bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Analytics for Educational Big Data and Its Application to Instructional Management
Authors: Zhang Nan 
Published: February 2025

Abstract
In today’s rapid development of information technology, the use of big data to carry out educational management work in colleges and universities has gradually become the main model for the development of educational management in colleges and universities. Based on data mining technology, this paper constructs a model for student academic early warning and designs an academic early warning system using an XGBoost model. The study analyzes the student achievement and behavior data from Lanzhou University of Technology, clusters the student-generated in-school behaviors using the DBSCAN method, and examines the relationship between borrowing behavior, consumption behavior, and student achievement. It was concluded that there exists a difference between students with good grades and those with failing grades. The performance of the XGB model proposed in this paper was tested with an accuracy of 96%. Subsequently, the distribution of students’ academic performance was studied from different majors and courses, and the Pearson coefficient was used to study the correlation between different semester GPAs and students’ grades. Moreover, finally, it was concluded that second-semester learning indexes have a greater impact on students’ academic performance through feature correlation.
The Role of Cloud Computing Platform in Civic Education Resource Sharing in Colleges and Universities
Authors: Yang Ting 
Published: February 2025

Abstract
In order to improve the efficiency of resource sharing, this paper introduces cloud computing technology and develops a sharing platform for Civics and Political Science teaching resources based on cloud computing technology. A specific implementation of the cloud computing-based educational resource sharing program is carried out, starting with the hardware and software environment, then moving on to network configuration, and finally verifying the operation results. By comparing the performance with other sharing platforms, the number of requests processed per unit of time and the average response time of the sharing platform designed in this paper is higher than the data of the traditional control platform. The average value of resource sharing agility is higher than the traditional sharing system by 0.128. The average value of the resource sharing degree of the design sharing system in this paper is higher than the traditional sharing system by 0.2, and the efficiency, breadth, as well as depth of resource sharing are significantly better than the traditional sharing system. A questionnaire survey was conducted to examine the perception and use of the cloud computing platform for sharing. 61.67% of the students were interested in the employment examination and research column, which indicates that the educational sharing resources of the platform are in line with the student’s situation and meet their demand.
Research on the Application of Machine Learning for Marxist Education Integrating Chinese Excellent Traditional Culture
Authors: Wang Handong 
Published: February 2025

Abstract
The rapid development of artificial intelligence has brought a profound impact on college education, and is triggering a profound change in educational concepts and educational methods. This project starts from the two dimensions of teaching assessment and resource recommendation of Marxist education, and centers on the application of machine learning technology in it. Based on the evaluation of Marxist education integrating Chinese traditional culture, an improved cuckoo search algorithm is used to optimize the number of hidden layer nodes of the extreme learning machine, and the ICS-ELM model is proposed. Combining similarity calculation and the k-means clustering algorithm, a recommendation model for Marxist education resources is constructed using data mining. Through experiments, it has been found that the ICS-ELM model performs better in terms of RMSE value and R value, and can better evaluate the effects of Marxist education. The response time of the Marxist education resource recommendation model is within 210ms, and the MAE and RMSE are within 0.677 and 0.823, respectively, which shows fast response speed and high accuracy of resource recommendation, which can improve the cognitive level of teachers and students and students’ learning interest. The application of machine learning technology provides a more intelligent, personalized, and efficient teaching mode for Marxist education.
Study on the Role of Blockchain Technology in Supply Chain Carbon Emission Transparency Enhancement and Financial Regulation
Authors: Zhou Xingyao , Wan Fujun , Wang Qiaohui , Song Wenting 
Published: February 2025

Abstract
Combined with related literature, this paper argues that the application of blockchain technology to supply chain carbon emissions and financial regulation can play a role in reducing the credit risk of corporate supply chain finance. To verify the inference, the initial model of supply chain financial credit risk assessment is constructed using factor analysis and logistic regression analysis. The study selects 105 sample enterprises that utilize blockchain technology in supply chain carbon emissions and financial regulation to construct a final model for supply chain financial credit risk assessment as

P
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1
-

1

1
+

e

5.366
-
5.594

F

1

+
1.114

F

2

+
3.368

F

4

P = 1 - {1 \over {1 + {e^{5.366 - 5.594{F_1} + 1.114{F_2} + 3.368{F_4}}}}}

. The risk-taking capacity of Enterprise Z, which utilizes blockchain technology for supply chain management, is calculated at 92.05% using this model, which is 32.05% higher than the standard value. The conclusion supports the inference of this paper.
The role of digital media technology in promoting the reform of traditional curricula in higher music education
Authors: Xiao Yuyang 
Published: February 2025

Abstract
In the era of rapid development of science and technology, the traditional music education mode can no longer meet the needs of the times. Therefore, the study introduces digital media technology such as virtual reality and augmented reality in the reform of traditional music course teaching and proposes a higher music education method based on digital media technology, with a view to promoting the current reform process of higher music education courses. Taking instrumental music performance and music aesthetics as examples, we study the teaching effect of the music teaching methods proposed in this paper. In this way, the advantages of digital media technology in the reform of the traditional curriculum of higher music are explored. Before the teaching experiment, the experimental group and the control group had the same level of instrumental performance and music aesthetics, with no obvious difference. After the teaching experiment, the experimental group made more significant progress in instrumental performance and music aesthetics, while the post-test instrumental performance and music aesthetics of the control group remained similar to the results of the pre-test, and the p-value of the post-test results of the two groups in instrumental performance and music aesthetics were all less than 0.05. This paper’s method of higher music education based on digital media enhances the sense of scene of the music curriculum through VR and AR technologies, which is beneficial to the students’ learning. The sense of scene is conducive to students’ better sense of presence and immersion in learning, mobilizing students’ learning enthusiasm, changing the mode of traditional music teaching in which students passively receive knowledge, which has an important role to play in promoting students’ music development, and also provides insights for reforming the traditional music education mechanism.
Research on Multimodal Image Tampering Detection and Counterfeit Image Recognition Techniques under Deep Learning Framework
Authors: Zhang Meijing , Ding Qiang , Yan Xiafei 
Published: February 2025

Abstract
The continuous development of deep forgery technology has led to the proliferation of image tampering and forgery, which contributes to the spread of false information. In order to achieve the detection and recognition of forged images, a forged image feature recognition method and an image tampering detection model are proposed. Among them, the forged image feature recognition method transforms the image into YCbCr colour space and performs DCT transform and LBP transform on the divided image blocks to achieve the description of edge texture and feature learning. The image tampering detection model with improved Faster R-CNN, on the other hand, uses deep learning techniques to learn the features of the original tampered image and introduces an attention mechanism to optimize the classical network. The forged image feature recognition method achieves a reduction in training time consumption (319.9s) while maintaining higher accuracy in feature recognition. The image tampering monitoring model achieves a localisation accuracy (F1) of 0.835 and 0.818 for the two datasets, respectively. Compared with the initial two-stream Faster R-CNN algorithm, the accuracy of splicing detection has improved by 2.7%, copy-movement detection by 2.7%, and tampering detection by removal has improved by 3%, and it has better robustness.
Study on the extension path of agricultural products live marketing mode of rural e-commerce platform based on network traffic analysis
Authors: Zhou Zhengxi , Luo Zhiming 
Published: February 2025

Abstract
In rural e-commerce live streaming scenarios, network traffic analysis has an important impact on the consumer viewing experience. This paper builds a theoretical model of how viewers’ viewing experiences affect their buying habits in agricultural e-commerce live streaming scenarios. It also looks at the role of network traffic analysis and the live streaming marketing model as moderators. Finally, it uses sample data from merchant accounts on rural e-commerce platforms to do real-world analyses. Consumers’ viewing experience significantly affects their purchasing behavior (regression coefficient = 1.832, p<0.05). Web traffic analysis mediates between consumer viewing experience and purchase behavior. The live streaming marketing model moderates the positive relationship between consumer viewing experience and web traffic analysis. Because of this, this paper suggests that live marketing of agricultural products on rural e-commerce platforms based on network traffic analysis can broaden the marketing model by making basic network facilities better, using the power of local universities, working together with farmers’ chambers of commerce and leading businesses, and creating unique brands.
Multi-scale spatio-temporal data modelling and brain-like intelligent optimisation strategies in power equipment operation and inspection
Authors: Zhang Guoliang , Zhang Peng , Zhou Fei , Kong Qingyu , Fan Xiaoyu , Chu Zongbo , Xia Weishang 
Published: February 2025

Abstract
Aiming at the problem that the fault samples of UHV converter equipment are few and cannot effectively support the intelligent operation and inspection of the equipment, this paper proposes a brain-like learning sample spatio-temporal correlation generation technique for the operation and inspection of UHV converter equipment. In this technique, GPNN fuses the temporal evolution law and similarity of nearby samples to intercept typical fault samples and then combines the SNNs model of brain-like computing to construct an intelligent diagnosis model for UHV converter equipment. The improved K-SVD dictionary learning algorithm is used to extract the time-domain features of the UHV converter faults, combined with the empirical wavelet singular entropy to obtain the frequency-domain features, and the KPCA algorithm is used to fuse the multiscale time-frequency features to obtain the multiscale spatial and temporal features of the faults of UHV converter equipment. The GPNN model for generating multi-scale spatio-temporal sequence fault samples is constructed by combining GAN with the nearest neighbor interpolation algorithm. The fault samples generated by the GPNN model are used as inputs and combined with the SNNs model for intelligent diagnosis of UHV converter equipment faults. The consistency between the fault samples generated by the GPNN model and the actual samples reaches more than 90.57%, the accuracy of the brain-like intelligent fault recognition model reaches up to 98.06%, and its training time is only 37.14 seconds. Learning the multi-scale features of the samples through the GPNN model, combined with brain-like computing technology, can support the training of brain-like models for health assessment, fault diagnosis, and trend prediction of UHV converter equipment.
Exploring the dynamic relationship between oil painting art and modern social values by combining data mining techniques
Authors: Zeng Maoquan 
Published: February 2025

Abstract
Oil painting is a kind of mass medium with unique charm and influence, which has a wide dissemination effect. If social values can be spread through oil painting, they can have a wider and more effective effect. In this paper, the Fisher score algorithm is used to assess the degree of correlation between the color features of oil paintings and the classes they belong to. The grey scale co-production matrix is used to extract texture features of oil paintings. The regularized limit learning machine model is introduced to improve the accuracy of algorithmic feature extraction. The oil painting artworks’ feature vectors are calculated, the RELM model is employed to recognize them, and the plain Bayesian classifier confirms the accuracy of the recognition results. A simulation analysis of the oil paintings is carried out. The highest pixel value is 1880 when the luminance of the red histogram is between 10-15 in the oil painting of “Sleeping Venus”, and the lowest pixel value is 184 in this interval in the “Unknown Woman”. The four grey gradient covariance matrices eigenmeans of the oil paintings are calculated, and the eigenquantities ASM, CON, CORRLN, and IDM are rotated by 90°. The eigenmeans are 0.047, 28.766, 0.125, 0.18. Based on the results of the experimental analysis, the correlation between the characteristics of oil painting art and the values of modern society is discussed.
Reform of Traditional Music Teaching Methods and Cultivation of Students’ Musical Creativity on Digital Platforms
Authors: Zhang Qingru , Wang Yan , Du Yuelin , Li Wei , Li Hua 
Published: February 2025

Abstract
Due to the rapid development of modern science and technology, the teaching methods and modes in traditional music have become increasingly obsolete and outdated, and digital teaching resources are the key to the reform of traditional music teaching methods. In this study, after extracting musical features such as range and average energy of music sounds during the teaching process, it is used as input data for the recurrent neural network model. The model is then optimized using a long-term memory artificial neural network and a self-attention mechanism, and the proposed combinatorial neural network CLSA model is used to visualize the music sounds. Subsequently, a digital music teaching platform is constructed based on the theory of multiple intelligences and the proposed model to assist in the reform and innovation of traditional music teaching methods. After research, the CLSA model proposed in this paper shows good performance in separating vocals and accompaniment, which has reached the performance of the current mainstream model. It is also found that after one semester of experimental teaching, the musical creativity of the students in the experimental class is significantly improved compared with the level before the semester (P<0.05), which indicates that the music teaching based on the digital platform has a good effect. This study provides a reference for relevant music teachers so that the level and teaching effect of digital music teaching can be improved and the deep integration of digital teaching resources and music teaching can be realised.
A Study on Sentiment Visual Analysis of Educational Public Opinion Based on Online Big Data
Authors: Wei Xiong 
Published: February 2025

Abstract
With the rapid development of the Internet, the Internet has become a new platform for gathering public opinion. Therefore, studying the sentiment of public opinion regarding education on the Internet is of great value in understanding the current situation of education. In this study, a web crawler is used to collect data related to education public opinion online, and an attention mechanism is used to extract data related to the sentiment of education public opinion. Subsequently, the convolutional neural network is used to extract sentiment features, and the sentiment features are classified and processed by the softmax classifier. Finally, the sentiment visualisation system for educational public opinion is designed by combining the sentiment analysis method. It is verified that the accuracy and F1 value of the sentiment analysis model proposed in this paper are the highest compared to the comparative models. The period of 2021-2022 is the high incidence period of online education public opinion events, and there are two obvious peaks of sentiment intensity in the typical cases of education public opinion A and B, which are the early stage of the outbreak of the online public opinion and the period of the official investigation and update, respectively. In this paper, we use visualization to show changes in people’s emotions related to education public opinion, hoping that it can provide a reference for managing education public opinion by relevant departments.
Research on data visualisation strategies for online information dissemination based on user experience
Authors: Wei Xiong 
Published: February 2025

Abstract
Information data visualisation is a new technology and trend emerging from the development of online information dissemination. This paper integrates the cross-field knowledge of journalism and communication, cognitive psychology and computer science, conducts two information visualisation experiments of colour symbols and cognitive load under the guidance of dual coding theory, and finds that contrasting colour schemes and symbol markers with meaningful associations help the audience’s cognition and memory of infographics. The errors caused by pie charts were much more significant than those caused by line and bar charts, and the errors caused by bar charts were much smaller than those caused by line charts. The cognitive load that pie charts cause is significantly higher than that of line and bar charts. Based on the visual representation of information visualisation, the principle of dual coding theory information processing and the results of two experiments, this paper proposes three principles of data visualisation strategy for information dissemination. This study provides theoretical support for the information visualisation design of data visualisation products, and the research conclusions and design methods can be applied to related information visualisation interfaces, which have good theoretical research value and practical significance.
Predicting HR Management Strategies and Employee Satisfaction in Enterprises Based on Deep Generative Models
Authors: Kang Ping 
Published: February 2025

Abstract
With the arrival of the digital era, the digital environment has brought new operation modes and work styles for enterprises, and at the same time, it also requires enterprises to make innovations and reforms in human resource management. The use of a deep generative model is used to generate facial expression recognition technology after optimizing the human resource management strategy in this paper. This technology extracts the facial expression features of employees and analyzes their emotional predictions in various simulated scenarios, all within the context of the optimized enterprise human resource management strategy. This, in turn, reflects the employees’ satisfaction with their work. Firstly, in order to verify the accuracy of the recognition technology proposed in this paper, recognition experiments are carried out on the Cohn-Kanade expression library, and from the analysis results, it is known that the recognition technology of this paper achieves a recognition rate of 98.25% in the Cohn-Kanade expression library, which is a high recognition rate. Finally, the selected 618 subjects were divided into 4 groups for the context simulation experiment to analyze mood prediction. The findings show that subjects’ positive mood is higher in different contexts, which suggests that employees are more content with their jobs in an optimized enterprise management environment.
Design of Learning Progress Tracking and Feedback Mechanism Based on Data Visualisation Technology in Music Teaching
Authors: Lin Peihan 
Published: February 2025

Abstract
In this paper, we first constructed a knowledge tracking model and embedded a self-attention mechanism to optimise the model and overcome the problem of data sparsity. Subsequently, the structures of the input layer, embedding layer, and hidden layer are sequentially designed to build the knowledge tracking model. Using neurons, the learning matrix was passed into a linear transformation, and a linear model was employed to obtain the predicted values. Individual student feedback on learning progress is calculated using the EVA evaluation model, and the timeliness feedback for all teaching subjects is compiled into an ensemble that constitutes the overall immediate feedback. Stage feedback is established in the same way to constitute a feedback mechanism for music teaching, and a strategy for supporting feedback teaching decisions is proposed. The experimental samples are selected, the teaching practice environment is established, and the results of visual tracking of learning progress are analyzed. The mean values of the total music learning strategy scores of the experimental class and the control class were 26.4136 and 19.229, respectively. Further analyses showed that there was a significant difference between the experimental class and the control class under the 95% confidence intervals of the music learning dimensions, assuming that the ANOVA values were all 0.05. The number of irrelevant learning behaviours of the students in the experimental and control classes was 95 and 137, respectively, and overall it seems that feedback on students’ learning progress can reduce the number of invalid behaviours.