ACM Computing Surveys最新文献

Embodied Intelligence: A Synergy of Morphology, Action, Perception and Learning

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-12 DOI: 10.1145/3717059

Huaping Liu, Di Guo, Angelo Cangelosi

引用次数: 0

Compact Data Structures for Network Telemetry

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-12 DOI: 10.1145/3716819

Shir Landau-Feibish, Zaoxing Liu, Jennifer Rexford

引用次数: 0

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-12 DOI: 10.1145/3716845

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao, Muhammad Shafique, Kiamal Pekmestzi, Dimitrios Soudris

{"title":"Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques","authors":"Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao, Muhammad Shafique, Kiamal Pekmestzi, Dimitrios Soudris","doi":"10.1145/3716845","DOIUrl":"https://doi.org/10.1145/3716845","url":null,"abstract":"The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, over the last 15 years, the semiconductor industry has established power efficiency as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, which has resulted in novel approximation techniques for all the layers of the traditional computing stack. More specifically, during the last decade, a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories) have been proposed in the literature. The current article is Part I of a comprehensive survey on Approximate Computing. It reviews its motivation, terminology and principles, as well it classifies the state-of-the-art software & hardware approximation techniques, presents their technical details, and reports a comparative quantitative analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"61 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401618","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

Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-11 DOI: 10.1145/3716846

Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell

{"title":"Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art","authors":"Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell","doi":"10.1145/3716846","DOIUrl":"https://doi.org/10.1145/3716846","url":null,"abstract":"Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries. Most of these systems are developed with modular sub-components for decision-making, planning, and control that may be hand-engineered or learning-based. While these approaches perform well under the situations they were specifically designed for, they can perform especially poorly in out-of-distribution scenarios that will undoubtedly arise at test-time. The rise of foundation models trained on multiple tasks with impressively large datasets has led researchers to believe that these models may provide “common sense” reasoning that existing planners are missing, bridging the gap between algorithm development and deployment. While researchers have shown promising results in deploying foundation models to decision-making tasks, these models are known to hallucinate and generate decisions that may sound reasonable, but are in fact poor. We argue there is a need to step back and simultaneously design systems that can quantify the certainty of a model’s decision, and detect when it may be hallucinating. In this work, we discuss the current use cases of foundation models for decision-making tasks, provide a general definition for hallucinations with examples, discuss existing approaches to hallucination detection and mitigation with a focus on decision problems, present guidelines, and explore areas for further research in this exciting field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393479","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

Deep Learning Based Image Aesthetic Quality Assessment- A Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-08 DOI: 10.1145/3716820

Maedeh Daryanavard Chounchenani, Asadollah Shahbahrami, Reza Hassanpour, Georgi Gaydadjiev

{"title":"Deep Learning Based Image Aesthetic Quality Assessment- A Review","authors":"Maedeh Daryanavard Chounchenani, Asadollah Shahbahrami, Reza Hassanpour, Georgi Gaydadjiev","doi":"10.1145/3716820","DOIUrl":"https://doi.org/10.1145/3716820","url":null,"abstract":"Image Aesthetic Quality Assessment (IAQA) spans applications such as the fashion industry, AI-generated content, product design, and e-commerce. Recent deep learning advancements have been employed to evaluate image aesthetic quality. A few surveys have been conducted on IAQA models; however, details of recent deep learning models and challenges have not been fully mentioned. This paper aims to fill these gaps by providing a review of deep learning IAQA over the past decade, based on input, process, and output phases. Methodologies for deep learning-based IAQA can be categorized into general and task-specific approaches, depending on the type and diversity of input images. The processing phase involves considerations related to network architecture, learning structures, and feature extraction methods. The output phase generates results such as scoring, distribution, attributes, and description. Despite achieving a maximum accuracy of 91.5%, further improvements in deep learning models are still required. Our study highlights several challenges, including adapting models for task-specific methodology, accounting for environmental factors influencing aesthetics, the lack of substantial datasets with appropriate labels, imbalanced data, preserving image aspect ratio and integrity in network architecture design, and the need for explainable AI to understand the causative factors behind aesthetic judgments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"54 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371537","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

AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-07 DOI: 10.1145/3716628

Zehang Deng, Yongjian Guo, Changzhou Han, Wanlun Ma, Junwu Xiong, Sheng Wen, Yang Xiang

引用次数: 0

A Survey on Exploring Real and Virtual Social Network Rumors: State-of-the-Art and Research Challenges

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-06 DOI: 10.1145/3716498

Qiang He, Songyangjun Zhang, Yuliang Cai, Wei Yuan, Lianbo Ma, Keping Yu

{"title":"A Survey on Exploring Real and Virtual Social Network Rumors: State-of-the-Art and Research Challenges","authors":"Qiang He, Songyangjun Zhang, Yuliang Cai, Wei Yuan, Lianbo Ma, Keping Yu","doi":"10.1145/3716498","DOIUrl":"https://doi.org/10.1145/3716498","url":null,"abstract":"This survey reviews the phenomenon of rumor propagation in social networks, defining rumors and their manifestations, and highlighting the societal confusion, panic, and harm they cause. It explores the psychological, social, and technical factors contributing to rumors and their impact on reputation, panic, and decision-making. The review covers theoretical frameworks of rumor propagation, analyzing progressive and non-progressive diffusion models in social networks. It also introduces the metaverse, discussing its impact on information spread and the new challenges it poses for rumor dissemination. Detection and intervention methods using AI, network analysis, and multimodal representation are highlighted, alongside policy and public education strategies. Additionally, the survey addresses challenges such as fake news, deepfakes, and the role of social bots and automated accounts. Future research directions are discussed, including the development of sophisticated detection algorithms, real-time monitoring, cross-platform and cross-cultural rumor detection, privacy protection, and automated coping mechanisms. The survey advocates for integrated strategies combining technological, social, and legal approaches to manage rumor propagation complexities and maintain information authenticity and social stability.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"47 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258431","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

Deep Learning Library Testing: Definition, Methods and Challenges

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-06 DOI: 10.1145/3716497

Xiaoyu Zhang, Weipeng Jiang, Chao Shen, Qi Li, Qian Wang, Chenhao Lin, Xiaohong Guan

{"title":"Deep Learning Library Testing: Definition, Methods and Challenges","authors":"Xiaoyu Zhang, Weipeng Jiang, Chao Shen, Qi Li, Qian Wang, Chenhao Lin, Xiaohong Guan","doi":"10.1145/3716497","DOIUrl":"https://doi.org/10.1145/3716497","url":null,"abstract":"Recently, software systems powered by deep learning (DL) techniques have significantly facilitated people’s lives in many aspects. As the backbone of these DL systems, various DL libraries undertake the underlying optimization and computation. However, like traditional software, DL libraries are not immune to bugs. These bugs may be propagated to programs and software developed based on DL libraries, thereby posing serious threats to users’ personal property and safety. Studying the characteristics of DL libraries, their associated bugs, and the corresponding testing methods is crucial for enhancing the security of DL systems and advancing the widespread application of DL technology. This paper provides an overview of the testing research on various DL libraries, discusses the strengths and weaknesses of existing methods, and provides guidance and reference for the application of DL library testing methods. This paper first introduces the workflow of DL underlying libraries and the characteristics of three kinds of DL libraries involved, namely DL framework, DL compiler, and DL hardware library. Subsequently, this paper constructs a literature collection pipeline and comprehensively summarizes existing testing methods on these DL libraries to analyze their effectiveness and limitations. It also reports findings and the challenges of existing DL library testing in real-world applications for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"44 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258430","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

ENDEMIC: End-to-End Network Disruptions - Examining Middleboxes, Issues, and Countermeasures - A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-05 DOI: 10.1145/3716372

Ilies Benhabbour, Marc Dacier

{"title":"ENDEMIC: End-to-End Network Disruptions - Examining Middleboxes, Issues, and Countermeasures - A Survey","authors":"Ilies Benhabbour, Marc Dacier","doi":"10.1145/3716372","DOIUrl":"https://doi.org/10.1145/3716372","url":null,"abstract":"Network middleboxes are important components in modern networking systems, impacting approximately 40% of network paths according to recent studies [1]. This survey paper delves into their endemic presence, enriches the original 2002 RFC with over two decades of findings, and emphasizes the significance of their impact in terms of security and performance. Furthermore, it categorizes network middleboxes based on their functions, objectives, and alterations. In today’s world, network middleboxes emerge as a dual-edged sword. While important for network operations, they also pose security risks. We present the various challenges they introduce, including their contribution to Internet ossification, their potential for censorship, monitoring, and traffic differentiation. Substantial effort remains to make their presence more visible to end users. This paper explores potential solutions, ranging from prevention and detection to curative measures. Ultimately, we aim to establish this survey as a foundational resource for addressing challenges revolving around the notion of network middleboxes, thereby fostering further research and innovation in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"136 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191817","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

Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-02-05 DOI: 10.1145/3716371

Ahmad Jan Mian, Muhammad Adil, Bouziane Brik, Saad Harous, Sohail Abbas

{"title":"Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions","authors":"Ahmad Jan Mian, Muhammad Adil, Bouziane Brik, Saad Harous, Sohail Abbas","doi":"10.1145/3716371","DOIUrl":"https://doi.org/10.1145/3716371","url":null,"abstract":"Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers on wheels.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"62 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191818","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