Efficient page layout analysis on small devices

Previously we have designed and implemented new image browsing facilities to support effective offiine image contents on mobile devices with limited capabilities： low bandwidth, small display, and slow processing. In this letter, we fulfill the automatic production of cartoon contents fitting small-screen display, and introduce a clustering method useful for various types of cartoon images as a prerequisite stage for preserving semantic meaning. The usage of neural networks is to properly cut the various forms of pages. Texture information that is useful for grayscale image segmentation gives us a good clue for page layout analysis using the multilayer perceptron （MLP） based x-y recursive algorithm. We also automatically frame the segment MLP using agglomerative segmentation. Our experimental results show that the combined approaches yield good results of segmentation for several cartoons.     

Understanding the performance of universities to promote technological innovation and transformation: An application to China based on a new network data envelopment analysis cross-efficiency model

When evaluating the performance of Research and Development (R&D) and Scientific and Technological (S&T) transformation activities in universities, the existing studies do not contain prior strategy analysis and inequality analysis. In this regard, prior strategy reflects the relative attention paid by universities to R&D and S&T transformation activities, while inequality analysis assists us in better understanding technological inequality in efficiency across universities. Additionally, this study contributes to the existing literatures by proposing new concepts of window-based cross-efficiency, window-based relative weights and a network Data Envelopment Analysis model. Our framework is able to evaluate the efficiency, perform priority analysis and identify the sources of technological inequality. Empirically, this study focuses on universities in Chinese provinces from 2007 to 2019. This study is of high policy practice, since it provides policy makers with how each university has performed during the examined period and what are the underling factors driving efficiency changes.