Mohamed Mostafa, Ahmad S Almogren, Muhammad Al-Qurishi, Majed Alrubaian
Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.
{"title":"Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review","authors":"Mohamed Mostafa, Ahmad S Almogren, Muhammad Al-Qurishi, Majed Alrubaian","doi":"10.1145/3700748","DOIUrl":"https://doi.org/10.1145/3700748","url":null,"abstract":"Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488348","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}
ive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document summarization to gain insights into the challenges, widely used datasets, evaluation metrics, approaches, and methods. This study reviews research articles published between 2011 and 2023 from popular electronic databases. In total, 226 journal and conference publications were included in this review. The in-depth analysis of these papers helps researchers understand the challenges, widely used datasets, evaluation metrics, approaches, and methods. This paper identifies and discusses potential opportunities and directions, along with a generic conceptual framework and guidelines on abstractive summarization models and techniques for research in abstractive text summarization.
{"title":"Single-Document Abstractive Text Summarization: A Systematic Literature Review","authors":"Abishek Rao, Shivani Aithal, Sanjay Singh","doi":"10.1145/3700639","DOIUrl":"https://doi.org/10.1145/3700639","url":null,"abstract":"ive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document summarization to gain insights into the challenges, widely used datasets, evaluation metrics, approaches, and methods. This study reviews research articles published between 2011 and 2023 from popular electronic databases. In total, 226 journal and conference publications were included in this review. The in-depth analysis of these papers helps researchers understand the challenges, widely used datasets, evaluation metrics, approaches, and methods. This paper identifies and discusses potential opportunities and directions, along with a generic conceptual framework and guidelines on abstractive summarization models and techniques for research in abstractive text summarization.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449294","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}
Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao, Soummya Kar, Sridhar Tayur
Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.
{"title":"A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research","authors":"Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao, Soummya Kar, Sridhar Tayur","doi":"10.1145/3700874","DOIUrl":"https://doi.org/10.1145/3700874","url":null,"abstract":"Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449253","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}
Bin Jiang, Jiacheng Feng, Xuerong Cui, Jian Wang, Yongxin Liu, Houbing Song
The Internet of Underwater Things (IoUT) pertains to a system that utilizes technology of Internet of Things (IoT) for data collection, communication, and control in the underwater environment. The monitoring and management of various parameters in the underwater domain are gathered through the deployment of underwater sensors, communication devices, and controllers. It is crucial in emerging ocean engineering. However, due to the instability of the underwater environment and the particularity of the underwater communication transmission medium, it is vulnerable to security threats, which may damage the system or cause data errors. In this survey, we will discuss the challenges, solutions and future directions of IoUT from security and reliability respectively. In order to ensure the normal operation of IoUT, we analyze the underwater security problems and solutions of the IoUT. Then, we discuss the reliability issue and improved strategies of IoUT system in detail. Finally, we come up with our views about the theories, challenges and future prospects of IoUT security after the comparative analysis.
{"title":"Security and Reliability of Internet of Underwater Things: Architecture, Challenges, and Opportunities","authors":"Bin Jiang, Jiacheng Feng, Xuerong Cui, Jian Wang, Yongxin Liu, Houbing Song","doi":"10.1145/3700640","DOIUrl":"https://doi.org/10.1145/3700640","url":null,"abstract":"The Internet of Underwater Things (IoUT) pertains to a system that utilizes technology of Internet of Things (IoT) for data collection, communication, and control in the underwater environment. The monitoring and management of various parameters in the underwater domain are gathered through the deployment of underwater sensors, communication devices, and controllers. It is crucial in emerging ocean engineering. However, due to the instability of the underwater environment and the particularity of the underwater communication transmission medium, it is vulnerable to security threats, which may damage the system or cause data errors. In this survey, we will discuss the challenges, solutions and future directions of IoUT from security and reliability respectively. In order to ensure the normal operation of IoUT, we analyze the underwater security problems and solutions of the IoUT. Then, we discuss the reliability issue and improved strategies of IoUT system in detail. Finally, we come up with our views about the theories, challenges and future prospects of IoUT security after the comparative analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448773","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}
Muhammed GOLEC, GUNEET KAUR WALIA, MOHIT KUMAR, FELIX CUADRADO, Sukhpal Singh Gill, STEVE UHLIG
Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on clod start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence (AI)/Machine Learning (ML)-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.
{"title":"Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions","authors":"Muhammed GOLEC, GUNEET KAUR WALIA, MOHIT KUMAR, FELIX CUADRADO, Sukhpal Singh Gill, STEVE UHLIG","doi":"10.1145/3700875","DOIUrl":"https://doi.org/10.1145/3700875","url":null,"abstract":"Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on clod start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence (AI)/Machine Learning (ML)-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448775","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}
Bo Zhang, Yansong Gao, Boyu Kuang, Changlong Yu, Anmin Fu, Willy Susilo
In recent years, frequent Advanced Persistent Threat (APT) attacks have caused disastrous damage to critical facilities, leading to severe information leakages, economic losses, and even social disruptions. Via sophisticated, long-term, and stealthy network intrusions, APT attacks are often beyond the capabilities of traditional intrusion detection methods. Existing methods employ various techniques to enhance APT detection at different stages, but this makes it difficult to fairly and objectively evaluate the capability, value, and orthogonality of available techniques. Overly focusing on hardening specific APT detection stages cannot address some essential challenges from a global perspective, which would result in severe consequences. To holistically tackle this problem and explore effective solutions, we abstract a unified framework that covers the complete process of APT attack detection, with standardized summaries of state-of-the-art solutions and analysis of feasible techniques. Further, we provide an in-depth discussion of the challenges and countermeasures faced by each component of the detection framework. In addition, we comparatively analyze public datasets and outline the capability criteria to provide a reference for standardized evaluations. Finally, we discuss insights into potential areas for future research.
{"title":"A Survey on Advanced Persistent Threat Detection: A Unified Framework, Challenges, and Countermeasures","authors":"Bo Zhang, Yansong Gao, Boyu Kuang, Changlong Yu, Anmin Fu, Willy Susilo","doi":"10.1145/3700749","DOIUrl":"https://doi.org/10.1145/3700749","url":null,"abstract":"In recent years, frequent Advanced Persistent Threat (APT) attacks have caused disastrous damage to critical facilities, leading to severe information leakages, economic losses, and even social disruptions. Via sophisticated, long-term, and stealthy network intrusions, APT attacks are often beyond the capabilities of traditional intrusion detection methods. Existing methods employ various techniques to enhance APT detection at different stages, but this makes it difficult to fairly and objectively evaluate the capability, value, and orthogonality of available techniques. Overly focusing on hardening specific APT detection stages cannot address some essential challenges from a global perspective, which would result in severe consequences. To holistically tackle this problem and explore effective solutions, we abstract a unified framework that covers the complete process of APT attack detection, with standardized summaries of state-of-the-art solutions and analysis of feasible techniques. Further, we provide an in-depth discussion of the challenges and countermeasures faced by each component of the detection framework. In addition, we comparatively analyze public datasets and outline the capability criteria to provide a reference for standardized evaluations. Finally, we discuss insights into potential areas for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444402","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}
Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
Clustering is a fundamental machine learning task which aims at assigning instances into groups so that similar samples belong to the same cluster while dissimilar samples belong to different clusters. Shallow clustering methods usually assume that data are collected and expressed as feature vectors within which clustering is performed. However, clustering high-dimensional data, such as images, texts, videos, and graphs, poses significant challenges for clustering tasks, such as indiscriminate representation and intricate relationships among instances. Over the past decades, deep learning has achieved remarkable success in effective representation learning and modeling complex relationships. Motivated by these advancements, Deep Clustering seeks to improve clustering outcomes through deep learning techniques, garnering considerable interest from both academia and industry. Despite many contributions to this vibrant area of research, the lack of systematic analysis and a comprehensive taxonomy has hindered progress in this field. In this survey, we first explore how deep learning can be integrated into deep clustering and identify two fundamental components: the representation learning module and the clustering module. Then we summarize and analyze the representative design of these two modules. Furthermore, we introduce a novel taxonomy of deep clustering based on how these two modules interact, specifically through multistage, generative, iterative, and simultaneous approaches. In addition, we present well-known benchmark datasets, evaluation metrics, and open-source tools to clearly demonstrate different experimental approaches. Finally, we examine the practical applications of deep clustering and propose challenging areas for future research.
{"title":"A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions","authors":"Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester","doi":"10.1145/3689036","DOIUrl":"https://doi.org/10.1145/3689036","url":null,"abstract":"Clustering is a fundamental machine learning task which aims at assigning instances into groups so that similar samples belong to the same cluster while dissimilar samples belong to different clusters. Shallow clustering methods usually assume that data are collected and expressed as feature vectors within which clustering is performed. However, clustering high-dimensional data, such as images, texts, videos, and graphs, poses significant challenges for clustering tasks, such as indiscriminate representation and intricate relationships among instances. Over the past decades, deep learning has achieved remarkable success in effective representation learning and modeling complex relationships. Motivated by these advancements, Deep Clustering seeks to improve clustering outcomes through deep learning techniques, garnering considerable interest from both academia and industry. Despite many contributions to this vibrant area of research, the lack of systematic analysis and a comprehensive taxonomy has hindered progress in this field. In this survey, we first explore how deep learning can be integrated into deep clustering and identify two fundamental components: the representation learning module and the clustering module. Then we summarize and analyze the representative design of these two modules. Furthermore, we introduce a novel taxonomy of deep clustering based on how these two modules interact, specifically through multistage, generative, iterative, and simultaneous approaches. In addition, we present well-known benchmark datasets, evaluation metrics, and open-source tools to clearly demonstrate different experimental approaches. Finally, we examine the practical applications of deep clustering and propose challenging areas for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444403","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}
María Gutiérrez, Mª Angeles Moraga, Félix Garcia, Coral Calero
This work presents a structured view of the state-of-the-art research on Artificial Intelligence (AI), from the point of view of efficiency and reduction of the energy consumption of AI Software. We analysed the current research on energy consumption of AI algorithms and its improvements, which gave us a starting literature corpus of 2688 papers that we identified as Green AI with a software perspective. We structure this corpus into Green IN AI and Green BY AI, which led us to discover that only 36 of them could be considered Green IN AI. After some quick insights about Green BY AI, we then introduce our main contribution: a systematic mapping of Green IN AI. We provide an in-depth analysis of the AI models that observed during the mapping, and what solutions have been proposed for improving their energy efficiency. We also analyse the energy evaluation methodologies employed in Green IN AI, discovering that most papers opt for a software-based energy estimation approach and a 27% of all papers not documenting their methodology. We finish by synthetising our insights from the mapping into a Decalogue of Good Practices for Green AI.
本作品从人工智能软件的能效和降低能耗的角度，对人工智能（AI）的最新研究成果进行了结构化的分析。我们分析了当前有关人工智能算法能耗及其改进的研究，由此建立了一个包含 2688 篇论文的文献语料库，并将其确定为从软件角度出发的绿色人工智能。我们将该语料库分为 "绿色人工智能"（Green IN AI）和 "绿色人工智能"（Green BY AI），结果发现其中只有 36 篇可被视为 "绿色人工智能"（Green IN AI）。在对 "Green BY AI "进行了一些简单了解之后，我们介绍了我们的主要贡献：对 "Green IN AI "进行系统映射。我们深入分析了映射过程中观察到的人工智能模型，以及为提高其能效而提出的解决方案。我们还分析了 "绿色 IN "人工智能中采用的能源评估方法，发现大多数论文选择了基于软件的能源估算方法，27%的论文没有记录其方法。最后，我们将从图谱中获得的见解综合为《绿色人工智能良好实践十诫》（Decalogue of Good Practices for Green AI）。
{"title":"Green IN Artificial Intelligence from a Software perspective: State-of-the-Art and Green Decalogue","authors":"María Gutiérrez, Mª Angeles Moraga, Félix Garcia, Coral Calero","doi":"10.1145/3698111","DOIUrl":"https://doi.org/10.1145/3698111","url":null,"abstract":"This work presents a structured view of the state-of-the-art research on Artificial Intelligence (AI), from the point of view of efficiency and reduction of the energy consumption of AI Software. We analysed the current research on energy consumption of AI algorithms and its improvements, which gave us a starting literature corpus of 2688 papers that we identified as Green AI with a software perspective. We structure this corpus into Green IN AI and Green BY AI, which led us to discover that only 36 of them could be considered Green IN AI. After some quick insights about Green BY AI, we then introduce our main contribution: a systematic mapping of Green IN AI. We provide an in-depth analysis of the AI models that observed during the mapping, and what solutions have been proposed for improving their energy efficiency. We also analyse the energy evaluation methodologies employed in Green IN AI, discovering that most papers opt for a software-based energy estimation approach and a 27% of all papers not documenting their methodology. We finish by synthetising our insights from the mapping into a Decalogue of Good Practices for Green AI.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440700","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}
Qian Wang, Hong-NingH Dai, Jinghua Yang, Cai Guo, Peter Childs, Maaike Kleinsmann, Yike Guo, Pan Wang
With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering (NPR) and, latterly, neural-style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalised methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation and development of AI artwork methods face many challenges. This paper is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.
{"title":"Learning-based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation","authors":"Qian Wang, Hong-NingH Dai, Jinghua Yang, Cai Guo, Peter Childs, Maaike Kleinsmann, Yike Guo, Pan Wang","doi":"10.1145/3698105","DOIUrl":"https://doi.org/10.1145/3698105","url":null,"abstract":"With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering (NPR) and, latterly, neural-style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalised methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation and development of AI artwork methods face many challenges. This paper is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440701","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}
As social media platforms evolve from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to users, and textual content is sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual connections between the modalities, e.g., text and image. Hence, many researchers have developed automatic techniques for detecting possible cross-modal discordance in web-based content. We analyze, categorize, and identify existing approaches in addition to the challenges and shortcomings they face in order to unearth new research opportunities in the field of multi-modal misinformation detection.
{"title":"Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities","authors":"Sara Abdali, Sina Shaham, Bhaskar Krishnamachari","doi":"10.1145/3697349","DOIUrl":"https://doi.org/10.1145/3697349","url":null,"abstract":"As social media platforms evolve from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to users, and textual content is sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual connections between the modalities, e.g., text and image. Hence, many researchers have developed automatic techniques for detecting possible cross-modal discordance in web-based content. We analyze, categorize, and identify existing approaches in addition to the challenges and shortcomings they face in order to unearth new research opportunities in the field of multi-modal misinformation detection.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440699","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}
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