ACM Computing Surveys最新文献_第2页

Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey 面部表情分析及其在物联网系统中的潜力：当代调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-24 DOI: 10.1145/3737456

Zixuan Shangguan, Yanjie Dong, Song Guo, Victor Leung, Jamal Deen, Xiping Hu

{"title":"Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey","authors":"Zixuan Shangguan, Yanjie Dong, Song Guo, Victor Leung, Jamal Deen, Xiping Hu","doi":"10.1145/3737456","DOIUrl":"https://doi.org/10.1145/3737456","url":null,"abstract":"Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios. IoT-enhanced MaE analysis enables real-time monitoring of patient emotions, facilitating improved mental health care in smart healthcare. Similarly, IoT-based MiE detection enhances surveillance accuracy and threat detection in smart security. Our work aims to provide a comprehensive overview of research progress in facial expression analysis and explores its potential integration with IoT systems. We discuss the distinctions between our work and existing surveys, elaborate on advancements in MaE and MiE analysis techniques across various learning paradigms, and examine their potential applications in IoT. We highlight challenges and future directions for the convergence of facial expression-based technologies and IoT systems, aiming to foster innovation in this domain. By presenting recent developments and practical applications, our work offers a systematic understanding of the ways of facial expression analysis to enhance IoT systems in healthcare, security, and beyond.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"45 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130231","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 Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review 基于功能的XAI方法基准框架：来自系统文献综述的见解和基础

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-24 DOI: 10.1145/3737445

Dulce Canha, Sylvain Kubler, Kary Främling, Guy Fagherazzi

{"title":"A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review","authors":"Dulce Canha, Sylvain Kubler, Kary Främling, Guy Fagherazzi","doi":"10.1145/3737445","DOIUrl":"https://doi.org/10.1145/3737445","url":null,"abstract":"Artificial Intelligence (AI) is transforming industries, offering new opportunities to manage and enhance innovation. However, these advancements bring significant challenges for scientists and businesses, with one of the most critical being the ‘trustworthiness” of AI systems. A key requirement of trustworthiness is transparency , closely linked to explicability . Consequently, the exponential growth of eXplainable AI (XAI) has led to the development of numerous methods and metrics for explainability. Nevertheless, this has resulted in a lack of standardized and formal definitions for fundamental XAI properties (e.g., what do soundness, completeness, and faithfulness of an explanation entail? How is the stability of an XAI method defined?). This lack of consensus makes it difficult for XAI practitioners to establish a shared foundation, thereby impeding the effective benchmarking of XAI methods. This survey paper addresses these challenges with two primary objectives. First, it systematically reviews and categorizes XAI properties, distinguishing them between human-centered (relying on empirical studies involving explainees) or functionally-grounded (quantitative metrics independent of explainees). Second, it expands this analysis by introducing a hierarchically structured, functionally grounded benchmark framework for XAI methods, providing formal definitions of XAI properties. The framework’s practicality is demonstrated by applying it to two widely used methods: LIME and SHAP.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130230","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 of Causal Inference in Banking, Finance, and Insurance 银行、金融和保险因果推理的综合综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-23 DOI: 10.1145/3736752

Satyam Kumar, Yelleti Vivek, Vadlamani Ravi, Indranil Bose

引用次数: 0

Attack Vectors for Face Recognition Systems: A Comprehensive Review 攻击向量的人脸识别系统：一个全面的审查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-22 DOI: 10.1145/3736753

Roberto Leyva, Epiphaniou Gregory, Carsten Maple

{"title":"Attack Vectors for Face Recognition Systems: A Comprehensive Review","authors":"Roberto Leyva, Epiphaniou Gregory, Carsten Maple","doi":"10.1145/3736753","DOIUrl":"https://doi.org/10.1145/3736753","url":null,"abstract":"Face Recognition Systems (FRS) are critical and essential components for user authentication via biometrics. To name a few, baking, e-Commerce, and border control are entities propelling their progress. These are of immense importance due to their economic and social relevance. FRS widespread usage leads to security vulnerabilities that need to be identified and mitigated. This paper provides a comprehensive review of potential attacks on recently discovered vulnerabilities from 2017–2024. Our work is significant regarding FRS development because their impact in terms of security. The novelty is a systematic review to properly categorize threat vectors and their severity towards FRS over the past eight years. We categorize, summarize, and analyze the threat vectors towards FRS to this end. We also elaborate on the threat taxonomy for existing Architecture Reference Architecture (ARA) to identify threats on user-based authentication FRS. Our findings show the most persistent attack vectors, usage trends, severity, functionality, and level of sophistication required to perform them. We present a comprehensive description of each to create more resilient and trustable systems for this fast-growing technology. This paper can be used by researchers and practitioners interested in the state-of-the-art FRS attack vectors to develop more secure systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122626","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

The Ubiquitous Skiplist: A Survey of What Cannot be Skipped About the Skiplist and its Applications in Data Systems 无处不在的Skiplist：关于Skiplist及其在数据系统中的应用不能跳过的调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-22 DOI: 10.1145/3736754

Lu Xing, Venkata Sai Pavan Kumar Vadrevu, Walid G. Aref

引用次数: 0

Maintainability and Scalability in Machine Learning: Challenges and Solutions 机器学习中的可维护性和可扩展性：挑战和解决方案

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-22 DOI: 10.1145/3736751

Karthik Shivashankar, Ghadi Al Hajj, Antonio Martini

引用次数: 0

Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives 微服务系统中的智能根源定位：综述与新视角

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-22 DOI: 10.1145/3736755

Nan Fu, Guang Cheng, Yue Teng, Guangye Dai, Shui Yu, Zihan Chen

{"title":"Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives","authors":"Nan Fu, Guang Cheng, Yue Teng, Guangye Dai, Shui Yu, Zihan Chen","doi":"10.1145/3736755","DOIUrl":"https://doi.org/10.1145/3736755","url":null,"abstract":"Root cause localization is the process of monitoring system behavior and analyzing fault patterns from behavioral data. It is applicable in software development, network operations, and cloud computing. However, with the advent of microservice architectures and cloud-native technologies, root cause localization becomes an arduous task. Frequent updates in systems result in large-scale data and complex dependencies. Traditional analysis methods relying on manual experience and predefined rules have limited data processing and cannot learn new fault patterns from historical knowledge. Artificial Intelligence techniques have emerged as powerful tools to leverage historical knowledge and are now widely used in root cause localization. In this paper, we provide a structured overview and a qualitative analysis of root cause localization in microservice systems. To begin with, we review the literature in this area and abstract a workflow of root cause localization, including multimodal data collection, intelligent root cause analysis, and performance evaluation. In particular, we highlight the role played by Artificial Intelligence techniques. Finally, we discuss some open challenges and research directions and propose an end-to-end framework from a new perspective, providing insights for future works.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"56 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122744","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 and Experimental Study of Real-Time Scheduling Methods for 802.1Qbv TSN Networks 802.1Qbv TSN网络实时调度方法综述与实验研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-21 DOI: 10.1145/3736715

Chuanyu Xue, Tianyu Zhang, Yuanbin Zhou, Mark Nixon, Andrew Loveless, Song Han

{"title":"A Survey and Experimental Study of Real-Time Scheduling Methods for 802.1Qbv TSN Networks","authors":"Chuanyu Xue, Tianyu Zhang, Yuanbin Zhou, Mark Nixon, Andrew Loveless, Song Han","doi":"10.1145/3736715","DOIUrl":"https://doi.org/10.1145/3736715","url":null,"abstract":"Time-sensitive networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., industry automation, automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN’s many traffic shapers due to its ability to achieve deterministic timing guarantees. Many scheduling methods for TAS shapers have been recently developed that claim to improve system schedulability. However, these scheduling methods have not yet been thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding of their performance using different evaluation metrics in diverse application scenarios. In this paper, we fill this gap by presenting a systematic review and experimental study on existing TAS-based scheduling methods for TSN. We first review and categorize the system models employed in these works along with the specific problems they aim to solve, and outline the fundamental and additional considerations in the designs of TAS-based scheduling methods. We then perform an extensive evaluation on seventeen representative solutions using both high-fidelity simulations and a real-life sixteen-node TSN testbed, and comparing their performance in terms of schedulability, scalability, and schedule quality. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight important findings. We also summarize the open issues and future research directions in this area. We expect this work will provide foundational knowledge and performance benchmarks for future studies on real-time TSN scheduling and beyond.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"16 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113794","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

Conformal Prediction: A Data Perspective 适形预测：一个数据视角

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-21 DOI: 10.1145/3736575

Xiaofan Zhou, Baiting Chen, Yu Gui, Lu Cheng

引用次数: 0

Self-supervised Learning for Electroencephalogram: A Systematic Survey 脑电图自监督学习的系统研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-05-20 DOI: 10.1145/3736574

Weining Weng, Yang Gu, Shuai Guo, Yuan Ma, Zhaohua Yang, Yuchen Liu, Yiqiang Chen

{"title":"Self-supervised Learning for Electroencephalogram: A Systematic Survey","authors":"Weining Weng, Yang Gu, Shuai Guo, Yuan Ma, Zhaohua Yang, Yuchen Liu, Yiqiang Chen","doi":"10.1145/3736574","DOIUrl":"https://doi.org/10.1145/3736574","url":null,"abstract":"Electroencephalography (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult and requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representations and proposes a systematic survey of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive survey of SSL for EEG analysis, including taxonomy, methodology, and technical details of the existing EEG-based SSL frameworks, and discuss the differences between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets, and further explore its application in large-scale pre-trained foundation models for EEG signals. 4) Finally, we discuss the potential directions for future SSL-EEG research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144104236","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