TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

The paper "TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting" by Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, and Ursula Eicker, has been accepted for publication in the International Conference on Machine Learning and Applications (ICMLA), 2023. TL;DR: The hydrometric prediction of water quantity

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Analysis of error rate in hierarchical menu selection in immersive augmented reality

Our paper Analysis of error rate in hierarchical menu selection in immersive augmented reality has been accepted for publication in SPIE AR|VR|MR. The work is co-authored by Majid Pourmemar and Charalambos Poullis. The paper is now available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12449/124491L/Analysis-of-error-rate-in-hierarchical-menu-selection-in-immersive/10.1117/12.
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Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

The preprint of our paper Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction is available on arxiv.org. The work is co-authored by Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, and Charalambos Poullis. TL;DR: We present a sequence-to-sequence architecture to extract the
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