ACM Computing Surveys最新文献_第5页

A Survey of Symbol Name Recovery in Software Reverse Engineering 软件逆向工程中符号名称恢复研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-09-01 DOI: 10.1145/3844948

Hongcheng Fan, Jielun Wu, Xincheng He, Yang Feng, Yibiao Yang, Baowen Xu, Qingkai Shi

引用次数: 0

Trustworthy Intelligent Vehicular Networks: A Survey 可信赖的智能车联网：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-09-01 DOI: 10.1145/3844946

Xiao Zhang, Nishaant Madhankumar, Deniz Acikbas, Rohit Raval, Juexing Wang, Zheng Song, Xin Xia, Xinyu Lei, Di Zhang

{"title":"Trustworthy Intelligent Vehicular Networks: A Survey","authors":"Xiao Zhang, Nishaant Madhankumar, Deniz Acikbas, Rohit Raval, Juexing Wang, Zheng Song, Xin Xia, Xinyu Lei, Di Zhang","doi":"10.1145/3844946","DOIUrl":"https://doi.org/10.1145/3844946","url":null,"abstract":"Intelligent Vehicular Networks (IVNs) serve as a core infrastructure for next-generation smart transportation, yet their large-scale deployment is severely hindered by insufficient trustworthiness, fragmented technologies, and difficult integration of multi-domain systems. Existing IVN surveys often lack a systematic taxonomy for trustworthy design and fail to comprehensively address practical challenges in V2X deployment and emerging 6G-enabled evolution. Most prior reviews overlook the joint optimization of cognition, communication, and computation layers, and rarely conduct a unified analysis of security, privacy, ethics, and trust issues across the full IVN pipeline. They also provide limited insights into real-world indoor and outdoor use cases and long-term developmental trends toward 2030. This survey proposes a three-layer hierarchical taxonomy of trustworthy IVNs, encompassing cognition, communication, and computation, to systematically organize and evaluate state-of-the-art technologies. We review sensing, communication, and computing in IVNs, while analyzing trustworthy risks and ethical dilemmas within each layer. We further validate practical IVN implementations through representative indoor and outdoor case studies and forecast key trends including 6G, AI-native networking, intelligent reflecting surfaces, integrated sensing and communication, and large language models. This survey provides a standardized analytical framework for researchers and offers actionable references for the secure, ethical, and trustworthy development and deployment of next-generation IVNs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"74 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860957","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 Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods 影响节点识别综述：从基于结构中心性的方法到基于学习的方法

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-09-01 DOI: 10.1145/3844941

Amir Sheikhahmadi, Laleh Tafakori, Mahdi Jalili

{"title":"A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods","authors":"Amir Sheikhahmadi, Laleh Tafakori, Mahdi Jalili","doi":"10.1145/3844941","DOIUrl":"https://doi.org/10.1145/3844941","url":null,"abstract":"Identifying influential nodes in complex networks is a fundamental challenge in network science. This problem involves measuring the influence of nodes and identifying those that exert the greatest impact on network dynamics and information dissemination. Existing approaches can be broadly divided into two categories: structural centrality-based methods and machine-learning-based methods. This article first provides background formulation on the influential node identification problem, followed by a comprehensive review of state-of-the-art methods and algorithms proposed to find the most influential nodes in complex networks. To trace the evolution of these approaches, the commonly used evaluation metrics are also presented. We then propose a framework that evaluates and compares the algorithms along key dimensions such as scalability, interpretability, computational cost, and appropriate application contexts. Finally, the article explores promising research directions, including hybrid methods, improved interpretability, more realistic performance evaluation in dynamic and large-scale networks, and the use of large language models. This survey provides a comprehensive reference for researchers and practitioners, offering clear insights into the past developments, current capabilities, and future opportunities in the field of influential node identification.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"39 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860958","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 on Table Mining with Large Language Models: Challenges, Advancements and Prospects 基于大型语言模型的表挖掘研究：挑战、进展与展望

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-09-01 DOI: 10.1145/3844608

Mingyue Cheng, Qingyang Mao, Qi Liu, Yitong Zhou, Yupeng Li, Jiahao Wang, Jiaying Lin, Jiawei Cao, Enhong Chen

{"title":"A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects","authors":"Mingyue Cheng, Qingyang Mao, Qi Liu, Yitong Zhou, Yupeng Li, Jiahao Wang, Jiaying Lin, Jiawei Cao, Enhong Chen","doi":"10.1145/3844608","DOIUrl":"https://doi.org/10.1145/3844608","url":null,"abstract":"Table mining is a popular research field that involves complicated technologies, including information retrieval, data mining, visual and textual understanding and logical reasoning. With the emergence of Large Language Models (LLMs), the field has witnessed considerable advancements, presenting new paradigms for table understanding, extraction, and reasoning. In this survey, we conduct a comprehensive review of the literature on table mining with LLMs. We begin by introducing the fundamental overview of tabular data and possible challenges in LLM-based table mining. Specifically, we explore the challenges unique to this domain, such as heterogeneous table structures, contextual ambiguity, and domain-specific knowledge requirements. Then, we summarize representative tabular tasks in table preparation and mining, categorizing existing methods along dimensions including task scope, model architecture, and application scenarios. Next, we describe advanced LLM-based learning strategies in table mining, including foundation models and training-free methods. We further review studies of trustworthy LLM-based table mining and some domain-specific applications. Finally, we discuss prospects and future directions in the field of LLM-based table mining, including issues of generalization, interpretability, efficiency, etc . We hope this survey provides a comprehensive resource for researchers and practitioners, paving the way for further exploration. The repository is at: https://github.com/USTCAGI/Awesome-LLM-Table-Mining.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"4 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860960","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 on Transformer-Based Long-Range Dependency Modeling in Intelligent Document Understanding 智能文档理解中基于变压器的远程依赖模型研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-08-30 DOI: 10.1145/3844505

Xumu Jiang, Yu Zhang, Alireza Abbasi

{"title":"A Survey on Transformer-Based Long-Range Dependency Modeling in Intelligent Document Understanding","authors":"Xumu Jiang, Yu Zhang, Alireza Abbasi","doi":"10.1145/3844505","DOIUrl":"https://doi.org/10.1145/3844505","url":null,"abstract":"Transformers have revolutionized Natural Language Processing (NLP) by capturing complex patterns in data, but they still face challenges in modeling long-range dependency, especially in tasks like Intelligent Document Understanding (IDU), where maintaining coherence across lengthy and varied documents is crucial. This survey reviews Transformer-based models for addressing long-range dependency challenges in IDU. It introduces a taxonomy of key approaches and discusses representative models within memory-focused, attention-focused, and augmentation-focused strategies. Then it explores the types of input modalities and datasets used in IDU, as well as practical applications in generation, text classification, information extraction, multimodal document understanding and sequence modeling. This survey also discusses practical deployment challenges that arise when applying these models to long and complex documents in real-world document processing pipelines. Finally, we outline future research directions, including enhancing memory efficiency, optimizing attention mechanisms, improving long-sequence processing, and advancing multimodal integration. We also emphasize the importance of developing models that can effectively handle complex multimodal documents, incorporate domain-specific knowledge (e.g., legal, medical) and generalize across domains for broader applicability. Overall, this paper highlights advancements in Transformer-based models for long-range dependency handling in IDU and identifies key research directions for building more scalable and structurally aware document understanding systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"50 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148858729","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

Motif Counting in Complex Networks: A Comprehensive Survey 复杂网络中的母题计数：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-08-30 DOI: 10.1145/3844607

Haozhe Yin, Kai Wang, Wenjie Zhang, Yizhang He, Ying Zhang, Xuemin Lin

{"title":"Motif Counting in Complex Networks: A Comprehensive Survey","authors":"Haozhe Yin, Kai Wang, Wenjie Zhang, Yizhang He, Ying Zhang, Xuemin Lin","doi":"10.1145/3844607","DOIUrl":"https://doi.org/10.1145/3844607","url":null,"abstract":"Motif counting plays a crucial role in understanding the structural properties of networks. By computing motif frequencies, researchers can draw key insights into the structural properties of the underlying network. As networks become increasingly complex, different graph models have been proposed, giving rise to diverse motif patterns. These variations introduce unique computational challenges that require specialized algorithms tailored to specific motifs within different graph structures. This survey provides a comprehensive and structured overview of motif counting techniques across general graphs, heterogeneous graphs, and hypergraphs. We categorize existing algorithms according to their underlying computational strategies, emphasizing key similarities and distinctions. In addition to reviewing current methodologies, we examine their strengths, limitations, and computational trade-offs. Furthermore, we explore future directions in motif counting, including scalable implementations to improve efficiency in large-scale networks, algorithmic adaptations for dynamic, temporal, and attributed graphs, and deeper integration with large language models (LLMs) and graph-based retrieval-augmented generation (GraphRAG). By offering a detailed analysis of these approaches, this survey aims to support researchers and practitioners in advancing motif counting for increasingly complex network data.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"18 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148858728","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

Auditing Artificial Intelligence Systems: A Survey of Current Frameworks, Principles and Approaches 审计人工智能系统：当前框架、原则和方法综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-08-29 DOI: 10.1145/3844500

Usman Shahbaz, Amin Beheshti, Babak Abedin, David Orsmond, Yuankai Qi

{"title":"Auditing Artificial Intelligence Systems: A Survey of Current Frameworks, Principles and Approaches","authors":"Usman Shahbaz, Amin Beheshti, Babak Abedin, David Orsmond, Yuankai Qi","doi":"10.1145/3844500","DOIUrl":"https://doi.org/10.1145/3844500","url":null,"abstract":"Over the past decade, the exponential integration of artificial intelligence (AI) systems across various sectors has been propelled by significant advances in machine learning algorithms, data availability, and computational power. This progress has produced highly effective AI systems, but also underscores the critical need for effective auditing to critically evaluate these technologies. In this paper, we conduct a systematic review of the literature on methodologies, frameworks, and techniques for auditing AI systems, focusing on legal and ethical considerations and compliance with regulations. By reviewing key academic databases, including Google Scholar, IEEE, ACM, and Springer, we establish the scope of our survey and derive topics from our research questions. Our findings reveal gaps in current auditing practices and highlight the importance of incorporating AI value chain stages and AI maturity levels into auditing frameworks. This approach enables us to distinguish and recommend existing frameworks and methodologies that are most suitable for the specific contexts of different organisations, thus enhancing the effectiveness of AI system evaluations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"40 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842897","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

Putting the Human Back in the Loop: A Review of Interactive Bayesian Optimization 把人放回循环：交互式贝叶斯优化的回顾

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-08-29 DOI: 10.1145/3805804

John Dudley, Antti Oulasvirta, Liwei Chan, Per Ola Kristensson

{"title":"Putting the Human Back in the Loop: A Review of Interactive Bayesian Optimization","authors":"John Dudley, Antti Oulasvirta, Liwei Chan, Per Ola Kristensson","doi":"10.1145/3805804","DOIUrl":"https://doi.org/10.1145/3805804","url":null,"abstract":"Problems in science and engineering are often cast as optimization tasks, where the parameters of some system must be decided in order to maximize or minimize a given objective or set of objectives. Bayesian optimization offers a flexible tool for this task, thanks to its sample-efficiency and its ability to handle opaque-box functions. This paper reviews research on human-in-the-loop Bayesian optimization , a natural development of Bayesian optimization that is rapidly gaining traction. While researchers have looked into ways to include humans in the Bayesian optimization process, there is little understanding of how this is best done and what kinds of benefits are achievable. We review the growing body of literature in this space and identify five main areas of interaction with different properties and advantages: (1) initializing; (2) evaluating explicitly; (3) evaluating implicitly; (4) steering; and (5) reviewing. The review suggests that appropriate interaction techniques can increase the efficacy and efficiency of a system and promote user engagement. We synthesize practical guidance to design human involvement via such techniques. We conclude that the scope and usefulness of Bayesian optimization can be greatly extended by involving humans in the optimization process.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"348 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842898","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-Driven Reliable SFC Deployment: Challenges, Opportunities, and Perspectives 人工智能驱动的可靠SFC部署：挑战、机遇和前景

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-08-27 DOI: 10.1145/3840282

Yihan Zhong, Danyang Zheng, Chao Wang, Anubha Mittal, Xiaojun Cao

{"title":"AI-Driven Reliable SFC Deployment: Challenges, Opportunities, and Perspectives","authors":"Yihan Zhong, Danyang Zheng, Chao Wang, Anubha Mittal, Xiaojun Cao","doi":"10.1145/3840282","DOIUrl":"https://doi.org/10.1145/3840282","url":null,"abstract":"Artificial Intelligence (AI) techniques are driving increasing volumes of reliability-sensitive Service Function Chain (SFC) requests from applications such as autonomous driving, smart manufacturing, and intelligent healthcare. These applications require not only feasible deployment but also continuous service availability under failures, disruptions, and uncertainty. Existing reliable SFC deployment studies have largely relied on rule-based approaches, which provide explicit formulations and interpretable decision rules but often struggle with dynamic, large-scale, and multi-objective requirements involving reliability, latency, cost, and security. AI-driven solutions therefore offer a promising direction for enabling adaptive and data-driven deployment decisions. This survey first clarifies the workflow of SFC deployment and the distinction between conventional and reliable SFC deployment. We then review rule-based reliable SFC deployment, analyzing its major design rationales, representative solution paradigms, and limitations. Building on this analysis, we examine AI-driven methodologies for reliable SFC deployment, with a particular focus on deep reinforcement learning approaches, which dominate the existing literature. Finally, we discuss open challenges and future directions toward adaptive, robust, and scalable reliable SFC deployment.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836211","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

Recent Advances in 3D Object and Scene Generation: A Survey 三维物体和场景生成的最新进展：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-08-26 DOI: 10.1145/3843767

Xiang Tang, Ruotong Li, Xiaopeng Fan

{"title":"Recent Advances in 3D Object and Scene Generation: A Survey","authors":"Xiang Tang, Ruotong Li, Xiaopeng Fan","doi":"10.1145/3843767","DOIUrl":"https://doi.org/10.1145/3843767","url":null,"abstract":"In recent years, the demand for 3D content has grown exponentially with the intelligent upgrade of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitations of traditional manual modeling approaches, such as labor-intensive workflows and prolonged production cycles, revolutionary advances have been achieved through the convergence of novel 3D representation paradigms and artificial intelligence generative technologies. In this survey, we conduct a systematic review of the cutting-edge achievements in static 3D object and scene generation, as well as establish a comprehensive technical framework through systematic categorization. We start our analysis with mainstream 3D object representations. Subsequently, we delve into the technical pathways of 3D object generation based on four mainstream deep generative models: Variational Autoencoders, Generative Adversarial Networks, Autoregressive Models, and Diffusion Models. Regarding scene generation, we focus on three dominant paradigms: layout-guided generation, lifting based on 2D priors, and rule-driven modeling. Finally, we critically examine persistent challenges in 3D generation and propose potential research directions for future investigation. This survey aims to provide readers with a structured understanding of state-of-the-art 3D generation technologies while inspiring researchers to undertake more exploration in this domain. Project page: Awesome-3D-Object-and-Scene-Generation.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815618","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