Electronic health record (EHR) datasets come in various schemas and can contain a range of data types, measurement units, and variables that share duplicate semantic content. The process of bringing such datasets into a common schema with consistent values, so that it is possible to perform queries uniformly, is known as harmonization. However, performing this process manually can be both time-consuming and prone to errors. In this work, we present a web-based platform that semi-automates the harmonization and linking of EHR datasets through a human-in-the-loop framework, guiding users with the use of large language models (LLMs). Our solution is a two-stage harmonization pipeline that keeps schema metadata processing online while handling patient-level data locally, to align with HIPAA data privacy principles. In the first stage, users harmonize and link only non-identifiable schema information. In the second stage, sensitive value-level harmonization occurs entirely on the user's system, so no private and protected health information ever leaves their environment. Throughout both stages, we expect that LLM-powered suggestions could potentially speed up the harmonization and linking processes.

Surveillance-related datasets that have been released in recent years focus only on one specific problem at a time (e.g., pedestrian detection, face detection, or face recognition), while most of them were collected using visible spectrum (VIS) cameras. Even though some cross-spectral datasets were presented in the past, they were acquired in a constrained setup, which limited the performance of methods for the aforementioned problems under a cross-spectral setting. This work introduces a new dataset, named EDGE19, that can be used in addressing the problems of pedestrian detection, face detection, and face recognition in images captured using trail cameras under the VIS and NIR spectra. Data acquisition was performed in an outdoor environment, during both day and night, under unconstrained acquisition conditions. The collection of images is accompanied by a rich set of annotations, consisting of person and facial bounding boxes, unique subject identifiers, and labels that characterize facial images as frontal, profile, or back faces. Moreover, the performance of several state-of-the-art methods was evaluated for each of the scenarios covered by our dataset. The baseline results we obtained highlight the difficulty of current methods in the tasks of cross-spectral pedestrian detection, face detection, and face recognition due to unconstrained conditions, including low resolution, pose variation, illumination variation, occlusions, and motion blur.

Many approaches for action recognition focus on general actions, such as "running" or "walking". This work presents a method for recognizing carrying actions in single images, by utilizing privileged information, such as annotation, available only during training, following the learning using privileged information paradigm. In addition, we introduce a dataset for carrying actions, formed using images extracted from YouTube videos depicting several scenarios. We accompany the dataset with a variety of different annotation types that include human pose, object and scene attributes. The experimental results demonstrate that our method, boosted sample averaged F1 score performance by 15.4% and 4.15%, respectively, in the validation and testing partitions of our dataset, when compared to an end-to-end CNN model, trained only with the observable information.