ABSTRACT
Detecting students' classroom behaviors from instructional videos is important for instructional assessment, analyzing students' learning status, and improving teaching quality. To achieve effective detection of student classroom behavior based on videos, this paper proposes a classroom behavior detection model based on the improved SlowFast. First, a Multi-scale Spatial-Temporal Attention (MSTA) module is added to SlowFast to improve the ability of the model to extract multi-scale spatial and temporal information in the feature maps. Second, Efficient Temporal Attention (ETA) is introduced to make the model more focused on the salient features of the behavior in the temporal domain. Finally, a spatio-temporal-oriented student classroom behavior dataset is constructed. The experimental results show that, compared with SlowFast, our proposed MSTA-SlowFast has a better detection performance with mean average precision (mAP) improvement of 5.63% on the self-made classroom behavior detection dataset.
Subject(s)
Learning , Students , Humans , Videotape RecordingABSTRACT
@#Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis, including tongue image segmentation and tongue color classification, improving their diagnostic accuracy. Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results. A new dataset was constructed for tongue image segmentation. Tongue color was marked to build a classified dataset for network training. In this research, the Inception + Atrous Spatial Pyramid Pooling (ASPP) + UNet (IAUNet) method was proposed for tongue image segmentation, based on the existing UNet, Inception, and atrous convolution. Moreover, the Tongue Color Classification Net (TCCNet) was constructed with reference to ResNet, Inception, and Triple-Loss. Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification. IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+ for tongue segmentation. TCCNet for tongue color classification was compared with VGG16 and GoogLeNet. Results IAUNet can accurately segment the tongue from original images. The results showed that the Mean Intersection over Union (MIoU) of IAUNet reached 96.30%, and its Mean Pixel Accuracy (MPA), mean Average Precision (mAP), F1-Score, G-Score, and Area Under Curve (AUC) reached 97.86%, 99.18%, 96.71%, 96.82%, and 99.71%, respectively, suggesting IAUNet produced better segmentation than other methods, with fewer parameters. Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors. The experiment yielded ideal results, with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%, respectively. Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones. IAUNet can not only produce ideal tongue segmentation, but have better effects than those of PSPNet, SegNet, UNet, and DeepLabV3+, the traditional networks. As for tongue color classification, the proposed network, TCCNet, had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.
ABSTRACT
The objective of the present study was to incorporate strontium into calcium phosphate cement combined with a lower single-dose local administration of bone morphogenetic protein-2 to enhance its in vivo biodegradation and bone tissue growth. After the creation of a rodent critical-sized femoral metaphyseal bone defect, strontium-modified calcium phosphate cement was prepared by mixing sieved granules of calcium phosphate cement and 5% SrCO3 for medical use, and then strontium-modified calcium phosphate cement with dripped bone morphogenetic protein-2 solution (5 µg) was implanted into the defect of OVX rats until death at eight weeks. The defected area in distal femurs of rats was harvested for evaluation by histology, micro-CT, and biomechanics. The results of our study show that a lower single-dose local administration of bone morphogenetic protein-2 combined local usage of strontium-modified calcium phosphate cement can increase the healing of defects in OVX rats. Furthermore, treatments with single-dose local administration of bone morphogenetic protein-2 and strontium-modified calcium phosphate cement showed a stronger effect on accelerating the local bone formation than calcium phosphate cement and strontium-modified calcium phosphate cement used alone. The results from our study demonstrate that combination of a lower single-dose local administration of bone morphogenetic protein-2 and strontium-modified calcium phosphate cement had an additive effect on local bone formation in osteoporosis rats.
