Artificial Intelligence

This large dataset includes six small datasets, including two types, one contains the original node relationship information and node feature information, please use it through the common network construction methods; the other is the dataset which has been processed, including the direct edge information and node's association information, which can be used to construct the network directly through the network construction methods.

We have developed three datasets, referred to as ER-C, Mito-C and Nucleus-C, respectively, for benchmarking robustness of DNN models against corruptions and adversarial attacks in semantic segmentation of fluorescence microscopy images. Degraded images in these three datasets are synthesized from raw images along with their manually annotated segmentation labels in the ER, Mito, and Nucleus datasets [1,2], respectively.

The large and diverse access to data sources in healthcare has boosted the application of novel computer techniques that can extract meaningful information to improve patients' prognoses and other important medical uses. However, current systems require the professional to manually type the information, which increases the risk of transcription errors and cross-contamination. We propose an automated system that allows healthcare professionals to dictate clinical information that can be transcribed and analyzed.

This dataset includes environmental perception, vehicle motion status, and battery consumption during vehicle operation. The dataset is collected by human operators during the safe and smooth operation of vehicles. By dividing the hazardous areas in the current environment of the vehicle and processing them, a radar map of the hazardous areas is generated; The vehicle motion status section contains the position information (x, y, z) and kinematic information (v, w, a, alpha, beta, gamma) of the vehicle.