bluuebunny/crossref_metadata_2025_split huggingface dataset:
A study on the impact of digital economy on driving up the income of rural residents in the context of big data
Authors: Li Yiqun , Fei Yun 
Published: May 2024

Abstract
The digital economy is promoting rural development and rural residents’ income growth and realizing the improvement of supply efficiency and the change of factor power. In this paper, we first construct the measurement system of rural residents’ income level and digital economy development level, respectively, and then use big data technology to collect relevant data from 2013 to 2020 and calculate the development level of the two by using principal component analysis and entropy weight method respectively. Then, the level of digital economy development was taken as an explanatory variable. Rural residents’ income was taken as an explanatory variable. Empirical regression analysis was carried out to explore the impact of digital economy on the improvement of rural residents’ income. The mediation effect and regional heterogeneity test were carried out. The results show that the coefficients of the digital economy are always significant and all positive at a 1% level when control variables are added gradually, and the Sobel statistic of entrepreneurial activity is 0.061 and 0.045. This study provides theoretical support for the analysis of how the rural digital economy can promote the revitalization of rural industries and promote the commonwealth of farmers and rural areas.
A Study on the Extension of Chinese Traditional Culture in College Art Education Combined with Gray Correlation Analysis Methods
Authors: Li Qiao 
Published: May 2024

Abstract
This study incorporates gray correlation analysis from gray statistical theory to assess the quality of traditional culture and art education. It establishes an evaluation index system and a gray correlation analysis model, examining the relationship between contemporary art education and traditional culture. The model is applied to identify the correlation, key characteristics, and principal influencing factors of traditional Chinese culture and art education in higher education institutions. Additionally, it examines the advantages of random sequences of factors through specific data processing techniques. The structural model tests the influencing factors on the quality of traditional culture and art education, with an analysis of path coefficients. The results showed that 97.4% of teachers chose traditional culture and art teaching resources, mainly by referring to the network aspect. The results of the weight distribution indicate that the teaching process U3 (0.3613) is the most important in traditional culture and art teaching, and the innovative activities in teaching content (0.8113) hold the largest weight. The top three gray correlation values of the indicators of the second-level factor layer are U43>U22>U32. The direct influence effect of excellent traditional culture permeating the teaching process is 0.232, which has the greatest influence on the quality of teaching. The direct impact of excellent conventional culture permeating teaching content is 0.184, which has the second largest influence on teaching quality. This paper successfully constructs a gray correlation model to analyze the relationship between traditional culture and contemporary art, which has high scientific validity.
Exploring the Teaching Reform Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning
Authors: Liang Li 
Published: May 2024

Abstract
Deep learning significantly enhances the comprehension of ideological and political education (IPE) concepts and facilitates the advancement of IPE teaching in colleges and universities through bilinear blended teaching reforms. This innovative teaching approach employs association rules from big data and an enhanced Apriori algorithm to analyze students’ online learning data. An analytical model of student learning behaviors in IPE, utilizing a Support Vector Machine (SVM) for learner classification, forms the basis for developing an online learner profile. This profile integrates assessments of learning outcomes, group identification, and style classification to tailor learning activities to individual needs. The efficacy of this reform was tested over one year, comparing the impacts of IPE across four different regions. Results indicate that students in the experimental group significantly outperformed their counterparts in the control group. For instance, in Shanghai, scores from the experimental group averaged 89.63 compared to 83.25 in the control group, yielding a significant difference of 6.38 points. Statistical analysis showed a t-value of 6.21 and a p-value of 0.000, affirming the positive impact of the bilinear blended teaching reform on enhancing student ideological engagement and promoting educational reform in colleges.
Knowledge mapping analysis of Chinese traditional culture under the strategy of common wealth
Authors: Guo Han 
Published: May 2024

Abstract
With the continuous improvement of people’s living standards, their attention to the spiritual world and culture, especially traditional culture, is gradually increasing. With the help of the graph-based data structure of a knowledge graph, the connections between entities in conventional culture can be more intuitively displayed in the form of nodes and edges. Traditional knowledge is represented by the Trans series model in this paper, and the attention mechanism is integrated into the Graph Attention Network Model (GAT). The descriptive information of entities and relationships was introduced for pre-training initialization, and a Graph Attention Network Connection Prediction Model (BFGAT) was proposed to integrate the descriptive information and structural features and the BFGAT model was optimized by using information filtering operations. After the experimental study of the performance of the modified model, it is found that the score of the BFGAT model is 0.594 under the Hits@10 evaluation index in the dataset WN188RR, which is 0.01 and 0.018 higher than the two variant models, respectively, and the performance is better than that of other models. In the analysis of the ability of the knowledge graph to disseminate Chinese traditional culture, the number of searches for “Confucius” in Chinese traditional culture in the Americas is higher. The search frequency of Confucius has increased significantly from 2013 to 2014, from 830,000 times in 2013 to 2.95 million times in 2014, and the search frequency of Confucius in the Americas has reached 7.59 million times in 2023. It can be seen that the knowledge graph technology based on the knowledge graph attention network link prediction model has higher accuracy for keyword recognition and is also conducive to the wide dissemination of traditional Chinese culture.
3D Communication of Animated Character Image Visualization Based on Digital Media Perspectives
Authors: Huang Yan , Chen Sirui 
Published: May 2024

Abstract
In this study, we initiate the process by pre-processing the character image data and establishing coordinate axes. Utilizing the parallax principle, we construct a three-dimensional model of the animated character image and design a multidimensional, time-sequential visualization scheme based on clustering techniques. To enhance the effectiveness of cluster visualization, we incorporate color and transparency into the model and develop a three-dimensional communication framework to facilitate user interaction and improve animated image visualization. Subsequently, we evaluate the advantages and feasibility of this three-dimensional communication approach, focusing on user experience and user engagement. We also explore the visual perception capabilities of this method against contrasting background colors of light and dark. The findings demonstrate a credible expression of animated video images, with a credibility index of 4.690. Additionally, user perception is quantified at an average of 4.895, which not only exceeds the four-point benchmark but also outperforms alternative methods. This research offers a substantial reference for the advancement of visual communication studies concerning animated character images.
Modern graphic design application innovation based on graphic abstract processing method
Authors: Gao Xueqiang 
Published: May 2024

Abstract
The art of abstraction reflects the rich imagination of human beings to a certain extent and is now widely used in movies, games, advertisements, and other modern design fields. In this paper, a new edge tangential flow algorithm applicable to image abstraction is proposed, which first estimates the gradient strength and gradient direction of each point in a given input image by using the Sobel operator and then establishes the edge graph of the input image by using the edge information transfer strategy. On this basis, the application and value of abstract art and this graphical abstract processing method in modern graphic design are discussed, and the essence of abstract form art is perfectly embodied so as to make the two perfectly integrated. The results show that in the application of graphic abstract processing in graphic design, the average value of user attraction, understanding, and memory dimension scores are 0.53, 0.675, and 0.683, respectively, which verifies the effectiveness of this graphic design practice through users.
Classification of Styles and Expressive Characteristics in Higher Vocals Teaching - Based on Emotional Calculation Approach
Authors: Liu Lijuan 
Published: May 2024

Abstract
Higher vocational vocal music teaching not only lies in enhancing students’ grasp of notes but also in deep emotional expression and emotional transmission. How to realize the precise classification of emotions expressed by musical style and features is an urgent problem to be solved. In this paper, starting from the classification of acoustic features and music styles, the STFT algorithm is used to extract the music features, and the music emotion model is constructed by combining the two-dimensional emotional psychological model. Based on the Turkish music emotion dataset, the acoustic data are preprocessed by using methods such as frame splitting, pre-emphasis, windowing, and mute frames, a deep confidence network extracts the music features, and the SVM model is introduced to establish the emotion classification model of music style and expression features. To verify the effectiveness of the model, this paper uses the dataset to analyze the music style and emotion classification dimensions, respectively. It is found that the classification accuracy of the Renaissance music category in classical music reaches 97.94%, and the accuracy of the music emotion classification of the model when the music data preprocessing slice granularity is 5 s is 0.838. The DBN-SVM model can realize the emotion classification of the music style and expression characteristics, which can provide a reference for the teachers to carry out targeted vocal music teaching and for the students to realize the release of emotion and emotional resonance when singing in vocal music. It provides a reference for teachers to carry out targeted vocal music teaching and for students to realize emotional release and resonance during vocal singing.
Development Path of Teaching Method Innovation in Art Colleges and Universities under the Background of Internet
Authors: Xu Xueqian 
Published: May 2024

Abstract
The informatization teaching method is a big challenge to the traditional form of teaching in art college education. This paper studies the construction of an informatization teaching method combining multimedia technology and multimodal media information retrieval technology. In view of the demand and development conception of teaching methods in art colleges and universities under the background of the Internet, it examines the willingness of teachers of art colleges and universities to teach informatization as well as the degree of satisfaction of their teaching ability. It optimizes the retrieval of learning resources based on the multi-category classification method of ELM classifiers and proposes an integrated mode of informatization teaching in art disciplines. According to the factors affecting the informatization teaching ability of art teachers, teaching design and teacher implementation are carried out in a case-by-case manner to analyze and summarize the program effects of the informatization teaching approach. The correlation coefficient between teachers’ willingness to adopt informative teaching methods and their satisfaction with their information teaching ability is 0.614, which is moderately correlated. After the implementation of the teaching program, the total score of the teacher’s informatization teaching ability reaches 80.052, which improves the level of informatization teaching compared to the level of informatization in pre-preparation. The informatization teaching method’s program design can enhance the teacher’s informatization teaching level, which can have an impact on the innovative development of the teaching method in art colleges and universities through practical experience.
The Construction of a Shared Resource Base for Teaching Chinese Culture under the Architecture of Disciplinary Knowledge Mapping
Authors: Wang Youjing , Hou Yongguang 
Published: May 2024

Abstract
This paper proposes to use knowledge mapping to construct a shared resource base for Chinese culture teaching to realize the optimization and innovation of Chinese culture teaching. The internal data and external data of the Chinese teaching resource base are obtained through two ways textbook informatization and automatic semantic annotation. Then the data are preprocessed to ensure the accuracy of the data in the resource base. On this basis, the LSTM-CRF model is used to identify the entity naming of the knowledge points of Chinese teaching resources. Then, the named knowledge points of Chinese culture teaching resources are integrated and stored to complete the systematic construction of the Chinese culture teaching resource base. The empirical analysis of the proposed resource base is carried out. The data present that the average oral score of the students in the photo group is 37.2583, while that of the students in the experimental group is 43.1465, and the difference between the average scores of the two classes is about 5 points, which is a large score difference. The two-tailed significance sig value is 0.005 if equal variances are used, and the two-tailed significance sig value is 0.008 if equal variances are not used, both of which are less than 0.05, indicating that the method of this paper has a positive effect on improving the speaking scores of the students of the comprehensive Chinese culture class.
AIGC Enabling Non-Genetic Design Methods and Practices
Authors: Li Zujian , Ma Zhehao , Xu Boshen , Lei Shanshan , Cheng Yin , Xu Feng 
Published: May 2024

Abstract
Artificial Intelligence Generated Content (AIGC) technology aligns seamlessly with the design requirements of non-genetic heritage, offering a viable pathway for its modernization. This paper delineates the specific design needs of non-genetic heritage and utilizes a diffusion model to create themed images and animations related to this heritage. Additionally, AIGC is employed to enhance the creation of virtual reality interactive imagery. The Long Short-Term Memory (LSTM) network is deployed to classify time-series gesture data, facilitating the training and categorization of Chinese Sign Language (CSL) gestures for virtual interactive engagement with non-heritage themes. We have integrated the AIGC operation process into the theme of non-genetic inheritance, thereby constructing a robust development trajectory for AIGC-enhanced non-genetic heritage. The experimental setup is crafted to ascertain the optimal number of iterations and training durations through the control variable method. We evaluate the efficacy of the diffusion model for anti-implicit writing analysis and the performance of the speech recognition, text dialogue, and text response modules within the non-heritage multimodal interactive framework using Word Error Rate (WER) and Mean Opinion Score (MOS). A descriptive analysis of users’ interactive experiences with non-heritage content is also conducted. The results indicate that the speech recognition module achieved a WER of 0.365, while the text response module garnered an MOS of 4.49 with a standard deviation of 0.56. This multimodal, non-heritage virtual interaction, leveraging multiple modalities, enriches users’ experiences and deepens their understanding and appreciation of non-heritage content. Consequently, this enhances the high-quality development of non-genetic heritage.
Modeling of the accurate application of the Kernel Efficiency Method in the proportioning process of shield tunnel construction
Authors: Wang Yangxin , Li Jinkui 
Published: May 2024

Abstract
The existing control strategies for subway construction struggle to address the challenges posed by the impact of large-diameter shield-driven underpass subway tunnels on soil settlement. In this paper, based on the mechanism of ground settlement of the shield tunnel under the KNX method, the influence of the underpass tunnel on the existing tunnel and its surrounding soil stress-strain characteristics is calculated by the Peck superposition method. The KNX method is optimized by optimizing the ratio of KNX slurry and the grouting pressure to control the existing tunnel and the ground settlement. The geotechnical general finite element analysis software is being introduced to conduct numerical simulation and engineering application experiments to verify its effectiveness. The results show that when the bonding force of the grouting material is 350Pa-s, the maximum value of the variation is 3.91mm, and the mobility is more suitable, which can effectively fill the void of the shield machine and reduce the settlement of the foundation. When the grouting pressure is 280kN/m2, the maximum contraction of the tunnel is 6.25mm, which can ensure the safety of the existing tunnel on the basis of the tunnel so that the actual settlement of the soil in the tunnel is minimized. Comprehensively, the optimization of the ratio of the KNP method in the control of the deformation characteristics of the soil and structures under the shield tunnel has effectiveness and can effectively provide theoretical and technical guidance for the construction of subway tunnels under the large-diameter shield under the subway.
Transformation Research on the Teaching Mode of Ideological and Political Courses in Colleges and Universities under Big Data Environment
Authors: Yang Dan 
Published: May 2024

Abstract
The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.
A Spectral Analytical Study of Melodic Innovations in the Opera “Aleko” and Rachmaninoff
Authors: Yang Dongdong , Li Ying 
Published: May 2024

Abstract
The opera “Aleko” is a graduation work composed by Rachmaninoff, for which the opera was born in Russia in the nineteenth century and has a strong literary and artistic value. This study tries to extract the time-frequency melodic characteristics of the opera “Aleko” by using the spectral analysis technique as a research tool combined with the Fourier transform and constant Q transform. From the perspective of quantitative analysis, the innovative laws of Rachmaninoff’s melodic features are summarized with the intention of revealing the great artistic charm of this opera work. The results of the spectral analysis show that the amplitude of the first part of “Aleko” is 27.5dB, which forms its unique “Rach’s style” through its innovative “stepped” meandering tones. At the same time, the rich melodic layers of “Aleko” emphasize the drama of the opera and make the internal structure of the phrases stronger.
Application of AI technology in personalized recommendation system for financial services
Authors: Yue Xiaowen 
Published: May 2024

Abstract
How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.