ACM Computing Surveys最新文献_第4页

Methodology of Algorithm Engineering 算法工程方法论

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-22 DOI: 10.1145/3769071

Jan Mendling, Henrik Leopold, Henning Meyerhenke, Benoit Depaire

引用次数: 0

Memorization in Deep Learning: A Survey 深度学习中的记忆：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-22 DOI: 10.1145/3769076

Jiaheng Wei, Yanjun Zhang, Leo Zhang, Ming Ding, Chao Chen, Kok-Leong Ong, Jun Zhang, Yang Xiang

{"title":"Memorization in Deep Learning: A Survey","authors":"Jiaheng Wei, Yanjun Zhang, Leo Zhang, Ming Ding, Chao Chen, Kok-Leong Ong, Jun Zhang, Yang Xiang","doi":"10.1145/3769076","DOIUrl":"https://doi.org/10.1145/3769076","url":null,"abstract":"Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various domains, yet understanding the details of DNN decision-making and learning processes remains a significant challenge. Recent investigations have uncovered an interesting memorization phenomenon in which DNNs tend to memorize specific details from examples rather than learning general patterns, affecting model generalization, security, and privacy. This raises critical questions about the nature of generalization in DNNs and their susceptibility to security breaches. In this survey, we present a systematic framework to organize memorization definitions based on the generalization and security/privacy domains and summarize memorization evaluation methods at both the example and model levels. Through a comprehensive literature review, we explore DNN memorization behaviors and their impacts on security and privacy. We also introduce privacy vulnerabilities caused by memorization and the phenomenon of forgetting and explore its connection with memorization. Furthermore, we spotlight various applications leveraging memorization mechanisms. This survey offers the first-in-kind understanding of memorization in DNNs, providing insights into its challenges and opportunities for enhancing AI development while addressing critical ethical concerns.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116225","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 Systematic Literature Review on Bias Evaluation and Mitigation in Automatic Speech Recognition Models for Low-Resource African Languages 非洲低资源语言语音自动识别模型偏差评估与缓解的系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-20 DOI: 10.1145/3769089

Joyce Nakatumba-Nabende, Sulaiman Kagumire, Caroline Kantono, Peter Nabende

{"title":"A Systematic Literature Review on Bias Evaluation and Mitigation in Automatic Speech Recognition Models for Low-Resource African Languages","authors":"Joyce Nakatumba-Nabende, Sulaiman Kagumire, Caroline Kantono, Peter Nabende","doi":"10.1145/3769089","DOIUrl":"https://doi.org/10.1145/3769089","url":null,"abstract":"With recent advancements in speech recognition, it is crucial to ensure that automatic speech recognition (ASR) systems do not exhibit systematic biases, such as those related to gender, age, accent, and dialect. Although research has extensively examined systematic biases such as those related to gender, age, accent, and dialect, for high-resource languages, research on low-resource African languages remains limited. This systematic literature review synthesizes evidence on bias evaluation and mitigation in ASR models for African languages, adhering to the PRISMA reporting guidelines. Our analysis reveals that most biases stem from data imbalances and limited linguistic diversity in training datasets, resulting in disproportionately high error rates for underrepresented speaker groups. Mitigation strategies in African contexts have primarily focused on data-centric methods, including dataset expansion, augmentation, and transfer learning. In contrast, more advanced approaches, including fairness-aware modeling, bias-aware loss functions, adversarial debiasing, and speaker-adaptive techniques, are rarely applied. Gender, accent, and dialect biases dominate the few African studies available, while age and racial biases are almost absent. The limited number of African languages covered highlights the urgent need for more representative and inclusive research. Addressing these gaps will support the development of fairer and more robust ASR technologies across the continent.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"75 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093598","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 the Use of Agent-Based Modeling and Simulation for the Vehicle Routing Problem 基于agent的车辆路径问题建模与仿真研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-20 DOI: 10.1145/3769070

Ahmed Laatabi, Benoit Gaudou, Chihab Hanachi, Patricia Stolf

引用次数: 0

Blockchain Smart Contract Security: Threats and Mitigation Strategies in a Lifecycle Perspective 区块链智能合约安全：生命周期视角下的威胁和缓解策略

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-19 DOI: 10.1145/3769013

Detian Liu, Jianbiao Zhang, Yifan Wang, Hong Shen, Zhaoqian Zhang, Tao Ye

引用次数: 0

A Survey on Deep Learning for Monte Carlo Path Tracing 蒙特卡罗路径跟踪的深度学习研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-19 DOI: 10.1145/3768618

Run Yan, Hui Guo, Libo Huang, Nong Xiao, Shen Li, Yongwen Wang, Yashuai Lv, Gang Chen

{"title":"A Survey on Deep Learning for Monte Carlo Path Tracing","authors":"Run Yan, Hui Guo, Libo Huang, Nong Xiao, Shen Li, Yongwen Wang, Yashuai Lv, Gang Chen","doi":"10.1145/3768618","DOIUrl":"https://doi.org/10.1145/3768618","url":null,"abstract":"Recent strides in hardware-accelerated ray tracing have propelled algorithms once deemed suitable only for offline rendering, like Monte Carlo path tracing, into interactive frame rates. While path tracing has been regarded as a practical utility in animating scenes for the film industry, achieving visually noise-free imagery often mandates thousands of samples per pixel and considerable computation time. Regrettably, this poses a difficulty for video games and virtual reality applications, which demand high frame rates and resolutions, thereby constraining the computational overhead of path tracing. Two extant approaches, in-process sampling, and post-processing reconstruction methods, i.e., denoising and upsampling, address this challenge. The giant evolution of deep learning technology has emerged as pivotal in path tracing processing. We explore and advance Monte Carlo path tracing technology based on deep learning. Moreover, we illustrate the merits and demerits of diverse designs and technologies, propose potential future development trends, and aim to provide researchers with a comprehensive understanding of the cutting-edge in deep learning-driven Monte Carlo path tracing.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"55 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089119","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

Community Search over Heterogeneous Information Networks: A Survey 异构信息网络上的社区搜索研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-18 DOI: 10.1145/3768576

Lihua Zhou, Jialong Wang, Yixin Song, Lizhen Wang, Hongmei Chen

引用次数: 0

A Survey of Learned Indexes for the Multi-dimensional Space 多维空间学习指标研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-17 DOI: 10.1145/3768575

Abdullah Al-Mamun, Hao Wu, Qiyang He, Jianguo Wang, Walid G. Aref

{"title":"A Survey of Learned Indexes for the Multi-dimensional Space","authors":"Abdullah Al-Mamun, Hao Wu, Qiyang He, Jianguo Wang, Walid G. Aref","doi":"10.1145/3768575","DOIUrl":"https://doi.org/10.1145/3768575","url":null,"abstract":"A recent research trend involves treating database index structures as Machine Learning (ML) models. In this domain, single or multiple ML models are trained to learn the mapping from keys to positions inside a data set. This class of indexes is known as “Learned Indexes.” Learned indexes have demonstrated improved search performance and reduced space requirements for one-dimensional data. The concept of one-dimensional learned indexes has naturally been extended to multi-dimensional (e.g., spatial) data, leading to the development of “Learned Multi-dimensional Indexes.” This survey presents a taxonomy that classifies and categorizes both learned one- and multi-dimensional indexes, and surveys the existing literature on learned indexes according to this taxonomy with an emphasis on learned multi-dimensional index structures. Specifically, it reviews the current state of this research area, explains the core concepts behind each proposed method, and classifies these methods based on several well-defined criteria. Additionally, we present a timeline to illustrate the evolution of research on learned indexes. Finally, we highlight several open challenges and future research directions in this emerging and highly active field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"18 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084033","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

Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey 阿拉伯语光学字符识别的进展与挑战：综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-17 DOI: 10.1145/3768150

Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Hyun-Soo Kang

{"title":"Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey","authors":"Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Hyun-Soo Kang","doi":"10.1145/3768150","DOIUrl":"https://doi.org/10.1145/3768150","url":null,"abstract":"Optical character recognition (OCR) is a vital process that involves the extraction of handwritten or printed text from scanned or printed images, converting it into a format that can be understood and processed by machines. The automatic extraction of text through OCR plays a crucial role in digitizing documents, enhancing productivity, and preserving historical records. This paper offers an exhaustive review of contemporary applications, methodologies, and challenges associated with Arabic OCR. A thorough analysis is conducted on prevailing techniques utilized throughout the OCR process, with a dedicated effort to discern the most efficacious approaches that demonstrate enhanced outcomes. To ensure a thorough evaluation, a meticulous keyword-search methodology is adopted, encompassing a comprehensive analysis of articles relevant to Arabic OCR. In addition to presenting cutting-edge techniques and methods, this paper identifies research gaps within the realm of Arabic OCR. We shed light on potential areas for future exploration and development, thereby guiding researchers toward promising avenues in the field of Arabic OCR. The outcomes of this study provide valuable insights for researchers, practitioners, and stakeholders involved in Arabic OCR, ultimately fostering advancements in the field and facilitating the creation of more accurate and efficient OCR systems for the Arabic language.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"79 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084032","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

Common Threads in Incremental Data Flow Analysis: A Comprehensive Survey 增量数据流分析中的常见线程：综合调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-09-15 DOI: 10.1145/3768155

Anushri Jana, Uday Khedker

引用次数: 0