tags and metasurfaces) and the corresponding techniques, which provide efficient information embedding and fine-grained signal manipulation for mmWave backscatter technologies. These efforts have greatly enabled a variety of appealing applications, such as long-range localization, roadside-to-vehicle communication, coverage optimization and large-scale identification. In this paper, we carry out a comprehensive survey to systematically summarize the works related to the topic of mmWave backscatter. Firstly, we introduce the scope of this survey and provide a taxonomy to distinguish two categories of mmWave backscatter research based on the operating principle of the backscatter transmitter: modulation-based and relay-based. Furthermore, existing works in each category are grouped and introduced in detail, with their common applications, platforms and technologies, respectively. Finally, we elaborate on potential directions and discuss related surveys in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"56 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599055","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":"Secure Robotics: Navigating Challenges at the Nexus of Safety, Trust, and Cybersecurity in Cyber-Physical Systems","authors":"Adam Haskard, Damith Herath","doi":"10.1145/3723050","DOIUrl":"https://doi.org/10.1145/3723050","url":null,"abstract":"The growing pervasiveness of robotic and embodied artificial intelligence systems in daily life and within cyber-physical environments highlights a complex web of challenges at the intersection of robotic safety, human-to-robot trust, and cybersecurity. This article explores these challenges by emphasising the crucial role of security in establishing and maintaining trust between humans and robots, which is integral to successfully adopting and operating these systems in human environments. Safety considerations include mitigating the risks of physical harm and environmental damage due to robotic malfunctions or cyberattacks, particularly in autonomous robots requiring high built-in safety measures. From a cybersecurity perspective, these systems face unique challenges due to their complex, interconnected software and hardware components that necessitate robust protection against data breaches to ensure secure data communication. Additionally, the dynamic interaction of these systems with the physical environment adds a layer of complexity, which makes the safety, security, and reliability of these interactions a vital component of the overall security strategy. This paper reviews these areas within the cyber-physical systems paradigm by focusing on engineering fail-safe mechanisms, the importance of trust and ethical responsibility in human-robot interactions, and the need for resilient cybersecurity measures. At this nexus, a table of crossover challenges illustrates the intricacy of integrating safety, trust, and security in robotic systems. This paper introduces “secure robotics” as a new paradigm to address these collective challenges with a novel model to provide a structured methodology for evaluating and enhancing robotic system performance that symbolises the convergence of theoretical constructs with empirical analysis. By defining secure robotics, the paper establishes a framework for advancing robotics in the cyber-physical era in alignment with current technological trends while anticipating future developments. This framework positions secure robotics as a key contributor to the evolution of cyber-physical systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"2 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599058","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":"Overview and Challenges of Distributed Decision Making in Resource Contested and Dynamic Environments","authors":"Claudia Szabo, Robin Baker, Glen Pearce, Eyoel Teffera, Anthony Perry","doi":"10.1145/3719001","DOIUrl":"https://doi.org/10.1145/3719001","url":null,"abstract":"Understanding the advantages and disadvantages of distributed decision making approaches as they are developed for and deployed in contested and dynamic environments is critical to ensure that recent advancements are used in practice to their maximum potential. In this survey, we focus on the use of decision making algorithms in two pespectives, namely, context and situational awareness (CSA) and decision making based on findings from CSA. We introduce taxonomies of required characteristics and analyse how they are met by existing approaches. Our analysis finds that evaluation of decision making approaches needs to mature to consider critical attributes such as the used network bandwidth, fault tolerance, and robustness among others. The broad majority of experimental analyses focused on showing that the approach works, typically in a small scale scenario, and that attributes such as runtime, network bandwith, and size weight and power, were critically overlooked. None of the approaches consider large action spaces or sparse rewards. We discuss trade-offs and challenges of existing work and highlight research opportunities.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"80 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598923","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":"Towards a Better Understanding of Evaluating Trustworthiness in AI Systems","authors":"Nils Kemmerzell, Annika Schreiner, Haroon Khalid, Michael Schalk, Letizia Bordoli","doi":"10.1145/3721976","DOIUrl":"https://doi.org/10.1145/3721976","url":null,"abstract":"With the increasing integration of artificial intelligence into various applications across industries, numerous institutions are striving to establish requirements for AI systems to be considered trustworthy, such as fairness, privacy, robustness, or transparency. For the implementation of Trustworthy AI into real-world applications, these requirements need to be operationalized, which includes evaluating the extent to which these criteria are fulfilled. This survey contributes to the discourse by outlining the current understanding of trustworthiness and its evaluation. Initially, existing evaluation frameworks are analyzed, from which common dimensions of trustworthiness are derived. For each dimension, the literature is surveyed for evaluation strategies, specifically focusing on quantitative metrics. By mapping these strategies to the machine learning lifecycle, an evaluation framework is derived, which can serve as a foundation towards the operationalization of Trustworthy AI.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"53 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576284","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":"Testbeds and Evaluation Frameworks for Anomaly Detection within Built Environments: A Systematic Review","authors":"Mohammed Alosaimi, Omer Rana, Charith Perera","doi":"10.1145/3722213","DOIUrl":"https://doi.org/10.1145/3722213","url":null,"abstract":"The Internet of Things (IoT) has revolutionized built environments by enabling seamless data exchange among devices such as sensors, actuators, and computers. However, IoT devices often lack robust security mechanisms, making them vulnerable to cyberattacks, privacy breaches, and operational anomalies caused by environmental factors or device faults. While anomaly detection techniques are critical for securing IoT systems, the role of testbeds in evaluating these techniques has been largely overlooked. This systematic review addresses this gap by treating testbeds as first-class entities essential for the standardized evaluation and validation of anomaly detection methods in built environments. We analyze testbed characteristics, including infrastructure configurations, device selection, user-interaction models, and methods for anomaly generation. We also examine evaluation frameworks, highlighting key metrics and integrating emerging technologies such as edge computing and 5G networks into testbed design. By providing a structured and comprehensive approach to testbed development and evaluation, this paper offers valuable guidance to researchers and practitioners in enhancing the reliability and effectiveness of anomaly detection systems. Our findings contribute to the development of more secure, adaptable, and scalable IoT systems, ultimately improving the security, resilience, and efficiency of built environments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"22 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576309","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":"I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey","authors":"Noah Lewis, Jean Luca Bez, Surendra Byna","doi":"10.1145/3722215","DOIUrl":"https://doi.org/10.1145/3722215","url":null,"abstract":"Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&amp;D.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"47 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575443","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 Readiness for AI: A 360-Degree Survey","authors":"Kaveen Hiniduma, Suren Byna, Jean Luca Bez","doi":"10.1145/3722214","DOIUrl":"https://doi.org/10.1145/3722214","url":null,"abstract":"Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&amp;D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that would be used for enhancing the quality, accuracy, and fairness of AI training and inference.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"18 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575444","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":"Vehicle Trajectory Data Processing, Analytics, and Applications: A Survey","authors":"Chenxi Liu, Zhu Xiao, Wangchen Long, Tong Li, Hongbo Jiang, Keqin Li","doi":"10.1145/3715902","DOIUrl":"https://doi.org/10.1145/3715902","url":null,"abstract":"Vehicles traveling through cities generate extensive vehicle trajectory collected by scalable sensors, providing excellent opportunities to address urban challenges such as traffic congestion and public safety. In this survey, we systematically review vehicle trajectory collection, preprocessing, analytics, and applications. First, we focus on the standard techniques for vehicle trajectory collection and corresponding datasets. Next, we introduce representative approaches for the latest advances in vehicle trajectory processing. We further discuss individual travel behavior and collective mobility analytics using vehicle trajectory. Since private cars constitute the majority of urban vehicles and form the basis for many recent research findings, we emphasize analytics based on private car trajectory data. We then compile vehicle trajectory-boosted applications from the perspective of computing vehicle trajectory. Finally, we go through unresolved problems with vehicle trajectory and outline potential future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"37 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569496","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":"Personalization in Public Transport Passenger Information Systems: A Systematic Review and Framework","authors":"Marloes Vredenborg, Anouk van Kasteren, Judith Masthoff","doi":"10.1145/3721478","DOIUrl":"https://doi.org/10.1145/3721478","url":null,"abstract":"In recent years, efforts to promote public transport usage have emphasized the integration of personalization into passenger information systems. Whilst numerous studies have explored this topic, a comprehensive overview of the state-of-the-art remains absent. To address this gap, we systematically reviewed 91 research papers published between 2000 and October 2024. Based on our review, we introduce a comprehensive framework that organizes the field along three key dimensions: personalization objects, attributes, and evaluation. Furthermore, we provide valuable insights for researchers and practitioners engaged in the study, design, and development of personalized passenger information systems and identify research opportunities to guide future advancements in the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"191 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569738","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 Source Code Representations for Machine Learning-Based Cybersecurity Tasks","authors":"Beatrice Casey, Joanna C. S. Santos, George Perry","doi":"10.1145/3721977","DOIUrl":"https://doi.org/10.1145/3721977","url":null,"abstract":"Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the current state of the field to better understand what exists and what’s not there yet. This paper presents a study of these existing ML-based approaches and demonstrates what type of representations were used for different cybersecurity tasks and programming languages. Additionally, we study what types of models are used with different representations. We have found that graph-based representations are the most popular category of representation, and Tokenizers and Abstract Syntax Trees (ASTs) are the two most popular representations overall ( <jats:italic>e.g.</jats:italic> , AST and Tokenizers are the representations with the highest count of papers, while graph-based representations is the category with the highest count of papers). We also found that the most popular cybersecurity task is vulnerability detection, and the language that is covered by the most techniques is C. Finally, we found that sequence-based models are the most popular category of models, and Support Vector Machines (SVMs) are the most popular model overall.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"28 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560704","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}