ACM Computing Surveys最新文献

{"title":"A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models","authors":"Haopeng Zhang, Philip S. Yu, Jiawei Zhang","doi":"10.1145/3731445","DOIUrl":"https://doi.org/10.1145/3731445","url":null,"abstract":"Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"23 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849789","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}

{"title":"Unravelling Digital Forgeries: A Systematic Survey on Image Manipulation Detection and Localization","authors":"Vijaya Kumar Kadha, Sambit Bakshi, Santos Kumar Das","doi":"10.1145/3731243","DOIUrl":"https://doi.org/10.1145/3731243","url":null,"abstract":"In recent years, deep learning has made significant strides, especially in computer vision applications and, more specifically, in information forensics. On the other hand, data-driven approaches have shown much promise in identifying manipulations in images and videos. However, most forensic tools ignore deep learning in favour of more traditional methodologies. This article thoroughly analyses the current state-of-the-art methods for detecting and localizing image alteration using classical and deep learning-based algorithms. In addition, this review includes the latest developments in the digital image forensics field, including Convolutional Neural Networks (CNNs), while incorporating insights from classical approaches and machine learning models. Further, the most significant data-driven techniques to address the issue of image manipulation detection and localization are presented and segregated into four subtopics: copy-move, splicing, object removal, and contrast enhancement. This study provides an exhaustive and up-to-date survey of the field for researchers and practitioners working in this domain. In addition, it covers the current challenges and future directions in deep learning for image manipulation detection and localization. Finally, this review’s discussion of relevant approaches and experiments will aid future exploration and development in this field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"28 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849787","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}

{"title":"War on JITs: Software-Based Attacks and Hybrid Defenses for JIT Compilers - A Comprehensive Survey","authors":"Quentin Ducasse, Pascal Cotret, Loïc Lagadec","doi":"10.1145/3731598","DOIUrl":"https://doi.org/10.1145/3731598","url":null,"abstract":"Programming Language Virtual Machines (VMs) are composed of several components that together execute and manage languages efficiently. They are deployed in virtually all computing systems through modern web browsers. However, vulnerabilities in any VM component pose a significant threat to security and privacy. In this paper, we present a survey of software attacks on Just-In-Time (JIT) compilers, which dynamically produce optimized code at run time. We first present an overview and categorization of software attacks and their vectors as presented in the literature, identifying three main attack classes: code injection, code-reuse and data-only attacks. We show how each can lead to arbitrary code execution. Next, we present a comprehensive taxonomy of defenses, including diversification, strict memory permissions and capability containment. While some were integrated in modern VMs, we draw recommendations for future protections. Securing JIT compilers remains challenging due to inherent conflicts with security principles, such as <jats:monospace>WX</jats:monospace> ( W ritable XOR e X ecutable), and the complexity of JIT optimizations. Finally, we examine how newer architectures, like ARMv8 and RISC-V, face similar threats. With RISC-V’s open architecture offering a promising platform for prototyping VM-specific protections and custom security instructions, we discuss hardware-assisted runtime protections and RISC-V extensions that could enhance VM security.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"43 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849790","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}

{"title":"State-of-the-Art and Challenges of Engineering ML- Enabled Software Systems in the Deep Learning Era","authors":"Gebremariam Assres, Guru Bhandari, Andrii Shalaginov, Tor-Morten Gronli, Gheorghita Ghinea","doi":"10.1145/3731597","DOIUrl":"https://doi.org/10.1145/3731597","url":null,"abstract":"Emerging from the software crisis of the 1960s, conventional software systems have vastly improved through Software Engineering (SE) practices. Simultaneously, Artificial Intelligence (AI) endeavors to augment or replace human decision-making. In the contemporary landscape, Machine Learning (ML), a subset of AI, leverages extensive data from diverse sources, fostering the development of ML-enabled (intelligent) software systems. While ML is increasingly utilized in conventional software development, the integration of SE practices in developing ML-enabled systems, especially across typical Software Development Life Cycle (SDLC) phases and methodologies in the post-2010 Deep Learning (DL) era, remains underexplored. Our survey of existing literature unveils insights into current practices, emphasizing the interdisciplinary collaboration challenges of developing ML-enabled software, including data quality, ethics, explainability, continuous monitoring and adaptation, and security. The study underscores the imperative for ongoing research and development with focus on data-driven hypotheses, non-functional requirements, established design principles, ML-first integration, automation, specialized testing, and use of agile methods.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"61 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849800","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}

{"title":"A Survey on the State of the Art of Causally Consistent Cloud Systems","authors":"Diana Freitas, Paul deGrandis, Tiago Boldt Sousa","doi":"10.1145/3731444","DOIUrl":"https://doi.org/10.1145/3731444","url":null,"abstract":"In geo-replicated distributed systems, data is redundantly stored across nodes at different geographical sites, increasing fault tolerance and ensuring low access latency by placing data closer to the end user. With data being concurrently updated across sites, replicas should converge to a consistent view of the data, which leads toward adopting fine-tuned consistency models, namely causal consistency (CC). On the one hand, CC respects the causality between operations, resulting in intuitive outcomes for end users and programmers. On the other hand, it avoids the latency penalty of stronger consistency models and bypasses their availability constraints in the presence of network partitions. Furthermore, when coupled with read-only transactions (ROTs) capable of extracting a unified view of the data, CC avoids the anomalies of weaker consistency models. ROTs, however, cause additional coordination overhead compared to non-transactional reads. This overhead is particularly unwelcome considering the prevalence of read operations in real-world applications, and hence the impact of ROTs on the overall performance of read-heavy systems. With this in mind, there has been a growing effort to optimize the latency and throughput of causally consistent ROTs and to understand how the design of existing systems impacts their performance. In light of these recent developments, the present work surveys the state of the art of causally consistent distributed systems, summarizing and comparing their core characteristics and trade-offs and examining how their design decisions impact the performance of ROTs. To this end, it first defines some key concepts and presents two impossibility results concerning the properties of ROT algorithms. It then reviews several causally consistent systems with ROT support by identifying their recurring strategies to ensure causality and summarizing each of their designs and properties, stressing their implications on the performance of ROTs. It also surveys two architectural approaches to CC, which present progress toward a standard implementation for causally consistent systems. Finally, it discusses the open challenges identified in the literature.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"49 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849795","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}

{"title":"A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting","authors":"Pierre-Daniel Arsenault, Shengrui Wang, Jean-Marc Patenaude","doi":"10.1145/3729531","DOIUrl":"https://doi.org/10.1145/3729531","url":null,"abstract":"Artificial Intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk decision-making domains, such as finance. The field of eXplainable AI (XAI) seeks to bridge this gap, aiming to make AI models more understandable. This survey, focusing on published work from 2018 to 2024, categorizes XAI approaches that predict financial time series. In this paper, explainability and interpretability are distinguished, emphasizing the need to treat these concepts separately as they are not applied the same way in practice. Through clear definitions, a rigorous taxonomy of XAI approaches, a complementary characterization, and examples of XAI’s application in the finance industry, this paper provides a comprehensive view of XAI’s current role in finance. It can also serve as a guide for selecting the most appropriate XAI approach for future applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"24 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846452","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}

{"title":"A Survey of Autonomous Driving from a Deep Learning Perspective","authors":"Jingyuan Zhao, Yuyan Wu, Rui Deng, Susu Xu, Jinpeng Gao, Andrew Burke","doi":"10.1145/3729420","DOIUrl":"https://doi.org/10.1145/3729420","url":null,"abstract":"Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception and detection, localization and mapping, and decision-making and control. We investigate specialized deep learning techniques, including convolutional neural networks, recurrent neural networks, self-attention transformers, and their variants, among others. These methods are applied within various learning paradigms—supervised, unsupervised and reinforcement learning—to suit the specific needs of autonomous driving. Our analysis evaluates the effectiveness, benefits, and limitations of these technologies, focusing on their integration with other intelligent algorithms to enhance system performance. Furthermore, we examine the architectures of autonomous systems, analyzing how knowledge and information are organized from modular, pipeline-based frameworks to comprehensive end-to-end models. By presenting an exhaustive overview of the progressing domain of autonomous driving and bridging various research areas, our survey aims to synthesize diverse research threads into a unified narrative. This effort not only aims to enhance our understanding but also pushes the boundaries of what is achievable in this interdisciplinary field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"49 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846455","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}

{"title":"Privacy Preserving Prompt Engineering: A Survey","authors":"Kennedy Edemacu, Xintao Wu","doi":"10.1145/3729219","DOIUrl":"https://doi.org/10.1145/3729219","url":null,"abstract":"Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a direct correlation between the performance of these models and their sizes. As a result, the sizes of these models have notably expanded in recent years, persuading researchers to adopt the term large language models (LLMs) to characterize the larger-sized PLMs. The size expansion comes with a distinct capability called in-context learning (ICL), which represents a special form of prompting and allows the models to be utilized through the presentation of demonstration examples without modifications to the model parameters. Although interesting, privacy concerns have become a major obstacle in its widespread usage. Multiple studies have examined the privacy risks linked to ICL and prompting in general, and have devised techniques to alleviate these risks. Thus, there is a necessity to organize these mitigation techniques for the benefit of the community. In this survey, we provide a systematic overview of the privacy protection methods employed during ICL and prompting in general. We review, analyze, and compare different methods under this paradigm. Furthermore, we provide a summary of the resources accessible for the development of these frameworks. Finally, we discuss the limitations of these frameworks and offer a detailed examination of the promising areas that necessitate further exploration.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"9 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846458","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}

{"title":"Security and Privacy Challenges of AIGC in Metaverse: A Comprehensive Survey","authors":"Shoulong Zhang, Haomin Li, Kaiwen Sun, Hejia Chen, Yan Wang, Shuai Li","doi":"10.1145/3729419","DOIUrl":"https://doi.org/10.1145/3729419","url":null,"abstract":"The Metaverse is a hybrid environment that integrates both physical and virtual realms. The Metaverse has been accessible due to many facilitating technologies. One of the essential technologies that contribute to the Metaverse is AIGC. It is crucial in creating artificial assets and presenting natural interactions efficiently and effectively. Nevertheless, AIGC models encounter external and internal obstacles in security, privacy, and ethics during every level of their development. To conduct a thorough analysis and investigation of risks and threats, we propose a new taxonomy system that categorizes the issues based on three primary factors: the stage of threat exposure, the specific area of the concerns, and the origin of the threats. Furthermore, we present specific unresolved questions that prompt additional investigation into the risks posed by AIGC and the steps taken to counteract them in Metaverse art creation and interactive methodologies. This thorough evaluation offers a broad perspective on the security measures AIGC uses in the Metaverse.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"108 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846456","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}

{"title":"Smart Road Traffic Monitoring: Unveiling the Synergy of IoT and AI for Enhanced Urban Mobility","authors":"Komal Saini, Sandeep Sharma","doi":"10.1145/3729217","DOIUrl":"https://doi.org/10.1145/3729217","url":null,"abstract":"Emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed intelligent transportation systems, providing novel solutions to the increasing complexity of managing traffic on roads as cities grow and traffic density rises, particularly in developing countries. Smart road traffic management systems seek to alleviate traffic-related issues, benefiting citizens and society. This study reports on a Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA)-based systematic literature review (SLR) of 75 papers published between 2014 and 2023. Like prior reviews, this SLR focuses on recent advances in the Internet of Things and Artificial Intelligence for traffic management, covering crucial practical issues such as scalability, data privacy, and resource constraints in growing regions. The study covers significant topics thoroughly, including AI approaches such as machine learning and deep learning, the incorporation of the Internet of Things, and artificial intelligence in traffic management, including the evaluation methods utilized in the examined studies. By synthesizing insights from various studies, recognizing research gaps, and proposing recommendations for future research, this work provides a comprehensive understanding of how recent advances in smart road traffic monitoring will lead to more effective models that improve urban mobility and benefit the community.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"8 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846512","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}