The Internet of Things (IoT) relies on resource-constrained devices deployed in unprotected environments. Given their constrained nature, IoT systems are vulnerable to security attacks. Data provenance, which tracks the origin and flow of data, provides a potential solution to guarantee data security, including trustworthiness, confidentiality, integrity, and availability in IoT systems. Different types of risks may be faced during data transmission in single-hop and multi-hop scenarios, particularly due to the interconnectivity of IoT systems, which introduces security and privacy concerns. Attackers can inject malicious data or manipulate data without notice, compromising data integrity and trustworthiness. Data provenance offers a way to record the origin, history, and handling of data to address these vulnerabilities. A systematic literature review of data provenance in IoT is presented, exploring existing techniques, practical implementations, security requirements, and performance metrics. Respective contributions and shortcomings are compared. A taxonomy related to the development of data provenance in IoT is proposed. Open issues are identified, and future research directions are presented, providing useful insights for the evolution of data provenance research in the context of the IoT.
{"title":"Security Approaches for Data Provenance in the Internet of Things: A Systematic Literature Review","authors":"Omair Faraj, David Megias, Joaquin Garcia-Alfaro","doi":"10.1145/3718735","DOIUrl":"https://doi.org/10.1145/3718735","url":null,"abstract":"The Internet of Things (IoT) relies on resource-constrained devices deployed in unprotected environments. Given their constrained nature, IoT systems are vulnerable to security attacks. Data provenance, which tracks the origin and flow of data, provides a potential solution to guarantee data security, including trustworthiness, confidentiality, integrity, and availability in IoT systems. Different types of risks may be faced during data transmission in single-hop and multi-hop scenarios, particularly due to the interconnectivity of IoT systems, which introduces security and privacy concerns. Attackers can inject malicious data or manipulate data without notice, compromising data integrity and trustworthiness. Data provenance offers a way to record the origin, history, and handling of data to address these vulnerabilities. A systematic literature review of data provenance in IoT is presented, exploring existing techniques, practical implementations, security requirements, and performance metrics. Respective contributions and shortcomings are compared. A taxonomy related to the development of data provenance in IoT is proposed. Open issues are identified, and future research directions are presented, providing useful insights for the evolution of data provenance research in the context of the IoT.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462204","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}
Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla
Rapid advancement in machine learning is increasing the demand for effective graph data analysis. However, real-world graph data often exhibits class imbalance, leading to poor performance of standard machine learning models on underrepresented classes. To address this, C lass- I mbalanced L earning on G raphs (CILG) has emerged as a promising solution that combines graph representation learning and class-imbalanced learning. This survey provides a comprehensive understanding of CILG’s current state-of-the-art, establishing the first systematic taxonomy of existing work and its connections to traditional imbalanced learning. We critically analyze recent advances and discuss key open problems. A continuously updated reading list of relevant papers and code implementations is available at https://github.com/yihongma/CILG-Papers.
{"title":"Class-Imbalanced Learning on Graphs: A Survey","authors":"Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla","doi":"10.1145/3718734","DOIUrl":"https://doi.org/10.1145/3718734","url":null,"abstract":"Rapid advancement in machine learning is increasing the demand for effective graph data analysis. However, real-world graph data often exhibits class imbalance, leading to poor performance of standard machine learning models on underrepresented classes. To address this, <jats:underline> C </jats:underline> lass- <jats:underline> I </jats:underline> mbalanced <jats:underline> L </jats:underline> earning on <jats:underline> G </jats:underline> raphs (CILG) has emerged as a promising solution that combines graph representation learning and class-imbalanced learning. This survey provides a comprehensive understanding of CILG’s current state-of-the-art, establishing the first systematic taxonomy of existing work and its connections to traditional imbalanced learning. We critically analyze recent advances and discuss key open problems. A continuously updated reading list of relevant papers and code implementations is available at https://github.com/yihongma/CILG-Papers.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"12 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462202","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}
Joseph Renzullo, Pemma Reiter, Westley Weimer, Stephanie Forrest
Machine learning (ML) pervades the field of Automated Program Repair (APR). Algorithms deploy neural machine translation and large language models (LLMs) to generate software patches, among other tasks. But, there are important differences between these applications of ML and earlier work, which complicates the task of ensuring that results are valid and likely to generalize. A challenge is that the most popular APR evaluation benchmarks were not designed with ML techniques in mind. This is especially true for LLMs, whose large and often poorly-disclosed training datasets may include problems on which they are evaluated. This paper reviews work in APR published in the field’s top five venues since 2018, emphasizing emerging trends in the field, including the dramatic rise of ML models, including LLMs. ML-based papers are categorized along structural and functional dimensions, and a variety of issues are identified that these new methods raise. Importantly, data leakage and contamination concerns arise from the challenge of validating ML-based APR using existing benchmarks, which were designed before these techniques were popular. We discuss inconsistencies in evaluation design and performance reporting and offer pointers to solutions where they are available. Finally, we highlight promising new directions that the field is already taking.
{"title":"Automated Program Repair: Emerging Trends Pose and Expose Problems for Benchmarks","authors":"Joseph Renzullo, Pemma Reiter, Westley Weimer, Stephanie Forrest","doi":"10.1145/3704997","DOIUrl":"https://doi.org/10.1145/3704997","url":null,"abstract":"Machine learning (ML) pervades the field of Automated Program Repair (APR). Algorithms deploy neural machine translation and large language models (LLMs) to generate software patches, among other tasks. But, there are important differences between these applications of ML and earlier work, which complicates the task of ensuring that results are valid and likely to generalize. A challenge is that the most popular APR evaluation benchmarks were not designed with ML techniques in mind. This is especially true for LLMs, whose large and often poorly-disclosed training datasets may include problems on which they are evaluated. This paper reviews work in APR published in the field’s top five venues since 2018, emphasizing emerging trends in the field, including the dramatic rise of ML models, including LLMs. ML-based papers are categorized along structural and functional dimensions, and a variety of issues are identified that these new methods raise. Importantly, data leakage and contamination concerns arise from the challenge of validating ML-based APR using existing benchmarks, which were designed before these techniques were popular. We discuss inconsistencies in evaluation design and performance reporting and offer pointers to solutions where they are available. Finally, we highlight promising new directions that the field is already taking.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"87 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462203","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}
Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal Patel, Li Liu
While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This paper presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim to provide both a position paper and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements.
{"title":"Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey","authors":"Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal Patel, Li Liu","doi":"10.1145/3718362","DOIUrl":"https://doi.org/10.1145/3718362","url":null,"abstract":"While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This paper presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim to provide both a position paper and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427140","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}
Tianen Liu, Shuai Wang, Zheng Dong, Borui Li, Tian He
With the development of sensing, wireless communication, and real-time computing technologies, vehicles are gradually becoming more and more intelligent. To provide safe autonomous mobility services, connected autonomous vehicles (CAVs) need to obtain complete information about their environment and process it in real-time to make driving decisions. However, the rapid increase in data volume puts pressure on CAVs to process tasks in real time. This survey analyzes CAVs delay optimization from the perception layer, communication layer, computation layer, and cross-layer. According to different coordination modes, each layer of CAVs is divided, and the problem of delay optimization is classified in fine granularity. This survey will help researchers gain insight into the mechanism of delay optimization on CAVs and highlight the key role of optimized delay in autonomous driving.
{"title":"From Perception to Computation: Revisiting Delay Optimization for Connected Autonomous Vehicles","authors":"Tianen Liu, Shuai Wang, Zheng Dong, Borui Li, Tian He","doi":"10.1145/3718361","DOIUrl":"https://doi.org/10.1145/3718361","url":null,"abstract":"With the development of sensing, wireless communication, and real-time computing technologies, vehicles are gradually becoming more and more intelligent. To provide safe autonomous mobility services, connected autonomous vehicles (CAVs) need to obtain complete information about their environment and process it in real-time to make driving decisions. However, the rapid increase in data volume puts pressure on CAVs to process tasks in real time. This survey analyzes CAVs delay optimization from the perception layer, communication layer, computation layer, and cross-layer. According to different coordination modes, each layer of CAVs is divided, and the problem of delay optimization is classified in fine granularity. This survey will help researchers gain insight into the mechanism of delay optimization on CAVs and highlight the key role of optimized delay in autonomous driving.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"1 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427139","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}
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Francesco Piccialli
The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today’s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it’s imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it’s crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.
{"title":"Green Federated Learning: A New Era of Green Aware AI","authors":"Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Francesco Piccialli","doi":"10.1145/3718363","DOIUrl":"https://doi.org/10.1145/3718363","url":null,"abstract":"The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today’s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it’s imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it’s crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417493","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}
Modern computing devices generate vast amounts of diverse data. It means that a fast transition through various computing devices leads to big data production. Big data with high velocity, volume, and variety presents challenges like data inconsistency, scalability, real-time analysis, and tool selection. Although numerous solutions have been proposed for big data processing, they are often limited in scope and effectiveness. This survey aims to address the lack of comprehensive analysis of big data challenges in relation to machine learning (ML) and the Internet of Things (IoT) environments, particularly concerning the 7Vs of big data. It emphasizes the significance of selecting suitable tools to address each unique big data characteristic, providing a structured approach to manage these challenges effectively. The article systematically reviews big data characteristics and associated techniques, with a detailed discussion of various tools and their applications. Additionally, it analyzes existing ML methods and techniques for IoT data analytics in big data contexts. Through a systematic literature review (SLR), we examine key aspects, including core concepts, benefits, limitations, and the impact of big data on ML algorithms and IoT data analytics. We highlight groundbreaking studies addressing big data challenges to impact future research and enhance big data-driven applications.
{"title":"A Comprehensive Survey on Big Data Analytics: Characteristics, Tools and Techniques","authors":"Mohammad Shahnawaz, Manish Kumar","doi":"10.1145/3718364","DOIUrl":"https://doi.org/10.1145/3718364","url":null,"abstract":"Modern computing devices generate vast amounts of diverse data. It means that a fast transition through various computing devices leads to big data production. Big data with high velocity, volume, and variety presents challenges like data inconsistency, scalability, real-time analysis, and tool selection. Although numerous solutions have been proposed for big data processing, they are often limited in scope and effectiveness. This survey aims to address the lack of comprehensive analysis of big data challenges in relation to machine learning (ML) and the Internet of Things (IoT) environments, particularly concerning the 7Vs of big data. It emphasizes the significance of selecting suitable tools to address each unique big data characteristic, providing a structured approach to manage these challenges effectively. The article systematically reviews big data characteristics and associated techniques, with a detailed discussion of various tools and their applications. Additionally, it analyzes existing ML methods and techniques for IoT data analytics in big data contexts. Through a systematic literature review (SLR), we examine key aspects, including core concepts, benefits, limitations, and the impact of big data on ML algorithms and IoT data analytics. We highlight groundbreaking studies addressing big data challenges to impact future research and enhance big data-driven applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"208 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417495","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}
The rise of data-sharing platforms, driven by public demand for open data and legislative mandates, has raised several pertinent issues. These encompass uncertainties over data accuracy, provenance and lineage, privacy concerns, consent management, and the lack of equitable incentives for data providers. The advanced nature of blockchain makes it well-suited to address these concerns. Yet, the limitations of blockchains, particularly their restricted performance, scalability, and high cost, make them less adept at managing the four “V” of big data - volume, variety, velocity, and veracity. As the body of work proposing blockchain-based data-sharing solutions grows, so does the confusion in selecting between these platforms, particularly in terms of sharing mechanisms, services, quality of services, and applications. In this paper, we aim to fill this knowledge gap through an in-depth survey of blockchain-based data-sharing architectures and applications. We first identify the key challenges of existing data-sharing techniques and lay out the foundations of blockchains. Our focus then shifts to the intersection of blockchain and data sharing, wherein we aim to clarify the existing landscape and propose a reference architecture for blockchain-based data sharing. Subsequently, we explore various industrial applications of blockchain-based data sharing, spanning healthcare, smart grids, transportation, and decarbonization. For each application, we draw from real-world deployments to present key lessons learned in the implementation of blockchain-based data sharing. Lastly, we shed light on current research challenges and open avenues for further study in this space. This paper aims to serve as a comprehensive resource for researchers/practitioners looking to navigate the complex terrain of blockchain-based data-sharing solutions.
{"title":"Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges","authors":"Thanh Linh Nguyen, Lam Nguyen, Thong Hoang, Dilum Bandara, Qin Wang, Qinghua Lu, Xiwei Xu, Liming Zhu, Shiping Chen","doi":"10.1145/3718082","DOIUrl":"https://doi.org/10.1145/3718082","url":null,"abstract":"The rise of data-sharing platforms, driven by public demand for open data and legislative mandates, has raised several pertinent issues. These encompass uncertainties over data accuracy, provenance and lineage, privacy concerns, consent management, and the lack of equitable incentives for data providers. The advanced nature of blockchain makes it well-suited to address these concerns. Yet, the limitations of blockchains, particularly their restricted performance, scalability, and high cost, make them less adept at managing the four “V” of big data - volume, variety, velocity, and veracity. As the body of work proposing blockchain-based data-sharing solutions grows, so does the confusion in selecting between these platforms, particularly in terms of sharing mechanisms, services, quality of services, and applications. In this paper, we aim to fill this knowledge gap through an in-depth survey of blockchain-based data-sharing architectures and applications. We first identify the key challenges of existing data-sharing techniques and lay out the foundations of blockchains. Our focus then shifts to the intersection of blockchain and data sharing, wherein we aim to clarify the existing landscape and propose a reference architecture for blockchain-based data sharing. Subsequently, we explore various industrial applications of blockchain-based data sharing, spanning healthcare, smart grids, transportation, and decarbonization. For each application, we draw from real-world deployments to present key lessons learned in the implementation of blockchain-based data sharing. Lastly, we shed light on current research challenges and open avenues for further study in this space. This paper aims to serve as a comprehensive resource for researchers/practitioners looking to navigate the complex terrain of blockchain-based data-sharing solutions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"28 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417498","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}
Computerised facial expression analysis is performed for a range of social and commercial applications and more recently its potential in medicine such as to detect Parkinson’s Disease (PD) is emerging. This has possibilities for use in telehealth and population screening. The advancement of facial expression analysis using machine learning is relatively recent, with majority of the published work being post-2019. We have performed a systematic review of the English-based publication on the topic from 2019 to 2024 to capture the trends and identify research opportunities that will facilitate the translation of this technology for recognising Parkinson’s disease. The review shows significant advancements in the field, with facial expressions emerging as a potential biomarker for PD. Different machine learning models, from shallow to deep learning, could detect PD faces. However, the main limitation is the reliance on limited datasets. Furthermore, while significant progress has been made, model generalization must be tested before clinical applications.
{"title":"Facial Expression Analysis in Parkinson's Disease Using Machine Learning: A Review","authors":"Guilherme Camargo, Quoc Ngo, Leandro Passos, Danilo Jodas, Joao Papa, Dinesh Kumar","doi":"10.1145/3716818","DOIUrl":"https://doi.org/10.1145/3716818","url":null,"abstract":"Computerised facial expression analysis is performed for a range of social and commercial applications and more recently its potential in medicine such as to detect Parkinson’s Disease (PD) is emerging. This has possibilities for use in telehealth and population screening. The advancement of facial expression analysis using machine learning is relatively recent, with majority of the published work being post-2019. We have performed a systematic review of the English-based publication on the topic from 2019 to 2024 to capture the trends and identify research opportunities that will facilitate the translation of this technology for recognising Parkinson’s disease. The review shows significant advancements in the field, with facial expressions emerging as a potential biomarker for PD. Different machine learning models, from shallow to deep learning, could detect PD faces. However, the main limitation is the reliance on limited datasets. Furthermore, while significant progress has been made, model generalization must be tested before clinical applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"16 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417494","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}
Paulo Roberto da Paz Ferraz Santos, Paulo Angelo Alves Resende, João José Costa Gondim, André Costa Drummond
Cyber attacks have become a growing threat in today’s interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This paper aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the paper poses some open questions and challenges, along with suggestions for future research directions.
{"title":"Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment","authors":"Paulo Roberto da Paz Ferraz Santos, Paulo Angelo Alves Resende, João José Costa Gondim, André Costa Drummond","doi":"10.1145/3717606","DOIUrl":"https://doi.org/10.1145/3717606","url":null,"abstract":"Cyber attacks have become a growing threat in today’s interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This paper aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the paper poses some open questions and challenges, along with suggestions for future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"67 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417745","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：