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As the push for a diversified use of information technologies in higher education teaching continues, a growing number of colleges and universities have come to adopt blended learning which combines traditional face-to-face lectures with online instruction to create flexible approaches of delivering content that are consistent with the requirements of new digital economy. At the same time, university students are required to have continuous growth in literacy skills. Metaliteracy is a comprehensive model for information literacy that can enhance blended learning experience. Embedding metaliteracy learning in a blended course is considered as a feasible approach to empower students in blended learning. Combining an analysis of data gathered through a survey administered at Xi’an Jiaotong-Liverpool University, a Sino-UK institution located in China, this paper reports the results of an investigation into the pedagogical issues including the metaliteracy learning experience of using an interactive communication environment and the benefits and challenges of integrating practices of metaliteracy with blended learning.

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As one of the essential pieces of evidence of crime scenes, footprint images cannot be ignored in the cracking of serial cases. Traditional footprint comparison and retrieval require much time and human resources, significantly affecting the progress of the case. With the rapid development of deep learning, the convolutional neural network has shown excellent performance in image recognition and retrieval. To meet the actual needs of public security footprint image retrieval, we explore the effect of convolution neural networks on footprint image retrieval and propose an ensemble deep neural network for image retrieval based on transfer learning. At the same time, based on edge computing technology, we developed a footprint acquisition system to collect footprint data. Experimental results on the footprint dataset we built show that our approach is useful and practical.