In this paper, we propose a novel General-NMF (General Non-negative Matrix Factorization) based digital watermarking scheme for copyright protection and integrity authentication of digital media content. Specifically, the proposed General-NMF algorithm is able to factorize a matrix C ${\in}R{^{s \times t}_{+}}$ decomposed into a basis matrix A ${\in}R{^{m \times n}_{+}}$ and a coefficient matrix B ${\in}R{^{p \times q}_{+}}$ by removing the dimension matching constraints required by the traditional NMF, where $s=m, n=p$ and $t=q$. In particular, $s=m \cdot l/n, t=l/p \cdot q$, and the variable l is the least common multiple of n and p. Furthermore, the generator factor of the random matrix and n are used as the keys of the proposed digital watermarking scheme. Experiment results show that the proposed digital watermarking scheme can effectively resist various attacks and tampering.

The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters.

Community detection is an important method to analyze the characteristics and structure of community networks, which can excavate the potential links between nodes and further discover subgroups from complex networks. However, most of the existing methods only unilaterally consider the direct link topology without comprehensively considering the internal and external characteristics of the community as well as the result itself, which fails to maximize the access to the network information, thus affecting the effectiveness of community detection. To compensate for this deficiency, we propose a new community detection method based on multi-constraint non-negative matrix factorization, named orthogonal regular sparse constraint non-negative matrix factorization (ORSNMF). Based on the network topology, the ORSNMF algorithm models the differences of the outside of the community, the similarities of the nodes inside the community, and the sparseness of the community membership matrices at the same time, which together guides the iterative learning process to better reflect the underlying information and inherent attributes of the community structure in order to improve the correct rate of dividing subgroups. An algorithm with convergence guarantee is also proposed to solve the model, and finally a large number of comparative experiments are conducted, and the results show that the algorithm has good results.