ACM Computing Surveys最新文献_第3页

Humanoid Robots and Humanoid AI: Review, Perspectives and Directions 类人机器人与类人人工智能：回顾、展望与方向

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-02 DOI: 10.1145/3770574

Longbing Cao

{"title":"Humanoid Robots and Humanoid AI: Review, Perspectives and Directions","authors":"Longbing Cao","doi":"10.1145/3770574","DOIUrl":"https://doi.org/10.1145/3770574","url":null,"abstract":"In the approximately century-long journey of robotics, humanoid robots made their debut around six decades ago. While current humanoids bear human-like appearances, none have embodied true humaneness, remaining distant from achieving human-like to human-level intelligence. The rapid recent advancements in generative AI and (multimodal) large language models have further reignited and escalated interest in humanoids towards real-time, interactive, and multimodal designs and applications, such as fostering humanoid workers, advisers, educators, medical professionals, caregivers, and receptionists. These unveil boundless opportunities of transforming 1) AI robotics into a research era of humanoid AI , and 2) AI robots into new-generation humanoid AI robots (AI humanoids). Our unique and comprehensive review of about 30 reported humanoids discloses a systematic terminology and a paradigmatic landscape of human-looking to human-like and human-level humanoids. It inspires comprehensive new perspectives and directions of humanoid AI as an area: transitioning from human-looking to humane humanoids, humanizing humanoids with functional and nonfunctional specifications, and cultivating technical and actionable advances of AI humanoids. Humanoid AI and AI humanoids nurture symbiotic advancements and future opportunities of synthesizing and transforming humanity modeling and conventional, generative to human-level AI into humanoid robotics.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"74 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209845","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

Learning-based Human Relighting: A Survey 基于学习的人类再照明：一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-02 DOI: 10.1145/3770081

Shumin Zhu, Wai Keung Wong, Xingxing Zou

引用次数: 0

Automated Market Makers in Cryptoeconomic Systems: A Taxonomy and Archetypes 加密经济系统中的自动做市商：分类和原型

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-26 DOI: 10.1145/3769669

Daniel Kirste, Niclas Kannengießer, Ricky Lamberty, Ali Sunyaev

引用次数: 0

A Survey on Reinforcement Learning Methods for UAV Systems 无人机系统强化学习方法综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-25 DOI: 10.1145/3769426

Hengsheng Chen, Yuanguo Lin, Mingjian Fu, Lina Yao, Michael Sheng

{"title":"A Survey on Reinforcement Learning Methods for UAV Systems","authors":"Hengsheng Chen, Yuanguo Lin, Mingjian Fu, Lina Yao, Michael Sheng","doi":"10.1145/3769426","DOIUrl":"https://doi.org/10.1145/3769426","url":null,"abstract":"In recent years, Unmanned Aerial Vehicles (UAVs) have attracted a lot of attention due to their flexibility and mobility. However, due to the increasingly complex environments faced by UAVs and the rising demands on UAV systems, traditional UAV control methods can no longer efficiently control the UAV under multi-constraint situations. Reinforcement Learning (RL), as an emerging robot control technology, is well suited to the needs of UAV systems in terms of its ability to interact with and learn from the environment. Therefore, RL-based UAV systems are gradually becoming a new trend in research. Nonetheless, as a new research field, it faces some challenges. To fully grasp the landscape of RL-based UAV systems, it is paramount to provide a comprehensive overview and analysis of the existing specific RL methods applied to UAV systems. In this survey, we first provide a comprehensive overview and summary of the application of RL in different UAV scenarios based on the classification of RL methods. After that, based on the existing relevant literature, we conduct a systematic analysis of the challenges and recent advancements when applying RL to UAV systems. Finally, we discuss the potential research directions for RL-based UAV systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"40 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133756","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

Physics-Guided Deep Learning for Dynamical Systems: A Survey 物理引导的动力系统深度学习：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-25 DOI: 10.1145/3766887

Rui Wang, Rose Yu

引用次数: 0

A Survey on Faults and Vulnerabilities in Network Topological Connectivity: Logical and Physical Perspectives 网络拓扑连通性中的故障与漏洞研究：逻辑和物理视角

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-25 DOI: 10.1145/3769668

Carlos Pedroso, Aldri Santos

{"title":"A Survey on Faults and Vulnerabilities in Network Topological Connectivity: Logical and Physical Perspectives","authors":"Carlos Pedroso, Aldri Santos","doi":"10.1145/3769668","DOIUrl":"https://doi.org/10.1145/3769668","url":null,"abstract":"Topological connectivity is essential in networked environments, as it enables the efficient exchange of information between distributed devices and increases service reliability. The devices’ interconnection with different characteristics allows the formation of networks that function as infrastructures, facilitating the provision of multiple services essential to social well-being. In critical scenarios such as disasters, ensuring connectivity is vital for the effective transmission of crucial data, such as security and public transport notifications. This survey highlights the importance of resilient topological connectivity, which maintains communication among network nodes despite faults or disruptions. First, we explore the challenges posed by vulnerabilities and faults, whether benign or malicious, physical or logical, that may disrupt the topology, resulting in problems such as packet loss, transmission delays, and reduced service availability. Then, we review the literature to identify the main faults and solutions developed to provide resilience to topological connectivity in different network scenarios, such as IoT, WSN, and TSN. Solutions are categorized with a focus on strategies such as resistance, recovery, adaptation, absorption, both active and reactive, offering a comprehensive understanding of how to strengthen topology performance and availability.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140567","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

Machine Learning Systems: A Survey from a Data-Oriented Perspective 机器学习系统：从面向数据的角度进行调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-24 DOI: 10.1145/3769292

Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil Lawrence

{"title":"Machine Learning Systems: A Survey from a Data-Oriented Perspective","authors":"Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil Lawrence","doi":"10.1145/3769292","DOIUrl":"https://doi.org/10.1145/3769292","url":null,"abstract":"Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-Oriented Architecture (DOA) is an emerging style that better equips systems to integrate ML models. Even though papers on deployed ML-based systems do not mention DOA, their authors make design decisions that implicitly follow DOA. Implicit decisions create a knowledge gap, limiting practitioners’ ability to implement ML-based systems. This paper surveys why, how, and to what extent practitioners have adopted DOA to implement ML-based systems. We overcome the knowledge gap by answering these questions and explicitly showing the design decisions and practices behind these systems. The survey follows a well-known systematic and semi-automated methodology for reviewing papers in software engineering. The majority of reviewed works partially adopt DOA. Such an adoption enables systems to address big data management, low-latency processing, resource management, security, and privacy requirements. Based on these findings, we formulate practical advice to facilitate the deployment of ML-based systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"28 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133757","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

LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights 软件安全法学硕士：漏洞检测技术和见解的调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-23 DOI: 10.1145/3769082

Ze Sheng, Zhicheng Chen, Shuning Gu, Heqing Huang, Guofei Gu, Jeff Huang

{"title":"LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights","authors":"Ze Sheng, Zhicheng Chen, Shuning Gu, Heqing Huang, Guofei Gu, Jeff Huang","doi":"10.1145/3769082","DOIUrl":"https://doi.org/10.1145/3769082","url":null,"abstract":"Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection. Traditional methods, including static and dynamic analysis, face limitations in efficiency, false-positive rates, and scalability with modern software complexity. Through code structure analysis, pattern identification, and repair suggestion generation, LLMs demonstrate a novel approach to vulnerability mitigation. This survey examines LLMs in vulnerability detection, analyzing problem formulation, model selection, application methodologies, datasets, and evaluation metrics. We investigate current research challenges, emphasizing cross-language detection, multimodal integration, and repository-level analysis. Based on our findings, we propose solutions addressing dataset scalability, model interpretability, and low-resource scenarios. Our contributions include: (1) a systematic analysis of LLM applications in vulnerability detection; (2) a unified framework examining patterns and variations across studies; and (3) identification of key challenges and research directions. This work advances the understanding of LLM-based vulnerability detection. The latest findings are maintained at https://github.com/OwenSanzas/LLM-For-Vulnerability-Detection","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127358","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

Survey on the Evaluation of Generative Models in Music 音乐生成模型评价综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-23 DOI: 10.1145/3769106

Alexander Lerch, Claire Arthur, Nick Bryan-Kinns, Corey Ford, Qianyi Sun, Ashvala Vinay

引用次数: 0

Graph Neural Networks for Integrated Circuit Design, Reliability, and Security: Survey and Tool 图神经网络集成电路设计，可靠性和安全性：调查和工具

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-22 DOI: 10.1145/3769081

Ziad El Sayed, Zeng Wang, Hana Selmani, Johann Knechtel, Ozgur Sinanoglu, Lilas Alrahis

引用次数: 0