EuroVisShort2022

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https://diglib.eg.org/handle/10.2312/2633199

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Evaluating Countable Texture Elements to Represent Bathymetric Uncertainty
(The Eurographics Association, 2022) Ware, Colin; Kastrisios, Christos; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Measurements of the depth of the seabed vary widely in both horizontal and vertical accuracy. To convey this information to mariners, Zones of Confidence (ZOC) are defined for charts. A mosaic of ZOCs can be represented as a chart overlay. This study evaluates two novel designs for textures to represent ZOCs. Both use textures with countable elements to represent different ZOC levels. One uses a texture made of lines where the number of lines in a texture cell represents the confidence level; the other uses dot clusters where the number of dots similarly represents the ZOC level. In the study, these were compared with three alternatives that used color to respond and accuracy as dependent variables. The dot clusters design yielded the fastest responses overall. A method using levels of color transparency proved to be the slowest and least accurate.
Data+Shift: Supporting Visual Investigation of Data Distribution Shifts by Data Scientists
(The Eurographics Association, 2022) Palmeiro, João; Malveiro, Beatriz; Costa, Rita; Polido, David; Moreira, Ricardo; Bizarro, Pedro; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Machine learning on data streams is increasingly more present in multiple domains. However, there is often data distribution shift that can lead machine learning models to make incorrect decisions. While there are automatic methods to detect when drift is happening, human analysis, often by data scientists, is essential to diagnose the causes of the problem and adjust the system. We propose Data+Shift, a visual analytics tool to support data scientists in the task of investigating the underlying factors of shift in data features in the context of fraud detection. Design requirements were derived from interviews with data scientists. Data+Shift is integrated with JupyterLab and can be used alongside other data science tools. We validated our approach with a think-aloud experiment where a data scientist used the tool for a fraud detection use case.
A Design Study of Visualizing Historical Book Movement
(The Eurographics Association, 2022) Xing, Yiwen; Dondi, Cristina; Borgo, Rita; Abdul-Rahman, Alfie; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Trading of 15th-century books is an area of great interest to historians. In this paper, we document the process behind an intensive design study and close collaboration with a domain expert on understanding crucial historical research questions, together with the result of the design study - BookTracker, a tool for mining and visualizing circulation and movement of the 15th-century book trade. The main contribution includes a summary of insights from the design study and BookTracker, a web application supporting historians in: (i) query-based search of user-defined path sequences, and (ii) analysis of the movement of the resulting user-defined path sequences through multiple visualization techniques. We discuss and summarize the value and logistics of conducting this design study, which could become generalizable lessons for the visualization design methodology.
DNC: Dynamic Neighborhood Change Faithfulness Metrics
(The Eurographics Association, 2022) Cai, Shijun; Meidiana, Amyra; Hong, Seok-Hee; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Faithfulness metrics measure how faithfully a visualization displays the ground truth information of the data. For example, neighborhood faithfulness metrics measure how faithfully the geometric neighbors of vertices in a graph drawing represent the ground truth neighbors of vertices in the graph. This paper presents a new dynamic neighborhood change (DNC) faithfulness metric for dynamic graphs to measure how proportional the geometric neighborhood change in dynamic graph drawings is to the ground truth neighborhood change in dynamic graphs. We validate the DNC metrics using deformation experiments, demonstrating that it can accurately measure neighborhood change faithfulness in dynamic graph drawings. We then present extensive comparison experiments to evaluate popular graph drawing algorithms using DNC, to recommend which layout obtains the highest neighborhood change faithfulness on a variety of dynamic graphs.
CellTrackVis: Analyzing the Performance of Cell Tracking Algorithms
(The Eurographics Association, 2022) Li, Weimin; Zhang, Xiang; Stern, Alan; Birtwistle, Marc; Iuricich, Federico; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Live-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimental pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.
DSS: Drawing Dynamic Graphs with Spectral Sparsification
(The Eurographics Association, 2022) Meidiana, Amyra; Hong, Seok-Hee; Pu, Yanyi; Lee, Justin; Eades, Peter; Seo, Jinwook; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
This paper presents DSS (Dynamic Spectral Sparsification), a sampling approach for drawing large and complex dynamic graphs which can preserve important structural properties of the original graph. Specifically, we present two variants: DSSI (Independent) which performs spectral sparsification independently on each dynamic graph time slice; and DSS-U (Union) which performs spectral sparsification on the union graph of all time slices. Moreover, for evaluation of dynamic graph drawing using sampling approach, we introduce two new metrics: DSQ (Dynamic Sampling Quality) to measure how faithfully the samples represent the ground truth change in the dynamic graph, and DSDQ (Dynamic Sampling Drawing Quality) to measure how faithfully the drawings of the sample represent the ground truth change. Experiments demonstrate that DSS significantly outperform random sampling on quality metrics and visual comparison. On average, DSS obtains over 80% (resp., 30%) better DSQ (resp., DSDQ) than random sampling, and visually better preserves the ground truth changes in dynamic graphs.
Visual Evaluation of Translation Alignment Data
(The Eurographics Association, 2022) Yousef, Tariq; Jänicke, Stefan; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Translation alignment plays a crucial role in various applications in natural language processing and digital humanities. With the recent advance in neural machine translation and contextualized language models, numerous studies have emerged on this topic, and several models and tools have been proposed. The performance of the proposed models has been always tested on standard benchmark data sets of different language pairs according to quantitative metrics such as Alignment Error Rate (AER) and F1. However, a detailed explanation on what alignment features contribute to these scores is missing. In order to allow analyzing the performance of alignment models, we present a visual analytics framework that aids researchers and developers in visualizing the output of their alignment models. We propose different visualization approaches that support assessing their own model's performance against alignment gold standards or in comparison to the performance of other models.
Application-oriented Analysis of Material Interface Reconstruction Algorithms in Time-varying Bijel Simulations
(The Eurographics Association, 2022) Bao, Xueyi; Karthikeyan, Nikhil; Schiller, Ulf D.; Iuricich, Federico; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Multimaterial interface reconstruction has been investigated over the years both from visualization and analytical point of view using different metrics. When focusing on visualization, interface continuity and smoothness are used to quantify interface quality. When the end goal is interface analysis, metrics closer to the physical properties of the material are preferred (e.g., curvature, tortuosity). In this paper, we re-evaluate three Multimaterial Interface Reconstruction (MIR) algorithms, already integrated in established visualization frameworks, under the lens of application-oriented metrics. Specifically, we analyze interface curvature, particle-interface distance, and medial axis-interface distance in a time-varying bijel simulation. Our analysis shows that the interface presenting the best visual qualities is not always the most useful for domain scientists when evaluating the material properties.
GROUPSET: A Set-Based Technique to Explore Time-Varying Data
(The Eurographics Association, 2022) Liu, Liqun; Vuillemot, Romain; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
We introduce GroupSet, a technique to facilitate the exploration of temporal charts using a set-based approach. GroupSet operates in a twofold way: first it classifies temporal data into categories (sets) for each time point, second it enables to explore such membership to categories (sets) over time. This approach enables to reveal temporal similarities of elements by categories (sets) memberships, usually hidden by overplot. We demonstrate the applicability of the technique to two case studies (traffic data and sport data) and report on usability feedback of an interactive prototype implementing the technique. Our code and datasets are published as an open-source project and we expect further research towards efficient set creation and temporal manipulation, which remain under-explored areas in the domain of set visualization and interaction.
Metaphoric Maps for Dynamic Vertex-weighted Graphs
(The Eurographics Association, 2022) Mchedlidze, Tamara; Schnorr, Christian; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
In this paper we study metaphoric maps of dynamic vertex-weighted graphs. Dynamic operations on such graphs allow a vertex to change the weight, vertices and edges appear and disappear. In the metaphoric map this is viewed as country shrink and growth, appearance and disappearance and change in the country adjacency. We present a force-based algorithm that supports these operations. In the design of the algorithm we prioritize the dynamic stability of the map, the accuracy in the size of countries and low complexity of the polygons representing the countries. We evaluate the algorithm based on the state-of-theart quality metrics for randomly generated inputs of various complexity.
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation
(The Eurographics Association, 2022) Kwon, Bum Chul; Lee, Jungsoo; Chung, Chaeyeon; Lee, Nyoungwoo; Choi, Ho-Jin; Choo, Jaegul; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Image classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted correlations ''data biases,'' and the visual features causing data biases ''bias factors.'' It is challenging to identify and mitigate biases automatically without human intervention. Therefore, we conducted a design study to find a human-in-the-loop solution. First, we identified user tasks that capture the bias mitigation process for image classification models with three experts. Then, to support the tasks, we developed a visual analytics system called DASH that allows users to visually identify bias factors, to iteratively generate synthetic images using a state-of-the-art image-toimage translation model, and to supervise the model training process for improving the classification accuracy. Our quantitative evaluation and qualitative study with ten participants demonstrate the usefulness of DASH and provide lessons for future work.
Visualization of Tonal Harmony for Jazz Lead Sheets
(The Eurographics Association, 2022) Bunks, Carey; Weyde, Tillman; Slingsby, Aidan; Wood, Jo; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Jazz improvisation is the extemporaneous expression of melody, and musicians commonly base their performances on chord progressions given by lead sheets. It is standard practice to commit a progression to memory by analyzing it for common patterns. This paper presents a visualization design intended to help reduce the amount of cognitive work needed to assimilate a song's chords and harmonic patterns. It does this using color, shapes, and glyphs as visual variables to convey meaning about tonal centers, chord functions, and harmonic structure.
Inferential Tasks as an Evaluation Technique for Visualization
(The Eurographics Association, 2022) Suh, Ashley; Mosca, Ab; Robinson, Shannon; Pham, Quinn; Cashman, Dylan; Ottley, Alvitta; Chang, Remco; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Designing suitable tasks for visualization evaluation remains challenging. Traditional evaluation techniques commonly rely on 'low-level' or 'open-ended' tasks to assess the efficacy of a proposed visualization, however, nontrivial trade-offs exist between the two. Low-level tasks allow for robust quantitative evaluations, but are not indicative of the complex usage of a visualization. Open-ended tasks, while excellent for insight-based evaluations, are typically unstructured and require time-consuming interviews. Bridging this gap, we propose inferential tasks: a complementary task category based on inferential learning in psychology. Inferential tasks produce quantitative evaluation data in which users are prompted to form and validate their own findings with a visualization. We demonstrate the use of inferential tasks through a validation experiment on two well-known visualization tools.
EuroVis 2022 Short Papers: Frontmatter
(The Eurographics Association, 2022) Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Explaining Black Box with Visual Exploration of Latent Space
(The Eurographics Association, 2022) Bodria, Francesco; Rinzivillo, Salvatore; Fadda, Daniele; Guidotti, Riccardo; Giannotti, Fosca; Pedreschi, Dino; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Autoencoders are a powerful yet opaque feature reduction technique, on top of which we propose a novel way for the joint visual exploration of both latent and real space. By interactively exploiting the mapping between latent and real features, it is possible to unveil the meaning of latent features while providing deeper insight into the original variables. To achieve this goal, we exploit and re-adapt existing approaches from eXplainable Artificial Intelligence (XAI) to understand the relationships between the input and latent features. The uncovered relationships between input features and latent ones allow the user to understand the data structure concerning external variables such as the predictions of a classification model. We developed an interactive framework that visually explores the latent space and allows the user to understand the relationships of the input features with model prediction.
How Effective are Uni- and Multivariate Typographic Encodings? Studying the Usage of FontWeight, Oblique Angle, and Spacing
(The Eurographics Association, 2022) Bäuerle, Andreas; Brath, Richard; El-Assady, Mennatallah; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Text is one of the most commonly used ways to transmit information. It is widely used in various visualizations and determines our understanding of the presented content. The information density of text can be enhanced by visualizing data in typographic attributes, such as font weight, letter spacing, or oblique angle. To increase information density the furthest, without the visualization losing performance or effectiveness, the perceivable granularity of the typographic attributes needs to be known. In an empirical experiment, the number of distinguishable levels in typographic attributes and the effects of changing the associated font size or facilitating multivariate encoding are assessed. Findings facilitate designing information-dense typographic visualizations without decreasing their performance or effectiveness.
Towards Multimodal Exploratory Data Analysis: SoniScope as a Prototypical Implementation
(The Eurographics Association, 2022) Enge, Kajetan; Rind, Alexander; Iber, Michael; Höldrich, Robert; Aigner, Wolfgang; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
The metaphor of auscultating with a stethoscope can be an inspiration to combine visualization and sonification for exploratory data analysis. This paper presents SoniScope, a multimodal approach and its prototypical implementation based on this metaphor. It combines a scatterplot with an interactive parameter mapping sonification, thereby conveying additional information about items that were selected with a visual lens. SoniScope explores several design options for the shape of its lens and the sorting of the selected items for subsequent sonification. Furthermore, the open-source prototype serves as a blueprint framework for how to combine D3.js visualization and SuperCollider sonification in the Jupyter notebook environment.
Face-Based Glyphs Revisited
(The Eurographics Association, 2022) Schlieder, Antonia; Wimmer, Philipp; Sadlo, Filip; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
While face-based glyphs have known advantages for certain visualization tasks, they suffer from mixing two rather different visual properties of faces: individual traits and emotion expressions. This paper proposes a set of actions on stylized face glyphs that are compatible with psychological evidence embodied in the facial action coding system [EFH02]. It shows how this set can be employed for distinguishing emotion expressions from other facial expressions, and derives an emotion-based glyph space to exploit the pre-attentive processing of emotion expressions. Finally, we report the results of an empirical user study comparing Chernoff-like glyphs with our emotion glyphs in a typical visualization task.
Blocks: Creating Rich Tables with Drag-and-Drop Interaction
(The Eurographics Association, 2022) Whilden, Allison; Karis, Dirk; Setlur, Vidya; Degtyar, Rodion; Que, Jonathan; Lymperopoulos, Filippos; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
We present Blocks, a formalism that enables the building of visualizations by specifying layout, data relationships, and level of detail (LOD) for specific portions of the visualization. Users can create and manipulate Blocks on a canvas interface through drag-and-drop interaction, controlling the LOD of the data attributes for tabular style visualizations. We conducted a user study to compare how 24 participants employ Blocks and Tableau to complete a target visualization task. Findings from the study suggest that Blocks is a useful mechanism for creating visualizations with embedded microcharts, conditional formatting, and custom layouts. We describe future directions for extending Blocks in visual analysis interfaces.
Augmented Intelligence with Interactive Voronoi Treemap for Scalable Grouping: a Usage Scenario with Wearable Data
(The Eurographics Association, 2022) Abuthawabeh, Ala; Baggag, Abdelkader; Aupetit, Michael; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
Interactive Voronoi Treemaps have been proposed to support arrangement and grouping tasks of data with snippet image representations. They rely on time-consuming manual actions to group data and cannot display more than a hundred images without occlusion. We propose visualizations designed to manage images visibility, evaluate group homogeneity, and shorten grouping task completion time while keeping control. It is supported by an automatic classifier forming an augmented intelligence system to tackle arrangement and grouping tasks at scale. We propose the usage scenario of a clinician using Interactive Voronoi Treemaps to group wearable data based on sleep visual patterns.

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