ACM Computing Surveys最新文献_第3页

Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey 自然语言处理和计算机视觉中的性别偏见：比较调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-02 DOI: 10.1145/3700438

Marion Bartl, Abhishek Mandal, Susan Leavy, Suzanne Little

引用次数: 0

Fog Computing Technology Research: A Retrospective Overview and Bibliometric Analysis 雾计算技术研究：回顾性概述和文献计量分析

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-11-02 DOI: 10.1145/3702313

Paola Vinueza-Naranjo, Janneth Chicaiza, Ruben Rumipamba-Zambrano

引用次数: 0

Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges 医学图像分析的对抗性攻击和防御调查：方法与挑战

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-30 DOI: 10.1145/3702638

Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, Hao Chen

{"title":"Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges","authors":"Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, Hao Chen","doi":"10.1145/3702638","DOIUrl":"https://doi.org/10.1145/3702638","url":null,"abstract":"Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition, we present a comprehensive survey on recent advances in adversarial attacks and defenses for medical image analysis with a systematic taxonomy in terms of the application scenario. We also provide a unified framework for different types of adversarial attack and defense methods in the context of medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models obtained by adversarial training under various scenarios. To the best of our knowledge, this is the first survey paper that provides a thorough evaluation of adversarially robust medical diagnosis models. By analyzing qualitative and quantitative results, we conclude this survey with a detailed discussion of current challenges for adversarial attack and defense in medical image analysis systems to shed light on future research directions. Code is available on GitHub.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"51 2 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556041","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

Backdoor Attacks against Voice Recognition Systems: A Survey 针对语音识别系统的后门攻击：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-26 DOI: 10.1145/3701985

Baochen Yan, Jiahe Lan, Zheng Yan

{"title":"Backdoor Attacks against Voice Recognition Systems: A Survey","authors":"Baochen Yan, Jiahe Lan, Zheng Yan","doi":"10.1145/3701985","DOIUrl":"https://doi.org/10.1145/3701985","url":null,"abstract":"Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy of VRSs. Unfortunately, existing literature lacks a thorough review on this topic. This paper fills this research gap by conducting a comprehensive survey on backdoor attacks against VRSs. We first present an overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then we propose a set of evaluation criteria to assess the performance of backdoor attack methods. Next, we present a comprehensive taxonomy of backdoor attacks against VRSs from different perspectives and analyze the characteristic of different categories. After that, we comprehensively review existing attack methods and analyze their pros and cons based on the proposed criteria. Furthermore, we review classic backdoor defense methods and generic audio defense techniques. Then we discuss the feasibility of deploying them on VRSs. Finally, we figure out several open issues and further suggest future research directions to motivate the research of VRSs security.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490790","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

Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning 使用联盟学习的协作式入侵检测系统的分类与调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-25 DOI: 10.1145/3701724

Aulia Arif Wardana, Parman Sukarno

{"title":"Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning","authors":"Aulia Arif Wardana, Parman Sukarno","doi":"10.1145/3701724","DOIUrl":"https://doi.org/10.1145/3701724","url":null,"abstract":"This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"3 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490644","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 Review on Blockchain Technology, Current Challenges, and AI-Driven Solutions 区块链技术、当前挑战和人工智能驱动的解决方案综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-24 DOI: 10.1145/3700641

Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa, Guido Perboli

引用次数: 0

Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review 用于社交网络假新闻检测的模态深度学习框架：系统性文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-23 DOI: 10.1145/3700748

Mohamed Mostafa, Ahmad S Almogren, Muhammad Al-Qurishi, Majed Alrubaian

{"title":"Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review","authors":"Mohamed Mostafa, Ahmad S Almogren, Muhammad Al-Qurishi, Majed Alrubaian","doi":"10.1145/3700748","DOIUrl":"https://doi.org/10.1145/3700748","url":null,"abstract":"Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"13 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488348","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

Single-Document Abstractive Text Summarization: A Systematic Literature Review 单文档摘要文本总结：系统性文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-18 DOI: 10.1145/3700639

Abishek Rao, Shivani Aithal, Sanjay Singh

引用次数: 0

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research 运筹学中的量子算法和量子启发算法系统映射研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-18 DOI: 10.1145/3700874

Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao, Soummya Kar, Sridhar Tayur

{"title":"A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research","authors":"Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao, Soummya Kar, Sridhar Tayur","doi":"10.1145/3700874","DOIUrl":"https://doi.org/10.1145/3700874","url":null,"abstract":"Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"19 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449253","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

Security and Reliability of Internet of Underwater Things: Architecture, Challenges, and Opportunities 水下物联网的安全性和可靠性：架构、挑战和机遇

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-10-17 DOI: 10.1145/3700640

Bin Jiang, Jiacheng Feng, Xuerong Cui, Jian Wang, Yongxin Liu, Houbing Song

{"title":"Security and Reliability of Internet of Underwater Things: Architecture, Challenges, and Opportunities","authors":"Bin Jiang, Jiacheng Feng, Xuerong Cui, Jian Wang, Yongxin Liu, Houbing Song","doi":"10.1145/3700640","DOIUrl":"https://doi.org/10.1145/3700640","url":null,"abstract":"The Internet of Underwater Things (IoUT) pertains to a system that utilizes technology of Internet of Things (IoT) for data collection, communication, and control in the underwater environment. The monitoring and management of various parameters in the underwater domain are gathered through the deployment of underwater sensors, communication devices, and controllers. It is crucial in emerging ocean engineering. However, due to the instability of the underwater environment and the particularity of the underwater communication transmission medium, it is vulnerable to security threats, which may damage the system or cause data errors. In this survey, we will discuss the challenges, solutions and future directions of IoUT from security and reliability respectively. In order to ensure the normal operation of IoUT, we analyze the underwater security problems and solutions of the IoUT. Then, we discuss the reliability issue and improved strategies of IoUT system in detail. Finally, we come up with our views about the theories, challenges and future prospects of IoUT security after the comparative analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"33 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448773","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