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The reversible watermarking technique is suitable for vector maps due to its reversibility after watermark extraction. In this paper, a novel reversible watermarking scheme based on the idea of nonlinear scrambling is proposed. It begins with feature point extraction. To avoid the high-precision vector data being illegally used by unauthorized users, the algorithm nonlinearly scrambles the relative position of feature points. Then based on the proposed reversible embedding, both scrambled feature points and nonfeature points are taken as cover data, the coordinates of which are modified to embed both watermark data and feature point identification data. Finally, combined with the scrambling secret key, the original vector data can be exactly recovered with watermark extraction. Comprehensive experimental results validate that the scheme could effectively prevent the high-precision vector data from being illegally used with maintaining the basic shape of each polyline, simultaneously. [ABSTRACT FROM AUTHOR]

We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only one ViT model at a time, but is capable of providing improved image recognition performance with various computational costs. Here, the trained ViT model, termed super vision transformer (SuperViT), is empowered with the versatile ability to solve incoming patches of multiple sizes as well as preserve informative tokens with multiple keeping rates (the ratio of keeping tokens) to achieve good hardware efficiency for inference, given that the available hardware resources often change from time to time. Experimental results on ImageNet demonstrate that our SuperViT can considerably reduce the computational costs of ViT models with even performance increase. For example, we reduce 2 × FLOPs of DeiT-S while increasing the Top-1 accuracy by 0.2% and 0.7% for 1.5 × reduction. Also, our SuperViT significantly outperforms existing studies on efficient vision transformers. For example, when consuming the same amount of FLOPs, our SuperViT surpasses the recent state-of-the-art EViT by 1.1% when using DeiT-S as their backbones. The project of this work is made publicly available at https://github.com/lmbxmu/SuperViT. [ABSTRACT FROM AUTHOR]

Consulting through message-updating in social network is regarded as a popular way of information seeking. However, most questions cannot receive answers or suggestions timely, and some questions even fail to get replies. Thus, identifying microblogs that contain questions (we call them 'question microblog') and recommending answers automatically are meaningful. We divide this problem into two submodules: question identification and answer recommendation. To the best of our knowledge, few attempts have been made to identify questions in microblogs due to standard features such as 5W1H (How, What, Where, When, Who, Why) are likely to be absent. The following challenging problem is how to provide users a relevant, credible, diversified and personalized answer after a microblog is recognized as a question. In this paper, we investigate the feasibility of integrating standard features and contextual features extracted from auxiliary resources and recommend a reasonable answer using collaborative filtering. Empirical results on Sina Microblog dataset demonstrate the efficacy and effectiveness of our method. [ABSTRACT FROM AUTHOR]