ACM Computing Surveys最新文献

Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance 用人类评估可解释的人工智能：信任、依赖、理解和表现

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-19 DOI: 10.1145/3856819

Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan

{"title":"Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance","authors":"Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan","doi":"10.1145/3856819","DOIUrl":"https://doi.org/10.1145/3856819","url":null,"abstract":"Explainable AI (XAI) is often evaluated through claims about trust, reliance, understanding, and performance, yet studies measure these constructs differently and frequently overlook confounding factors. This PRISMA-guided systematic review with narrative synthesis examines human-centered XAI evaluation across these four constructs. The review covers publications from 2015 to 2025. A merged search combined an OpenAlex-informed DOI/title catalog, prior surveys, citation chasing, and a Scopus coverage-completion stage identifying 9,026 records. A targeted revision update added two eligible publications, yielding 119 validated full-text publications. The synthesis highlights three patterns: trust and reliance can diverge; perceived understanding can exceed objective understanding; and interface salience or perceived accuracy can confound explanation effects. Aggregate advice acceptance, for example, cannot distinguish beneficial reliance from automation bias. We therefore recommend measuring acceptance separately for correct and incorrect AI outputs, testing understanding beyond self-report, documenting study context and interface conditions, and evaluating explanations under uncertainty or model failure. The review contributes construct definitions, a synthesis across designs and domains, an analysis of evaluation measures, and practical reporting guidelines. It also examines generated explanations and citation-based interfaces, supporting more reproducible and interpretable human-centered XAI evaluations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149218727","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

Facial Expression and Behavior Modeling from Digital Avatars to Expressive Humanoid Robots: A Review 面部表情和行为建模，从数字化身到富有表现力的人形机器人：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-15 DOI: 10.1145/3856804

Xu Zhang, Longbing Cao

{"title":"Facial Expression and Behavior Modeling from Digital Avatars to Expressive Humanoid Robots: A Review","authors":"Xu Zhang, Longbing Cao","doi":"10.1145/3856804","DOIUrl":"https://doi.org/10.1145/3856804","url":null,"abstract":"Endowing humanoid robots with expressive, controllable, and robust facial expressions and behaviors is essential for advancing human-like humanoid-human interaction across social, healthcare, and service domains. However, transforming the semantic richness and flexibility of virtual avatars into the mechanical, real-time, and calibration constraints of physical humanoids remains a fundamental challenge. This survey reviews facial expression and behavior modeling for both and analyzes their virtual-to-physical gaps from a deployment-oriented perspective. Our review is organized along three axes: (i) modeling paradigms , with emphasis on actuator compatibility and transferability; (ii) expression control strategies , assessed for their potential to enable robust robotic deployment; and (iii) deployment dimensions , which examine the challenges of parameter-to-actuator mapping, calibration, and real-time operation. This survey provides a critical synthesis of the trade-offs between expressive fidelity and hardware feasibility. We analyze persistent gaps between algorithmic advances and embodied realization, and review the datasets, metrics, and evaluation frameworks necessary for advancing expressive humanoids and their facial expression and behavior modeling. Finally, we identify open challenges and future research directions, including multimodal coordination, adaptive personalization, and standardized evaluation, providing guidance for next-generation expressive human-like humanoids, and systematically integrating advances in humanoid modeling with actionable deployment of humanoid facial expressions and behaviors.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"90 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187931","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

Explanation User Interfaces: A Systematic Literature Review 用户界面：系统的文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-14 DOI: 10.1145/3834765

Eleonora Cappuccio, Andrea Esposito, Francesco Greco, Giuseppe Desolda, Rosa Lanzilotti, Salvatore Rinzivillo

{"title":"Explanation User Interfaces: A Systematic Literature Review","authors":"Eleonora Cappuccio, Andrea Esposito, Francesco Greco, Giuseppe Desolda, Rosa Lanzilotti, Salvatore Rinzivillo","doi":"10.1145/3834765","DOIUrl":"https://doi.org/10.1145/3834765","url":null,"abstract":"Artificial Intelligence is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behavior of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system’s design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a platform to support Human-cEnteRed developMent of Explainable user interfaceS (HERMES) and guide practitioners and scholars in the design and evaluation of XUIs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"33 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172571","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

Federated Distillation: A Survey 联邦蒸馏：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-14 DOI: 10.1145/3857792

Lin Li, Jianping Gou, Baosheng Yu, Lan Du, Yi Zhang, Dacheng Tao

{"title":"Federated Distillation: A Survey","authors":"Lin Li, Jianping Gou, Baosheng Yu, Lan Du, Yi Zhang, Dacheng Tao","doi":"10.1145/3857792","DOIUrl":"https://doi.org/10.1145/3857792","url":null,"abstract":"Federated learning (FL) enables collaborative model training without centralizing clients’ raw data, but parameter exchange can impose substantial communication costs and usually requires compatible model architectures. Federated distillation (FD) instead transfers predictive or intermediate knowledge through knowledge distillation (KD), which can support heterogeneous models and reduce communication in suitable settings. These benefits are not automatic: their magnitude depends on the transmitted knowledge, reference data, communication schedule, and threat model. This survey provides a challenge-centered synthesis of FD. We first formalize a representative public-data, response-based FD protocol and then organize existing methods by the FL challenges they address, including data, system, and model heterogeneity, client drift, catastrophic forgetting, communication cost, and privacy risk. We further review applications in industrial engineering, computer vision, natural language processing, and healthcare, including recent federated adaptation of large language models. Finally, we identify five research priorities: richer transferred knowledge, joint treatment of interacting forms of heterogeneity, communication-aware distillation, threat-model-driven privacy protection, and standardized theory, benchmarks, and evaluation.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"49 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172507","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

Network Topology Obfuscation: Challenges, Solutions, and Comparisons 网络拓扑混淆：挑战、解决方案和比较

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-08 DOI: 10.1145/3857909

Changhao Qiu, Bangbang Ren, Lailong Luo, Deke Guo

引用次数: 0

FPGA-Based Neural Network Accelerators for Space Missions: A Survey 基于fpga的空间任务神经网络加速器综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-07 DOI: 10.1145/3857797

Pedro Antunes,Artur Podobas

引用次数: 0

A Preview of Computational Animal Linguistics 计算动物语言学预览

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-06 DOI: 10.1145/3850160

Tuan Dang, Atrey Desai, Hridayesh Lekhak, Tirza Panunto, Lindsay Pike, Theron Wang, Kenny Q. Zhu

引用次数: 0

Vulnerabilities and Defenses in Audio-Visual Attacks: A Survey From Audio to Multimodal Models 视听攻击中的漏洞和防御：从音频到多模态模型的调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-05 DOI: 10.1145/3857219

Jinming Wen, Xinyi Wu, Yuwen Li, Yanhao Jia, Shuai Zhao

{"title":"Vulnerabilities and Defenses in Audio-Visual Attacks: A Survey From Audio to Multimodal Models","authors":"Jinming Wen, Xinyi Wu, Yuwen Li, Yanhao Jia, Shuai Zhao","doi":"10.1145/3857219","DOIUrl":"https://doi.org/10.1145/3857219","url":null,"abstract":"With the rapid development of multimodal large language models (MLLMs) and the cost of large amounts of data and resources, researchers are more inclined to use public datasets and fine-tune open-source MLLMs to achieve excellent performance in audiovisual related tasks. While this trend accelerates progress, it also introduces security risks. Empirical studies demonstrate that the latest MLLMs can be manipulated to generate harmful content through crafted inputs, including adversarial perturbations and malicious queries, bypassing internal safeguards. To gain a deeper comprehension of the inherent security vulnerabilities associated with audio-visual-based multimodal models, a series of surveys investigates various types of attacks, including adversarial and backdoor attacks. While existing surveys on audio-visual attacks provide a comprehensive overview, they are limited to specific types of attacks, which lack a unified review of various types of attacks. To bridge this gap, this paper presents a comprehensive and systematic review of audio-visual attacks, which include adversarial, backdoor, and jailbreak attacks. Furthermore, we also review various types of attacks in the latest audio-visual-based MLLMs, a dimension notably absent in existing surveys. Drawing from extensive literature, this paper delineates both challenges and emergent trends for future research on audio-visual attacks and defenses.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"8 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086871","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

Agentic Software Issue Resolution with Large Language Models: A Survey 基于大型语言模型的代理软件问题解决：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-03 DOI: 10.1145/3849702

Zhonghao Jiang, David Lo, Zhongxin Liu

{"title":"Agentic Software Issue Resolution with Large Language Models: A Survey","authors":"Zhonghao Jiang, David Lo, Zhongxin Liu","doi":"10.1145/3849702","DOIUrl":"https://doi.org/10.1145/3849702","url":null,"abstract":"Software issue resolution task aims to address real-world issues in software repositories based on natural language descriptions provided by users, representing a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and generative capabilities, LLM-based approaches have made significant progress in automated software issue resolution. However, real-world software issue resolution is inherently complex and requires long-horizon reasoning, iterative exploration, and feedback-driven decision-making that demand agentic capabilities beyond conventional single-step approaches. Recently, LLM-based agentic systems have become a promising research direction for software issue resolution, since the related literature has experienced explosive growth. Advancements in agentic software issue resolution can not only greatly enhance software maintenance efficiency and quality but also provide a realistic environment for validating agentic systems’ reasoning, planning, and execution capabilities, bridging AI and software engineering. This work presents a systematic survey of 242 recent studies at the forefront of LLM-based agentic software issue resolution research. It outlines the general workflow of the task and establishes a taxonomy across three dimensions: benchmarks, techniques, and empirical studies. Furthermore, it highlights how reinforcement learning has become an increasingly important training paradigm for agentic systems in software engineering. Finally, it summarizes key challenges and outlines promising directions for future research. The artifacts’ page accompanying this survey is at https://github.com/ZhonghaoJiang/Awesome-Issue-Solving.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"31 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149086438","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

Critiquing AI Surveys with a Case Study on Temporal Features for Trustworthy Computer Vision 以可信计算机视觉的时间特征为例对人工智能调查的批判

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-03 DOI: 10.1145/3849866

Jack Napier, Zhe Wang, Jin Song Dong, Kelvin Ross, Shiping Chen, Zhe Hou

引用次数: 0