ACM Computing Surveys最新文献_第3页

Smart Water-IoT: Harnessing IoT and AI for Efficient Water Management 智能水-物联网：利用物联网和人工智能实现高效水管理

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-10 DOI: 10.1145/3744338

Vlastimil Slany, Eva Krcalova, Jiri Balej, Martin Zach, Tereza Kucova, Michal Prauzek, Radek Martinek

{"title":"Smart Water-IoT: Harnessing IoT and AI for Efficient Water Management","authors":"Vlastimil Slany, Eva Krcalova, Jiri Balej, Martin Zach, Tereza Kucova, Michal Prauzek, Radek Martinek","doi":"10.1145/3744338","DOIUrl":"https://doi.org/10.1145/3744338","url":null,"abstract":"The treatment, monitoring and distribution of drinking water is an integral component of critical national infrastructure and therefore places continually increasing demands on Water Distribution Networks (WDNs). This domain and its sub-sectors face several major problems, namely climate change and drought-induced rises in water consumption from surface and underground reservoirs, in addition to the existence of significant water leaks during transmission to end users. These problems can be addressed by deploying Internet of Things (IoT) systems and smart distribution grids to improve the efficiency and safety of water distribution and to easily detect leaks or unauthorised consumption. This type of smart grid is referred to as Smart Water-IoT (SW-IoT), a novel, comprehensive water management concept. This review article discusses the application of IoT components and artificial intelligence (AI) in five basic categories (agriculture, water treatment, security, water distribution networks and wastewater). Relevant legislation in the EU, USA, Canada, Australia, China, Japan and India is also reviewed. In this context, the mandatory implementation of smart remote data reading solutions into the critical infrastructure of EU member states is outlined to highlight the importance of responsible water handling. The article provides a detailed analysis of the current research in SW-IoT and defines the main research challenges for future investigation.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"65 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260143","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 Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions 大型语言模型的不确定性量化研究综述：分类、开放研究挑战和未来方向

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-07 DOI: 10.1145/3744238

Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar

{"title":"A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions","authors":"Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar","doi":"10.1145/3744238","DOIUrl":"https://doi.org/10.1145/3744238","url":null,"abstract":"The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect responses, which are expressed with striking confidence. Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt, driving significant research efforts devoted to quantifying the uncertainty of LLMs. This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses. We present existing methods within a relevant taxonomy, unifying ostensibly disparate methods to aid understanding of the state of the art. Furthermore, we highlight applications of uncertainty quantification methods for LLMs, spanning chatbot and textual applications to embodied artificial intelligence applications in robotics. We conclude with open research challenges in uncertainty quantification of LLMs, seeking to motivate future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"251 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237657","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 Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas 系统视角下的事件预测方法综述：整合不同研究领域

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-06 DOI: 10.1145/3743672

Janik-Vasily Benzin, Stefanie Rinderle-Ma

{"title":"A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas","authors":"Janik-Vasily Benzin, Stefanie Rinderle-Ma","doi":"10.1145/3743672","DOIUrl":"https://doi.org/10.1145/3743672","url":null,"abstract":"Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user’s desired future state. If the predicted future events do not comply with this state, actions are taken towards achieving desirable future states. Evidently, event prediction is valuable in many application domains such as business and natural disasters. The diversity of application domains results in a diverse range of methods that are scattered across various research areas which, in turn, use different terminology for event prediction methods. Consequently, sharing methods and knowledge for developing future event prediction methods is restricted. To facilitate knowledge sharing on account of a comprehensive integration and assessment of event prediction methods, we take a systems perspective to integrate event prediction methods into a single system, elicit requirements, and assess existing work with respect to the requirements. Based on the assessment, we identify open challenges and discuss future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"20 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236967","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 Kolmogorov-Arnold Network 关于Kolmogorov-Arnold网络的综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-06 DOI: 10.1145/3743128

Shriyank Somvanshi, Syed Aaqib Javed, Md Monzurul Islam, Diwas Pandit, Subasish Das

{"title":"A Survey on Kolmogorov-Arnold Network","authors":"Shriyank Somvanshi, Syed Aaqib Javed, Md Monzurul Islam, Diwas Pandit, Subasish Das","doi":"10.1145/3743128","DOIUrl":"https://doi.org/10.1145/3743128","url":null,"abstract":"This review study explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs set themselves apart from traditional neural networks by employing learnable, spline-parameterized functions rather than fixed activation functions, allowing for flexible and interpretable representations of high-dimensional functions. The review explores Kan’s architectural strengths, including adaptive edge-based activation functions that enhance parameter efficiency and scalability across varied applications such as time series forecasting, computational biomedicine, and graph learning. Key advancements including Temporal-KAN (T-KAN), FastKAN, and Partial Differential Equation (PDE) KAN illustrate KAN’s growing applicability in dynamic environments, significantly improving interpretability, computational efficiency, and adaptability for complex function approximation tasks. Moreover, the paper discusses KANs integration with other architectures, such as convolutional, recurrent, and transformer-based models, showcasing its versatility in complementing established neural networks for tasks that require hybrid approaches. Despite its strengths, KAN faces computational challenges in high-dimensional and noisy data settings, sparking continued research into optimization strategies, regularization techniques, and hybrid models. This paper highlights KANs expanding role in modern neural architectures and outlines future directions to enhance its computational efficiency, interpretability, and scalability in data-intensive applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"7 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236966","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

Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey 近10年来视觉任务的对抗性攻击研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-06 DOI: 10.1145/3743126

Chiyu Zhang, Lu Zhou, Xiaogang Xu, Jiafei Wu, Zhe Liu

引用次数: 0

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs 破译RLHF: llm从人类反馈中强化学习的关键分析

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-05 DOI: 10.1145/3743127

Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande, Bruno Castro da Silva

引用次数: 0

Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities 面向生成人工智能的消息代理：调查、挑战和机遇

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-05 DOI: 10.1145/3742891

Alaa Saleh, Roberto Morabito, Schahram Dustdar, Sasu Tarkoma, Susanna Pirttikangas, Lauri Lovén

{"title":"Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities","authors":"Alaa Saleh, Roberto Morabito, Schahram Dustdar, Sasu Tarkoma, Susanna Pirttikangas, Lauri Lovén","doi":"10.1145/3742891","DOIUrl":"https://doi.org/10.1145/3742891","url":null,"abstract":"In today’s digital world, GenAI is becoming increasingly prevalent by enabling unparalleled content generation capabilities for a wide range of advanced applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models spanning the distributed edge-cloud continuum, placing increasing demands on communication infrastructures, highlighting the necessity for robust communication solutions. Central to this need are message brokers, which serve as essential channels for data transfer within various system components. This survey aims to delve into a comprehensive analysis of traditional and modern message brokers based on a variety of criteria, highlighting their critical role in enabling efficient data exchange in distributed AI systems. Furthermore, we explore the intrinsic constraints that the design and operation of each message broker might impose, highlighting their impact on real-world applicability. Finally, this study explores the enhancement of message broker mechanisms tailored to GenAI environments. It considers key factors such as scalability, semantic communication, and distributed inference that can guide future innovations and infrastructure advancements in the realm of GenAI data communication.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"25 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228643","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

Graph Deep Learning for Time Series Forecasting 图深度学习用于时间序列预测

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-03 DOI: 10.1145/3742784

Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi

{"title":"Graph Deep Learning for Time Series Forecasting","authors":"Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi","doi":"10.1145/3742784","DOIUrl":"https://doi.org/10.1145/3742784","url":null,"abstract":"Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors leverage pairwise relationships by conditioning forecasts on graphs spanning the time series collection. The conditioning takes the form of architectural inductive biases on the forecasting architecture, resulting in a family of models called spatiotemporal graph neural networks. These biases allow for training global forecasting models on large collections of time series while localizing predictions w.r.t. each element in the set (nodes) by accounting for correlations among them (edges). Recent advances in graph neural networks and deep learning for time series forecasting make the adoption of such processing framework appealing and timely. However, most studies focus on refining existing architectures by exploiting modern deep-learning practices. Conversely, foundational and methodological aspects have not been subject to systematic investigation. To fill this void, this tutorial paper aims to introduce a comprehensive methodological framework formalizing the forecasting problem and providing design principles for graph-based predictors, as well as methods to assess their performance. In addition, together with an overview of the field, we provide design guidelines and best practices, as well as an in-depth discussion of open challenges and future directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210783","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

Benchmarking Relaxed Differential Privacy in Private Learning: A Comparative Survey 私教学习中放松差别隐私的基准：一项比较调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-03 DOI: 10.1145/3729216

Zhaolong Zheng, Lin Yao, Haibo Hu, Guowei Wu

{"title":"Benchmarking Relaxed Differential Privacy in Private Learning: A Comparative Survey","authors":"Zhaolong Zheng, Lin Yao, Haibo Hu, Guowei Wu","doi":"10.1145/3729216","DOIUrl":"https://doi.org/10.1145/3729216","url":null,"abstract":"Differential privacy (DP), a rigorously quantifiable privacy preservation technique, has found widespread application within the domain of machine learning. As DP techniques are implemented in machine learning algorithms, a significant and intricate trade-off between privacy and utility emerges, garnering extensive attention from researchers. In the pursuit of striking a delicate equilibrium between safeguarding sensitive data and optimizing its utility, researchers have introduced various variants of Relaxed Differential Privacy (RDP) definitions. These nuanced formulations, however, exhibit substantial diversity in their underlying principles and interpretations of the core concept of DP, thereby engendering a current void in the comprehensive synthesis of these related works. The principal objective of this article is twofold. Firstly, it aims to provide a comprehensive summary of pertinent research endeavors pertaining to RDP within the realm of machine learning. Secondly, it endeavors to empirically assess the impact on both privacy and utility stemming from machine learning algorithms founded upon these RDP definitions. Additionally, this article undertakes a systematic analysis of the foundational principles underpinning distinct variants of relaxed definitions, culminating in the development of a taxonomy that categorizes these RDP definitions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"8 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210784","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 Employing Large Language Models for Text-to-SQL Tasks 文本转sql任务中使用大型语言模型的研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-03 DOI: 10.1145/3737873

Liang Shi, Zhengju Tang, Nan Zhang, Xiaotong Zhang, Zhi Yang

引用次数: 0