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Sunghee Choi

Publications and source records attributed to Sunghee Choi.

3 recordsLinked to original sources

The Containment Polytope of a Triangle and a Convex Polygon

The containment polytope $\mathcal{C}$ for a triangle $P$ and a convex $n$-gon $Q$ is a four-dimensional polytope representing the similarity transforms (translation, rotation, scaling) that place $P$ inside $Q$. While it is known that the complexity of $\mathcal{C}$ and its silhouette $Λ$, which represents all the largest placements of $P$ inside $Q$ for all orientations, can be quadratic in $n$, we show that the silhouette can be partitioned into a linear number of planar convex chains and steps in $O(n \log n)$ time. Based on the linear number of convex chains and steps, we can compute the complexity of silhouette $Λ$ in $O(n \log n)$ time and query the largest placement of $P$ inside $Q$ for a given orientation in $O(\log n)$ time. We explain geometrically how the triangle $P$ rotates while continuously scaling and translating to be maximal inside $Q$, leading to a parameterized algorithm for containment problems.

cs.CG↗

Progressive Transmission and Inference of Deep Learning Models

Modern image files are usually progressively transmitted and provide a preview before downloading the entire image for improved user experience to cope with a slow network connection. In this paper, with a similar goal, we propose a progressive transmission framework for deep learning models, especially to deal with the scenario where pre-trained deep learning models are transmitted from servers and executed at user devices (e.g., web browser or mobile). Our progressive transmission allows inferring approximate models in the middle of file delivery, and quickly provide an acceptable intermediate outputs. On the server-side, a deep learning model is divided and progressively transmitted to the user devices. Then, the divided pieces are progressively concatenated to construct approximate models on user devices. Experiments show that our method is computationally efficient without increasing the model size and total transmission time while preserving the model accuracy. We further demonstrate that our method can improve the user experience by providing the approximate models especially in a slow connection.

cs.LG↗

Improved Explanatory Efficacy on Human Affect and Workload through Interactive Process in Artificial Intelligence

Despite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence. We, therefore, propose explanatory efficacy, a novel metric for evaluating the strength of the cyclic relationship the interface exhibits. Furthermore, in a user study, we evaluated the perceived affect and workload and recorded the EEG signals of our participants as they interacted with our custom-built, iterative explanatory interface to build personalized recommendation systems. We found that systems for perceptually driven iterative tasks with greater explanatory efficacy are characterized by statistically significant hemispheric differences in neural signals with 62.4% accuracy, indicating the feasibility of neural correlates as a measure of explanatory efficacy. These findings are beneficial for researchers who aim to study the circular ecosystem of the human-artificial intelligence partnership.

cs.HC↗