Publications:

• Liebers, Carina; Prochazka, Marvin; Pfützenreuter, Niklas; Liebers, Jonathan; Auda, Jonas; Gruenefeld, Uwe; Schneegass, Stefan: Pointing It out! Comparing Manual Segmentation of 3D Point Clouds between Desktop, Tablet, and Virtual Reality. In: International Journal of Human–Computer Interaction (2023), p. 1-15. doi:10.1080/10447318.2023.2238945PDFFull textCitationAbstractDetails

  

  Scanning everyday objects with depth sensors is the state-of-the-art approach to generating point clouds for realistic 3D representations. However, the resulting point cloud data suffers from outliers and contains irrelevant data from neighboring objects. To obtain only the desired 3D representation, additional manual segmentation steps are required. In this paper, we compare three different technology classes as independent variables (desktop vs. tablet vs. virtual reality) in a within-subject user study (N = 18) to understand their effectiveness and efficiency for such segmentation tasks. We found that desktop and tablet still outperform virtual reality regarding task completion times, while we could not find a significant difference between them in the effectiveness of the segmentation. In the post hoc interviews, participants preferred the desktop due to its familiarity and temporal efficiency and virtual reality due to its given three-dimensional representation.

• Abdrabou, Yasmeen; Rivu, Sheikh Radiah; Ammar, Tarek; Liebers, Jonathan; Saad, Alia; Liebers, Carina; Gruenefeld, Uwe; Knierim, Pascal; Khamis, Mohamed; Makela, Ville; Schneegass, Stefan: Understanding Shoulder Surfer Behavior and Attack Patterns Using Virtual Reality. In: Proceedings of the 2022 International Conference on Advanced Visual Interfaces, Vol 2022 (2023), p. 1-9. doi:10.1145/3531073.3531106PDFFull textCitationDetails
• Liebers, Carina; Agarwal, Shivam; Beck, Fabian: CohExplore: Visually Supporting Students in Exploring Text Cohesion. In: Gillmann, Christina; Krone, Michael; Lenti, Simone (Ed.): EuroVis 2023 - Posters. The Eurographics Association, 2023. doi:10.2312/evp.20231058PDFCitationAbstractDetails

  A cohesive text allows readers to follow the described ideas and events. Exploring cohesion in text might aid students enhancing their academic writing. We introduce CohExplore, which promotes exploring and reflecting on cohesion of a given text by visualizing computed cohesion related metrics on an overview and detailed level. Detected topics are color-coded, semantic similarity is shown via lines, while connectives and co-references in a paragraph are encoded using text decoration. Demonstrating the system, we share insights about a student-authored text.

• Liebers, Carina; Agarwal, Shivam; Krug, Maximilian; Pitsch, Karola; Beck, Fabian: VisCoMET: Visually Analyzing Team Collaboration in Medical Emergency Trainings. In: Computer Graphics Forum (2023). doi:10.1111/cgf.14819PDFCitationAbstractDetails

  Handling emergencies requires efficient and effective collaboration of medical professionals. To analyze their performance, in an application study, we have developed VisCoMET, a visual analytics approach displaying interactions of healthcare personnel in a triage training of a mass casualty incident. The application scenario stems from social interaction research, where the collaboration of teams is studied from different perspectives. We integrate recorded annotations from multiple sources, such as recorded videos of the sessions, transcribed communication, and eye-tracking information. For each session, an informationrich timeline visualizes events across these different channels, specifically highlighting interactions between the team members. We provide algorithmic support to identify frequent event patterns and to search for user-defined event sequences. Comparing different teams, an overview visualization aggregates each training session in a visual glyph as a node, connected to similar sessions through edges. An application example shows the usage of the approach in the comparative analysis of triage training sessions, where multiple teams encountered the same scene, and highlights discovered insights. The approach was evaluated through feedback from visualization and social interaction experts. The results show that the approach supports reflecting on teams’ performance by exploratory analysis of collaboration behavior while particularly enabling the comparison of triage training sessions.

• Abdrabou, Yasmeen; Rivu, Radiah; Ammar, Tarek; Liebers, Jonathan; Saad, Alia; Liebers, Carina; Gruenefeld, Uwe; Knierim, Pascal; Khamis, Mohamed; Mäkelä, Ville; Schneegass, Stefan; Alt, Florian: Understanding Shoulder Surfer Behavior Using Virtual Reality. In: Proceedings of the IEEE conference on Virtual Reality and 3D User Interfaces (IEEE VR). IEEE, Christchurch, New Zealand 2022. CitationAbstractDetails

  We explore how attackers behave during shoulder surfing. Unfortunately, such behavior is challenging to study as it is often opportunistic and can occur wherever potential attackers can observe other people’s private screens. Therefore, we investigate shoulder surfing using virtual reality (VR). We recruited 24 participants and observed their behavior in two virtual waiting scenarios: at a bus stop and in an open office space. In both scenarios, avatars interacted with private screens displaying different content, thus providing opportunities for shoulder surfing. From the results, we derive an understanding of factors influencing shoulder surfing behavior.

• Latif, Shahid; Agarwal, Shivam; Gottschalk, Simon; Chrosch, Carina; Feit, Felix; Jahn, Johannes; Braun, Tobias; Tchenko, Yanick Christian; Demidova, Elena; Beck, Fabian: Visually Connecting Historical Figures Through Event Knowledge Graphs. In: 2021 IEEE Visualization Conference (VIS) - Short Papers. IEEE, 2021. doi:10.1109/VIS49827.2021.9623313CitationAbstractDetails

  

  Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user’s query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.