EuroVA2025

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https://diglib.eg.org/handle/10.2312/3607188

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Browsing EuroVA2025 by Subject "Human centered computing → Visual analytics"

Now showing 1 - 2 of 2
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    Guided Visual Analysis of Time Series Data with Spiral Views and View Quality Measures
    (The Eurographics Association, 2025) Stoppacher, Stefanie; Rakuschek, Julian; Schreck, Tobias; Schulz, Hans-Jörg; Villanova, Anna
    Seasonal variations in energy consumption and temperature, like many other time series, exhibit periodically repeating patterns. Identifying and analyzing these cyclic patterns is crucial for understanding underlying trends and predicting future behavior. Spiral visualizations are commonly used to highlight periodicity, as they intuitively arrange seasonal data in spirals. We introduce encompassing user-guided enhancements to spiral visualizations, supporting the search and analysis of patterns in cyclic time series. A key element is a parameter space visualization by an interactive heat map, which highlights important quality measures, such as similarity and monotonicity, across different segments of the spiral. This approach helps users efficiently locate areas of interest that meet specific criteria, thereby streamlining the discovery of significant patterns. To further support analysis, the system offers a linked stacked area or line chart representation of selected segments, providing a clearer understanding of the quality measures. The effectiveness of the quality measures is demonstrated by use cases on several datasets.
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    Integrating Layer-Wise Relevance Propagation with Stable Diffusion for Enhanced Interpretability
    (The Eurographics Association, 2025) Auman, Christian; Bhati, Deepshikha; Arquilla, Kyle; Neha, Fnu; Guercio, Angela; Schulz, Hans-Jörg; Villanova, Anna
    Diffusion-based generative models, such as Stable Diffusion and DALL-E, have revolutionized artificial intelligence by enabling high-quality image generation from textual descriptions. Despite their success, these models raise ethical concerns, such as style appropriation and misuse, closely tied to the interpretability and transparency of the underlying mechanisms. This paper introduces a framework integrating Layer-wise Relevance Propagation (LRP) into the Stable Diffusion model to enhance interpretability. LRP assigns relevance scores to specific elements of textual prompts, allowing users to understand and visualize how input text influences image generation. We also present an interactive web-based visualization tool that supports intuitive exploration of diffusion processes. By improving interpretability, this approach fosters responsible use of generative AI technologies. A user study involving 35 participants demonstrates the tool's accessibility and effectiveness.