ACM Computing Surveys最新文献_第7页

Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-04 DOI: 10.1145/3721479

Vu Tuan Truong, Luan Ba Dang, Long Bao Le

{"title":"Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey","authors":"Vu Tuan Truong, Luan Ba Dang, Long Bao Le","doi":"10.1145/3721479","DOIUrl":"https://doi.org/10.1145/3721479","url":null,"abstract":"Diffusion models (DMs) have achieved state-of-the-art performance on various generative tasks such as image synthesis, text-to-image, and text-guided image-to-image generation. However, the more powerful the DMs, the more harmful they can potentially be. Recent studies have shown that DMs are prone to a wide range of attacks, including adversarial attacks, membership inference attacks, backdoor injection, and various multi-modal threats. Since numerous pre-trained DMs are published widely on the Internet, potential threats from these attacks are especially detrimental to the society, making DM-related security a topic worthy of investigation. Therefore, in this paper, we conduct a comprehensive survey on the security aspect of DMs, focusing on various attack and defense methods for DMs. First, we present crucial knowledge of DMs with five main types of DMs, including denoising diffusion probabilistic models, denoising diffusion implicit models, noise conditioned score networks, stochastic differential equations, and multi-modal conditional DMs. We provide a comprehensive survey of recent works investigating different types of attacks that exploit the vulnerabilities of DMs. Then, we thoroughly review potential countermeasures to mitigate each of the presented threats. Finally, we discuss open challenges of DM-related security and describe potential research directions for this topic.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546525","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

On Efficiency, Fairness and Security in AI Accelerator Resource Sharing: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-03-03 DOI: 10.1145/3721427

Jiahua Huang, Weiwei Lin, Wentai Wu, Yang Wang, Haocheng Zhong, Xinhua Wang, Keqin Li

{"title":"On Efficiency, Fairness and Security in AI Accelerator Resource Sharing: A Survey","authors":"Jiahua Huang, Weiwei Lin, Wentai Wu, Yang Wang, Haocheng Zhong, Xinhua Wang, Keqin Li","doi":"10.1145/3721427","DOIUrl":"https://doi.org/10.1145/3721427","url":null,"abstract":"The effective and efficient utilization of AI accelerators represents a critical issue for the practitioners engaged in the field of deep learning. Practical evidence from companies such as Alibaba, SenseTime, and Microsoft reveals that the utilization of production GPU clusters in the industry is generally between 25% and 50%. This indicates a significant opportunity for improvement. To this end, AI accelerator resource sharing has emerged as a promising approach to the performance optimization of multi-tenant clusters. This survey covers this line of studies from 2016 to 2024, focusing primarily on system efficiency while also including discussion on fairness, interference, and security in AI accelerator sharing. We revisit the fundamentals and key concepts, followed by a comprehensive review of recent advances in the field. We find that over 70% of the studies focus on efficiency improvement. We also observe that approximately half of the reviewed studies have made their source code publicly available, while fewer than one-third of the studies did not utilize a physical machine for experimentation. Finally, based on the limitations of existing research, we outline several directions for future research concerning the integration of sharing with large language models (LLMs), coordination between schedulers and application-layer metrics, and collaboration among heterogeneous accelerators.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"9 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538376","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

Review and Analysis of FPGA and ASIC Implementations of NIST Lightweight Cryptography Finalists

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-28 DOI: 10.1145/3721122

Evangelia Konstantopoulou, George Athanasiou, Nicolas Sklavos

引用次数: 0

Software Engineering for OpenHarmony: A Research Roadmap

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-26 DOI: 10.1145/3720538

Li Li, Xiang Gao, Hailong Sun, Chunming Hu, Xiaoyu Sun, Haoyu Wang, Haipeng Cai, Ting Su, Xiapu Luo, Tegawendé Bissyande, Jacques Klein, John Grundy, Tao Xie, Haibo Chen, Huaimin Wang

{"title":"Software Engineering for OpenHarmony: A Research Roadmap","authors":"Li Li, Xiang Gao, Hailong Sun, Chunming Hu, Xiaoyu Sun, Haoyu Wang, Haipeng Cai, Ting Su, Xiapu Luo, Tegawendé Bissyande, Jacques Klein, John Grundy, Tao Xie, Haibo Chen, Huaimin Wang","doi":"10.1145/3720538","DOIUrl":"https://doi.org/10.1145/3720538","url":null,"abstract":"Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS. OpenHarmony, a newly open-sourced mobile platform, has rarely been considered, although it is the one requiring the most attention as OpenHarmony is expected to occupy one-third of the market in China (if not in the world). To fill the gap, we present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony. Specifically, we start by presenting a tertiary study of mobile software engineering, attempting to understand what problems have been targeted by the mobile community and how they have been resolved. We then summarize the existing (limited) achievements of OpenHarmony and subsequently highlight the research gap between Android/iOS and OpenHarmony. This research gap eventually helps in forming the roadmap for conducting software engineering research for OpenHarmony.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"187 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507102","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

Igniting Language Intelligence: The Hitchhiker's Guide from Chain-of-Thought Reasoning to Language Agents

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-25 DOI: 10.1145/3719341

Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao

{"title":"Igniting Language Intelligence: The Hitchhiker's Guide from Chain-of-Thought Reasoning to Language Agents","authors":"Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao","doi":"10.1145/3719341","DOIUrl":"https://doi.org/10.1145/3719341","url":null,"abstract":"Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks. Additionally, theoretical proofs have illuminated their emergent reasoning capabilities, providing a compelling showcase of their advanced cognitive abilities in linguistic contexts. Critical to their remarkable efficacy in handling complex reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. The CoT reasoning approach has not only exhibited proficiency in amplifying reasoning performance but also in enhancing interpretability, controllability, and flexibility. In light of these merits, recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents, which adeptly adhere to language instructions and execute actions within varied environments. This survey paper orchestrates a thorough discourse, penetrating vital research dimensions, encompassing: (i) the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; (ii) the paradigm shift in CoT; and (iii) the burgeoning of language agents fortified by CoT approaches. Prospective research avenues envelop explorations into generalization, efficiency, customization, scaling, and safety. A repository for the related papers is available at https://github.com/Zoeyyao27/CoT-Igniting-Agent.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"31 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495336","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 Comprehensive Review on IoT Marketplace Matchmaking: Approaches, Opportunities and Challenges

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-24 DOI: 10.1145/3715904

Qi An, Frank Jiang, Azadeh Neiat, William Yeoh, Kumar Venayagamoorthy, Arkady Zaslavsky

{"title":"A Comprehensive Review on IoT Marketplace Matchmaking: Approaches, Opportunities and Challenges","authors":"Qi An, Frank Jiang, Azadeh Neiat, William Yeoh, Kumar Venayagamoorthy, Arkady Zaslavsky","doi":"10.1145/3715904","DOIUrl":"https://doi.org/10.1145/3715904","url":null,"abstract":"Service discovery matchmaking plays a vital role in the cyber marketplace for the Internet of Things (IoT), especially in peer-to-peer environments where buyers and sellers dynamically register and match resource profiles online. As the IoT marketplace expands, efficient resource allocation through matchmaking is increasingly important. However, the growing complexity of service discovery, coupled with data security and privacy challenges, complicates the identification of suitable services. To address these issues, this study conducts a comprehensive review of matchmaking algorithms within the IoT marketplace by examining their key attributes, strengths, and limitations as documented in academic literature. This paper categorises and summarises state-of-the-art approaches, identifying research gaps and proposing future directions. Our comparative analysis highlights the strengths and weaknesses of current methodologies, advocating for deep learning and context-aware solutions to improve service efficiency. Additionally, blockchain-based approaches are discussed for their potential to improve security, trust, and privacy-preserving transactions. This research lays a critical foundation for the advancement of secure, efficient IoT-enabled marketplaces.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"65 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486548","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 Edge Large Language Models: Design, Execution, and Applications

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-24 DOI: 10.1145/3719664

Yue Zheng, Yuhao Chen, Bin Qian, Xiufang Shi, Yuanchao Shu, Jiming Chen

引用次数: 0

Machine Learning for Infectious Disease Risk Prediction: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-24 DOI: 10.1145/3719663

Mutong Liu, Yang Liu, Jiming Liu

引用次数: 0

Public Datasets for Cloud Computing: A Comprehensive Survey 云计算公共数据集：全面调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-22 DOI: 10.1145/3719003

Guozhi Liu, Weiwei Lin, Haotong Zhang, Jianpeng Lin, Shaoliang Peng, Keqin Li

{"title":"Public Datasets for Cloud Computing: A Comprehensive Survey","authors":"Guozhi Liu, Weiwei Lin, Haotong Zhang, Jianpeng Lin, Shaoliang Peng, Keqin Li","doi":"10.1145/3719003","DOIUrl":"https://doi.org/10.1145/3719003","url":null,"abstract":"Publicly available datasets are vital to researchers because they permit the testing of new algorithms under a variety of conditions and ensure the verifiability and reproducibility of scientific experiments. In cloud computing research, there is a particular dependence on obtaining load traces and network traces from real cloud computing clusters, which are used for designing energy efficiency prediction, workload analysis, and anomaly detection solutions. To address the current lack of a comprehensive overview and thorough analysis of cloud computing datasets and to gain insight into their current status and future trends, in this paper, we provide a comprehensive survey of existing publicly cloud computing datasets. Firstly, we utilize a systematic mapping approach to analyze 968 scientific papers from 6 scientific databases, resulting in the retrieval of 42 datasets related to cloud computing. Secondly, we categorize these datasets based on 11 characteristics to assist researchers in quickly finding datasets suitable for their specific needs. Thirdly, we provide detailed descriptions of each dataset to assist researchers in gaining a clearer understanding of their characteristics. Fourthly, we select 12 mainstream datasets and conduct a comprehensive analysis and comparison of their characteristics. Finally, we discuss the weaknesses of existing datasets, identify challenges, provide recommendations for long-term dataset maintenance and updates, and outline directions for the future creation of new cloud computing datasets. Related resources are available at: https://github.com/ACAT-SCUT/Awesome-CloudComputing-Datasets.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"26 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470934","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

Out-of-Distribution Data: An Acquaintance of Adversarial Examples - A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-22 DOI: 10.1145/3719292

Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla

{"title":"Out-of-Distribution Data: An Acquaintance of Adversarial Examples - A Survey","authors":"Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla","doi":"10.1145/3719292","DOIUrl":"https://doi.org/10.1145/3719292","url":null,"abstract":"Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact DNNs’ reliability and robustness. Traditionally, research has addressed OOD detection and adversarial robustness as separate challenges. This survey focuses on the intersection of these two areas, examining how the research community has investigated them together. Consequently, we identify two key research directions: robust OOD detection and unified robustness. Robust OOD detection aims to differentiate between in-distribution (ID) data and OOD data, even when they are adversarially manipulated to deceive the OOD detector. Unified robustness seeks a single approach to make DNNs robust against both adversarial attacks and OOD inputs. Accordingly, first, we establish a taxonomy based on the concept of distributional shifts. This framework clarifies how robust OOD detection and unified robustness relate to other research areas addressing distributional shifts, such as OOD detection, open set recognition, and anomaly detection. Subsequently, we review existing work on robust OOD detection and unified robustness. Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470936","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