Xi Li^1,2, Weiwei Zhu², Qian Li^3,*, Changhui Hou^1,*, Yaozong Zhang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 369-391, 2024, DOI:10.32604/cmc.2024.054982 - 15 October 2024

Abstract Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key components:… More >

Shengchun Wang¹, Xiaozhong Yu¹, Lianye Liu², Jingui Huang^{1, *}, Tsz Ho Wong³, Chengcheng Jiang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627 - 23 July 2020

Abstract Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address More >