Knowledge graph can be used to clarify the curriculum knowledge system and describe the knowledge point and its structural relationship. In order to improve the learning efficiency and level of learners in the process of programming language learning, the typical examples related to knowledge can be incorporated into the knowledge graph. In this study, the knowledge graph of C# programming language is constructed as an example: first, the data is obtained; Second, the ontology is constructed and the data is processed. Then use the Neo4j graph database to store knowledge; Finally, knowledge retrieval and visualization are realized, and knowledge graph can be used to assist teaching and personalized learning.
Face inpainting is a challenging task in computer vision. Although deep learning-based methods that apply attention mechanism or utilize prior knowledge could reconstruct facial components, they may produce visual artifacts or lack detail texture. To solve mainly these two problems, we propose a multicolumn gated convolutional network (MGCN). MGCN is composed of three parallel branches with gated convolution to dynamically extract multispatial features, which could help to improve the global semantic coherence and achieve more effective performance in irregular mask. Specifically, for generating more plausible texture, we developed a diversified perceptual Markov random field to search correct feature patches in global rather than local images. Experiments on CelebA-HQ face and Flickr-Faces-HQ datasets demonstrate that MGCN achieves a more competitive performance than the state-of-the-art methods.
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