Intelligent recommendation system for College English courses based on graph convolutional networks.

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students' major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

An adaptively multi-correlations aggregation network for skeleton-based motion recognition.

Previous work based on Graph Convolutional Networks (GCNs) has shown promising performance in 3D skeleton-based motion recognition. We believe that the 3D skeleton-based motion recognition problem can be explained as a modeling task of dynamic skeleton-based graph construction. However, existing methods fail to model human poses with dynamic correlations between human joints, ignoring the information contained in the skeleton structure of the non-connected relationship during human motion modeling. In this paper, we propose an Adaptively Multi-correlations Aggregation Network(AMANet) to capture dynamic joint dependencies embedded in skeleton graphs, which includes three key modules: the Spatial Feature Extraction Module (SFEM), Temporal Feature Extraction Module (TFEM), and Spatio-Temporal Feature Extraction Module (STFEM). In addition, we deploy the relative coordinates of the joints of various parts of the human body via moving frames of Differential Geometry. On this basis, we design a Data Preprocessing Module (DP), enriching the characteristics of the original skeleton data. Extensive experiments are conducted on three public datasets(NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-Skeleton 400), demonstrating our proposed method's effectiveness.

Skeleton-based motion prediction: A survey.

Human motion prediction based on 3D skeleton data is an active research topic in computer vision and multimedia analysis, which involves many disciplines, such as image processing, pattern recognition, and artificial intelligence. As an effective representation of human motion, human 3D skeleton data is favored by researchers because it provide resistant to light effects, scene changes, etc. earlier studies on human motion prediction focuses mainly on RBG data-based techniques. In recent years, researchers have proposed the fusion of human skeleton data and depth learning methods for human motion prediction and achieved good results. We first introduced human motion prediction research background and significance in this survey. We then summarized the latest deep learning-based techniques for predicting human motion in recent years. Finally, a detailed paper review and future development discussion are provided.

QMEDNet: A quaternion-based multi-order differential encoder-decoder model for 3D human motion prediction.

In order to deal with the sequence information in the task of 3D human motion prediction effectively, many previous methods seek to predict the motion state of the next moment using the traditional recurrent neural network in Euclidean space. However human motion representation in Euclidean space has high distortion and shows a weak semantic expression when using deep learning models. In this work, we try to process human motion by mapping Euclidean space into a Hypercomplex vector space. We propose a novel model based on quaternion to predict the three-dimensional motion of a human body. The core idea of this study is to use the fusion information to understand and process the human motion state in quaternion space. The multi-order differential information is fused both in the encoder and decoder of feature extraction and mapped to the quaternion space, respectively. The encoder takes graph convolution as the basic unit and the decoder adopts gated recurrent units. Numerous experiments have been carried out to prove that the multi-order information in quaternion space can help build a more reasonable description for 3D human motion. The performance of the proposed QMEDNet is superior to most of the advanced short and long-term motion prediction methods in both public datasets, Human 3.6M and CMU Mocap.