Artificial Intelligence

This dataset is the supporting simulated data for the paper titled "Hidden Border Tunnels: Research on Excavation Monitoring and Excavation Path Prediction." These data are generated through physical simulations and are used to validate the effectiveness of the algorithms proposed in the paper. The dataset includes the coordinates of simulated vibration events, as well as the prediction results of excavation events by various machine learning models, such as RNN, LSTM, GRU, Transformer, CNN_Transformer, and LSTMTransformer.

NICU-Care is a high-quality video dataset designed to support visual recognition tasks in Neonatal Intensive Care Unit (NICU) scenarios, including nursing action recognition, object detection, and semantic segmentation. It was constructed in a standardized simulated NICU environment, capturing multi-view RGB videos of professional nurses performing six types of routine caregiving procedures on simulated infants. The dataset provides fine-grained temporal annotations and pixel-level segmentation masks for key objects like nurse hands, medical tools, and infant body parts.

Furthermore, we introduce A Multi-Modal Continuous Emotion Annotation Dataset for VR Action Games (MMEAD-VRAG), the first multi-modal time-series dataset incorporating both physiological and behavioral signals in VR action gaming scenarios. A comparative analysis with existing state-of-the-art datasets reveals that MMEAD-VRAG exhibits fewer limitations in terms of data collection methodology, dataset scale, and participant diversity.

This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.