Rotation independent hierarchical representation for Open and Closed Curves and its Applications

The algorithm used for the segmentation of an image,and scheme used for the representation of the segmentationresult are mostly selected based on the final image analysis orinterpretation objective. The boundary based imagesegmentation and representation system developed by Naborssegments and stores the result as a graph-tree hierarchicalstructure that is capable of supporting diverse applications. Thispaper shows that Nabors’ hierarchical representation of curves isnot invariant to rotation, and proposes an enhancedrepresentation which retains its structure and remains invariantunder rotation. The curve matching algorithm which matchestwo curves based on their hierarchical representation makes iteasy to determine if a curve is a section of a larger curve. Thepotential of the representation is illustrated by developing imageregistration and image stitching methods based on the newrepresentation

Trusting Language Models in Education

Language Models are being widely used in Education. Even though modern deep learning models achieve very good performance on question-answering tasks, sometimes they make errors. To avoid misleading students by showing wrong answers, it is important to calibrate the confidence - that is, the prediction probability - of these models. In our work, we propose to use an XGBoost on top of BERT to output the corrected probabilities, using features based on the attention mechanism. Our hypothesis is that the level of uncertainty contained in the flow of attention is related to the quality of the model's response itself