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Large Language Models for Multi-Choice Question Classification of Medical Subjects
The aim of this paper is to evaluate whether large language models trained on multi-choice question data can be used to discriminate between medical subjects.
Semantically Selective Augmentation for Deep Compact Person Re-Identification
We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks.
Non-Verbal Communication Analysis in Victim-Offender Mediations
In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field.
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