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Artificial Intelligence as a Service (AIaaS) for Cloud, Fog and the Edge: State-of-the-Art Practices
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-31 DOI: 10.1145/3712016
Naeem Syed, Adnan Anwar, Zubair Baig, Sherali Zeadally
{"title":"Artificial Intelligence as a Service (AIaaS) for Cloud, Fog and the Edge: State-of-the-Art Practices","authors":"Naeem Syed, Adnan Anwar, Zubair Baig, Sherali Zeadally","doi":"10.1145/3712016","DOIUrl":"https://doi.org/10.1145/3712016","url":null,"abstract":"Artificial Intelligence (AI) fosters enormous business opportunities that build and utilize private AI models. Implementing AI models at scale and ensuring cost-effective production of AI-based technologies through entirely in-house capabilities is a challenge. The success of the Infrastructure as a Service (IaaS) and Software as a Service (SaaS) Cloud Computing models can be leveraged to facilitate a cost-effective and scalable AI service paradigm, namely, ‘AI as a Service.’ We summarize current state-of-the-art solutions for AI-as-a-Service (AIaaS), and we discuss its prospects for growth and opportunities to advance the concept. To this end, we perform a thorough review of recent research on AI and various deployment strategies for emerging domains considering both technical as well as survey articles. Next, we identify various characteristics and capabilities that need to be met before an AIaaS model can be successfully designed and deployed. Based on this we present a general framework of an AIaaS architecture that integrates the required aaS characteristics with the capabilities of AI. We also compare various approaches for offering AIaaS to end users. Finally, we illustrate several real-world use cases for AIaaS models, followed by a discussion of some of the challenges that must be addressed to enable AIaaS adoption.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"39 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072449","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
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-31 DOI: 10.1145/3711680
Jiayi Kuang, Ying Shen, Jingyou Xie, Haohao Luo, Zhe Xu, Ronghao Li, Yinghui Li, Xianfeng Cheng, Xika Lin, Yu Han
{"title":"Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey","authors":"Jiayi Kuang, Ying Shen, Jingyou Xie, Haohao Luo, Zhe Xu, Ronghao Li, Yinghui Li, Xianfeng Cheng, Xika Lin, Yu Han","doi":"10.1145/3711680","DOIUrl":"https://doi.org/10.1145/3711680","url":null,"abstract":"Visual Question Answering (VQA) is a challenge task that combines natural language processing and computer vision techniques and gradually becomes a benchmark test task in multimodal large language models (MLLMs). The goal of our survey is to provide an overview of the development of VQA and a detailed description of the latest models with high timeliness. This survey gives an up-to-date synthesis of natural language understanding of images and text, as well as the knowledge reasoning module based on image-question information on the core VQA tasks. In addition, we elaborate on recent advances in extracting and fusing modal information with vision-language pretraining models and multimodal large language models in VQA. We also exhaustively review the progress of knowledge reasoning in VQA by detailing the extraction of internal knowledge and the introduction of external knowledge. Finally, we present the datasets of VQA and different evaluation metrics and discuss possible directions for future work.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072452","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
Separation of Duty in Information Security
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-30 DOI: 10.1145/3715959
Sebastian Groll, Ludwig Fuchs, Günther Pernul
{"title":"Separation of Duty in Information Security","authors":"Sebastian Groll, Ludwig Fuchs, Günther Pernul","doi":"10.1145/3715959","DOIUrl":"https://doi.org/10.1145/3715959","url":null,"abstract":"Separation of Duty (SoD) is a fundamental security principle that ensures that critical tasks or functions are divided upon multiple users in order to prevent fraud. The topic of SoD spans over many different areas like Identity and Access Management, Workflows, Petri nets or high-level enterprise management. In this survey paper we conduct a systematic and stand-alone literature review on SoD. We develop a multi-level classification scheme and analyse the state of the art and current trends in SoD research as well as the current challenges and potential research gaps. To the best of our knowledge, this is the first effort to comprehensively survey and structure SoD literature.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"20 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143057123","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
Knowledge Distillation on Graphs: A Survey
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-30 DOI: 10.1145/3711121
Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh Chawla
{"title":"Knowledge Distillation on Graphs: A Survey","authors":"Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh Chawla","doi":"10.1145/3711121","DOIUrl":"https://doi.org/10.1145/3711121","url":null,"abstract":"Graph Neural Networks (GNNs) have received significant attention for demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limited devices because of model sizes and scalability constraints imposed by the multi-hop data dependency. In addition, real-world graphs usually possess complex structural information and features. Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, Knowledge Distillation on Graphs (KDG) has been introduced to build a smaller but effective model, leading to model compression and performance improvement. Recently, KDG has achieved considerable progress, with many studies proposed. In this survey, we systematically review these works. Specifically, we first introduce the challenges and bases of KDG, then categorize and summarize the existing work of KDG by answering the following three questions: 1) what to distillate, 2) who to whom, and 3) how to distillate. We offer in-depth comparisons and elucidate the strengths and weaknesses of each design. Finally, we share our thoughts on future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"53 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056579","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
Resource Sharing on the Internet: A Comprehensive Survey on ISP Peering
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-29 DOI: 10.1145/3715906
Anindo Mahmood, Murat Yuksel
{"title":"Resource Sharing on the Internet: A Comprehensive Survey on ISP Peering","authors":"Anindo Mahmood, Murat Yuksel","doi":"10.1145/3715906","DOIUrl":"https://doi.org/10.1145/3715906","url":null,"abstract":"Peering has rapidly emerged as a critical element of the Internet architecture in ensuring low latency, bandwidth efficient and high Quality-of-Service operation for Internet Service Providers (ISPs). In this paper, we delve into the multifaceted domain of peering, providing a comprehensive overview of its fundamental elements. We investigate the various peering models and policies available to ISPs, as well as key players of the ecosystem. We also explore security risks associated with peering and available safeguards to mitigate them. Our goal is to review current industry practices and research literature to shed valuable insights into the complexities of ISP peering.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"147 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056630","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
Advances in Set Function Learning: A Survey of Techniques and Applications
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-29 DOI: 10.1145/3715905
Jiahao Xie, Guangmo Tong
{"title":"Advances in Set Function Learning: A Survey of Techniques and Applications","authors":"Jiahao Xie, Guangmo Tong","doi":"10.1145/3715905","DOIUrl":"https://doi.org/10.1145/3715905","url":null,"abstract":"Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors where the order of features matters, set function learning demands methods that are invariant to permutations of the input set, presenting a unique and complex problem. This survey provides a comprehensive overview of the current development in set function learning, covering foundational theories, key methodologies, and diverse applications. We categorize and discuss existing approaches, focusing on deep learning approaches, such as DeepSets and Set Transformer based methods, as well as other notable alternative methods beyond deep learning, offering a complete view of current models. We also introduce various applications and relevant datasets, such as point cloud processing and multi-label classification, highlighting the significant progress achieved by set function learning methods in these domains. Finally, we conclude by summarizing the current state of set function learning approaches and identifying promising future research directions, aiming to guide and inspire further advancements in this promising field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"53 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056758","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
User-Centred Privacy and Data Protection: An Overview of Current Research Trends and Challenges for the Human-Computer Interaction Field
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-29 DOI: 10.1145/3715903
Shirlei Aparecida de Chaves, Fabiane Benitti
{"title":"User-Centred Privacy and Data Protection: An Overview of Current Research Trends and Challenges for the Human-Computer Interaction Field","authors":"Shirlei Aparecida de Chaves, Fabiane Benitti","doi":"10.1145/3715903","DOIUrl":"https://doi.org/10.1145/3715903","url":null,"abstract":"A user-focused technological approach is essential for privacy and data protection, so a systematic mapping study was conducted to review how researchers approach such matters. Of 8867 papers, 231 were systematically selected and analysed. Through thematic analysis, we identified three main themes: improving privacy policies, raising privacy awareness, and controlling information disclosure. Notably, 45% of the studies lacked user involvement, highlighting a diverse landscape in the extent of real user participation in research evaluations. This study provides valuable insights for researchers and practitioners in promoting privacy-preserving human-computer interaction.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"36 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143057124","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
Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: A Survey
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-28 DOI: 10.1145/3714457
Dinh-Viet-Toan Le, Louis Bigo, Dorien Herremans, Mikaela Keller
{"title":"Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: A Survey","authors":"Dinh-Viet-Toan Le, Louis Bigo, Dorien Herremans, Mikaela Keller","doi":"10.1145/3714457","DOIUrl":"https://doi.org/10.1145/3714457","url":null,"abstract":"– Music is frequently associated with the notion of language as both domains share several similarities, including the ability for their content to be represented as sequences of symbols. In computer science, the fields of Natural Language Processing (NLP) and Music Information Retrieval (MIR) reflect this analogy through a variety of similar tasks, such as author detection or content generation. This similarity has long encouraged the adaptation of NLP methods to process musical data, in particular symbolic music data, and the rise of Transformer neural networks has considerably strengthened this practice. This survey reviews NLP methods applied to symbolic music generation and information retrieval following two axes. We first propose an overview of representations of symbolic music inspired by text sequential representations. We then review a large set of computational models, in particular deep learning models, that have been adapted from NLP to process these musical representations for various MIR tasks. These models are described and categorized through different prisms with a highlight on their music-specialized mechanisms. We finally present a discussion surrounding the adequate use of NLP tools to process symbolic music data. This includes technical issues regarding NLP methods which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"36 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055593","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
Point Cloud-Based Deep Learning in Industrial Production: A Survey
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-27 DOI: 10.1145/3715851
Yi Liu, Changsheng Zhang, Xingjun Dong, Jiaxu Ning
{"title":"Point Cloud-Based Deep Learning in Industrial Production: A Survey","authors":"Yi Liu, Changsheng Zhang, Xingjun Dong, Jiaxu Ning","doi":"10.1145/3715851","DOIUrl":"https://doi.org/10.1145/3715851","url":null,"abstract":"With the rapid development of 3D acquisition technology, point clouds have received increasing attention. In recent years, point cloud-based deep learning has been applied to various industrial scenarios, promoting industrial intelligence. However, there is still a lack of review on the application of point cloud-based deep learning in industrial production. To bridge this gap and inspire future research, this paper provides a review of current point cloud-based deep learning methods applied to industrial production from the perspective of different application scenarios, including pose estimation, defect inspection, measurement and estimation, etc. Considering the real-time requirement of industrial production, this paper also summarizes real-time point cloud-based deep learning methods in each application scenario. Then, this paper introduces commonly used evaluation metrics and public industrial point cloud datasets. Finally, from the aspects of the dataset, speed and industrial product specificity, the challenges faced by current point cloud-based deep learning methods in industrial production are discussed, and future research directions are prospected.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"121 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050684","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 of Benchmarks for Improvement of Software's Non-Functional Properties
IF 16.6 1区 计算机科学
ACM Computing Surveys Pub Date : 2025-01-27 DOI: 10.1145/3711119
Aymeric Blot, Justyna Petke
{"title":"A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties","authors":"Aymeric Blot, Justyna Petke","doi":"10.1145/3711119","DOIUrl":"https://doi.org/10.1145/3711119","url":null,"abstract":"Despite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about the representativeness of current benchmarks of real-world software. To address these issues and facilitate further research on improvement of non-functional properties of software, we conducted a comprehensive survey on the benchmarks used in the field thus far. We searched five major online repositories of research work, collecting 5499 publications (4066 unique), and systematically identified relevant papers to construct a rich and diverse corpus of 425 relevant studies. We find that execution time is the most frequently improved property in research work (63%), while multi-objective improvement is rarely considered (7%). Static approaches for improvement of non-functional software properties are prevalent (51%), with exploratory approaches (18% evolutionary and 15% non-evolutionary) increasingly popular in the last 10 years. Only 39% of the 425 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (63 papers). We also provide recommendations for future work, noting, for instance, lack of benchmarks for non-functional improvement that covers Python, JavaScript, or mobile devices. All the details regarding the 425 identified papers are available on our dedicated webpage: https://bloa.github.io/nfunc_survey.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"20 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050573","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
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