<i>Background</i>: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged ...

<p>Distributed version control systems are popular for storing source code, but they are notoriously ill suited for storing large binary files. <p>We report on the results from a set of experiments designed to characterize the behavior of some widely used distributed version control systems with respect to scaling. The experiments measured commit times and repository sizes when storing single files ...

Background:The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to ...

This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters ...

<i>Background and objective</i>: The number of publications on the use of chatbots for health is recently increasing, however to our knowledge, there are no publications summarizing what is known about using chatbots for public health yet. The objective of this work is to provide an overview of the existing scientific literature on the use of chatbots for public health, for which purpose have chatbots ...

Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms’ hyperparameters and classification performance, ...

Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced ...

Highly automated vehicles will change our personal mobility in the future. To ensure the safety and the comfort of their passengers, the cars have to rely on as many information regarding their current surrounding traffic situation, as they can obtain. In addition to classical sensors like cameras or radar sensors, automated vehicles use data from a so called High Definition Street Map. Through such ...

Infection incidences in people with diabetes can create sever health complications mainly due to the effect of stress hormones, such as cortisol and adrenaline, which increases glucose production and insulin resistance in the body. The proposed electronic disease surveillance monitoring network (EDMON) relies on self-recorded data from people with Type 1 diabetes and dedicated algorithms to detect ...

Patients increasingly collect health-related data using mobile health apps and sensors. Studies have shown that this data can be beneficial for both clinicians and patients if used during medical consultations. However, such data is almost never used outside controlled situations or medical trials. This paper explains why the usage of self-collected health data is not widespread by identifying ...