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Eye Tracking for Target Acquisition in Sparse Visualizations

In this paper, we present a novel marker-free method for identifying screens of interest when using head-mounted eye-tracking for visualization in cluttered and multi-screen environments. We offer a solution to discerning visualization entities from sparse backgrounds by incorporating edge-detection into the existing pipeline. Our system allows for both more efficient screen identification and improved accuracy over the state-of-the-art ORB algorithm.

The source code for this project is available on our Github.



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Detecting Negative Emotion for Mixed Initiative Visual Analytics


Prateek Panwar and Christopher Collins

The work describes an efficient model to detect negative mind states caused by visual analytics tasks. We have developed a method for collecting data from multiple sensors, including GSR and eye-tracking, and quickly generating labelled training data for the machine learning model. Using this method we have created a dataset from 28 participants carrying out intentionally difficult visualization tasks. We have concluded the paper by discussing the best performing model, Random Forest, and its future applications for providing just-in-time assistance for visual analytics.


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