Ilir Jusufi

TL;DR: This paper proposes a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined, and provides a survey of work in information visualization related to comparison.

TL;DR: This survey is intended to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

TL;DR: This work discusses performance as it relates to visualization and survey existing approaches in performance visualization and develops a taxonomy for the contexts in which different performance visualizations reside and describes the state of the art research pertaining to each.

TL;DR: The results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning.

TL;DR: A new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships is presented, which emphasizes the code's structural behavior, which is much more familiar to the application developer.