Computer Graphics & Visual Computing (CGVC) 2024

Permanent URI for this collection

City, University of London, UK | 12 - 13 September 2024
Visualisation Design and Evaluation Methods
Multi-level Visualization for Exploration of Structures in Missing Data
Sarah Alsufyani, Matthew Forshaw, Silvia Del Din, Alison Yarnall, Lynn Rochester, and Sara Johansson Fernstad
Interplay of Visual Analytics and Topic Modeling in Gameplay Analysis
Laleh Moussavi, Gennady Andrienko, Natalia Andrienko, and Aidan Slingsby
Does Empirical Evidence from Healthy Aging Studies Predict a Practical Difference Between Visualizations for Different Age Groups?
Shan Shao, Yiran Li, Andrew Meso, and Nicolas S. Holliman
Map Augmentation and Sketching for Cycling Experience Elicitation
Mirela Reljan-Delaney, Jo D. Wood, and Alex S. Taylor
Creating Data Art: Authentic Learning and Visualisation Exhibition
Jonathan C. Roberts
3D Rendering and Virtual Reality (VR)
Assessing the Reliability of Integrated Gradients-Based Saliency Maps for 3D Point Cloud Semantic Segmentation Models
Jorge F. Ciprián-Sánchez, Josafat-Mattias Burmeister, Tim Cech, Rico Richter, and Jürgen Döllner
Real-time Data-Oriented Virtual Forestry Simulation for Games
Benjamin Williams, Tom Oliver, Davin Ward, and Chris Headleand
Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing
Andreas Polychronakis, George Alex Koulieris, and Katerina Mania
DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction
Adeyemi Ademola, David Sinclair, Babis Koniaris, Samantha Hannah, and Kenny Mitchell
Computer Graphics
Semantic UV Mapping to Improve Texture Inpainting for 3D Scanned Indoor Scenes
Jelle Vermandere, Maarten Bassier, Suzanna Cuypers, and Maarten Vergauwen
EBBVH: A Novel Method for Constructing Bounding Volume Hierarchies
Matthew Houghton and Kristian Spoerer
View-Consistent Virtual Try-on of Glasses using a Hybrid NeRF-Mesh Rendering Approach
Arne Rak, Tristan Wirth, Thomas Lindemeier, Volker Knauthe, and Arjan Kuijper
Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings
Daniel Atzberger, Adrian Jobst, Willy Scheibel, and Jürgen Döllner
A Stereo-Integrated Novel View Synthesis Pipeline for the Enhancement of Road Surface Reconstruction Dataset
Mochuan Zhan, Terence Morley, and Martin Turner
Serial Gaussian Blue Noise Stippling
Abdalla G. M. Ahmed
Information Visualisation
Visualizing Complex Data Decisions: Design Study for Ethical Factors in AI Clinical Decision Support Systems
Svitlana Surodina, Daria Volkova, Alfie Abdul-Rahman, and Rita Borgo
Authoring Visualisation of Routinely Collected Data Using LLMs
Amir Hosseini, Jo Wood, and Mai Elshehaly
Visual Storytelling: A Methodological Approach to Designing and Implementing a Visualization Poster
Rhiannon S. Owen and Jonathan C. Roberts
Use of Notebooks and Role of Map features in Mapping Minority Women Bicycle Riding
Mirela Reljan-Delaney, Jo D. Wood, and Alex S. Taylor
Multi-fidelity Multi-disciplinary Optimisation of Propeller Design by Visual Analytics
Shubham Shubham, Andrea Spinelli, and Timoleon Kipouros
Geographic Visualisation
Exploring Data Analysts' Uncertainty Reasoning Strategies for Effective Uncertainty Visualization Design
Christoph Kinkeldey, Mirela Reljan-Delaney, Georgia Panagiotidou, and Jason Dykes
Comparing Distance Metrics in Space-time Clustering to Provide Visual Summaries of Traffic Congestion
Peter Baudains and Nicolas S. Holliman
Reflections on the Evolution of the BookTracker Visualization Platform
Yiwen Xing, Cristina Dondi, Rita Borgo, and Alfie Abdul-Rahman
Min-Max Modifiable Nested Octrees M3NO: Indexing Point Clouds with Arbitrary Attributes in Real Time
Paul Hermann, Michel Krämer, Tobias Dorra, and Arjan Kuijper
Machine Learning and LLM-enabled Visual Analytics
Visual Interpretation of Tagging: Advancing Understanding in Task-Oriented Dialogue Systems
Yazhuo Zhou, Yiwen Xing, Alfie Abdul-Rahman, and Rita Borgo
LLM-Assisted Visual Analytics: Opportunities and Challenges
Maeve Hutchinson, Radu Jianu, Aidan Slingsby, and Pranava Madhyastha
Investigation of the Organisation of the Extracellular Matrix Using Fibre Tracing
Youssef Arafat, Cristina Cuesta-Apausa, Esther Castellano, and Constantino Carlos Reyes-Aldasoro
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
Daniel Sikar, Artur d'Avila Garcez, Robin Bloomfield, Tillman Weyde, Kaleem Peeroo, Naman Singh, Maeve Hutchinson, Dany Laksono, and Mirela Reljan-Delaney

BibTeX (Computer Graphics & Visual Computing (CGVC) 2024)
@inproceedings{
10.2312:cgvc.20242023,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Slingsby, Aidan
and
Hunter, David
}, title = {{
Computer Graphics and Visual Computing (CGVC): Frontmatter}},
author = {
Slingsby, Aidan
and
Hunter, David
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20242023}
}
@inproceedings{
10.2312:cgvc.20241212,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Multi-level Visualization for Exploration of Structures in Missing Data}},
author = {
Alsufyani, Sarah
and
Forshaw, Matthew
and
Din, Silvia Del
and
Yarnall, Alison
and
Rochester, Lynn
and
Fernstad, Sara Johansson
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241212}
}
@inproceedings{
10.2312:cgvc.20241213,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Interplay of Visual Analytics and Topic Modeling in Gameplay Analysis}},
author = {
Moussavi, Laleh
and
Andrienko, Gennady
and
Andrienko, Natalia
and
Slingsby, Aidan
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241213}
}
@inproceedings{
10.2312:cgvc.20241214,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Does Empirical Evidence from Healthy Aging Studies Predict a Practical Difference Between Visualizations for Different Age Groups?}},
author = {
Shao, Shan
and
Li, Yiran
and
Meso, Andrew I.
and
Holliman, Nicolas S.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241214}
}
@inproceedings{
10.2312:cgvc.20241215,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Map Augmentation and Sketching for Cycling Experience Elicitation}},
author = {
Reljan-Delaney, Mirela
and
Wood, Jo D.
and
Taylor, Alex S.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241215}
}
@inproceedings{
10.2312:cgvc.20241216,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Creating Data Art: Authentic Learning and Visualisation Exhibition}},
author = {
Roberts, Jonathan C.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241216}
}
@inproceedings{
10.2312:cgvc.20241217,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Assessing the Reliability of Integrated Gradients-Based Saliency Maps for 3D Point Cloud Semantic Segmentation Models}},
author = {
Ciprián-Sánchez, Jorge F.
and
Burmeister, Josafat-Mattias
and
Cech, Tim
and
Richter, Rico
and
Döllner, Jürgen
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241217}
}
@inproceedings{
10.2312:cgvc.20241218,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Real-time Data-Oriented Virtual Forestry Simulation for Games}},
author = {
Williams, Benjamin
and
Oliver, Tom
and
Ward, Davin
and
Headleand, Chris
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241218}
}
@inproceedings{
10.2312:cgvc.20241219,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing}},
author = {
Polychronakis, Andreas
and
Koulieris, George Alex
and
Mania, Katerina
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241219}
}
@inproceedings{
10.2312:cgvc.20241220,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction}},
author = {
Ademola, Adeyemi
and
Sinclair, David
and
Koniaris, Babis
and
Hannah, Samantha
and
Mitchell, Kenny
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241220}
}
@inproceedings{
10.2312:cgvc.20241221,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Semantic UV Mapping to Improve Texture Inpainting for 3D Scanned Indoor Scenes}},
author = {
Vermandere, Jelle
and
Bassier, Maarten
and
Cuypers, Suzanna
and
Vergauwen, Maarten
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241221}
}
@inproceedings{
10.2312:cgvc.20241222,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
EBBVH: A Novel Method for Constructing Bounding Volume Hierarchies}},
author = {
Houghton, Matthew
and
Spoerer, Kristian
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241222}
}
@inproceedings{
10.2312:cgvc.20241223,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
View-Consistent Virtual Try-on of Glasses using a Hybrid NeRF-Mesh Rendering Approach}},
author = {
Rak, Arne
and
Wirth, Tristan
and
Lindemeier, Thomas
and
Knauthe, Volker
and
Kuijper, Arjan
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241223}
}
@inproceedings{
10.2312:cgvc.20241224,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings}},
author = {
Atzberger, Daniel
and
Jobst, Adrian
and
Scheibel, Willy
and
Döllner, Jürgen
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241224}
}
@inproceedings{
10.2312:cgvc.20241225,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
A Stereo-Integrated Novel View Synthesis Pipeline for the Enhancement of Road Surface Reconstruction Dataset}},
author = {
Zhan, Mochuan
and
Morley, Terence
and
Turner, Martin
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241225}
}
@inproceedings{
10.2312:cgvc.20241226,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Serial Gaussian Blue Noise Stippling}},
author = {
Ahmed, Abdalla G. M.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241226}
}
@inproceedings{
10.2312:cgvc.20241227,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Visualizing Complex Data Decisions: Design Study for Ethical Factors in AI Clinical Decision Support Systems}},
author = {
Surodina, Svitlana
and
Volkova, Daria
and
Abdul-Rahman, Alfie
and
Borgo, Rita
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241227}
}
@inproceedings{
10.2312:cgvc.20241228,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Authoring Visualisation of Routinely Collected Data Using LLMs}},
author = {
Hosseini, Amir
and
Wood, Jo
and
Elshehaly, Mai
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241228}
}
@inproceedings{
10.2312:cgvc.20241229,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Visual Storytelling: A Methodological Approach to Designing and Implementing a Visualization Poster}},
author = {
Owen, Rhiannon S.
and
Roberts, Jonathan C.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241229}
}
@inproceedings{
10.2312:cgvc.20241230,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Use of Notebooks and Role of Map features in Mapping Minority Women Bicycle Riding}},
author = {
Reljan-Delaney, Mirela
and
Wood, Jo D.
and
Taylor, Alex S.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241230}
}
@inproceedings{
10.2312:cgvc.20241231,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Multi-fidelity Multi-disciplinary Optimisation of Propeller Design by Visual Analytics}},
author = {
Shubham, Shubham
and
Spinelli, Andrea
and
Kipouros, Timoleon
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241231}
}
@inproceedings{
10.2312:cgvc.20241232,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Exploring Data Analysts' Uncertainty Reasoning Strategies for Effective Uncertainty Visualization Design}},
author = {
Kinkeldey, Christoph
and
Reljan-Delaney, Mirela
and
Panagiotidou, Georgia
and
Dykes, Jason
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241232}
}
@inproceedings{
10.2312:cgvc.20241233,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Comparing Distance Metrics in Space-time Clustering to Provide Visual Summaries of Traffic Congestion}},
author = {
Baudains, Peter
and
Holliman, Nicolas S.
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241233}
}
@inproceedings{
10.2312:cgvc.20241234,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Reflections on the Evolution of the BookTracker Visualization Platform}},
author = {
Xing, Yiwen
and
Dondi, Cristina
and
Borgo, Rita
and
Abdul-Rahman, Alfie
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241234}
}
@inproceedings{
10.2312:cgvc.20241235,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Min-Max Modifiable Nested Octrees M3NO: Indexing Point Clouds with Arbitrary Attributes in Real Time}},
author = {
Hermann, Paul
and
Krämer, Michel
and
Dorra, Tobias
and
Kuijper, Arjan
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241235}
}
@inproceedings{
10.2312:cgvc.20241236,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Visual Interpretation of Tagging: Advancing Understanding in Task-Oriented Dialogue Systems}},
author = {
Zhou, Yazhuo
and
Xing, Yiwen
and
Abdul-Rahman, Alfie
and
Borgo, Rita
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241236}
}
@inproceedings{
10.2312:cgvc.20241237,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
LLM-Assisted Visual Analytics: Opportunities and Challenges}},
author = {
Hutchinson, Maeve
and
Jianu, Radu
and
Slingsby, Aidan
and
Madhyastha, Pranava
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241237}
}
@inproceedings{
10.2312:cgvc.20241238,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
Investigation of the Organisation of the Extracellular Matrix Using Fibre Tracing}},
author = {
Arafat, Youssef
and
Cuesta-Apausa, Cristina
and
Castellano, Esther
and
Reyes-Aldasoro, Constantino Carlos
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241238}
}
@inproceedings{
10.2312:cgvc.20241239,
booktitle = {
Computer Graphics and Visual Computing (CGVC)},
editor = {
Hunter, David
and
Slingsby, Aidan
}, title = {{
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others}},
author = {
Sikar, Daniel
and
Garcez, Artur d'Avila
and
Bloomfield, Robin
and
Weyde, Tillman
and
Peeroo, Kaleem
and
Singh, Naman
and
Hutchinson, Maeve
and
Laksono, Dany
and
Reljan-Delaney, Mirela
}, year = {
2024},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {
10.2312/cgvc.20241239}
}

Browse

Recent Submissions

Now showing 1 - 29 of 29
  • Item
    Computer Graphics and Visual Computing (CGVC): Frontmatter
    (The Eurographics Association, 2024) Slingsby, Aidan; Hunter, David; Slingsby, Aidan; Hunter, David
  • Item
    Multi-level Visualization for Exploration of Structures in Missing Data
    (The Eurographics Association, 2024) Alsufyani, Sarah; Forshaw, Matthew; Din, Silvia Del; Yarnall, Alison; Rochester, Lynn; Fernstad, Sara Johansson; Hunter, David; Slingsby, Aidan
    Missing data refers to the absence of a value in the dataset where it was expected to be present. This absence is common across various fields. It can be caused by a range of factors in the data collection process, and may severely impact analysis through unreliable or biased results. Missing data visualization provides an effective approach to exploring the missing data, recognizing the missingness patterns and structures, and determining optimal solutions through interactive visual interfaces. This paper presents a visualization prototype that incorporates two novel techniques, the MissVisG glyph and the MissVis plot, to support the exploration of missing values in data. The visualization provides an overview of missing values, and helps identify patterns in the data to guide users in selecting appropriate methods for dealing with the missingness. A multi-step evaluation process is utilized to assess and ensure the usability and effectiveness of the visualization.
  • Item
    Interplay of Visual Analytics and Topic Modeling in Gameplay Analysis
    (The Eurographics Association, 2024) Moussavi, Laleh; Andrienko, Gennady; Andrienko, Natalia; Slingsby, Aidan; Hunter, David; Slingsby, Aidan
    Spatio-temporal event sequences consist of activities or occurrences involving various interconnected elements in space and time. Exploring these sequences with topic modeling is a relatively new and evolving research area. We use topic modeling to analyze football games, as an example of complex and under-explored spatio-temporal event data. A key challenge in topic modeling is selecting the most suitable number of topics for the downstream application. Selecting too few topics oversimplifies the data, merging distinct patterns, whereas selecting too many can fragment coherent themes into overlapping categories. We propose a visual analytics technique that uses dimensionality reduction on topics derived from multiple topic modeling runs, each with a different number of topics. Our technique organizes all the topics in a hierarchical layout based on their spatial similarity, making it easier to make an informed decision about selecting the most expressive set of topics that represent distinctive spatial patterns. We apply our visual analytics technique to a football dataset, illustrating how it can be used to select an appropriate set of topics for this data. We then use these topics to represent game episodes, which help us summarize game dynamics and uncover insights into the games.
  • Item
    Does Empirical Evidence from Healthy Aging Studies Predict a Practical Difference Between Visualizations for Different Age Groups?
    (The Eurographics Association, 2024) Shao, Shan; Li, Yiran; Meso, Andrew I.; Holliman, Nicolas S.; Hunter, David; Slingsby, Aidan
    When communicating critical information to decision-makers, one of the major challenges in visualization is whether the communication is affected by different perceptual or cognitive abilities, one major influencing factor is age. We review both visualization and psychophysics literature to understand where quantitative evidence exists on age differences in visual perception. Using contrast sensitivity data from the literature we show how the differences between visualizations for different age groups can be predicted using a new model of visible frequency range with age. The model assumed that at threshold values some visual data will not be visible to older people (spatial frequency > 2 and contrast <=0.01). We apply this result to a practical visualization and show an example that at higher levels of contrast, the visual signal should be perceivable by all viewers over 20. Universally usable visualization should use a contrast of 0.02 or higher and be designed to avoid spatial frequencies greater than eight cycles per degree to accommodate all ages. There remains much research to do on to translate psychophysics results to practical quantitative guidelines for visualization producers.
  • Item
    Map Augmentation and Sketching for Cycling Experience Elicitation
    (The Eurographics Association, 2024) Reljan-Delaney, Mirela; Wood, Jo D.; Taylor, Alex S.; Hunter, David; Slingsby, Aidan
    This work examines the use of maps for knowledge elicitation in the sphere of urban cycling. The study involved running 14 distinct workshops, each serving as a unique data collection session for a particular individual. In each workshop, the participant was provided with 12 different renditions of the geographical areas as well as drawing materials. The geographical area renditions contained regions specified by the participant as cycling locations during the preparatory correspondence. The outputs were analysed for patterns in map augmentations and thematic content in the sketches. We have found that participants engaged deeply with the map augmentation process expressing their preferences and giving new insights. Themes such as connectivity, scenic beauty, and temporality emerged prominently from the analysed data, shedding light on the subjective experiences and preferences of urban cyclists.
  • Item
    Creating Data Art: Authentic Learning and Visualisation Exhibition
    (The Eurographics Association, 2024) Roberts, Jonathan C.; Hunter, David; Slingsby, Aidan
    We present an authentic learning task designed for computing students, centred on the creation of data-art visualisations from chosen datasets for a public exhibition. This exhibition was showcased in the cinema foyer for two weeks in June, providing a real-world platform for students to display their work. Over the course of two years, we implemented this active learning task with two different cohorts of students. In this paper, we share our experiences and insights from these activities, highlighting the impact on student engagement and learning outcomes. We also provide a detailed description of the seven individual tasks that learners must perform: topic and data selection and analysis, research and art inspiration, design conceptualisation, proposed solution, visualisation creation, exhibition curation, and reflection. By integrating these tasks, students not only develop technical skills but also gain practical experience in presenting their work to a public audience, bridging the gap between academic learning and professional practice.
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    Assessing the Reliability of Integrated Gradients-Based Saliency Maps for 3D Point Cloud Semantic Segmentation Models
    (The Eurographics Association, 2024) Ciprián-Sánchez, Jorge F.; Burmeister, Josafat-Mattias; Cech, Tim; Richter, Rico; Döllner, Jürgen; Hunter, David; Slingsby, Aidan
    Deep learning models achieve high accuracy in the semantic segmentation of 3D point clouds; however, it is challenging to discern which patterns a model has learned and how it derives its output from the input. Recently, the Integrated Gradients method has been adopted to explain semantic segmentation models for 3D point clouds. This method can be used to generate saliency maps that visualize the contribution of input points to a particular model output. However, there is a lack of quantitative evaluation of the reliability of the generated saliency maps and the influence of the baseline selection (a central component of Integrated Gradients) on the method's results. In this paper, we quantitatively evaluate the reliability of saliency maps generated by the Integrated Gradients method for a 3D point cloud semantic segmentation model through well-known sanity checks from the image domain that we adapt to 3D point cloud segmentation. We perform these sanity checks for three different baselines to further evaluate the stability of the generated saliency maps concerning the baseline choice. Our results indicate that the Integrated Gradients method is sensitive to both the parameters of the model and training labels, unstable concerning the choice of baseline, and that, although it can identify points with high contributions to the model output, it fails to identify correctly if such contributions are positive or negative. Finally, we propose an averaging approach to aggregate the results of points that receive multiple scores from Integrated Gradients during the segmentation process and show that it produces saliency maps that better reflect high-contribution input points than previous approaches.
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    Real-time Data-Oriented Virtual Forestry Simulation for Games
    (The Eurographics Association, 2024) Williams, Benjamin; Oliver, Tom; Ward, Davin; Headleand, Chris; Hunter, David; Slingsby, Aidan
    The current frontier of virtual forestry algorithms remain largely unoptimised and ultimately unsuitable for real-time applications. Providing an optimisation strategy for the real-time simulation of virtual forestry would find particular utility in some areas, for example, in video games. With this motivation in mind, this paper presents a novel optimisation strategy for asymmetric plant competition models. In our approach, we utilise a data-oriented methodology with spatial hashing to enable the real-time simulation of virtual forests. Our approach also provides a significant improvement in performance when contrasted with existing serial implementations. Furthermore, we find that the introduction of our optimisation strategy can be used to simulate hundreds of thousands of virtual trees, in real-time, on a typical desktop machine.
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    Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing
    (The Eurographics Association, 2024) Polychronakis, Andreas; Koulieris, George Alex; Mania, Katerina; Hunter, David; Slingsby, Aidan
    This paper presents a rapid rendering pipeline for sphere tracing Signed Distance Functions (SDFs), showcasing a notable boost in performance compared to the current state-of-the-art. Existing methods endeavor to reduce the ray step count by adjusting step size using heuristics or by rendering multiple intermediate lower-resolution buffers to pre-calculate non-salient pixels at reduced quality. However, the accelerated performance with low-resolution buffers often introduces artifacts compared to fully sphere-traced scenes, especially for smaller features, which might go unnoticed altogether. Our approach significantly reduces steps compared to prior work while minimising artifacts. We accomplish this based on two key observations and by employing a single low-resolution buffer: Firstly, we perform SDF scaling in the low-resolution buffer, effectively enlarging the footprint of the implicit surfaces when rendered in low resolution, ensuring visibility of all SDFs. Secondly, leveraging the low-resolution buffer rendering, we detect when a ray converges to high-cost surface edges and can terminate sphere tracing earlier than usual, further reducing step count. Our method achieves a substantial performance improvement (exceeding 3× in certain scenes) compared to previous approaches, while minimizing artifacts, as demonstrated in our visual fidelity evaluation.
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    DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction
    (The Eurographics Association, 2024) Ademola, Adeyemi; Sinclair, David; Koniaris, Babis; Hannah, Samantha; Mitchell, Kenny; Hunter, David; Slingsby, Aidan
    Enabling online virtual reality (VR) users to dance and move in a way that mirrors the real-world necessitates improvements in the accuracy of predicting human motion sequences paving way for an immersive and connected experience. However, the drawbacks of latency in networked motion tracking present a critical detriment in creating a sense of complete engagement, requiring prediction for online synchronization of remote motions. To address this challenge, we propose a novel approach that leverages a synthetically generated dataset based on supervised foot anchor placement timings of rhythmic motions to ensure periodicity resulting in reduced prediction error. Specifically, our model compromises a discrete cosine transform (DCT) to encode motion, refine high frequencies and smooth motion sequences and prevent jittery motions. We introduce a feed-forward attention mechanism to learn based on dual-window pairs of 3D key points pose histories to predict future motions. Quantitative and qualitative experiments validating on the Human3.6m dataset result in observed improvements in the MPJPE evaluation metrics protocol compared with prior state-of-the-art.
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    Semantic UV Mapping to Improve Texture Inpainting for 3D Scanned Indoor Scenes
    (The Eurographics Association, 2024) Vermandere, Jelle; Bassier, Maarten; Cuypers, Suzanna; Vergauwen, Maarten; Hunter, David; Slingsby, Aidan
    This work aims to improve texture inpainting following clutter removal in scanned indoor meshes. This is achieved through a new UV mapping pre-processing step that leverages semantic information from indoor scenes to more accurately align the UV islands with the 3D representations of distinct structural elements, such as walls and floors. Semantic UV Mapping enhances traditional UV unwrapping algorithms by incorporating not only geometric features but also visual features derived from the existing texture. This segmentation improves UV mapping and simultaneously simplifies the 3D geometric reconstruction of the scene after the removal of loose objects. Each segmented element can then be reconstructed separately, using the boundary conditions of the adjacent elements. Since this is performed as a pre-processing step, other specialized methods for geometric and texture reconstruction can be employed in the future to further enhance the results.
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    EBBVH: A Novel Method for Constructing Bounding Volume Hierarchies
    (The Eurographics Association, 2024) Houghton, Matthew; Spoerer, Kristian; Hunter, David; Slingsby, Aidan
    We present an attempt to improve upon the construction of the most prevalent acceleration structure that is used in ray traced rendering techniques, the Bounding Volume Hierarchy. Our improvement is a novel technique for BVH construction called 'Edge-Based Bounding Volume Hierarchy'. This algorithm uses a hybrid top-down & bottom-up approach to improve performance for raytracing in large scenes, by up to 10x in some scenes.
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    View-Consistent Virtual Try-on of Glasses using a Hybrid NeRF-Mesh Rendering Approach
    (The Eurographics Association, 2024) Rak, Arne; Wirth, Tristan; Lindemeier, Thomas; Knauthe, Volker; Kuijper, Arjan; Hunter, David; Slingsby, Aidan
    In recent times, an increasing fraction of global purchases is conducted via the world wide web. For individual accessories, such as glasses, a purchase commonly involves trying on multiple products to fit individual aesthetic preferences. The experience of the try-on process differs greatly between online and offline shopping. While there are real-time methods that facilitate virtual try-on of glasses, they usually project them onto a 2D image. This leads to inconsistent positioning of the glasses model between different views, negatively influencing the shopping experience. We propose a strategy, that enables the virtual try-on of glasses using a Neural Radiance Field as head avatar and a meshed glasses model, leading to consistent positioning of the spectacle frame through multiple views while maintaining real world like visual quality. We contribute an approach for placing and aligning the glasses in relation to the human head in the given NeRF context. Furthermore, we propose a framework for realtime hybrid rendering of meshes and Neural Radiance Fields in the same scene. The proposed method requires training times around one minute and produces a freely explorable 3D model that achieves interactive framerates on end-consumer hardware.
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    Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings
    (The Eurographics Association, 2024) Atzberger, Daniel; Jobst, Adrian; Scheibel, Willy; Döllner, Jürgen; Hunter, David; Slingsby, Aidan
    Dimensionality reductions are a class of unsupervised learning algorithms that aim to find a lower-dimensional embedding for a high-dimensional dataset while preserving local and global structures. By representing a high-dimensional dataset as a twodimensional scatterplot, a user can explore structures within the dataset. However, dimensionality reductions inherit distortions that might result in false deductions. This work presents a visualization approach that combines a two-dimensional scatterplot derived from a dimensionality reduction with two pointwise filtering possibilities. Each point is associated with two pointwise metrics that quantify the correctness of its neighborhood and similarity to surrounding data points. By setting threshold for these two metrics, the user is supported in several scatterplot analytics tasks, e.g., class separation and outlier detection. We apply our visualization to a text corpus to detect interesting data points visually and discuss the findings.
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    A Stereo-Integrated Novel View Synthesis Pipeline for the Enhancement of Road Surface Reconstruction Dataset
    (The Eurographics Association, 2024) Zhan, Mochuan; Morley, Terence; Turner, Martin; Hunter, David; Slingsby, Aidan
    This proposal outlines a novel view synthesis pipeline designed for road reconstruction in autonomous driving scenarios that leverages virtual camera technology to synthesise images from unvisited camera poses, thereby enhancing and expanding current datasets. It consists of three main steps: data acquisition, data preprocessing and fusion, and then importantly interacting with new 3D view synthesis with geometric priors. The modular design allows each component to be independently optimised and upgraded, ensuring flexibility and adaptability to various datasets and task requirements. The proposed approach aims to improve the robustness, realism, and photometric consistency of novel view synthesis, effectively handling dynamic scenes and varying lighting conditions. Additionally, this research plans to open-source a low-cost stereo camera hardware solution with the included software implementation.
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    Serial Gaussian Blue Noise Stippling
    (The Eurographics Association, 2024) Ahmed, Abdalla G. M.; Hunter, David; Slingsby, Aidan
    We adapt the adaptive Gaussian Blue Noise (GBN) algorithm to iterate serially over the points, one by one, thus enabling its implementation on CPU. Towards that end, we propose an alternative kernel shaping model. Our implementation model is simpler and has a linear time complexity, replacing the quadratic complexity of the original model.
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    Visualizing Complex Data Decisions: Design Study for Ethical Factors in AI Clinical Decision Support Systems
    (The Eurographics Association, 2024) Surodina, Svitlana; Volkova, Daria; Abdul-Rahman, Alfie; Borgo, Rita; Hunter, David; Slingsby, Aidan
    Despite the proliferation of Artificial Intelligence (AI) technologies, their uptake in clinical settings has been lacking progress due to complexities of sociotechnical factors and intricacies of decision-making. Fairness and bias of predictive models, ethics and quality of training data, and corresponding compliance requirements become especially pressing while remaining fuzzy and implicit for various stakeholders who make the decisions. We present learnings and future directions from a design study with domain experts and propose a novel approach to encoding and collaborative reasoning on complex requirements for AI-Empowered Clinical Decision Support System (AI-CDSS) design based on Knowledge Graph (KG) representation. The insights will be useful to the community of visualization researchers who work on ethical AI-CDSS design and conduct design studies with clinical partners.
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    Authoring Visualisation of Routinely Collected Data Using LLMs
    (The Eurographics Association, 2024) Hosseini, Amir; Wood, Jo; Elshehaly, Mai; Hunter, David; Slingsby, Aidan
    The integration of routinely collected healthcare data into decision-making processes has the potential to revolutionise patient care and health outcomes. However, the complexity and heterogeneity of these datasets pose significant challenges for effective querying and analysis. Visualisation supports socio-technical processes where data analytics are augmented with human expertise to overcome data complexity. However, the authorship of effective visualisation is a challenging task, especially for users without a technical background, such as commissioners, clinicians and population health experts. This complexity calls for more efforts to develop natural language interfaces (NLIs) to democratise access to and understanding of routine data through visualisation. This short paper presents an innovative approach utilising Large Language Models (LLMs) to facilitate the querying and visualisation of routinely collected healthcare data. We present a preliminary framework for combining natural language queries with visualisation recommendation systems to retrieve and visualise relevant information from electronic health records (EHRs). We propose a human-in-the-loop approach for establishing accurate and efficient LLM-enabled information retrieval. Our preliminary findings suggest that LLMs can significantly streamline the visualisation authoring process, enabling stakeholders and healthcare professionals to access critical information rapidly and accurately. This work underscores the potential of LLM-driven solutions in advancing healthcare data utilisation and paves the way for future research in this promising intersection of artificial intelligence and medical informatics.
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    Visual Storytelling: A Methodological Approach to Designing and Implementing a Visualization Poster
    (The Eurographics Association, 2024) Owen, Rhiannon S.; Roberts, Jonathan C.; Hunter, David; Slingsby, Aidan
    We present a design study of developing a visualisation poster. Posters can be difficult to create, and the story on a poster is not always clear. Using a case-study approach we propose three important aspects: the poster should have a clear focus (especially a hero visualisation), envisioning its use helps to drive the important aspects, and third the essence (its fundamental concept and guiding idea) must be clear. We will use case studies that have focused on the use of the Five Design-Sheet method (FdS) as a way to sketch and plan a visualisation, before successfully implementing and creating the visual poster. The case studies serve as a practical illustration of the workflow, offering a means to explain the three key processes involved: (1) comprehending the data, (2) employing a design study with the FdS (Five Design-Sheet), (3) crafting, evaluating and refining the visualisation.
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    Use of Notebooks and Role of Map features in Mapping Minority Women Bicycle Riding
    (The Eurographics Association, 2024) Reljan-Delaney, Mirela; Wood, Jo D.; Taylor, Alex S.; Hunter, David; Slingsby, Aidan
    Visualization has greatly enhanced our understanding of cycling trends [fL18], enabled the depiction and analysis of largescale cycling data [BWB14, RMGZALD18], and facilitated the tracking and interpretation of personal behaviour through the dashboards of personal tracking devices [NKKW20]. Data and visualization can be either vast and generalized or intimate and personal. There are significant challenges associated with big data as certain subgroups are underrepresented in data collection, making their presence difficult to detect and more targeted and smaller data collection can complement and expose facets of the population that are not visible in big data. Ethnic minority women cyclists are one such group. Research into their attitudes and cycling habits is often outdated [Lim10] or originates from contexts where their ethnicity is the majority [GOF∗22]. This study aims to shed light on the experiences ofMuslim and BAME women cyclists, uncovering hidden realities and challenging dominant narratives. A small group of ethnic minority women participated in the research, keeping diaries of their cycling experiences and using GPS trackers. The collected data was presented back to them in the form of individual data notebooks, combining technology, visualization, and ultimately qualitative analysis. This empirical work provides a fresh perspective on how female cyclists interact with their environment and offers valuable understanding of the preferences and challenges faced by this growing and vibrant group. This paper builds upon the previously published work [RDWT23], shifting the focus away from the methodological execution of the study and instead emphasizing the participants' interactions with the maps and the unique insights gained.
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    Multi-fidelity Multi-disciplinary Optimisation of Propeller Design by Visual Analytics
    (The Eurographics Association, 2024) Shubham, Shubham; Spinelli, Andrea; Kipouros, Timoleon; Hunter, David; Slingsby, Aidan
    This paper introduces a comprehensive framework for multi-fidelity, multi-disciplinary optimization of propeller design using visual analytics. The proposed methodology integrates advanced data visualization techniques, surrogate modelling and optimisation methodologies to handle high-dimensional data across various disciplines, including aerodynamics, aeroacoustics, and structures. By leveraging multi-fidelity simulations, the framework balances accuracy with computational efficiency, enabling detailed exploration and optimization of propeller designs. Interactive visualization tools in the framework facilitate the identification of optimal design parameters and trade-offs, highlighting its potential to improve decision-making in engineering design processes in terms of confidence and knowledge creation.
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    Exploring Data Analysts' Uncertainty Reasoning Strategies for Effective Uncertainty Visualization Design
    (The Eurographics Association, 2024) Kinkeldey, Christoph; Reljan-Delaney, Mirela; Panagiotidou, Georgia; Dykes, Jason; Hunter, David; Slingsby, Aidan
    Despite its proven positive effects, visual data analysis rarely includes information about data uncertainty. Building on past research, we explore the hypothesis that effective uncertainty visualizations must support reasoning strategies that enable data analysts to utilize uncertainty information ('uncertainty reasoning strategies', UnReSt). Through this work, we seek to gain insights into the reasoning strategies employed by domain experts for incorporating uncertainty into their visual analysis. Additionally, we aim to explore effective ways of designing uncertainty visualizations that support these strategies. For this purpose, we developed a methodology involving online meetings that included think-aloud protocols and interviews. We applied the methodology in a user study with five domain experts from the field of epidemiology. Our findings identify, describe, and discuss the UnReSt employed by our participants, allowing for initial recommendations as a foundation for future design guidelines: uncertainty visualization should (i) visually support data analysts in adapting or developing UnReSt, (ii) not facilitate ignoring the uncertainty, (iii) aid in the definition of acceptable levels of uncertainty, and (iv) not hide uncertain parts of the data by default. We reflect on the methodology we developed and applied in our study, addressing challenges related to the recruiting process, the examination of an existing tool along with familiar tasks and data, the design of bespoke prototypes in collaboration with visualization experts, and the timing of the meetings. We encourage visualization researchers to adapt this methodology to gain deeper insights into the UnReSt of data analysts and how uncertainty visualization can effectively support them. The supplemental materials can be found at https://osf.io/s2nwf/.
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    Comparing Distance Metrics in Space-time Clustering to Provide Visual Summaries of Traffic Congestion
    (The Eurographics Association, 2024) Baudains, Peter; Holliman, Nicolas S.; Hunter, David; Slingsby, Aidan
    Smart Cities are characterised by their ability to collect and process large volumes of sensor data. Visual analytics is then often required to make this data actionable and to allow decisions to be made in support of the well-being of inhabitants. In this study, using Bus Open Data, we consider how space-time clustering can be used to generate visual summaries of traffic congestion. Using a space-time extension of DBSCAN, our clustering procedure is evaluated with respect to both Euclidean distance and street network distance. Results show that network-based distance metrics improve the clustering procedure by generating clusters with less uncertainty. Moreover, congestion clusters derived from network-based distances are also more likely to last longer and to precede future congestion appearing nearby. We suggest that network-based distances might provide greater opportunity for more impactful traffic control room decision-making and we discuss steps towards a near real-time system design that can be used in support of operational decision-making.
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    Reflections on the Evolution of the BookTracker Visualization Platform
    (The Eurographics Association, 2024) Xing, Yiwen; Dondi, Cristina; Borgo, Rita; Abdul-Rahman, Alfie; Hunter, David; Slingsby, Aidan
    Understanding the trade data of historical books is crucial for researchers investigating the distribution and provenance of Incunabula (books printed between 1450 and 1500). We incrementally developed BookTracker, a platform featuring multiple visualization and visual analytics applications to support these research efforts. This platform leverages data from the Material Evidence in Incunabula (MEI) database, which meticulously records detailed information on the provenance, ownership, and use of 15th-century printed books. BookTracker began with a focus on providing visualization and visual analytical solutions to effectively present each book provenance's chronological and geographical information. Through three years of collaborative work with domain experts, we continually explored the Material Evidence in Incunabula (MEI) data and discovered more possibilities for visualization to represent this rich information. Gradually, a suite of specialized visualization tools for specific analytical purposes was developed, including DanteSearchVis, DanteExploreVis, KURF2022, KURF2023, and OwnershipTracker. These tools now comprise the BookTracker platform, which has evolved to explore various features and aspects of the data. This paper details the evolution of BookTracker's design and development alongside domain experts, highlighting the reflections and lessons learned from its application in various research projects. We discuss this long-term collaborative visualization project, hoping to offer our experience as a case study for similar research in the future.
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    Min-Max Modifiable Nested Octrees M3NO: Indexing Point Clouds with Arbitrary Attributes in Real Time
    (The Eurographics Association, 2024) Hermann, Paul; Krämer, Michel; Dorra, Tobias; Kuijper, Arjan; Hunter, David; Slingsby, Aidan
    We present a data structure that allows 3D point clouds with arbitrary attributes to be indexed in real time. We focus on large datsets from mobile mapping systems such as airborne and terrestrial laser scanners. Compared to traditional indexing approaches running offline, our data structure can be created incrementally while the points are being recorded. This allows the data to be used (i.e. analyzed or visualized) already during acquisition or immediately after it has finished. The data structure enables queries based on spatial extent and value ranges of arbitrary attributes. This is in contrast to existing works, which focus on either spatial or attribute indexing, typically are not real-time capable, or only support a limited set of attributes. Our approach combines Modifiable Nested Octrees and extended Binned Min-Max Octrees. Using a subset of the well known AHN4 dataset with 138 million points, we evaluate the approach, assess quality and query performance, and compare it with an existing state-of-the-art solution. On commodity hardware, our data structure can process 1.97 million points per second, which is more than most commercially available laser scanners can record. When filtering points by attribute value ranges, it also reduces the number of octree nodes that have to be loaded, and it substantially outperforms naive sequential point filtering.
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    Visual Interpretation of Tagging: Advancing Understanding in Task-Oriented Dialogue Systems
    (The Eurographics Association, 2024) Zhou, Yazhuo; Xing, Yiwen; Abdul-Rahman, Alfie; Borgo, Rita; Hunter, David; Slingsby, Aidan
    In task-oriented dialogue systems, tagging tasks leverage Large Language Models (LLMs) to understand dialogue semantics. The specifics of how these models capture and utilize dialogue semantics for decision-making remain unclear. Unlike binary or multi-classification, tagging involves complex multi-to-multi relationships between features and predictions, complicating attribution analyses. To address these challenges, we introduce a novel interactive visualization system that enhances understanding of dialogue semantics through attribution analysis. Our system offers a multi-level and layer-wise visualization framework, revealing the evolution of attributions across layers and allowing users to interactively probe attributions. With a dual-view for streamlined comparisons, users can effectively compare different LLMs. We demonstrate our system's effectiveness with a common task-oriented dialogue task: slot filling. This tool aids NLP experts in understanding attributions, diagnosing models, and advancing dialogue understanding development by identifying potential sources of model hallucinations.
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    LLM-Assisted Visual Analytics: Opportunities and Challenges
    (The Eurographics Association, 2024) Hutchinson, Maeve; Jianu, Radu; Slingsby, Aidan; Madhyastha, Pranava; Hunter, David; Slingsby, Aidan
    We explore the integration of large language models (LLMs) into visual analytics (VA) systems to transform their capabilities through intuitive natural language interactions. We survey current research directions in this emerging field, examining how LLMs are integrated into data management, language interaction, visualisation generation, and language generation processes. We highlight the new possibilities that LLMs bring to VA, especially how they can change VA processes beyond the usual use cases. We especially highlight building new visualisation-language models, allowing access of a breadth of domain knowledge, multimodal interaction, and opportunities with guidance. Finally, we carefully consider the prominent challenges of using current LLMs in VA tasks. Our discussions in this paper aim to guide future researchers working on LLM-assisted VA systems and help them navigate common obstacles when developing these systems.
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    Investigation of the Organisation of the Extracellular Matrix Using Fibre Tracing
    (The Eurographics Association, 2024) Arafat, Youssef; Cuesta-Apausa, Cristina; Castellano, Esther; Reyes-Aldasoro, Constantino Carlos; Hunter, David; Slingsby, Aidan
    Throughout history, the observation of medical and biological samples has been of high importance and has led to many discoveries. When this process relies on human observation, it can be time-consuming, especially with the advent of technological advancements that generate more and more images at faster rates. Additionally, some features of the samples can be undetectable by the naked eye, but with the aid of visual computing techniques, these hidden details can be revealed. The morphological characteristics of the extracellular matrix play a vital role in cancer and other health conditions. Visual observations of the ECM can provide valuable insights; however, the task may be tedious and sometimes it is hard to quantify the differences between samples. In this work, a tracing algorithm is proposed. Furthermore, morphological characteristics of the extracellular matrix can be extracted with the algorithm to quantify and compare different biological populations. Experiments revealed that the removal of interactions in fibroblasts affected their ability to form a healthy extracellular matrix as compared with a wild type population. Here, an investigation of the morphological differences between the ECM of two populations was conducted. Five images of mutant and five images of wild type cells growing in culture were compared. A deconvolutional convolutional neural network was used as a pre-processing filtering method to remove noise from the images. The images are then traced by the proposed algorithm, Trace Ridges, to extract morphological features and visually present the edges and gaps extracted. Trace Ridges combines methods of Edge detection, watershed, and morphological characteristics to delineate fibre-like structures. Two morphological characteristics provided statistical differences between the populations: number of fibres (p−value = 0.00091) and relative area of gaps between the fibres (p−value = 0.014). The number of fibres detected in wild type was higher than mutant while the relative gaps area size of mutant was higher than that of WT. Trace Ridges was able to successfully delineate the ECM fibres of mutant and wild type cells and extract morphological features to show the difference between the populations.
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    The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
    (The Eurographics Association, 2024) Sikar, Daniel; Garcez, Artur d'Avila; Bloomfield, Robin; Weyde, Tillman; Peeroo, Kaleem; Singh, Naman; Hutchinson, Maeve; Laksono, Dany; Reljan-Delaney, Mirela; Hunter, David; Slingsby, Aidan
    This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques to measure the distances between the predictions of a trained neural network and class centroids. By analyzing these distances, the MLM provides a comprehensive view of the model's misclassification tendencies, enabling decision-makers to identify the most common and critical sources of errors. The MLM allows for the prioritization of model improvements and the establishment of decision thresholds based on acceptable risk levels. The approach is evaluated on the MNIST dataset using a Convolutional Neural Network (CNN) and a perturbed version of the dataset to simulate distribution shifts. The results demonstrate the effectiveness of the MLM in assessing the reliability of predictions and highlight its potential in enhancing the interpretability and risk mitigation capabilities of neural networks. The implications of this work extend beyond image classification, with ongoing applications in autonomous systems, such as self-driving cars, to improve the safety and reliability of decision-making in complex, real-world environments.