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1. MobiPDF: Reconstructing PDF contents for Mobile Devices | |||
Peng Jin,Ligang He,Yilian Zhou,Cheng Chang,Liangtang Lei,Xiaorui Zhang,Hao Chen | |||
Computer Science and Technology 22 May 2020 | |||
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Abstract:The PDF format is widely used in academia with its elegant style and unmodifiable features. With the popularity of mobile terminals, more and more people use mobile devices to browse and obtain documents. However, once the PDF document is generated, the style is fixed. It is difficult to read on a small-sized mobile screen. In this work, we develop a system called MobiPDF to reconstruct the content in a PDF document and render it in a way so that it can viewed properly on a mobile screen. In particular, the content of the PDF document is first decomposed into different areas. The PDF file is then converted into an HTML format file and also to an image file. Next, the texts and their rendering information are extracted from the HTML file according to the positions of the decomposed areas. In addition, the non-text information such as figures and tables is extracted from the image file based on its positions. The extracted text and non-text contents together with the rendering information are reconstructed as a new HTML file according to the size of the user\'s mobile screen. We have developed MobiPDF as a web service, which is hosted on the Tencent Cloud and can be accessed publically. | |||
TO cite this article:Peng Jin,Ligang He,Yilian Zhou, et al. MobiPDF: Reconstructing PDF contents for Mobile Devices[OL].[22 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752113 |
2. Research on Cache Strategy of Edge Image Based on Popularity Prediction | |||
ZOU Sheng,LIU Liang | |||
Computer Science and Technology 25 March 2019 | |||
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Abstract:With the advent of the era of Internet of Everything, the edge computing used to make up for the lack of cloud computing comes into being. However, due to the light-weighting of edge clouds in edge computing, the problem of limited resources is caused, especially in terms of storage resources. At the same time, considering the problem that there are a lot of redundant images in the virtual machine or the container, this paper solves the problem of insufficient storage resources from the perspective of optimizing the edge image cache. To this end, this paper chooses Kubernetes as the edge platform, and uses the value of Baidu index as the popularity value, and proposes an edge image cache algorithm based on popularity prediction, namely b-GRU. Firstly, based on the feature analysis of the acquired data, the prediction of the image popularity based on GRU is performed. Then, the image cache replacement based on the popularity prediction is performed. Finally, the comparison experiment of b-GRU shows that the storage space of b-GRU is only 41% of LRU and LFU storage space under the condition of guaranteeing a certain cache hit ratio, which proves the effectiveness of this strategy. | |||
TO cite this article:ZOU Sheng,LIU Liang. Research on Cache Strategy of Edge Image Based on Popularity Prediction[OL].[25 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4748074 |
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