GNNs have been widely used in deep learning on graphs. They learn effective node representations. However, most methods ignore the heterogeneity. Methods designed for heterogeneous graphs, on the other hand, fail to learn complex semantic representations because they only use meta-paths instead of meta-graphs. Furthermore, they cannot fully capture the content-based correlations, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors. We propose a novel Higher-order Attribute-Enhancing (HAE) framework enhancing node embedding in a layer-by-layer manner. Under the HAE framework, we propose a Higher-order Attribute-Enhancing GNN (HAE\textsubscript{GNN}) for heterogeneous network embeding. HAE\textsubscript{GNN} simultaneously incorporates meta-paths and meta-graphs for rich, heterogeneous semantics, and leverages the self-attention mechanism to explore content-based nodes' interactions. The unique higher-order architecture of HAE\textsubscript{GNN} allows examining the first-order as well as higher-order neighborhoods. Moreover, HAE\textsubscript{GNN} shows good explainability as it learns the importances of different meta-paths and meta-graphs. HAE\textsubscript{GNN} is also memory-efficient, for it avoids per meta-path based matrix calculation. Experimental results not only show HAE\textsubscript{GNN}'s superior performance against the state-of-the-art methods in node classification, node clustering, and visualization, but also demonstrate its superiorities in terms of memory efficiency and explainability.

In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction method and model fusion technology are proposed to solve the problem of basic medical insurance fraud identification. The feature second-level extraction algorithm proposed in this paper can effectively extract important features and improve the prediction accuracy of subsequent algorithms. In order to solve the problem of unbalanced simulation allocation in the medical insurance fraud identification scenario, a sample division method based on the idea of sample proportion equilibrium is proposed. Based on the above methods of feature extraction and sample division, a new training and fitting model fusion algorithm (tree hybrid bagging, THBagging) is proposed. This method makes full use of the balanced idea of the tree model algorithm based on Boosting to fuse, and finally achieves the effect of improving the accuracy of basic medical insurance fraud identification.

Usually, in addition to the main content, web pages contain additional information in the form of noise, such as navigation elements, sidebars and advertisements. This kind of noise has nothing to do with the main content, it will affect the tasks of data mining and information retrieval so that the sensor will be damaged by the wrong data and interference noise. Because of the diversity of web page structure, it is a challenge to detect relevant information and noise in order to improve the true reliability of sensor networks. In this paper, we propose a visual block construction method based on page type conversion (VB-PTC). This method uses a combination of site-level noise reduction based on hashtree and page-level noise reduction based on linked clusters to eliminate noise in web articles, and it successfully converts multi-record complex pages to multi-record simple pages, effectively simplifying the rules of visual block construction. In the aspect of multi-record content extraction, according to the characteristics of different fields, we use different extraction methods, combined with regular expression, natural language processing and symbol density detection methods which greatly improves the accuracy of multi-record content extraction. VB-PTC can be effectively used for information retrieval, content extraction and page rendering tasks.

With the rising elderly population in China, detecting whether an elderly person has fallen is one of the problems that people need to pay attention to today, however, most of the current detection methods are affected by problems such as expensive, vulnerable to environment and not easy to implement. In order to solve the above problems, this paper proposes a fall detection method with YOLOv5s as the basic network model, which first enhances the original image, and then improves the loss function and NMS non-maximal suppression. The final results show that applying this improved algorithm model can effectively perform fall detection.