A REVIEW OF THE USE OF STORYTELLING TO IMPROVE STUDENTS’ ORAL PROFICIENCY IN EFL TEACHING

This review examines how storytelling has been applied in EFL teaching in secondary and higher education to enhance students’ oral proficiency. It aims to offer some insight into using storytelling as an effective tool for designing speaking classes in EFL contexts. The review found that as a teaching strategy, storytelling can be effective in promoting oral proficiency in students of English in both secondary and higher education contexts. The main difference lies with teachers as the main storytelling designers in secondary education and students in higher education. Similarly, in secondary education the application of storytelling tends to happen in the classroom, while in higher education, storytelling is more likely to be used outside the classroom by creating digital stories. Based on the analysis of the implementation of storytelling in both contexts, the review closes with some guidelines for the application of storytelling in EFL teaching.  Article visualizations

RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network

In recent years, convolutional neural network has shown good performance in many image processing and computer vision tasks. However, a standard CNN model is not invariant to image rotations. In fact, even slight rotation of an input image will seriously degrade its performance. This shortcoming precludes the use of CNN in some practical scenarios. Thus, in this paper, we focus on designing convolutional layer with good rotation invariance. Specifically, based on a simple rotation-invariant coordinate system, we propose a new convolutional operation, called Rotation-Invariant Coordinate Convolution (RIC-C). Without additional trainable parameters and data augmentation, RIC-C is naturally invariant to arbitrary rotations around the input center. Furthermore, we find the connection between RIC-C and deformable convolution, and propose a simple but efficient approach to implement RIC-C using Pytorch. By replacing all standard convolutional layers in a CNN with the corresponding RIC-C, a RIC-CNN can be derived. Using MNIST dataset, we first evaluate the rotation invariance of RIC-CNN and compare its performance with most of existing rotation-invariant CNN models. It can be observed that RIC-CNN achieves the state-of-the-art classification on the rotated test dataset of MNIST. Then, we deploy RIC-C to VGG, ResNet and DenseNet, and conduct the classification experiments on two real image datasets. Also, a shallow CNN and the corresponding RIC-CNN are trained to extract image patch descriptors, and we compare their performance in patch verification. These experimental results again show that RIC-C can be easily used as drop in replacement for standard convolutions, and greatly enhances the rotation invariance of CNN models designed for different applications