ACM Computing Surveys最新文献_第2页

Secure UAV (Drone) and the Great Promise of AI 安全无人机和人工智能的巨大前景

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-17 DOI: 10.1145/3673225

Behrouz Zolfaghari, Mostafa Abbasmollaei, Fahimeh Hajizadeh, Naoto Yanai, Khodakhast Bibak

{"title":"Secure UAV (Drone) and the Great Promise of AI","authors":"Behrouz Zolfaghari, Mostafa Abbasmollaei, Fahimeh Hajizadeh, Naoto Yanai, Khodakhast Bibak","doi":"10.1145/3673225","DOIUrl":"https://doi.org/10.1145/3673225","url":null,"abstract":"UAVs have found their applications in numerous applications from recreational activities to business in addition to military and strategic fields. However, research on UAVs is not going on as quickly as the technology. Especially, when it comes to the security of these devices, the academia is lagging behind the industry. This gap motivates our work in this paper as a stepping stone for future research in this area. A comprehensive survey on the security of UAVs and UAV-based systems can help the research community keep pace with, or even lead the industry. Although there are several reviews on UAVs or related areas, there is no recent survey broadly covering various aspects of security. Moreover, none of the existing surveys highlights current and future trends with a focus on the role of an omnipresent technology such as AI. This paper endeavors to overcome these shortcomings. We conduct a comprehensive review on security challenges of UAVs as well as the related security controls. Then we develop a future roadmap for research in this area with a focus on the role of AI. The future roadmap is established based on the identified current trends, under-researched topics, and a future look-ahead.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141333580","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

An Introduction to the Compute Express Link (CXL) Interconnect 计算高速链路 (CXL) 互连简介

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-14 DOI: 10.1145/3669900

Debendra Das Sharma, Robert Blankenship, Daniel Berger

引用次数: 0

Macro Ethics Principles for Responsible AI Systems: Taxonomy and Directions 负责任的人工智能系统的宏观伦理原则：分类与方向

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-13 DOI: 10.1145/3672394

Jessica Woodgate, Nirav Ajmeri

引用次数: 0

Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box 为 XAI 制定以实例为中心的反事实算法基准：从白箱到黑箱

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-12 DOI: 10.1145/3672553

Catarina Moreira, Yu-Liang Chou, Chihcheng Hsieh, Chun Ouyang, João Pereira, Joaquim Jorge

{"title":"Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box","authors":"Catarina Moreira, Yu-Liang Chou, Chihcheng Hsieh, Chun Ouyang, João Pereira, Joaquim Jorge","doi":"10.1145/3672553","DOIUrl":"https://doi.org/10.1145/3672553","url":null,"abstract":"This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have little impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A counterfactual inspection analysis is strongly recommended to ensure a robust examination of counterfactual explanations and the potential identification of biases.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308990","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 Practical tutorial on Explainable AI Techniques 可解释人工智能技术实用教程

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-12 DOI: 10.1145/3670685

Adrien Bennetot, Ivan Donadello, Ayoub El Qadi El Haouari, Mauro Dragoni, Thomas Frossard, Benedikt Wagner, Anna Sarranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia Díaz-Rodríguez

{"title":"A Practical tutorial on Explainable AI Techniques","authors":"Adrien Bennetot, Ivan Donadello, Ayoub El Qadi El Haouari, Mauro Dragoni, Thomas Frossard, Benedikt Wagner, Anna Sarranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia Díaz-Rodríguez","doi":"10.1145/3670685","DOIUrl":"https://doi.org/10.1145/3670685","url":null,"abstract":"The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for their behaviour. As opaque Machine Learning models are increasingly being employed to make important predictions in critical domains, there is a danger of creating and using decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing DNNs with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box. The article aims to rectify the lack of a practical XAI guide by applying XAI techniques in particular day-to-day models, datasets and use-cases. In each chapter, the reader will find a description of the proposed method as well as one or several examples of use with Python notebooks. These can be easily modified in order to be applied to specific applications. We also explain what the prerequisites are for using each technique, what the user will learn about them, and which tasks they are aimed at.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308996","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 Survey of Hardware Improvements to Secure Program Execution 安全程序执行硬件改进调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-12 DOI: 10.1145/3672392

Lianying Zhao, He Shuang, Shengjie Xu, Wei Huang, Rongzhen Cui, Pushkar Bettadpur, David Lie

{"title":"A Survey of Hardware Improvements to Secure Program Execution","authors":"Lianying Zhao, He Shuang, Shengjie Xu, Wei Huang, Rongzhen Cui, Pushkar Bettadpur, David Lie","doi":"10.1145/3672392","DOIUrl":"https://doi.org/10.1145/3672392","url":null,"abstract":"Hardware has been constantly augmented for security considerations since the advent of computers. There is also a common perception among computer users that hardware does a relatively better job on security assurance compared to software. Yet, the community has long lacked a comprehensive study to answer questions such as how hardware security support contributes to security, what kind of improvements have been introduced to improve such support and what its advantages/disadvantages are. By generalizing various security goals, we taxonomize hardware security features and their security properties that can aid in securing program execution, considered as three aspects, i.e., state correctness, runtime protection and input/output protection. Based on this taxonomy, the survey systematically examines 1) the roles: how hardware is applied to achieve security; and 2) the problems: how reported attacks have exploited certain defects in hardware. We see that hardware’s unique advantages and problems co-exist and it highly depends on the desired security purpose as to which type to use. Among the survey findings are also that code as part of hardware (aka. firmware) should be treated differently to ensure security by design; and how research proposals have driven the advancement of commodity hardware features.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308991","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

Lexical Semantic Change through Large Language Models: a Survey 通过大型语言模型实现词汇语义变化：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-10 DOI: 10.1145/3672393

Francesco Periti, Stefano Montanelli

{"title":"Lexical Semantic Change through Large Language Models: a Survey","authors":"Francesco Periti, Stefano Montanelli","doi":"10.1145/3672393","DOIUrl":"https://doi.org/10.1145/3672393","url":null,"abstract":"Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC and we propose a classification framework characterized by three dimensions: meaning representation, time-awareness, and learning modality. The framework is exploited to i) review the measures for change assessment, ii) compare the approaches on performance, and iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141299114","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

Databases in Edge and Fog Environments : A Survey 边缘和雾环境中的数据库：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-04 DOI: 10.1145/3666001

Luís Manuel Meruje Ferreira, Fabio Coelho, José Pereira

引用次数: 0

Machine Learning with Confidential Computing: A Systematization of Knowledge 利用保密计算进行机器学习：知识系统化

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-03 DOI: 10.1145/3670007

Fan Mo, Zahra Tarkhani, Hamed Haddadi

{"title":"Machine Learning with Confidential Computing: A Systematization of Knowledge","authors":"Fan Mo, Zahra Tarkhani, Hamed Haddadi","doi":"10.1145/3670007","DOIUrl":"https://doi.org/10.1145/3670007","url":null,"abstract":"Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML’s pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential Computing has been utilized in both academia and industry to mitigate privacy and security issues in various ML scenarios. In this paper, the conjunction between ML and Confidential Computing is investigated. We systematize the prior work on Confidential Computing-assisted ML techniques that provide i) confidentiality guarantees and ii) integrity assurances, and discuss their advanced features and drawbacks. Key challenges are further identified, and we provide dedicated analyses of the limitations in existing Trusted Execution Environment (TEE) systems for ML use cases. Finally, prospective works are discussed, including grounded privacy definitions for closed-loop protection, partitioned executions of efficient ML, dedicated TEE-assisted designs for ML, TEE-aware ML, and ML full pipeline guarantees. By providing these potential solutions in our systematization of knowledge, we aim to build the bridge to help achieve a much stronger TEE-enabled ML for privacy guarantees without introducing computation and system costs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251607","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

“Are you feeling sick?” A systematic literature review of cybersickness in virtual reality "你感觉不舒服吗？虚拟现实中的网络病症系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-03 DOI: 10.1145/3670008

Nilotpal Biswas, Anamitra Mukherjee, Samit Bhattacharya

{"title":"“Are you feeling sick?” A systematic literature review of cybersickness in virtual reality","authors":"Nilotpal Biswas, Anamitra Mukherjee, Samit Bhattacharya","doi":"10.1145/3670008","DOIUrl":"https://doi.org/10.1145/3670008","url":null,"abstract":"Cybersickness (CS), also known as visually induced motion sickness (VIMS) is a condition that can affect individuals when they interact with virtual reality (VR) technology. This condition is characterized by symptoms such as nausea, dizziness, headaches, eye fatigue, etc., and can be caused by a variety of factors. Finding a feasible solution to reduce the impact of CS is extremely important as it will greatly enhance the overall user experience and make VR more appealing to a wider range of people. We have carefully compiled a list of 223 highly pertinent studies to review the current state of research on the most essential aspects of CS. We have provided a novel taxonomy that encapsulates various aspects of CS measurement techniques found in the literature. We have proposed a set of CS mitigation guidelines for both developers and users. We have also discussed various CS-inducing factors and provided a taxonomy that tries to capture the same. Overall, our work provides a comprehensive overview of the current state of research in CS with a particular emphasis on different measurement techniques and CS mitigation strategies, identifies research gaps in the literature, and provides recommendations for future research in the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251747","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