Jin Han’s research while affiliated with Nanjing University of Information Science and Technology and other places

Comprehensively extracting spatio-temporal features is essential to research topic trend prediction. This necessity arises from the fact that research topics exhibit both temporal trend features and spatial correlation features. This study proposes a Temporal Graph Attention Network (T-GAT) to extract the spatio-temporal features of research topics and predict their trends. In this model, a temporal convolutional layer is employed to extract temporal trend features from multivariate topic time series. Additionally, a multi-head graph attention layer is introduced to capture spatial correlation features among research topics. This layer learns attention scores from the data by using scaled dot product operations and updates edge weights between topics accordingly, thereby mitigating the issue of over-smoothing. Furthermore, we introduce WFtopic-econ and WFtopic-polit, two domain-specific datasets for Chinese research topics constructed from the Wanfang Academic Database. Extensive experiments demonstrate that T-GAT outperforms baseline models in prediction accuracy, with RMSE and MAE being reduced by 4.8% to 7.1% and 14.5% to 18.4%, respectively, while R2 improved by 4.8% to 7.9% across varying observation time steps on the WFtopic-econ dataset. Moreover, on the WFtopic-polit dataset, RMSE and MAE were reduced by 4.0% to 5.3% and 10.0% to 10.7%, respectively, and R2 improved by 7.6% to 14.4%. These results validate the effectiveness of integrating graph attention with temporal convolution to model the spatio-temporal evolution of research topics, providing a robust tool for scholarly trend analysis and decision making.

Predicting trends across multiple research topics presents a significant challenge for multivariate time series forecasting. In the temporal dimension, the fluctuation in popularity of individual research topics reveals trend features, while in the spatial dimension, the interdependence among frequently co-occurring research topics reveals correlation features. However, existing methods often overlook this spatial correlation feature or regard it as static and unchanging, leading to suboptimal prediction results. To address these shortcomings, this paper proposes a novel spatio-temporal graph neural network model, the Temporal Graph Attention Network (T-GAT). This model employs temporal convolution to extract the temporal trend features of research topics and utilizes multi-head graph attention to extract the spatial correlation features among these topics. Notably, the graph attention mechanism is capable of dynamically adjusting the weights of neighboring nodes based on the attention scores learned from the data, thereby reducing the risk of over-smoothing. Experiments conducted on the dataset from the Wanfang Academic Journal Database demonstrate that the proposed model outperforms baseline methods.

With the development of information technology, cloud computing technology has brought many conveniences to all aspects of work and life. With the continuous promotion, popularization and vigorous development of e-government and e-commerce, the number of documents in electronic form is getting larger and larger. Electronic document is an indispensable main tool and real record of e-government and business activities. How to scientifically and effectively manage electronic documents? This is an important issue faced by governments and enterprises in improving management efficiency, protecting state secrets or business secrets, and reducing management costs. This paper discusses the application of cloud computing technology in the construction of electronic file management system, proposes an architecture of electronic file management system based on cloud computing, and makes a more detailed discussion on key technologies and implementation. The electronic file management system is built on the cloud architecture to enable users to upload, download, share, set security roles, audit, and retrieve files based on multiple modes. An electronic file management system based on cloud computing can make full use of cloud storage, cloud security, and cloud computing technologies to achieve unified, reliable, and secure management of electronic files.

Evidence plays an extremely important role in legal proceedings, historical research, and diplomatic disputes. In recent years, more and more evidence data has been presented in the form of electronic data. Therefore, the extraction, organization, and validation of knowledge in electronic evidence also seem more and more important. In order to effectively organize the evidence data and construct the Chain of Evidence that can meet the practical needs, this paper uses Knowledge Graph based method to conduct relevance of evidence research. Firstly, through Knowledge Extraction, Knowledge Fusion, Knowledge Reasoning, the evidence data Knowledge Graph is constructed. After that, the Chain of Evidence will be built through evidence correlation and evidence influence evaluation, and finally different forms of knowledge presentation are carried out for different users. In this paper, the work and process of evidence chain association based on knowledge map are introduced, and the possible research direction in the future is prospected.

With the electronic transformation of traditional paper media, the integration of government new media platforms, the establishment of various institutions on social, music, video, shopping and other websites, and the rise of We-Media, massive new media information resources come out. In order to solve the problem of organizing and managing such information resources, and meet the needs of people’s deep use, propose an intelligent catalog system based on the development status of new media information resources. The intelligent catalog system for new media information resources combines massive data, bibliography theories, various catalogs, emerging intelligent technologies, catalog applications, etc., consisting of catalog data, catalog set, catalog application and support and guarantee. Metadata, classification and coding, and knowledge graph are key technologies for the construction of intelligent catalog system. Through the combination of bibliographic theory and information technologies, new media information resources can be better managed and utilized, and it can be fulfilled to provide personalized, diversified, integrated, accurate knowledge services for the users.

SWOT analysis is a classical analysis tool of competitive intelligence. The affect depends on whether all internal and external factors were analyzed. The construction of index system of SWOT analysis requires a lot of human resource. To resolve this issue, a method for computer aided index system establishment of SWOT analysis is proposed. This method can be automated extract and classify the key words. And the index system of SWOT analysis is generated by words classified information contained in words categories. In addition, the method can generate suggestions of indexes weight according to the amount, frequency and timing of key words contained in their classes.

SWOT analysis is a classical analysis tool of competitive intelligence. The affect depends on whether all internal and external factors were analyzed. The construction of index system of SWOT analysis requires a lot of human resource. To resolve this issue, a method for computer aided index system establishment of SWOT analysis is proposed. This method can be automated extract and classify the key words. And the index system of SWOT analysis is generated by words classified information contained in words categories. In addition, the method can generate suggestions of indexes' weight according to the amount, frequency and timing of key words contained in their classes.