ACM Computing Surveys最新文献_第7页

Graph and Sequential Neural Networks in Session-based Recommendation: A Survey 基于会话的推荐中的图和序列神经网络：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-18 DOI: 10.1145/3696413

Zihao Li, Chao Yang, Yakun Chen, Xianzhi Wang, Hongxu Chen, Guandong Xu, Lina Yao, Michael Sheng

引用次数: 0

A Survey on Video Diffusion Models 视频传播模型调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-18 DOI: 10.1145/3696415

Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai, Han Hu, Hang Xu, Zuxuan Wu, Yu-Gang Jiang

{"title":"A Survey on Video Diffusion Models","authors":"Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai, Han Hu, Hang Xu, Zuxuan Wu, Yu-Gang Jiang","doi":"10.1145/3696415","DOIUrl":"https://doi.org/10.1145/3696415","url":null,"abstract":"The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are gradually superseding methods based on GANs and auto-regressive Transformers, demonstrating exceptional performance not only in image generation and editing, but also in the realm of video-related research. However, existing surveys mainly focus on diffusion models in the context of image generation, with few up-to-date reviews on their application in the video domain. To address this gap, this paper presents a comprehensive review of video diffusion models in the AIGC era. Specifically, we begin with a concise introduction to the fundamentals and evolution of diffusion models. Subsequently, we present an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks. We conduct a thorough review of the literature in these three key areas, including further categorization and practical contributions in the field. Finally, we discuss the challenges faced by research in this domain and outline potential future developmental trends. A comprehensive list of video diffusion models studied in this survey is available at https://github.com/ChenHsing/Awesome-Video-Diffusion-Models.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"29 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374638","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 of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability 基于混合的数据扩充调查：分类、方法、应用和可解释性

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-17 DOI: 10.1145/3696206

Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang, Kunpeng Zhang

{"title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","authors":"Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang, Kunpeng Zhang","doi":"10.1145/3696206","DOIUrl":"https://doi.org/10.1145/3696206","url":null,"abstract":"Data augmentation (DA) is indispensable in modern machine learning and deep neural networks. The basic idea of DA is to construct new training data to improve the model’s generalization by adding slightly disturbed versions of existing data or synthesizing new data. This survey comprehensively reviews a crucial subset of DA techniques, namely Mix-based Data Augmentation (MixDA), which generates novel samples by combining multiple examples. In contrast to traditional DA approaches that operate on single samples or entire datasets, MixDA stands out due to its effectiveness, simplicity, computational efficiency, theoretical foundation, and broad applicability. We begin by introducing a novel taxonomy that categorizes MixDA into Mixup-based, Cutmix-based, and mixture approaches based on a hierarchical perspective of the data mixing operation. Subsequently, we provide an in-depth review of various MixDA techniques, focusing on their underlying motivations. Owing to its versatility, MixDA has penetrated a wide range of applications, which we also thoroughly investigate in this survey. Moreover, we delve into the underlying mechanisms of MixDA’s effectiveness by examining its impact on model generalization and calibration while providing insights into the model’s behavior by analyzing the inherent properties of MixDA. Finally, we recapitulate the critical findings and fundamental challenges of current MixDA studies while outlining the potential directions for future works. Different from previous related surveys that focus on DA approaches in specific domains (e.g., computer vision and natural language processing) or only review a limited subset of MixDA studies, we are the first to provide a systematical survey of MixDA, covering its taxonomy, methodology, application, and explainability. Furthermore, we provide promising directions for researchers interested in this exciting area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"192 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374636","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 Quality Assurance of Smart Contracts 智能合约质量保证调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-14 DOI: 10.1145/3695864

Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang, Xiaoxuan Yang, Liehuang Zhu

{"title":"Survey on Quality Assurance of Smart Contracts","authors":"Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang, Xiaoxuan Yang, Liehuang Zhu","doi":"10.1145/3695864","DOIUrl":"https://doi.org/10.1145/3695864","url":null,"abstract":"As blockchain technology continues to advance, the secure deployment of smart contracts has become increasingly prevalent, underscoring the critical need for robust security measures. This surge in usage has led to a rise in security breaches, often resulting in substantial financial losses for users. This paper presents a comprehensive survey of smart contract quality assurance, from understanding vulnerabilities to evaluating the effectiveness of detection tools. Our work is notable for its innovative classification of forty smart contract vulnerabilities, mapping them to established attack patterns. We further examine nine defense mechanisms, assessing their efficacy in mitigating smart contract attacks. Furthermore, we develop a labeled dataset as a benchmark encompassing ten common vulnerability types, which serves as a critical resource for future research. We also conduct comprehensive experiments to evaluate fourteen vulnerability detection tools, providing a comparative analysis that highlights their strengths and limitations. In summary, this survey synthesizes state-of-the-art knowledge in smart contract security, offering practical recommendations to guide future research and foster the development of robust security practices in the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"28 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374639","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

Alert Prioritisation in Security Operations Centres: A Systematic Survey on Criteria and Methods 安全行动中心的警报优先级：关于标准和方法的系统调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-14 DOI: 10.1145/3695462

Fatemeh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris

{"title":"Alert Prioritisation in Security Operations Centres: A Systematic Survey on Criteria and Methods","authors":"Fatemeh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris","doi":"10.1145/3695462","DOIUrl":"https://doi.org/10.1145/3695462","url":null,"abstract":"Security Operations Centres (SOCs) are specialised facilities where security analysts leverage advanced technologies to monitor, detect, and respond to cyber incidents. However, the increasing volume of security incidents has overwhelmed security analysts, leading to alert fatigue. Effective alert prioritisation (AP) becomes crucial to address this problem through the utilisation of proper criteria and methods. Human-AI teaming (HAT) has the potential to significantly enhance AP by combining the complementary strengths of humans and AI. AI excels in processing large volumes of alert data, identifying anomalies, uncovering hidden patterns, and prioritising alerts at scale, all at machine speed. Human analysts can leverage their expertise to investigate prioritised alerts, re-prioritise them based on additional context, and provide valuable feedback to the AI system, reducing false positives and ensuring critical alerts are prioritised. This work provides a comprehensive review of the criteria and methods for AP in SOC. We analyse the advantages and disadvantages of the different categories of AP criteria and methods based on HAT, specifically considering automation, augmentation, and collaboration. We also identify several areas for future research. We anticipate that our findings will contribute to the advancement of AP techniques, fostering more effective security incident response in SOCs.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"68 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374667","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

State of the Art and Potentialities of Graph-level Learning 图层面学习的现状与潜力

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-13 DOI: 10.1145/3695863

Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò

{"title":"State of the Art and Potentialities of Graph-level Learning","authors":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò","doi":"10.1145/3695863","DOIUrl":"https://doi.org/10.1145/3695863","url":null,"abstract":"Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures. While these methods benefit from good interpretability, they often suffer from computational bottlenecks as they cannot skirt the graph isomorphism problem. Conversely, deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations. As a result, these deep graph learning methods have been responsible for many successes. Yet, no comprehensive survey reviews graph-level learning starting with traditional learning and moving through to the deep learning approaches. This article fills this gap and frames the representative algorithms into a systematic taxonomy covering traditional learning, graph-level deep neural networks, graph-level graph neural networks, and graph pooling. In addition, the evolution and interaction between methods from these four branches within their developments are examined to provide an in-depth analysis. This is followed by a brief review of the benchmark datasets, evaluation metrics, and common downstream applications. Finally, the survey concludes with an in-depth discussion of 12 current and future directions in this booming field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374640","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

Image steganography approaches and their detection strategies: a survey 图像隐写术方法及其检测策略概览

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-10 DOI: 10.1145/3694965

Meike Helena Kombrink, Zeno Jean Marius Hubert Geradts, Marcel Worring

{"title":"Image steganography approaches and their detection strategies: a survey","authors":"Meike Helena Kombrink, Zeno Jean Marius Hubert Geradts, Marcel Worring","doi":"10.1145/3694965","DOIUrl":"https://doi.org/10.1145/3694965","url":null,"abstract":"Steganography is the art and science of hidden (or covered) communication. In digital steganography, the bits of images, videos, audio and text files are tweaked to represent the information to hide. This paper covers the current methods for hiding information in images, alongside steganalysis methods which aim to detect the presence of steganography. By reviewing 456 references, this paper discusses the different approaches that can be taken toward steganography and its much less widely studied counterpart. Currently in research older steganography approaches are more widely used than newer methods even though these show greater potential. New methods do have flaws, therefore more research is needed to make these practically applicable. For steganalysis one of the greatest challenges is the generalisability. Often one scheme can detect the presence of one specific hiding method. More research is needed to combine current schemes and/or create new generalisable schemes. To allow readers to compare results between different papers in our work performance indications of all steganalysis methods are outlined and a comparison of performance is included. This comparison is given using ’topological sorting’ graphs, which compares detection results from all papers (as stated in the papers themselves) on different steganographic schemes.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374641","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

Multimodal Recommender Systems: A Survey 多模式推荐系统：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-09-10 DOI: 10.1145/3695461

Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang

{"title":"Multimodal Recommender Systems: A Survey","authors":"Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang","doi":"10.1145/3695461","DOIUrl":"https://doi.org/10.1145/3695461","url":null,"abstract":"The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short videos, news and etc. , understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, M ultimodal R ecommender S ystem (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to four categories, i.e., Modality Encoder , Feature Interaction , Feature Enhancement and Model Optimization . Besides, to make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper. To access more details of the surveyed papers, such as implementation code, we open source a repository.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"40 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374645","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 Survey on Biclustering-based Collaborative Filtering 基于双聚类的协同过滤综合调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-22 DOI: 10.1145/3674723

Miguel G. Silva, Sara C. Madeira, Rui Henriques

{"title":"A Comprehensive Survey on Biclustering-based Collaborative Filtering","authors":"Miguel G. Silva, Sara C. Madeira, Rui Henriques","doi":"10.1145/3674723","DOIUrl":"https://doi.org/10.1145/3674723","url":null,"abstract":"Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation success is challenged by the diversity of user preferences, structural sparsity of user-item ratings, and inherent subjectivity of rating scales. The increasing user base and item dimensionality of e-commerce and e-entertainment platforms creates opportunities, while further raising generalization and scalability needs. Moved by the need to answer these challenges, user-based and item-based clustering approaches for CF became pervasive. However, classic clustering approaches assess user (item) rating similarity across all items (users), neglecting the rich diversity of item and user profiles. Instead, as preferences are generally simultaneously correlated on subsets of users and items, biclustering approaches provide a natural alternative, being successfully applied to CF for nearly two decades and synergistically integrated with emerging deep learning CF stances. Notwithstanding, biclustering-based CF principles are dispersed, causing state-of-the-art approaches to show accentuated behavioral differences. This work offers a structured view on how biclustering aspects impact recommendation success, coverage, and efficiency. To this end, we introduce a taxonomy to categorize contributions in this field and comprehensively survey state-of-the-art biclustering approaches to CF, highlighting their limitations and potentialities.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"82 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439849","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

Object-Centric Learning with Capsule Networks: A Survey 利用胶囊网络进行以对象为中心的学习：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2024-06-21 DOI: 10.1145/3674500

Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah

{"title":"Object-Centric Learning with Capsule Networks: A Survey","authors":"Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah","doi":"10.1145/3674500","DOIUrl":"https://doi.org/10.1145/3674500","url":null,"abstract":"Capsule networks emerged as a promising alternative to convolutional neural networks for learning object-centric representations. The idea is to explicitly model part-whole hierarchies by using groups of neurons called capsules to encode visual entities, then learn the relationships between these entities dynamically from data. However, a major hurdle for capsule network research has been the lack of a reliable point of reference for understanding their foundational ideas and motivations. This survey provides a comprehensive and critical overview of capsule networks which aims to serve as a main point of reference going forward. To that end, we introduce the fundamental concepts and motivations behind capsule networks, such as equivariant inference. We then cover various technical advances in capsule routing algorithms as well as alternative geometric and generative formulations. We provide a detailed explanation of how capsule networks relate to the attention mechanism in Transformers and uncover non-trivial conceptual similarities between them in the context of object-centric representation learning. We also review the extensive applications of capsule networks in computer vision, video and motion, graph representation learning, natural language processing, medical imaging, and many others. To conclude, we provide an in-depth discussion highlighting promising directions for future work.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"75 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435748","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