Abstract: In complex and dynamic urban traffic scenarios, the accurate prediction of trajectories of surrounding traffic participants (vehicles, pedestrians, etc) with interactive behaviours plays an ...

Abstract: Generating discriminative node representations for heterogeneous graphs based on contrastive learning with graph neural networks has become an important topic in data mining. Many existing ...

Why play matters: Shifting math from rigid drills to playful exploration helps reduce anxiety and fosters a lasting love for ...

AI-powered math tutors are changing how students learn, making complex topics easier to understand with step-by-step guidance, interactive visuals, and personalized support. From solving calculus ...

We propose a novel deep learning framework, STGCN, to tackle time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem ...