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Video information hiding and transmission over noisy channels leads to errors on video and degradation of the visual quality notably. In this paper, a video signal fusion scheme is proposed to combine sensed host signal and the hidden signal with quantization index modulation (QIM) technology in the compressive sensing (CS) and discrete cosine transform (DCT) domain. With quantization based signal fusion, a realistic solution is provided to the receiver, which can improve the reconstruction video quality without requiring significant extra channel resource. The extensive experiments have shown that the proposed scheme can effectively achieve the better trade-off between robustness and statistical invisibility for video information hiding communication. This will be extremely important for low-resolution video analytics and protection in big data era

Simple graph convolution (SGC) achieves competitive classification accuracy to graph convolutional networks (GCNs) in various tasks while being computationally more efficient and fitting fewer parameters. However, the width of SGC is narrow due to the over-smoothing of SGC with higher power, which limits the learning ability of graph representations. Here, we propose AdjMix, a simple and attentional graph convolutional model, that is scalable to wider structure and captures more nodes features information, by simultaneously mixing the adjacency matrices of different powers. We point out that the key factor of over-smoothing is the mismatched weights of adjacency matrices, and design AdjMix to address the over-smoothing of SGC and GCNs by adjusting the weights to matching values. Experiments on citation networks including Pubmed, Citeseer, and Cora show that our AdjMix improves over SGC by 2.4%, 2.2%, and 3.2%, respectively, while achieving same performance in terms of parameters and complexity, and obtains better performance in terms of classification accuracy, parameters, and complexity, compared to other baselines.

Amorphallus konjac corms are important agriculture products in Yichang, Hubei Province, China. The Erwinia carotovora infected Amorphallus konjac corms are processed to food as normal corms. The contents of elements and L: -Proline in the normal and infected Amorphallus konjac corms are analyzed for food safety. Even growing in the almost same soil condition, the contents of Pb, Cd, Mn and L: -Proline in infected corms are significantly higher than those of normal corms (show data as suggestion by peers). Our study suggested that the infected corms are not suitable for food purpose.