Abstract With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks.

Currently, there are more than 2,400 stocks in Chinese A-shares market and there is almost one IPO share coming into the emerging market per day. The rapid growth of Chinese stock market makes investors difficult to manage portfolio. In the paper, a Chinese A-shares Network (CAN) is constructed using a topic model (i.e., Latent Dirichlet Allocation) to efficiently divide all the A-shares to individual sectors in a probabilistic way in terms of the Business Scope Descriptions (BSD) of the listed companies until December 31, 2011. In the meanwhile, a novel visualization profile is proposed to friendly show stock-sectors relationships. Experimental results validate the effectiveness of the CAN system: the stocks in the same ``sector" defined by the CAN have higher pair wise correlations than those by the experts.

It is a great challenge to perform robust tracking for a mobile robot owing to dynamic environments. Also, fast motion or abrupt jerk of the robotic camera poses a severe threat for continuous tracking. To address these problems, a novel attention model is proposed motivated by human attention mechanism which consists of low level salient feature and high level scene semantics. The low level layer extracts color and motion feature to obtain combined feature probability map. In semantic level, the ADM(attention distribution map) is computed by applying an attenuation function on the combined feature map which is motivated by human's foveal vision. The object position is found using CAMSHIFT algorithm in ADM. And this layer also generates a region-based SSG(scene semantics graph). When robot moves abnormally, the model detects candidate regions in color saliency map and then attention shifts from one region to the next and check it by elastically matching SSG until the target is recovered. Experiments in several kinds of environments give promising results and show that this model is robust for mobile robotic tracking. When camera moves steadily, a little fast or even jerks very abruptly, it can keep continuous tracking.