Spatial database is a spatial information database and is the core component of geographic information systems (GIS). Aiming at the problem that time complexity of k-nearest neighbor (kNN) querying... Show More

Spatial database is a spatial information database and is the core component of geographic information systems (GIS). Aiming at the problem that time complexity of k-nearest neighbor (kNN) querying algorithms are proportionate to scale of training samples, an efficient query method for spatial database based on the Spark framework and the reversed k-nearest neighbor (RkNN) is proposed. Firstly, based on the Spark framework, a two-layer indexing structure based on grid and Voronoi diagram is constructed, and an efficient filtering and a refining processing algorithm are proposed. Secondly, the filtering step of proposed algorithm is used to obtain the candidates, and the refining step is used to remove the candidates. Finally, the candidate sets from different regions are merged to get the final result. Results of experiments on real-world datasets validate that the proposed method has better query performance and better stability and significantly improves the processing speed.

This paper firstly examines the development history of China's equipment manufacturing industry and the research status quo of domestic and foreign information technology upgrading of equipment... Show More

This paper firstly examines the development history of China's equipment manufacturing industry and the research status quo of domestic and foreign information technology upgrading of equipment manufacturing industry, then gives the definition of information technology and equipment manufacturing industry upgrading, clearly points out the shortcomings of China's information technology development and analyses the mechanism of information technology upgrading of equipment manufacturing industry, and finally explores two paths for the upgrading of production equipment and upgrading of production processes, and gives differentiated solutions for regional development imbalance of the boosting path gives a differentiated programme. This paper provides theoretical references for the development of this field in China by exploring specific paths on the basis of the research on the mechanism of information technology's facilitating effect on the equipment manufacturing industry at home and abroad.

Compared with single-modal methods, emotion recognition research is increasingly focusing on the use of multi-modal methods to improve accuracy. Despite the advantages of multimodality, challenges... Show More

Compared with single-modal methods, emotion recognition research is increasingly focusing on the use of multi-modal methods to improve accuracy. Despite the advantages of multimodality, challenges such as feature fusion and redundancy remain. In this study, we propose a multi-modal multi-label emotion recognition decision system based on graph convolution. Our approach utilizes text, speech, and video data for feature extraction, while combining tag attention to capture fine-grained modal dependencies. The two-stage feature reconstruction module facilitates complementary feature fusion while preserving mode-specific information. Emotional decisions are made using a fully connected layer to optimize performance without adding complexity to the model. Experimental results on IEMOCAP, CMU-MOSEI and MELD datasets show that our algorithm has higher accuracy than existing models, highlighting the effectiveness and innovation of our proposed algorithm.

In recent years, integrating text and image data for sentiment analysis in social networks has become a key approach. However, techniques for capturing complex cross-modal information and... Show More

In recent years, integrating text and image data for sentiment analysis in social networks has become a key approach. However, techniques for capturing complex cross-modal information and effectively fusing multimodal features still have shortcomings. We design a multimodal sentiment analysis model called the Dynamic Graph-Text Fusion Network (DGFN) to address these challenges. Text features are captured by leveraging the neighborhood information aggregation properties of Graph Convolutional Networks, treating words as nodes and integrating their features through their adjacency relationships. Additionally, the multi-head attention mechanism is utilized to extract rich semantic information from different subspaces simultaneously. For image feature extraction, a convolutional attention module is employed. Subsequently, an attention-based fusion module integrates the text and image features. Experimental results on the two datasets show significant improvements in sentiment classification accuracy and F1 scores, validating the effectiveness of the proposed DGFN model.

In promoting the goal of “double carbon”, talents are the key support. Internal “intelligence cultivation” and external “intelligence introduction” are very important. Through the investigation of... Show More

In promoting the goal of “double carbon”, talents are the key support. Internal “intelligence cultivation” and external “intelligence introduction” are very important. Through the investigation of 133 household units and 3 universities in Northeast China, this paper discusses the relationship between talent market demand and discipline construction. Put forward the talent training mode in colleges and universities, strengthen the recognition of teachers and students on the goal of “double carbon”, and establish the evaluation mechanism of “focusing on internal and internal integration”. Through the investigation of 112 potential import targets, the results show that most overseas talents view China's “double carbon” goal positively. However, due to the lack of international exchange and scientific research conditions, this paper puts forward some strategic suggestions for the return of talents from domestic universities to promote more outstanding talents to join.