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The improvement of strategies for information-based educational and teaching abilities of newly appointed teachers should be substantially investigated urgently based on the policy level of various countries or the reality of colleges and universities. However, from the perspective of the Technological Pedagogical Content Knowledge（TPACK）hierarchy, there are only a few studies on the strategies to improve the information-based educational and teaching abilities of newly appointed teachers. Therefore, this study analyzed the relationships between the three cores and four composite elements, designed the ability improvement strategy corresponding to each element, designed a hierarchical improving strategy which corresponds to 20 training contents, and used quasi-experimental research methods to apply the strategy to the 2019 newly recruited teacher training system of Guangdong University of Finance and Economics. Through the implementation of the training strategy for one semester and effect evaluation, combined with the comparative analysis of the training evaluation data of newly appointed teachers who did not implement this strategy in 2018, it shows that the implementation of the strategy can significantly improve the information-based educational and teaching ability of newly appointed teachers, with good results.

With the rapidly growing number of images over the Internet, efficient scalable semantic image retrieval becomes increasingly important. This paper presents a novel approach for semantic image retrieval by combining Convolutional Neural Network (CNN) and Markov Random Field (MRF). As a key step, image concept detection, that is, automatically recognizing multiple semantic concepts in an unlabeled image, plays an important role in semantic image retrieval. Unlike previous work that uses single-concept classifiers one by one, we detect semantic multiconcept by using a multiconcept scene classifier. In other words, our approach takes multiple concepts as a holistic scene for multiconcept scene learning. Specifically, we first train a CNN as a concept classifier, which further includes two types of classifiers: a single-concept fully connected classifier that is best suited to single-concept detection and a multiconcept scene fully connected classifier that is good for holistic scene detection. Then we propose an MRF-based late fusion approach that is able to effectively learn the semantic correlation between the single-concept classifier and multiconcept scene classifier. Finally, the semantic correlation among the subconcepts of images is cought to further improve detection precision. In order to investigate the feasibility and effectiveness of our proposed approach, we conduct comprehensive experiments on two publicly available image databases. The results show that our proposed approach outperforms several state-of-the-art approaches.

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