To efficiently improve the effects of image Super Resolution (SR), a multi-stage cascade residual convolution neural network model was proposed. Firstly, two-stage SR image reconstruction method was used to reconstruct the 2-times SR image and then reconstruct the 4-times SR image; secondly, residual layer and jump layer were used to predict the texture information of the high resolution space in the first and second stages, and deconvolution layer was used to reconstruct 2-times and 4-times SR images. Finally, two multi-task loss functions were constructed respectively by the results of two stages. And the loss of the first stage guided that of the second one, which accelerated the network training and enhanced the supervision and guidance of the network learning. The experimental results show that compared with bilinear algorithm, bicubic algorithm, Super Resolution using Convolutional Neural Network (SRCNN) algorithm and Fast Super Resolution Convolutional Neural Network (FSRCNN) algorithm, the proposed model can better construct the details and texture of images, which avoids the image over smoothing after iterating, and achieves higher Peak Signal-to-Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM).

With the development of e-commerce, online shopping Web sites provide reviews for helping a customer to make the best choice. However, the number of reviews is huge, and the content of reviews is typically redundant and non-standard. Thus, it is difficult for users to go through all reviews in a short time and find the distinguishing characteristics of a product from the reviews. To resolve this problem, a method to mine top- k emerging patterns was proposed and applied to mining reviews of different products. Based on the proposed method, a prototype, called ReviewScope, was designed and implemented. ReviewScope can find significant comments of certain goods as decision basis, and provide visualization results. The case study on real world data set of JD.com demonstrates that ReviewScope is effective, flexible and user-friendly.