Porto, Portugal 3 June 2019
Towards Supporting Interpretability of Clustering Results with Uncertainty Visualization
Uni- and Multi-modal Uncertainty Visualization in 2D Scalar Field Ensembles
Detection of Confirmation and Distinction Biases in Visual Analytics Systems
Examining the Components of Trust in Map-Based Visualizations
Trust in Information Visualization
(The Eurographics Association, 2019)Cognitive bias is a systematic error that introduces drifts and distortions in the human judgment in terms of visual decomposition in the direction of the dominant instance. It has a significant role in decision-making ...
(The Eurographics Association, 2019)Prior research suggests that perceived transparency is often associated with perceived trust. For some data types, greater transparency in data visualization is also associated with an increase in the amount of information ...
(The Eurographics Association, 2019)Trust is an important factor that mediates whether a user will rely and build on the information displayed in a visualization. Research in other fields shows that there are different mechanisms of trust building: Users ...
(The Eurographics Association, 2019)Interpretation of machine learning results is a major challenge for non-technical experts, with visualization being a common approach to support this process. For instance, interpretation of clustering results is usually ...
(The Eurographics Association, 2019)The aim of uncertainty-aware scalar field visualization is to convey the most likely case, but also the uncertainty associated with it. In scientific simulations, uncertainty can be modeled using an ensemble approach. ...
(The Eurographics Association, 2019)