bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Color Intelligent Matching and Optimization of Traditional Art Colors in Modern Visual Communication Designs
Authors: Wang Qin 
Published: November 2024

Abstract
This paper provides a detailed description of the key technologies of the ICC color management system, including device color matching, adaptive color gamut matching, and the color beauty optimization algorithm. A series of engineering experiments of color matching and cyclic optimization were carried out, and the case study method was used to obtain evaluation indexes such as color difference, color gamut, visual effect, and PBI index value and explore the advantages of ICC color management system application. The results demonstrate a significant reduction in the color difference value of the color block before and after the intelligent matching output, typically by 2–6 times. The maximum color difference in primary and secondary matching is no more than 4, and the color matching degree increases, indicating that intelligent matching is effective. In the interior decoration design case study, the PBI index increased from less than 50% to more than 50% after the optimization of the ICC color management system, while the mean value of the overall evaluation was higher, and the subjects were more agreeable to the design color evaluation.
Exploring the role of artificial intelligence in psychoeducational personalized learning environments
Authors: Wu Xiaoyu 
Published: November 2024

Abstract
In the deepening educational changes, personalized education has become a topic of great concern. The study mainly explores the use of artificial intelligence technology to construct a personalized learning environment and uses the assessment of learning performance in psychological education as an entry point for specific analysis. Comparative experiments are conducted to test the feasibility of improving the standard ant colony clustering algorithm to construct a model for psychological education performance assessment. The personalized learning system is then constructed with the performance assessment model, and students majoring in psychological education in a certain school are selected to conduct teaching experiments to explore the influence of the personalized learning system based on the improved ant colony clustering assessment model on psychological education. The grades of psychological education classes classified using the model of this paper are normally distributed, and it is reasonable and objective to use dynamic student achievement clustering to evaluate their grades. After the teaching experiment, students in the practice group were overall 2.98% to 6.72% higher than students in the other group in psychoeducational learning achievement, technology acceptance, and satisfaction, and 28.14% lower in learning anxiety. The personalized learning system designed in the study was able to improve students’ psychoeducational learning performance and experience, and reduce their learning anxiety, resulting in positive experimental results.
An integrated analytical approach for multimodal remote sensing data in marine ecosystem damage early warning
Authors: Yang Min 
Published: November 2024

Abstract
With the rapid economic development, the increase of human activities in coastal areas, the continuous discharge of pollutants from land-based sources into the sea, and the increase in the protection of offshore by the sea-related management departments, the judgment of early warning level of marine ecosystems is therefore of great significance. The article is based on the survey and monitoring data of Ocean A in 2022-2023 and utilizes MNDWI and Otsu to extract marine multimodal remote sensing data. Then, the environmental status of A ocean is analyzed. By constructing the early warning indicator system of A marine ecosystem based on the P-R-S model and exploring the integrated analysis method of BP ANN in the marine ecosystem, the condition of A marine ecosystem was warned, and the results were analyzed. The results show that from 2020 to 2023, the degree of sustainable development of A marine ecosystem will be in the state of “light warning”-a strong degree of sustainable development.
User Behavior Analysis and Prediction Model Construction in Higher Education Management Information Systems
Authors: Yu Shanshan , Zhong Lihua , Liu Yuwang 
Published: November 2024

Abstract
The education management system is an important tool for universities to manage academic information and information of staff and students. The article constructs a user behavior analysis model based on machine learning and a user behavior prediction model based on LR-XGBoost to analyze and predict user behavior in the educational management information system of colleges and universities. Specifically, after the collection of user behavior data in the university education management information system, descriptive statistics and cluster analysis are performed. Then, the relationship between user behavior and dropout/leaving is explored by the LR-XGBoost model, and the prediction performance is tested by comparing the prediction accuracy of the prediction model with other models. Cluster 3 users had significantly higher means on 12 behaviors in the college education management information system than those in clusters 2 and 1. Among the 12 behaviors, only closing web pages had a p-value greater than 0.05 and did not pass the z-test. The LR-XGBoost prediction model had the best performance with an accuracy of 95.51% and 94.53% for behavioral prediction and anomalous behavior prediction, respectively, and F1 values of 92.34% and 95.33%, respectively. It has a clear overall advantage in predicting user behavior for the next 1-10 days.
Learning analytics in online education: data-driven insights into student success
Authors: Zhu Peixia 
Published: November 2024

Abstract
In order to gain insights into the key factors affecting students’ success in online education, this paper extracts students’ online learning behavioral feature indicators through the behavioral record data in the online learning platform, applies the attribute approximation algorithm based on the Bayesian Fuzzy Rough Set (IDB-BRS) model to attribute approximation of the behavioral indicators, and utilizes the improved Apriori algorithm to mine the association rules between online learning behaviors and learning effects. The improved Apriori algorithm is used to establish association rules between online learning behaviors and learning effects. In comparison to the VPFRS model attribute approximation algorithm, the IDB-BRS model attribute approximation algorithm does not necessitate pre-given parameters and achieves superior classification accuracy and approximation time in the Soybean, Credit, and Balance datasets, thereby offering greater practical value. The association rules reveal that students who carefully study course resources, actively submit assignments, and study online frequently contribute positively to their success in online learning. This paper holds significant implications for enhancing the effectiveness of learning in online education.
Research on Art and Design Talent Cultivation Mode of Vocational Colleges under Rural Revitalization Strategy
Authors: Yu Wenbin 
Published: November 2024

Abstract
This paper summarizes the characteristics and needs of art design talent training in colleges and universities under the rural revitalization policy and constructs the art talent training model under the concept of rural revitalization from the five dimensions of professionalism, nurturing, practice, assessment and regulation. Comprehensive rural revitalization policy and art design professional talent cultivation objectives, the design of the questionnaire, the questionnaire for the reliability test, followed by the formal distribution of the questionnaire, to obtain the data of the research satisfaction analysis, professional design data is derived from the scale test. The independent sample t-test is used to verify the effectiveness of the talent training mode in this paper. After analysis, it was concluded that there is a significant difference between the traditional training mode and the training mode of this paper in the students’ professional skills, P<0.05. In addition, the satisfaction rate of art design majors in the training mode of this paper is 49.5%. This paper aims to improve the integration of rural revitalization policy and art design talent cultivation in vocational colleges, thus laying the foundation for the development of art design talents.
Intelligent Promotion Strategies for the Integration of Traditional Culture and Ideological and Political Education in Higher Education
Authors: Zhao Yu 
Published: November 2024

Abstract
Traditional culture and civic education are two important parts of the higher education system. Based on knowledge graph technology, this paper explores a strategy to promote the intelligent integration of the two. Firstly, we construct a knowledge map for the field of civics and traditional culture and optimize the traditional named entity recognition algorithm by using the BERT module for word feature extraction and pre-training. Through the search and recognition of stored domain knowledge, a smart Q&A system that integrates ideology, politics, and traditional culture can be constructed and used for teaching. The F1 value of the constructed BERT-BiLSTM-CRF algorithm is 76.7, which outperforms the comparison algorithm, and the ablation experiments show that the F1 value of the algorithm decreases by 7.9% when the BERT module is missing, which verifies the reasonableness of the design of this paper. The results of the teaching experiments on the performance of Civics and the interest in learning traditional culture show that the application of an intelligent Q&A system for integrated teaching effectively improves the students’ performance in Civics and the interest in learning traditional culture.
Content dissemination strategy of political ideological education in colleges and universities under the new media environment
Authors: Liu Yanqin , Deng Chao 
Published: November 2024

Abstract
New media technology can provide powerful support for the dissemination of political and ideological education content in colleges and universities. This paper relies on the hybrid recommendation algorithm to construct a personalized recommendation system for ideological and political education content in colleges and universities, which provides a brand new strategy for the dissemination of ideological and political education content. The popular resource penalty factor and time decay penalty factor are introduced into the original Pearson similarity calculation to improve the accuracy of the recommendation algorithm. This paper employs the statistical analysis method to examine the influence of the personalized recommendation system on the spread of ideological and political content in colleges and universities. The big data analysis module in the system predicts users’ forwarding behavior and the system’s dissemination effect, while the SIR model, which incorporates forwarding probability, conducts simulation experiments to replicate users’ ability to disseminate information. It has been found that the vividness, originality, interactivity, and interestingness of the recommended content of the system have a significant impact on the number of user likes and positive comments, which measure the dissemination effect. The big data analysis module is able to predict the forwarding behavior of users and the depth of dissemination of civic and political education content with accuracy exceeding 90% and 80%, respectively. Simultaneously, the system’s core users disseminate civic and political education content quickly and widely, allowing the system to effectively disseminate these contents in this paper.
Functional Analysis of Physical Education Program Construction in Colleges and Universities on Promoting Students’ Mental Health
Authors: Pei Hongliang 
Published: November 2024

Abstract
College and university physical education programs play a crucial role in enhancing students’ fitness and mental health, serving as a vital means of fostering their mental well-being. This paper outlines the structural design of college physical education courses and suggests effective strategies to enhance students’ mental health through their construction. This paper utilizes the mental health level as an index to gauge the impact of college physical education curriculum construction on students’ mental health. It develops a method for evaluating the mental health of college students using a Krill swarm algorithm-optimized support vector machine. It uses the evaluation indexes of somatization and depression from the SCL-90 as inputs for the KH-SVM evaluation model, which then outputs the students’ mental health status. We selected 660 students enrolled in 2022 at W College as research subjects for the mental health assessment. With the exception of the terror factor (P=0.145>0.145), the P values for other mental health level factors like somatization and obsessive-compulsive disorder before and after the optimization and construction of college physical education courses are less than 0.05, indicating significant differences and improvements in the psychological problems associated with these factors. After the optimization and construction of physical education courses, the total mean score of SCL-90 students was 146.73, which was 28.78 points lower than the score of 175.48 before the experiment, which was at a healthy level.
Music genre classification using deep learning: a comparative analysis of CNNs and RNNs
Authors: Xu Wenyi 
Published: November 2024

Abstract
Music categorization as a key component of music information retrieval is an important means of dealing with massive amounts of music information. Based on this, this paper proposes the use of deep learning algorithms to automatically identify music genre factions in a classification method. After denoising the audio using wavelet thresholding, the method of combining audio characteristics and spectral image features is used to enhance the audio data, short-time Fourier transforms and Meier transforms are used to realize audio feature extraction, and then convolutional neural network and recurrent neural network are used as the classifiers respectively to construct the music genre classification model. The GTZAN dataset is used as an experimental object to explore the effects of data enhancement and feature extraction on the music classification effect and to compare and analyze the performance of the CNN model and the RNN model. When the data enhancement methods based on audio features and image features are used simultaneously, the music classification accuracy of the model is above 90%, and the model using a short-time Fourier transform to extract audio features has a better classification effect. The CNN model is 1.3% more effective than the RNN model in classifying music genres, and this result is still consistent even if the audio is processed with variable speed pitch change and structural change, so the CNN model has better music genre classification.
Construction of Virtual Reality Teaching Environment for Civic and Political Education in Colleges and Universities and Its Teaching Quality Assessment
Authors: Gou Wenyan , Zhang Xi 
Published: November 2024

Abstract
Based on the need to adapt to the future development of education, the application of VR technology in the field of education is becoming more and more extensive and in-depth, and the application of VR technology to ideological and political education in colleges and universities is inevitable to adapt to the development of the times. After building a virtual reality online teaching environment for ideology and politics, the study designed a quality assessment system for ideology and politics teaching based on a VR teaching environment based on the principle of indicator construction and combined the subjective hierarchical analysis method and objective CRITIC method to assign the weights of indicators. By combining the fuzzy comprehensive evaluation method with five schools as examples, the research sample’s teaching quality was comprehensively evaluated. The findings indicate that the KMO value for each dimension is greater than 0.7, and the indicators have a strong correlation. Among the evaluation results of the four dimensions, the comprehensive evaluation of the C2 school’s Civics education environment is poor, but the comprehensive evaluation of the Civics education input and Civics education effectiveness is excellent, while the average value of the water of the VR Civics teaching quality of the five schools is at 63.01, which is on the passing line.
Design and Implementation of Virtual Reality Teaching Scenarios for Chinese Language Chinese Education
Authors: Guo Jian 
Published: November 2024

Abstract
By superimposing virtual information on the real teaching scene, it can inject new vitality into the teaching of Chinese in the Chinese language with the characteristics of and immersive learning experience. On the background of exploring virtual reality technology in Chinese education of Chinese language, the article introduces virtual reality technology in teaching, realizes voice interaction and gesture interaction functions, and builds a virtual reality teaching scene by using natural interaction technology. The learning experience test experiment has shown that the virtual reality teaching scene design proposed in this paper has achieved preliminary results with an average score of above 3.6. Through the analysis of student’s learning experiences and the pre-test and post-test analysis of learning motivation in the experimental and control classes, it is learned that the virtual reality teaching scene designed in this paper for Chinese language education has achieved good results.
A Study of FEDER Reconstruction of Ideological Education Model in Digital Media Technology and VR Environment
Authors: Li Qingxiang , Qin Liping 
Published: November 2024

Abstract
Digital media technology plays a crucial role in reproducing historical scenes and facilitating virtual interactions with political figures in ideological education, with virtual reality (VR) serving as a crucial technological support for the reconstruction of a new ideological education model. In order to improve the teaching immersion, this paper designs a multimodal gesture recognition algorithm. The acquired gesture data are regarded as curves, weights are assigned according to the degree of gesture fluctuation, and weight-based Pearson correlation coefficient algorithms (WPCCs) are proposed to calculate gesture similarity. The data and model are trained in a distributed manner using the FEDER learning technique finally. The optimized algorithm achieves a recognition rate of 96.66% for eight common gestures, which is 37.36% less time-consuming than the DTW algorithm. After applying the algorithm to virtual reality ideology education, students experienced a strong sense of presence (mean = 4.12); the post-test learning motivation score was 0.42 points higher than the pre-test (p<0.05); and the mean scores of perceived ease of use and emotional experience evaluations were 3.97 points and 4.33 points, respectively. It indicates that the algorithm in this paper has excellent performance and application efficiency.
Informatization Construction and Innovative Change Path of Educational Management in Higher Education Institutions
Authors: Li Xiaobo , Zhu Wei , Peng Yu 
Published: November 2024

Abstract
This paper adopts a combination of system dynamics and entropy power methods to establish a system model suitable for exploring sustainability and informationization construction of education management in higher vocational colleges and universities. It explores the influencing role of each element in the system and analyzes the causal relationship between each factor. Then, using structural equation modeling, the mediating role of teachers’ educational technology competence was analyzed to explore an innovative path for management information technology construction in higher vocational colleges. The results show that the system sustainability of performance assessment effectiveness, informationization training growth rate and information resource funding growth rate are all greater than 16. The management effectiveness scores for infrastructure funding utilization rate and information management personnel growth rate in 2025 are 24.7317 and 24.3988, respectively. The educational technology competence of teachers in higher vocational colleges and universities plays a mediator role in the situation of informatization infrastructure construction (indirect effect value of 0.112). Therefore, in the construction of informationization of education management in higher vocational colleges and universities, more attention should be paid to the aspects of informationization training, information resources, performance, infrastructure, talents and teachers’ education technology.
A Study of Evolutionary Trends of Classical Music Works Based on Data Mining
Authors: Li Yeye 
Published: November 2024

Abstract
Classical music is one of the most influential types of music, and Chinese classical music has entered a promising period of development after decades of accumulation. In this paper, some works are selected from many different types of classical music datasets to be produced as datasets, and the data of classical music works are pre-processed by labeling and segmentation. The chromaticity vectors of classical music works are solved using PCP features and combined with the MFCC algorithm to obtain the note feature changes of classical music works. The acquired classical music features are input into the bidirectional LSTM model, and then the self-attention mechanism is introduced to assign weights to the classical music feature vectors so as to realize the chord recognition of classical music works. The average accuracy of feature recognition of classical music works using chromaticity vectors is 63.47%, and the average misdetection rate and omission rate for notes of classical music works are only 0.11% and 0.08%, respectively. When the Quarter Length grows from 0.2 to 9.9, there are obvious discrete changes between different classical music works data, and the frequency of some Pitches is only within 10 times. The values of scale variance of classical music works in the duration fluctuated between 0.01 and 0.14, and the maximum accuracy of the chord recognition model of classical music works was 0.8379. Combining data mining and deep learning can understand chord and note changes in classical music works, and provide support for exploring the evolutionary trend of classical music works.