ACM Computing Surveys最新文献_第9页

A Survey on Image Segmentation and Super-resolution Reconstruction in Visual Sensor Networks 视觉传感器网络中图像分割与超分辨率重建研究进展

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-01 DOI: 10.1145/3757730

Xiaofang Li, Zhizhong Ma, Ruili Wang, Zhixin Sun, Manna Dai, Yi Wang, Zhenguang Liu, Hong Ye

引用次数: 0

A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects 事件因果关系鉴定综述：分类、挑战、评估和展望

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-01 DOI: 10.1145/3756009

Qing Cheng, Zefan Zeng, Xingchen Hu, Yuehang Si, Zhong Liu

{"title":"A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects","authors":"Qing Cheng, Zefan Zeng, Xingchen Hu, Yuehang Si, Zhong Liu","doi":"10.1145/3756009","DOIUrl":"https://doi.org/10.1145/3756009","url":null,"abstract":"Event Causality Identification (ECI) has become an essential task in Natural Language Processing (NLP), focused on automatically detecting causal relationships between events within texts. This comprehensive survey systematically investigates fundamental concepts and models, developing a systematic taxonomy and critically evaluating diverse models. We begin by defining core concepts, formalizing the ECI problem, and outlining standard evaluation protocols. Our classification framework divides ECI models into two primary tasks: Sentence-level Event Causality Identification (SECI) and Document-level Event Causality Identification (DECI). For SECI, we review models employing feature pattern-based matching, machine learning classifiers, deep semantic encoding, prompt-based fine-tuning, and causal knowledge pre-training, alongside data augmentation strategies. For DECI, we focus on approaches utilizing deep semantic encoding, event graph reasoning, and prompt-based fine-tuning. Special attention is given to recent advancements in multi-lingual and cross-lingual ECI, as well as zero-shot ECI leveraging Large Language Models (LLMs). We analyze the strengths, limitations, and unresolved challenges associated with each approach. Extensive quantitative evaluations are conducted on four benchmark datasets to rigorously assess the performance of various ECI models. We conclude by discussing future research directions and highlighting opportunities to advance the field further.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763365","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

Practical Android Software Protection in the Wild 实用的Android软件保护

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-01 DOI: 10.1145/3757735

Eduardo Blazquez, Juan Tapiador

{"title":"Practical Android Software Protection in the Wild","authors":"Eduardo Blazquez, Juan Tapiador","doi":"10.1145/3757735","DOIUrl":"https://doi.org/10.1145/3757735","url":null,"abstract":"Software protection refers to a range of methods used to protect applications against reverse engineering. Although this term is commonly used, distinctions arise in the specific tools and techniques utilized, such as packers, protectors, and obfuscators, as each category employs different strategies to defend applications against analysis. Given the growing importance of protecting intellectual property and sensitive user information stored in mobile applications, these protective measures have become indispensable. This paper presents a taxonomy categorizing and describing the main techniques used to secure Android applications. Additionally, we analyze the available software tools designed to aid developers in protecting their applications, as well as their prevalence in the wild using a longitudinal dataset comprising nearly 2.5 million apps, including malicious software, pre-installed applications, and regular market application. Our key findings show that, although the use of software protection techniques has been steadily increasing over the last decade, they are still used only by a small fraction of applications in the Android ecosystem. Games and financial applications are by far the ones that most commonly use some form of protection, and we also observe noticeable differences between marketplaces.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"27 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763366","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 Review on Lightweight Cryptographic Mechanisms for Industrial Internet of Things Systems 工业物联网系统轻量级加密机制综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-01 DOI: 10.1145/3757734

Saad Khan, Pedro Afonso Ferreira Lopes Martins, Bruno Sousa, Vasco Pereira

{"title":"A Comprehensive Review on Lightweight Cryptographic Mechanisms for Industrial Internet of Things Systems","authors":"Saad Khan, Pedro Afonso Ferreira Lopes Martins, Bruno Sousa, Vasco Pereira","doi":"10.1145/3757734","DOIUrl":"https://doi.org/10.1145/3757734","url":null,"abstract":"The integration of Industrial Internet of Things (IIoT) devices within Industrial Control Systems (ICS) presents significant cybersecurity challenges, primarily due to the limited resources of these devices. Traditional cryptographic algorithms are often unsuitable for IIoT environments due to their high computational, memory, and energy requirements. Lightweight Cryptographic Algorithms have emerged as efficient and secure alternatives, specifically designed for resource-constrained environments. This paper systematically reviews lightweight symmetric cryptographic mechanisms, specifically Block and Stream ciphers, and evaluates their critical attributes from an IIoT perspective. In addition, the security strengths and vulnerabilities of these algorithms against known cryptanalytic attacks, including Differential, Linear, Related Key, and others, are discussed. The paper also discusses current standardization efforts by organizations such as the National Institute of Standards and Technology (NIST), International Organization for Standardisation (ISO)/International Electro-technical Commission (IEC), highlighting their applicability in ICS environments. Finally, it identifies open research issues and future directions for improving lightweight cryptographic security in ICS, providing valuable insights for security practitioners and researchers seeking to robustly secure IIoT deployments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"27 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763367","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

Applications of AI in Space Domain 人工智能在空间领域的应用

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-30 DOI: 10.1145/3757317

Luciana Rebelo, Francesco Basciani, Patrizio Pelliccione

引用次数: 0

Robust Recommender System: A Survey and Future Directions 鲁棒推荐系统：综述及未来发展方向

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-29 DOI: 10.1145/3757057

Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, Huawei Shen, Xueqi Cheng

{"title":"Robust Recommender System: A Survey and Future Directions","authors":"Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, Huawei Shen, Xueqi Cheng","doi":"10.1145/3757057","DOIUrl":"https://doi.org/10.1145/3757057","url":null,"abstract":"With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters “dirty” data, where noise or malicious information can lead to abnormal recommendations. Research on improving robustness of recommender systems against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on robust recommender systems. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks, and regularization, purification, self-supervised learning for defending against malicious attacks. Additionally, we summarize evaluation metrics and commonly used datasets for assessing robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to provide readers with a comprehensive understanding of robust recommender systems and to identify key pathways for future research and development. To facilitate ongoing exploration, we maintain a continuously updated GitHub repository with related research: https://github.com/Kaike-Zhang/Robust-Recommender-System.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"31 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766192","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

Fuzzy Sets-Based Approaches for Improved Medical Diagnosis: An Analysis and Overview of Major Research Directions 基于模糊集的医学诊断改进方法：主要研究方向分析与综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-29 DOI: 10.1145/3757058

Amit K. Shukla, Priyanka Mehra, Pranab K Muhuri

引用次数: 0

Fact Checking Knowledge Graphs -- A Survey 事实核查知识图谱——一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-19 DOI: 10.1145/3749838

Umair Qudus, Michael Röder, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo

引用次数: 0

Four Decades of Symbolic Knowledge Extraction from Sub-Symbolic Predictors. A Survey 从子符号预测器提取符号知识的四十年。一项调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-15 DOI: 10.1145/3749097

Federico Sabbatini

引用次数: 0

Survey and Analysis for the Challenges in Computer Science to the Automation of Grading Systems 计算机科学对评分系统自动化挑战的调查与分析

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-14 DOI: 10.1145/3748521

Joan Lu, Bhavya Krishna Balasubramanian, Mike Joy, Qiang Xu

{"title":"Survey and Analysis for the Challenges in Computer Science to the Automation of Grading Systems","authors":"Joan Lu, Bhavya Krishna Balasubramanian, Mike Joy, Qiang Xu","doi":"10.1145/3748521","DOIUrl":"https://doi.org/10.1145/3748521","url":null,"abstract":"Assessment is essential to educational system. Automatic grading reduces the time and effort taken by tutors to assess the answers written by the students. To understand recent computational methods used for automatic grading, a review has been conducted. 4084 papers were initially identified using a keyword search. After filtering, the number was reduced to 57. It was found that statistical models are normally used in Automatic-Short-Answer-Grading (ASAG); vector-based similarity measures are the most popular among projects; pilot datasets are mostly used; standard datasets for evaluation are missing. Evidence shows that machine learning and deep learning are most popularly adopted methods and generative AI, e.g., LLMs and ChatGPT are also jump to the chance, which indicates that integrating AI in education is an inevitable trend. Also, most investigations prefer to adopt multiple approaches to improve computational quality, dataset analysis, and evaluation results. The identified research gaps will be a useful reference guide to users/researchers beneficial to formative/summative assessment. We concluded that the presented outcome, analysis and discussions are informative to academia and pedagogical practitioners who are interested in further developing/using ASAG systems. Although research into ASAG is still rudimentary, it is a promising area with impact on academic circles/commercially educational markets.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"13 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622740","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