NewEvery arXiv paper, its researchers & institutions — mapped.
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#user study

10 results
eess.IV2019

Human Perceptual Evaluations for Image Compression

Yash Patel, Srikar Appalaraju, R. Manmatha

The paper conducts human user studies to show that deep‑learning image compression methods optimized for higher MS‑SSIM scores can actually look worse to people than traditional co…

#image compression#perceptual evaluation#user study#deep learning
cs.LG2019

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden +1

The paper reports a survey of ICU and emergency department clinicians to identify which types of explanations from machine‑learning models best foster trust and adoption in clinica…

#explainable ai#clinical decision support#trust in AI#user study
cs.HC2019

Visualising Geographically-Embedded Origin-Destination Flows: in 2D and immersive environments

Yalong Yang

The thesis investigates and evaluates visualization techniques for origin‑destination flows on geographic maps, creating designs for both 2D and immersive environments and testing…

#origin-destination flows#geographic visualization#2d visualization#immersive visualization
cs.HC2019

Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation

Yalong Yang, Tim Dwyer, Sarah Goodwin +1

The paper evaluates visual representations for dense many-to-many geographic flows, comparing bundled node‑link flow maps, OD Maps, and a new method called MapTrix.

#flow visualization#geographic data#user study#visual analytics
cs.HC2019

Maps and Globes in Virtual Reality

Yalong Yang, Bernhard Jenny, Tim Dwyer +3

The paper compares four VR visualizations of world maps—a 3D exocentric globe, a flat map, an egocentric globe, and a curved map—and finds that exocentric globes generally provide…

#virtual reality#geographic visualization#map rendering#user study
cs.LG2019

Assessing the Local Interpretability of Machine Learning Models

Dylan Slack, Sorelle A. Friedler, Carlos Scheidegger +1

The paper investigates how well humans can understand and predict the behavior of machine learning models using two notions of local interpretability—simulatability and what‑if exp…

#interpretability#local explanations#simulatability#user study
cs.HC2019

Investigating Direct Manipulation of Graphical Encodings as a Method for User Interaction

Bahador Saket, Samuel Huron, Charles Perin +1

The paper studies how users perform direct manipulation of graphical encodings in visualizations, presenting a qualitative study of 15 operations to identify strategies and design…

#direct manipulation#graphical encodings#visualization interaction#user study
cs.GR2019

Evaluating Ordering Strategies of Star Glyph Axes

Matthias Miller, Xuan Zhang, Johannes Fuchs +1

The paper reports a user study that compares similarity‑based and dissimilarity‑based ordering of dimensions in star glyph visualizations, showing that dissimilarity‑based layouts…

#star glyphs#dimension ordering#visual clustering#user study
cs.HC2019

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Stephen Smart, Keke Wu, Danielle Albers Szafir

The paper introduces an algorithm that automatically creates designer-quality sequential and diverging color ramps from a single seed color by learning patterns from a corpus of ex…

#color ramps#visualization design#automated color selection#aesthetic evaluation
cs.HC2019

Augmenting Music Sheets with Harmonic Fingerprints

Matthias Miller, Alexandra Bonnici, Mennatallah El-Assady

The paper introduces a visualization technique that adds harmonic fingerprint glyphs, based on the circle-of-fifths concept, to digital music sheets to help students and musicians…

#music notation#visualization#harmonic analysis#user study