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At present, the existing news recommendation system fails to fully consider the semantic information of news, meanwhile, the uneven popularity of news will also cause the phenomenon of long tail. Therefore, we propose a novel news recommendation model based on encoder graph neural network and Bat optimization in online social networks. Firstly, Bat optimization algorithm is used to improve the effect of news clustering. Secondly, the concept of metadata is introduced into the graph neural network, and the ontology of learning resources based on knowledge points is established to realize the correlation between news resources. Finally, the model combining Convolutional Neural Network (CNN) and attention network is used to learn the representation of news, and Gate Recurrent Unit (GRU) is used to learn the short-term preferences of users from their recent reading history. We carry out experiments on real news datasets, and compared with other advanced methods, the proposed model has better evaluation indexes. [ABSTRACT FROM AUTHOR]

In space art design, the recognition of expression is of great help to the understanding of art. It is very difficult to obtain occlusion expression data from robot environment. In particular, it is very challenging to recognize the occluded expression. In the case of facial occlusion, it is difficult to extract the features of occluded expressions by traditional methods. In order to reduce the dependence of expression recognition on individuals, this paper proposes a cycle-consistent adversarial network and K-SVD dictionary learning method for occluded expression recognition in education management under robot environment. Firstly, the new method uses the cyclic-consistent generation adversarial network as the skeleton model, which can generate the un-occluded expression image without the need of paired data sets. Meanwhile, in order to improve the discriminant ability and image generation ability of the network, a multi-scale discriminator is used to construct the discriminant network. Then, the least squares and cyclic sensing loss are used to strengthen the constraints on the network model and improve the image quality. By subtracting the error matrix from the test sample, a clear image of the expression classification stage can be recovered. The clear image samples are decomposed into identity features and expression features by using the collaborative representation of two dictionaries. Finally, it is classified according to the contribution of each expression feature to the joint sparse representation. Experiments conducted on CK+, RAF-DB and SFEW datasets, the results show that the average accuracy of the new model is 98.44%, 87.12% and 62.17%, respectively. Compared with the traditional convolutional neural network models and advanced methods, this model effectively improves the accuracy of facial recognition in the case of facial occlusion. [ABSTRACT FROM AUTHOR]

In order to realize fast and accurate search of sensitive regions in remote sensing images, we propose a multi-functional faster RCNN based on multi-scale feature fusion model for region search. The feature extraction network is based on ResNet50 and the dilated residual blocks are utilized for multi-layer and multi-scale feature fusion. We add a path aggregation network with a convolution block attention module (CBAM) attention mechanism in the backbone network to improve the efficiency of feature extraction. Then, the extracted feature map is processed, and RoIAlign is used to improve the pooling operation of regions of interest and it can improve the calculation speed. In the classification stage, an improved nonmaximum suppression is used to improve the classification accuracy of the sensitive region. Finally, we conduct cross validation experiments on Google Earth dataset and the DOTA dataset. Meanwhile, the comparison experiments with the state-of-the-art methods also prove the high efficiency of the proposed method in region search ability. [ABSTRACT FROM AUTHOR]

This paper presents a novel web-based crowdsourcing platform for the assessment of the subjective and objective quality of experience (QoE) of the video service in the cloud-server environment. The user has the option to enter subjective QoE data for video service by filling out a web questionnaire. The objective QoE data of the cloud-server, network condition, and the user device is automatically captured by the crowdsourcing platform. Our proposed system collects both objective and subjective QoE simultaneously in real-time. The paper presents the key technologies used in the development of the platform and describes the functional requirements and design ideas of the system in detail. The system collects real-time comprehensive data to enhance the quality of the user experience to provide a valuable reference. The system is tested in a real-time environment and the test results are given in terms of the system performance. The crowdsourcing platform has new features of real-time network monitoring, the client device, and cloud monitoring, which currently has not been provided by existing web platforms and crowdsourcing frameworks. The results show that 1MB buffer is filled 100% very soon after starting watching videos from the crowdsourcing platform. [ABSTRACT FROM AUTHOR]