EuroVisPosters2022

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633198

Browse

Now showing 1 - 20 of 35

Accurate Molecular Atom Selection in VR
(The Eurographics Association, 2022) Molina, Elena; Vázquez, Pere-Pau; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Accurate selection in cluttered scenes is complex because a high amount of precision is required. In Virtual Reality Environments, it is even worse, because it is more difficult for us to point a small object with our arms in the air. Not only our arms move slightly, but the button/trigger press reduces our weak stability. In this paper, we present two alternatives to the classical ray pointing intended to facilitate the selection of atoms in molecular environments. We have implemented and analyzed such techniques through an informal user study and found that they were highly appreciated by the users. This selection method could be interesting in other crowded environments beyond molecular visualization.
Chord2DS: An Extension to Chord Diagram to Show Data Elements from Two Heterogeneous Data Sources
(The Eurographics Association, 2022) Humayoun, Shah Rukh; Brahmadevara, Likhitha; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The standard Chord diagram, a radial layout, shows data elements in a circular fashion from one data source. In this paper, we propose an extension to the standard Chord diagram to show data elements from two heterogeneous data sources into one single diagram. The main Chord diagram is used for showing data elements and the relations between them from one data source, while we use an outer layer to show data elements from the second data source. The relationships between data elements from both data sources are shown through visual cues. The proposed solution uses space efficiently compared to using multiple diagrams in the scenarios of two heterogeneous data sources.
Situated Visualization in Motion for Video Games
(The Eurographics Association, 2022) Bucchieri, Federica; Yao, Lijie; Isenberg, Petra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We contribute a systematic review of situated visualizations in motion in the context of video games. Video games produce rich dynamic datasets during gameplay that are often visualized to help players succeed in a game. Often these visualizations are moving either because they are attached to moving game elements or due to camera changes. We want to understand to what extent this motion and contextual game factors impact how players can read these visualizations. In order to ground our work, we surveyed 160 visualizations in motion and their embeddings in the game world. Here, we report on our analysis and categorization of these visualizations.
A Case Study on Implementing Screen Reader Accessibility in Dynamic Visualizations
(The Eurographics Association, 2022) Costa, Rita; Malveiro, Beatriz; Palmeiro, João; Bizarro, Pedro; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Millions of people worldwide work in jobs where assessing dynamic data presented visually to them is a key part of their tasks. Since the data is only represented in a visual format, these occupations are out of reach for visually impaired people, making them unable to review hundreds of information-heavy cases per day and determine outcomes for each one in just a couple of minutes. In this work, we aim to shrink that gap by detailing the implementation of screen reader accessibility features to real-world visualizations used by fraud detection analysts. We propose a set of features that should be validated with users and, if proved to be useful, transformed into guidelines for creating these types of accessible charts.
Visual Exploration of Genetic Sequence Variants in Pangenomes
(The Eurographics Association, 2022) van den Brandt, Astrid; Jonkheer, Eef M.; van Workum, Dirk-Jan M.; Smit, Sandra; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
To study the genetic sequence variation underlying traits of interest, the field of comparative genomics is moving away from analyses with single reference genomes to pangenomes; abstract representations of multiple genomes in a species or population. Pangenomes are beneficial because they represent a diverse set of genetic material and therefore avoid bias towards a single reference. While pangenomes allow for a complete map of the genetic variation, their large size and complex data structure hinder contextualization and interpretation of analysis results. Current visualization strategies fall short because they are created for single references or do not illustrate links to metadata. We present a work in progress version of a novel visual analytics strategy for pangenomic variant analysis. Our strategy is designed through an intensive involvement of genome scientists. The current design uniquely exploits interactive sorting, aggregation, and linkage relations from different perspectives of the data, to help the genome scientists explore and evaluate variant-trait associations in the context of multiple references and metadata.
On Visualizing Music Storage Media for Modern Access to Historic Sources
(The Eurographics Association, 2022) Khulusi, Richard; Fricke, Heike; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Finding a balance between conserving historic objects and using them for research is one of the big issues in historic collections. Digitization holds the opportunity to offer a safe and non-destructible access to historic objects, making them available for research. With this poster, we want to give insight into our planned visualization system, using close and distant reading access for visual analysis approaches and allowing musicologists novel approaches to normally fragile and endangered media.
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback
(The Eurographics Association, 2022) Yu, Yuncong; Kruyff, Dylan; Jiao, Jiao; Becker, Tim; Behrisch, Michael; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome the uneconomic (re- )training with deep learning-based methods. Very high-dimensional time series emerge on an unprecedented scale due to increasing sensor usage and data storage. Visual pattern search is one of the most frequent tasks on such data. Automatic pattern retrieval methods often suffer from inefficient training, a lack of ground truth, and a discrepancy between the similarity perceived by the algorithm and the user. Our proposal is based on a query-aware locality-sensitive hashing technique to create a representation of multivariate time series windows. It features sub-linear training and inference time with respect to data dimensions. This performance gain allows an instantaneous relevance-feedback-driven adaption and converges to users' similarity notion. We are benchmarking PSEUDo in accuracy and speed with representative and state-of-the-art methods, evaluating its steerability through simulated user behavior, and designing expert studies to test PSEUDo's usability.
Visualizing Similarities between American Rap-Artists
(The Eurographics Association, 2022) Meinecke, Christofer; Schebera, Jeremias; Eschrich, Jakob; Wiegreffe, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Rap music is one of the biggest music genres in the world today. Since the early days of rap music, references not only to pop culture but also to other rap artists have been an integral part of the lyrics' artistry. In addition, rap musicians reference each other by adopting fragments of lyrics, for example, to give credit. This kind of text reuse can be used to create connections between individual artists. Due to the large amount of lyrics, only automated detection methods can efficiently detect similarities between the songs and the artists. Here, we present a visualization system for analyzing rap music lyrics. We also trained a network tailored specifically for rap lyrics to compute similarities in lyrics. Here a video of the prototype can be seen.
MOBS - Multi-Omics Brush for Subgraph Visualisation
(The Eurographics Association, 2022) Heylen, Dries; Peeters, Jannes; Ertaylan, Gökhan; Hooyberghs, Jef; Aerts, Jan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
One of the big opportunities in multi-omics analysis is the identification of interactions between molecular entities and their association with diseases. In analyzing and expressing these interactions in the search for new hypotheses, multi-omics data is often either translated into matrices containing pairwise correlations and distances, or visualized as node-link diagrams. A major problem when visualizing large networks however is the occurrence of hairball-like graphs, from which little to none information can be extracted. It is of interest to investigate subgroups of markers that are closely associated with each other, rather than just looking at the overload of all interactions. Hence, we propose MOBS (Multi-Omics Brush for Subgraph visualisation), a web-based visualisation interface that can provide both an overview and detailed views on the data. By means of a two dimensional brush on a heatmap that includes hierarchical clustering, relationships of interest can be extracted from a fully connected graph, to enable detailed analysis of the subgraph of interest.
Scientific Convergence and Divergence in Visualization and Visual Analytics
(The Eurographics Association, 2022) He, Jiangen; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present preliminary results of a visualization tool designed to visualize scientific evolution by using scientific publication data, especially convergence-divergence processes. It aims to increase the efficiency and accuracy of our understanding of scientific knowledge in a certain field with limited domain knowledge. We visualized 2,435 papers published in IEEE VIS and EuroVis to demonstrate the tool and provide a big picture of the scientific evolution in the visualization community.
Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems
(The Eurographics Association, 2022) Benvenuti, Dario; Fiordeponti, Giovanni; Cheng, Hao; Catarci, Tiziana; Fekete, Jean-Daniel; Santucci, Giuseppe; Angelini, Marco; Battle, Leilani; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-Computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context.
Visual Exploration of Preference-based Routes in Ski Resorts
(The Eurographics Association, 2022) Rauscher, Julius; Miller, Matthias; Keim, Daniel A.; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Ski resorts exhibit a variety of available pistes and lifts, to which every skier has intrinsic preferences. While novices tend to favor easy pistes, experts might opt for more advanced pistes. In large resorts, the vast possibilities render manual, optimized routing according to specific piste and lift preferences extremely tedious. So far, existing visualizations of ski resorts lack these routing capabilities.We present a visual analytics interface that allows the user to find an optimal route between arbitrary locations in a ski resort according to individual personal preferences. Furthermore, we encode steepness information along the pistes to expose segments that deviate from the difficulty classification and thus are incompatible with the given user preferences.
Enhancing Evaluation of Room Scale VR Studies to POI Visualizations in Minimaps
(The Eurographics Association, 2022) Ajdadilish, Batoul; Kohl, Steffi; Schröder, Kay; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Understanding and evaluating user behavior in virtual reality environments is challenging for researchers. Stereoscopic perception is highly dependent on the point of view, so it is necessary to account for multiple spatial positions. Robust tools and methods to analyze these spatio-temporal data are lacking. We propose a design solution for spatio-temporal data visualization for room-scale VR studies. Our result is a top-down minimap that plots 3D point of interest coordinates of room-scale virtual reality environments to a 2D visualization. The video stream from the head mount display is next to the minimap showing the top-down view of the scene, reflecting the visual stimuli that were perceivable by the user. Both views are linked such that replaying the user session is synchronized in time. The minimap enables researchers to review and replay the recorded user session for in-depth study, allowing them to gain insightful information about users' behavior in virtual environments.
Visualizing Prediction Provenance in Regression Random Forests
(The Eurographics Association, 2022) Médoc, Nicolas; Ciorna, Vasile; Petry, Frank; Ghoniem, Mohammad; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Random forest models are widely used in many application domains due to their performance and the fact that their constituent decision trees carry clear decision rules. Yet, the provenance of the predictions made by an entire forest is complex to grasp, which motivates application domain experts to adopt black-box testing strategies. We propose in this paper a coordinated multiple view system allowing to shed more light on prediction provenance, uncertainty and error in terms of bias and variance at the global model scale or at the local scale of decision paths and individual instances.
ANARI: ANAlytic Rendering Interface
(The Eurographics Association, 2022) Griffin, Kevin; Amstutz, Jefferson; DeMarle, Dave; Günther, Johannes; Progsch, Jakob; Sherman, William; Stone, John E.; Usher, Will; Kooten, Kees van; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The ANARI API enables users to build the description of a scene to generate imagery, rather than specifying the details of the rendering process, providing simplified visualization application development and cross-vendor portability to diverse rendering engines, including those using state-of-the-art ray tracing.
Automatic Segmentation of Tooth Images: Optimization of Multi-parameter Image Processing Workflow
(The Eurographics Association, 2022) Bressan Fogalli, Giovani; Line, Sérgio Roberto Peres; Baum, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The development of specific algorithms in image processing are usually related to dataset characteristics. Those characteristics will influence the number of instructions required to solve a problem. Normally, the more complex a set of instructions is, the more parameters need to be set. Dealing with such degrees of freedom, sometimes leading to subjective decision making, is time-consuming and frequently leads to errors or sub-optimal results of the developed model. Here, we deal with a model for segmentation of masks of tooth images containing a pattern of bands called Hunter-Schreger Bands (HSB). They appear on tooth surface when lit from the side. This segmentation process is only one step of a pipeline whose overall goal is human biometric identification to be used, e.g., in forensics. The segmentation algorithm, which exploits the anisotropy of the image, uses several parameters and choosing the optimal combination of them is challenging. The aim of this work was to utilize visual data analysis tools to optimize the chosen parameters and to understand their influence on the performance of the algorithm. Our results reveal that a slightly better combination of parameter values can be found starting from the experimentally determined initial parameters. This approach can be repeatedly performed to achieve even better parameterizations. To more deeply understand the influence of the parameters on the final result, more sophisticated visual interaction tools will be explored in future work.
Visualizing the Evolution of Multi-agent Game-playing Behaviors
(The Eurographics Association, 2022) Agarwal, Shivam; Latif, Shahid; Rothweiler, Aristide; Beck, Fabian; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Analyzing the training evolution of AI agents in a multi-agent environment helps to understand changes in learned behaviors, as well as the sequence in which they are learned. We train an existing Pommerman team from scratch and, at regular intervals, let it battle against another top-performing team. We define thirteen game-specific behaviors and compute their occurrences in 600 matches. To investigate the evolution of these behaviors, we propose a visualization approach and showcase its usefulness in an application example.
VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies
(The Eurographics Association, 2022) Dattolo, Antonina; Corbatto, Marco; Angelini, Marco; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through one usage scenarios related to the creation of a bibliography.
Digital Twins of Smart Farms
(The Eurographics Association, 2022) Zhao, Yuhang; Jiang, Zheyu; Pang, Shanchen; Lv, Zhihan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
In recent years, the development of Digital Twins has made rapid progress, and Digital Twins has gradually begun to combine various fields and applied to the current digitalization of the physical world. Digital Twins can play an important role in agriculture. Digital Twins can fully improve the yield and income of crop products and solve the problems of food security. In this paper, the development prospect of Digital Twins in agriculture is discussed.
Using Data Comics to Enhance Visualization Literacy
(The Eurographics Association, 2022) Boucher, Magdalena; Stoiber, Christina; Aigner, Wolfgang; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Visualization Literacy as a skill is becoming important, as growing amounts of data require complex ways of visualizing and interpreting them. Yet, it is hardly taught during general education, and not many resources conveying visualization knowledge in an easily accessible way exist. We draw on the notion of data comics, which are already well-suited for communicating visualization insights, but so far have not been explored in the context of teaching visualization skills. We aim to map the research landscape around this idea through a systematic literature research and present a first overview of related areas and how they might influence data comics used to enhance visualization literacy.

Browse

Browsing EuroVisPosters2022 by Issue Date

Results Per Page

Sort Options