In this study, we evaluate potential demographic bias in state-of-the-art deepfake image detection models across three key attributes: age, ethnicity, and gender. Unlike prior works that retrain detectors or analyse forensic manipulations, we systematically assess multiple pretrained checkpoints of leading deepfake detectors, each trained on different datasets, to ensure an unbiased evaluation framework. Our experiments employ synthetic images generated by recent diffusion and autoregressive models, alongside real images from balanced datasets, to measure subgroup-specific detection performance. Results reveal no systematic bias across demographic categories—variations in accuracy and precision remain within small statistical margins across all detectors and checkpoints. We further provide a taxonomy of image generative models, highlighting their evolution from pixel-space to latent-space diffusion architectures, to contextualize the diversity of synthetic data used in our evaluation. Overall, our findings suggest that modern deepfake image detectors, when tested in a cross-demographic setting using pretrained checkpoints, exhibit robust and fair performance across age, ethnicity, and gender.
{"title":"Bias-Free? An Empirical Study on Ethnicity, Gender, and Age Fairness in Deepfake Detection","authors":"Aditi Panda, Tanusree Ghosh, Tushar Choudhary, Ruchira Naskar","doi":"10.1145/3796544","DOIUrl":"https://doi.org/10.1145/3796544","url":null,"abstract":"In this study, we evaluate potential demographic bias in state-of-the-art deepfake image detection models across three key attributes: age, ethnicity, and gender. Unlike prior works that retrain detectors or analyse forensic manipulations, we systematically assess multiple pretrained checkpoints of leading deepfake detectors, each trained on different datasets, to ensure an unbiased evaluation framework. Our experiments employ synthetic images generated by recent diffusion and autoregressive models, alongside real images from balanced datasets, to measure subgroup-specific detection performance. Results reveal no systematic bias across demographic categories—variations in accuracy and precision remain within small statistical margins across all detectors and checkpoints. We further provide a taxonomy of image generative models, highlighting their evolution from pixel-space to latent-space diffusion architectures, to contextualize the diversity of synthetic data used in our evaluation. Overall, our findings suggest that modern deepfake image detectors, when tested in a cross-demographic setting using pretrained checkpoints, exhibit robust and fair performance across age, ethnicity, and gender.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"40 4 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153682","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}
Domain ontologies are essential for representing and reasoning about knowledge, yet addressing information uncertainty within them remains challenging. This review surveys approaches to modelling information uncertainty in domain ontologies from 2010 to 2024. It categorizes modelling formalisms, identifies information uncertainty types, and analyzes how information uncertainty is integrated into ontology components. It reviews reasoning techniques and emerging methods, including Machine Learning and Natural Language Processing. The review examines languages, tools, and evaluation strategies. The purpose is to map the landscape of information uncertainty modelling in domain ontologies, highlight research gaps and trends, and provide structured guidance for selecting suitable approaches.
{"title":"A Comprehensive Review of Information Uncertainty Modelling in Domain Ontologies","authors":"Deemah Alomair, Ridha Khedri, Wendy MacCaull","doi":"10.1145/3794841","DOIUrl":"https://doi.org/10.1145/3794841","url":null,"abstract":"Domain ontologies are essential for representing and reasoning about knowledge, yet addressing information uncertainty within them remains challenging. This review surveys approaches to modelling information uncertainty in domain ontologies from 2010 to 2024. It categorizes modelling formalisms, identifies information uncertainty types, and analyzes how information uncertainty is integrated into ontology components. It reviews reasoning techniques and emerging methods, including Machine Learning and Natural Language Processing. The review examines languages, tools, and evaluation strategies. The purpose is to map the landscape of information uncertainty modelling in domain ontologies, highlight research gaps and trends, and provide structured guidance for selecting suitable approaches.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135118","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}
Minxing Zhang, Yi Yang, Roy Xie, Bhuwan Dhingra, Shuyan Zhou, Jian Pei
Large Language Model (LLM)-based agents have recently emerged as a new paradigm that extends the capabilities of LLMs beyond text generation to dynamic interaction with external environments. A critical challenge lies in ensuring their generalizability – the ability to maintain consistently high performance across varied instructions, tasks, environments, and domains, especially those different from the agent’s fine-tuning data. Despite growing interest, the concept of generalizability in LLM-based agents remains underdefined, and systematic approaches to measure and improve it are lacking. We provide the first comprehensive review of generalizability in LLM-based agents. We begin by clarifying the definition and boundaries of agent generalizability. We then review existing benchmarks. Next, we categorize strategies for improving generalizability into three groups: methods targeting the backbone LLM, targeting agent components, and targeting their interactions. Furthermore, we introduce the distinction between generalizable frameworks and generalizable agents and outline how generalizable frameworks can be translated into agent-level generalizability. Finally, we identify future directions, including the development of standardized evaluation frameworks, variance- and cost-based metrics, and hybrid approaches that integrate methodological innovations with agent architecture-level designs. We aim to establish a foundation for principled research on building LLM-based agents that generalize reliably across diverse real-world applications.
{"title":"Generalizability of Large Language Model-Based Agents: A Comprehensive Survey","authors":"Minxing Zhang, Yi Yang, Roy Xie, Bhuwan Dhingra, Shuyan Zhou, Jian Pei","doi":"10.1145/3794858","DOIUrl":"https://doi.org/10.1145/3794858","url":null,"abstract":"Large Language Model (LLM)-based agents have recently emerged as a new paradigm that extends the capabilities of LLMs beyond text generation to dynamic interaction with external environments. A critical challenge lies in ensuring their <jats:italic toggle=\"yes\">generalizability</jats:italic> – the ability to maintain consistently high performance across varied instructions, tasks, environments, and domains, especially those different from the agent’s fine-tuning data. Despite growing interest, the concept of generalizability in LLM-based agents remains underdefined, and systematic approaches to measure and improve it are lacking. We provide the first comprehensive review of generalizability in LLM-based agents. We begin by clarifying the definition and boundaries of agent generalizability. We then review existing benchmarks. Next, we categorize strategies for improving generalizability into three groups: methods targeting the backbone LLM, targeting agent components, and targeting their interactions. Furthermore, we introduce the distinction between <jats:italic toggle=\"yes\">generalizable frameworks</jats:italic> and <jats:italic toggle=\"yes\">generalizable agents</jats:italic> and outline how generalizable frameworks can be translated into agent-level generalizability. Finally, we identify future directions, including the development of standardized evaluation frameworks, variance- and cost-based metrics, and hybrid approaches that integrate methodological innovations with agent architecture-level designs. We aim to establish a foundation for principled research on building LLM-based agents that generalize reliably across diverse real-world applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"244 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138700","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}
This meta-analysis examines music emotion recognition (MER) models published between 2014 and 2024, focusing on predictions of valence, arousal, and categorical emotions. A total of 553 studies were identified, of which 96 full-text articles were assessed, resulting in a final review of 34 studies. These studies reported 290 models, including 86 for emotion classification and 204 for regression. Using the best-performing model from each study, we found that valence and arousal were predicted with reasonable accuracy (r = 0.67 and r = 0.81, respectively), while classification models achieved an accuracy of 0.87 as measured with Matthews correlation coefficient. Across modelling approaches, linear and tree-based methods generally outperformed neural networks in regression tasks, whereas neural networks and support vector machines (SVMs) showed highest performance in classification tasks. We highlight key recommendations for future MER research, emphasizing the need for greater transparency, feature validation, and standardized reporting to improve comparability across studies.
{"title":"A Meta-Analysis of Music Emotion Recognition Studies","authors":"Tuomas Eerola, Cameron Anderson","doi":"10.1145/3796518","DOIUrl":"https://doi.org/10.1145/3796518","url":null,"abstract":"This meta-analysis examines music emotion recognition (MER) models published between 2014 and 2024, focusing on predictions of valence, arousal, and categorical emotions. A total of 553 studies were identified, of which 96 full-text articles were assessed, resulting in a final review of 34 studies. These studies reported 290 models, including 86 for emotion classification and 204 for regression. Using the best-performing model from each study, we found that valence and arousal were predicted with reasonable accuracy (r = 0.67 and r = 0.81, respectively), while classification models achieved an accuracy of 0.87 as measured with Matthews correlation coefficient. Across modelling approaches, linear and tree-based methods generally outperformed neural networks in regression tasks, whereas neural networks and support vector machines (SVMs) showed highest performance in classification tasks. We highlight key recommendations for future MER research, emphasizing the need for greater transparency, feature validation, and standardized reporting to improve comparability across studies.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"72 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138871","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}
Philipp Scharpf, Corinna Breitinger, Andreas Spitz, Norman Meuschke, André Greiner-Petter, Moritz Schubotz, Bela Gipp
This article provides a comprehensive systematic review of the literature on entity linking using Wikidata as the grounding knowledge base. Our review extends the scope of previous studies from two to eight dimensions of entity linking, which we classify into the following categories: definitions, tasks, types, domains, approaches, datasets, applications, and challenges. We find that datasets primarily address question-answering and news domains but underutilize Wikidata’s capabilities for hyper-relations, multilingualism, and time dependence. The research gaps we identify include the need for more robust datasets, hybrid methods combining rule-based and learning-based approaches, and improved handling of ambiguity, sparse entity types, data noise, and knowledge graph evolution.
{"title":"Entity Linking with Wikidata: A Systematic Literature Review","authors":"Philipp Scharpf, Corinna Breitinger, Andreas Spitz, Norman Meuschke, André Greiner-Petter, Moritz Schubotz, Bela Gipp","doi":"10.1145/3795134","DOIUrl":"https://doi.org/10.1145/3795134","url":null,"abstract":"This article provides a comprehensive systematic review of the literature on entity linking using Wikidata as the grounding knowledge base. Our review extends the scope of previous studies from two to eight dimensions of entity linking, which we classify into the following categories: definitions, tasks, types, domains, approaches, datasets, applications, and challenges. We find that datasets primarily address question-answering and news domains but underutilize Wikidata’s capabilities for hyper-relations, multilingualism, and time dependence. The research gaps we identify include the need for more robust datasets, hybrid methods combining rule-based and learning-based approaches, and improved handling of ambiguity, sparse entity types, data noise, and knowledge graph evolution.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"9 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095841","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}
Sung Une Lee, Harsha Perera, Yue Liu, Boming Xia, Qinghua Lu, Liming Zhu, Olivier Salvado, Jon Whittle
The rapid growth of AI underscores the need for responsible AI (RAI) practices. While many RAI checklists and frameworks exist, practitioners still struggle with how to use them in practice across roles and stages. We introduce the RAI Question Bank, a role- and lifecycle-tagged, evidence-oriented question set that simplifies interaction for executives, managers, and developers while preserving comprehensive coverage mapped to leading frameworks and regulations (e.g., EU AI Act). With comprehensive taxonomy and linkage between lower-level questions and higher-level themes, the Question Bank facilitates cohesive assessments. Two case studies show how it surfaces risks, prioritizes effort, and supports policy alignment.
人工智能的快速发展凸显了对负责任的人工智能（RAI）实践的需求。虽然存在许多RAI检查表和框架，但从业者仍然在努力解决如何在跨角色和阶段的实践中使用它们的问题。我们介绍了RAI题库，这是一个角色和生命周期标记的、面向证据的问题集，它简化了高管、经理和开发人员的交互，同时保留了对领先框架和法规（例如，EU AI Act）的全面覆盖。通过对较低层次问题和较高层次主题的综合分类和联系，题库促进了内聚性评估。两个案例研究展示了它是如何暴露风险、确定工作的优先级以及支持政策一致性的。
{"title":"Responsible AI Question Bank for Risk Assessment","authors":"Sung Une Lee, Harsha Perera, Yue Liu, Boming Xia, Qinghua Lu, Liming Zhu, Olivier Salvado, Jon Whittle","doi":"10.1145/3790096","DOIUrl":"https://doi.org/10.1145/3790096","url":null,"abstract":"The rapid growth of AI underscores the need for responsible AI (RAI) practices. While many RAI checklists and frameworks exist, practitioners still struggle with how to use them in practice across roles and stages. We introduce the RAI Question Bank, a role- and lifecycle-tagged, evidence-oriented question set that simplifies interaction for executives, managers, and developers while preserving comprehensive coverage mapped to leading frameworks and regulations (e.g., EU AI Act). With comprehensive taxonomy and linkage between lower-level questions and higher-level themes, the Question Bank facilitates cohesive assessments. Two case studies show how it surfaces risks, prioritizes effort, and supports policy alignment.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"2 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072563","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}
Youngjin Park, Anh Tong, Sehyun Lee, Jihyeon Seong, Qin Xie, Jaesik Choi
This paper presents a comprehensive cross-task analysis of time series methodologies, revealing fundamental connections that are often obscured by task-specific perspectives. Our contributions are fivefold. First, we introduce seven priority properties, along with exogenous integration, that characterize methodologies independent of application domain, enabling systematic comparison across traditional and modern approaches. Second, we classify neural architectures by transparency levels determined by two characteristics: parameter time-invariance and the explicitness of mathematical formulations. Locally time-invariant operations enable mechanistic understanding, but globally time-varying operations pose fundamental challenges to achieving it. Third, our hierarchical taxonomy guides the selection of methodologies. Fourth, we comparatively evaluate explanation methods by quantifying how closely they recover transparency, measuring explanation richness via breadth (granularity) and depth (mechanistic understanding): pointwise methods offer lower richness, component-level methods achieve medium richness, and concept-based methods achieve higher richness, sometimes at the cost of generalization. Finally, we identify an ongoing challenge from the absence of ground truth for temporal components and outline future research directions for time-varying modeling explanations. This survey provides methodological insights and practical frameworks in time series analysis.
{"title":"Towards Transparent Time Series Analysis: Exploring Methods and Enhancing Interpretability","authors":"Youngjin Park, Anh Tong, Sehyun Lee, Jihyeon Seong, Qin Xie, Jaesik Choi","doi":"10.1145/3794839","DOIUrl":"https://doi.org/10.1145/3794839","url":null,"abstract":"This paper presents a comprehensive cross-task analysis of time series methodologies, revealing fundamental connections that are often obscured by task-specific perspectives. Our contributions are fivefold. First, we introduce seven priority properties, along with exogenous integration, that characterize methodologies independent of application domain, enabling systematic comparison across traditional and modern approaches. Second, we classify neural architectures by transparency levels determined by two characteristics: parameter time-invariance and the explicitness of mathematical formulations. Locally time-invariant operations enable mechanistic understanding, but globally time-varying operations pose fundamental challenges to achieving it. Third, our hierarchical taxonomy guides the selection of methodologies. Fourth, we comparatively evaluate explanation methods by quantifying how closely they recover transparency, measuring explanation richness via breadth (granularity) and depth (mechanistic understanding): pointwise methods offer lower richness, component-level methods achieve medium richness, and concept-based methods achieve higher richness, sometimes at the cost of generalization. Finally, we identify an ongoing challenge from the absence of ground truth for temporal components and outline future research directions for time-varying modeling explanations. This survey provides methodological insights and practical frameworks in time series analysis.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"30 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072562","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}
Causal inference plays an important role in explanatory analysis and decision-making across a wide range of fields, including statistics, marketing, healthcare, and education. Its core objective is to estimate treatment effects and inform intervention policies. Most existing work focuses on the binary treatment setting, where each unit is assigned to either treatment or control. In practice, however, treatments are often more complex, encompassing multi-valued, continuous, or bundle interventions. We refer to such settings as complex treatments. In this paper, we provide a systematic and comprehensive survey of causal inference methods for complex treatments. We first revisit the problem formulation, core assumptions, and their possible variations under different settings. We sequentially review the representative methods for multi-valued, continuous, and bundle treatments. Within each setting, we organize the methods into two broad categories: those that rely on the unconfoundedness assumption and those that address violations of this assumption. We further discuss the intrinsic relationships among these methods and the assumption verification. Finally, we summarize available benchmark datasets and open-source codes, and outline several directions for future research.
{"title":"Causal Inference with Complex Treatments: A Survey","authors":"Yingrong Wang, Haoxuan Li, Minqin Zhu, Anpeng Wu, Baohong Li, Keting Yin, Ruoxuan Xiong, Fei Wu, Kun Kuang","doi":"10.1145/3789499","DOIUrl":"https://doi.org/10.1145/3789499","url":null,"abstract":"Causal inference plays an important role in explanatory analysis and decision-making across a wide range of fields, including statistics, marketing, healthcare, and education. Its core objective is to estimate treatment effects and inform intervention policies. Most existing work focuses on the binary treatment setting, where each unit is assigned to either treatment or control. In practice, however, treatments are often more complex, encompassing multi-valued, continuous, or bundle interventions. We refer to such settings as complex treatments. In this paper, we provide a systematic and comprehensive survey of causal inference methods for complex treatments. We first revisit the problem formulation, core assumptions, and their possible variations under different settings. We sequentially review the representative methods for multi-valued, continuous, and bundle treatments. Within each setting, we organize the methods into two broad categories: those that rely on the <jats:italic toggle=\"yes\">unconfoundedness assumption</jats:italic> and those that address violations of this assumption. We further discuss the intrinsic relationships among these methods and the assumption verification. Finally, we summarize available benchmark datasets and open-source codes, and outline several directions for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"178 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056017","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}
In recent years, the widespread informatization and rapid data explosion have increased the demand for high-performance heterogeneous systems that integrate multiple computing cores such as CPUs, Graphics Processing Units (GPUs), Application Specific Integrated Circuits (ASICs), and Field Programmable Gate Arrays (FPGAs). The combination of CPU and GPU is particularly popular due to its versatility. However, these heterogeneous systems face significant security and privacy risks. Advances in privacy-preserving techniques, especially hardware-based Trusted Execution Environments (TEEs), offer effective protection for GPU applications. Nonetheless, the potential security risks involved in extending TEEs to GPUs in heterogeneous systems remain uncertain and need further investigation. To investigate these risks in depth, we study the existing popular GPU TEE designs and summarize and compare their key implications. Additionally, we review existing powerful attacks on GPUs and traditional TEEs deployed on CPUs, along with the efforts to mitigate these threats. We identify potential attack surfaces introduced by GPU TEEs and provide insights into key considerations for designing secure GPU TEEs. This survey is timely as new TEEs for heterogeneous systems, particularly GPUs, are being developed, highlighting the need to understand potential security threats and build both efficient and secure systems.
{"title":"Confidential Computing on Heterogeneous CPU-GPU Systems: Survey and Future Directions","authors":"Qifan Wang, David Oswald","doi":"10.1145/3793532","DOIUrl":"https://doi.org/10.1145/3793532","url":null,"abstract":"In recent years, the widespread informatization and rapid data explosion have increased the demand for high-performance heterogeneous systems that integrate multiple computing cores such as CPUs, Graphics Processing Units (GPUs), Application Specific Integrated Circuits (ASICs), and Field Programmable Gate Arrays (FPGAs). The combination of CPU and GPU is particularly popular due to its versatility. However, these heterogeneous systems face significant security and privacy risks. Advances in privacy-preserving techniques, especially hardware-based Trusted Execution Environments (TEEs), offer effective protection for GPU applications. Nonetheless, the potential security risks involved in extending TEEs to GPUs in heterogeneous systems remain uncertain and need further investigation. To investigate these risks in depth, we study the existing popular GPU TEE designs and summarize and compare their key implications. Additionally, we review existing powerful attacks on GPUs and traditional TEEs deployed on CPUs, along with the efforts to mitigate these threats. We identify potential attack surfaces introduced by GPU TEEs and provide insights into key considerations for designing secure GPU TEEs. This survey is timely as new TEEs for heterogeneous systems, particularly GPUs, are being developed, highlighting the need to understand potential security threats and build both efficient and secure systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048426","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}
Sumair Aziz, Girija Chetty, Roland Goecke, Raul Fernandez-Rojas
Healthcare Embedded Systems (HES) use biosensors to capture physiological data, analyse it with advanced algorithms, and provide timely alerts during emergencies. These systems enhance healthcare delivery by supporting diagnosis, early symptom detection, and disease prediction. Despite extensive research on data analysis techniques in healthcare, selecting real-time methods for specific embedded hardware remains challenging. This review aims to summarise and synthesise existing literature to: (a) identify the healthcare challenges addressed by HES and the types of biosignals employed, (b) explore the embedded platforms utilised for implementing HES, and (c) examine the data analysis techniques used for real-time HES applications. A systematic search across three electronic databases (2015-2024), identified 50 relevant studies. These studies span various application domains, biosensing modalities, feature extraction methods, and machine learning and deep learning techniques. Raspberry Pi single-board computers emerged as the most popular embedded platform for implementing AI-based HES. Deep learning, especially convolutional neural networks, dominated, with cardiac health as the primary focus. While the reviewed studies demonstrate promising results, they are often constrained by specific experimental contexts. This review offers a comprehensive overview of real-time data analysis in HES and highlights key opportunities for future research to advance the field.
{"title":"A Systematic Literature Review of Healthcare Embedded Systems Using AI-based Biosignal Analysis","authors":"Sumair Aziz, Girija Chetty, Roland Goecke, Raul Fernandez-Rojas","doi":"10.1145/3793669","DOIUrl":"https://doi.org/10.1145/3793669","url":null,"abstract":"Healthcare Embedded Systems (HES) use biosensors to capture physiological data, analyse it with advanced algorithms, and provide timely alerts during emergencies. These systems enhance healthcare delivery by supporting diagnosis, early symptom detection, and disease prediction. Despite extensive research on data analysis techniques in healthcare, selecting real-time methods for specific embedded hardware remains challenging. This review aims to summarise and synthesise existing literature to: (a) identify the healthcare challenges addressed by HES and the types of biosignals employed, (b) explore the embedded platforms utilised for implementing HES, and (c) examine the data analysis techniques used for real-time HES applications. A systematic search across three electronic databases (2015-2024), identified 50 relevant studies. These studies span various application domains, biosensing modalities, feature extraction methods, and machine learning and deep learning techniques. Raspberry Pi single-board computers emerged as the most popular embedded platform for implementing AI-based HES. Deep learning, especially convolutional neural networks, dominated, with cardiac health as the primary focus. While the reviewed studies demonstrate promising results, they are often constrained by specific experimental contexts. This review offers a comprehensive overview of real-time data analysis in HES and highlights key opportunities for future research to advance the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048424","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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