"Piecing Data Connections Together Like a Puzzle": Effects of Increasing Task Complexity on the Effectiveness of Data Storytelling Enhanced Visualisations
研究通过实验探讨了数据故事讲述在不同认知复杂度任务中的有效性，发现其对低阶任务有效，但不一定帮助高阶任务的正确完成，尽管提高了效率。
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Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery
本文针对火星滑坡分割问题，提出了一种结合上下文渐进层扩展特征提取和基于Transformer上下文推理的U形网络TransCPLES，以提高分割精度和计算效率。
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From Reflection to Repair: A Scoping Review of Dataset Documentation Tools
Existing dataset documentation tools struggle to achieve real-world adoption due to ambiguous value propositions, misalignment with practical contexts, insufficient attention to human labor costs, and a lack of systemic integration. This study addresses these challenges through a mixed-methods systematic scoping review of 59 relevant publications, combining qualitative coding with quantitative synthesis to uncover the underlying motivations driving tool design and their relationship to institutional norms. The analysis identifies four key patterns that hinder adoption and advances a responsible AI design perspective that shifts emphasis from individual accountability to institutional solutions. The work advocates embedding sustainable documentation practices within organizational workflows and cultures, offering the HCI community actionable pathways toward institutionalizing responsible data stewardship.
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An Analysis of HPC and Edge Architectures in the Cloud
This study addresses the practical disparities and co-evolution between high-performance computing (HPC) and edge computing architectures within the cloud continuum. It presents the first large-scale empirical analysis based on 396 real-world, production-grade AWS architectures. Methodologically, we propose a multidimensional, data-driven framework encompassing service topology identification, storage type classification, architectural complexity quantification, and ML service integration statistics. Results reveal systematic differences—and complementary patterns—between HPC and edge architectures across four dimensions: core service composition (e.g., EC2 versus Greengrass/Lambda), storage design paradigms (parallel file systems versus distributed lightweight caches), complexity distributions, and ML embedding strategies. This work delivers the first industry-scale architectural benchmark for the cloud continuum, providing empirically grounded insights and methodological foundations for cross-domain architecture design, resource optimization, and cloud-native convergence of HPC and edge computing.
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"Piecing Data Connections Together Like a Puzzle": Effects of Increasing Task Complexity on the Effectiveness of Data Storytelling Enhanced Visualisations
研究通过实验探讨了数据故事讲述在不同认知复杂度任务中的有效性，发现其对低阶任务有效，但不一定帮助高阶任务的正确完成，尽管提高了效率。
0 citationsRead paper
Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery
本文针对火星滑坡分割问题，提出了一种结合上下文渐进层扩展特征提取和基于Transformer上下文推理的U形网络TransCPLES，以提高分割精度和计算效率。
0 citationsRead paper
From Reflection to Repair: A Scoping Review of Dataset Documentation Tools
Existing dataset documentation tools struggle to achieve real-world adoption due to ambiguous value propositions, misalignment with practical contexts, insufficient attention to human labor costs, and a lack of systemic integration. This study addresses these challenges through a mixed-methods systematic scoping review of 59 relevant publications, combining qualitative coding with quantitative synthesis to uncover the underlying motivations driving tool design and their relationship to institutional norms. The analysis identifies four key patterns that hinder adoption and advances a responsible AI design perspective that shifts emphasis from individual accountability to institutional solutions. The work advocates embedding sustainable documentation practices within organizational workflows and cultures, offering the HCI community actionable pathways toward institutionalizing responsible data stewardship.
0 citationsRead paper
An Analysis of HPC and Edge Architectures in the Cloud
This study addresses the practical disparities and co-evolution between high-performance computing (HPC) and edge computing architectures within the cloud continuum. It presents the first large-scale empirical analysis based on 396 real-world, production-grade AWS architectures. Methodologically, we propose a multidimensional, data-driven framework encompassing service topology identification, storage type classification, architectural complexity quantification, and ML service integration statistics. Results reveal systematic differences—and complementary patterns—between HPC and edge architectures across four dimensions: core service composition (e.g., EC2 versus Greengrass/Lambda), storage design paradigms (parallel file systems versus distributed lightweight caches), complexity distributions, and ML embedding strategies. This work delivers the first industry-scale architectural benchmark for the cloud continuum, providing empirically grounded insights and methodological foundations for cross-domain architecture design, resource optimization, and cloud-native convergence of HPC and edge computing.
0 citationsRead paper