bluuebunny/crossref_metadata_2025_split huggingface dataset:
Construction of Chinese Youth Football Players Cultivation System Based on Discrete Regression Algorithm
Authors: Hu Hongquan 
Published: February 2024

Abstract
In the course of the development of Chinese soccer, the development of youth soccer plays a very important role, so it is very important to build a scientific training system for youth soccer. In this paper, Bootstrap resampling technique is used to train the multidimensional logistic regression algorithm, and the genetic algorithm is used to optimize the parameters, which in turn improves the logistic regression algorithm. The HBase discrete regression algorithm model is constructed by combining the improved logistic regression algorithm with the HBase database system. Experiments were conducted on the running ability, performance prediction, and mental health disorder detection of Chinese youth soccer players, and finally the clubs in five places that improved the training system of youth soccer players through the HBase discrete regression algorithm were evaluated. The experimental results show that the maximum instantaneous explosive power of the athletes is close to 80 m/s2. In the performance prediction experiment, the error between the performance of the youth soccer players predicted based on the HBase discrete regression algorithm and the real value is less than 0.5. In the five clubs that have improved the cultivation system of the youth soccer athletes based on the HBase discrete regression algorithm, the overall average score of the evaluation of the cultivation system is more than 83 points.. This study has the potential to help improve the development of youth soccer in China.
Analysis of the Professionalism of College Teachers in the Information Age Based on Teaching According to Materials
Authors: Qiang Xiaojing 
Published: February 2024

Abstract
Aiming at the problem that it is difficult to objectively analyze the degree of teaching based on teaching materials in the traditional way, this paper establishes an index system based on the opinions of experts and the characteristics of teaching based on teaching materials. A BP neural network analyzes the index system to measure the level of teaching by university teachers based on teaching materials. This paper aims to improve the running speed of the BP neural network by employing the gradient-at-every-turn method and optimizing parameters. Moreover, the maximum-minimum value method is used to normalize the data, and the entropy value method is used to calculate the weights of the indicators to provide more accurate data for the BP neural network. Once the model construction was finished, it was put through its paces in teacher training and science colleges for trial operation. The absolute value difference between the output of the neural network and the expert evaluation is between 0.09 and 0.052, which are all less than 0.1. Most of the teachers’ scores are at the excellent level, around 0.8-0.9, but the score for diversified teaching methods is 0.6784, which is on the low side, proving that there is still room for improvement in this aspect.
Research on Innovative Parenting Mode of Ideological and Political Education in Colleges and Universities under the Background of Big Data
Authors: Che Yanqiu 
Published: February 2024

Abstract
In the context of the new era, the education concept has been transformed, and practical parenting has attracted much attention, and the talent cultivation mechanism of colleges and universities therefore urgently needs to be innovated to meet the requirements of the development of the times. In this paper, we optimize the original objective function of the sample of students’ civic education, use Taylor’s formula to expand the objective function approximation, calculate the complexity of the tree model, and complete the construction of XGBoost student learning behavior. Improve the K-means algorithm based on Canopy, combined with the improved SVM algorithm, to provide early warning for students’ academic performance in Civics. The assessment framework for teaching effect is designed to assess students’ Civics learning process. The results show that after the intervention of the model proposed in this paper, the students’ Civics performance improved from 60.59 to 65.63. The cognitive ability of Civics has a significant effect on social learning and individual learning behaviors at the same time, with the effects of 0.8623 and 0.9782. The innovative nurturing model of Civics in colleges and universities is improved by the proposed model in this paper.
The Embodiment and Application of Minimalism in Graphic Design Teaching Reform Based on Big Data Analysis
Authors: Wen Jie 
Published: February 2024

Abstract
In the context of the deep development of modern science and technology and multimedia technology, the design style of minimalism is also developing in a diversified direction, but there are few studies on its application in teaching reform. Therefore, this paper uses TF-IDF text mining, combined with the K-Means algorithm, to construct a knowledge construction mapping literature intelligent analysis model to reveal the embodiment of the application of minimalism in the teaching reform of graphic design. The use of CHI_TF-ID is intended to address the issues of TF-IDF mining that are both time-consuming and unsatisfactory. By using DBI and DI index, the clustering effect of the K-Means algorithm can be enhanced. A new approach to graphic design design teaching is proposed by using keywords. In the teaching practice, the average results of the experiment class were 85.82, and the average result was 6.96. In contrast, the average score of the control class was 80.43, a difference from the previous score of 79.04. Teaching minimalist graphic design can effectively enhance the effectiveness of teaching.
Teaching Reflections on the Integration of the course ideology and politics into Tourism Management Majors in the Context of the Internet
Authors: Jin Xiaohong 
Published: February 2024

Abstract
The primary task of education is to realize the goal of “establishing morality and educating people”, and to give full play to the role of the course ideology and politics Education, however, the degree of integration of the course ideology and politics Education in the current tourism management profession is not deep enough. In this paper, we start from the perspective of evaluation of the course ideology and politics Politics in the curriculum, use random search algorithm to optimize the hyperparameters of NN-Stacking algorithm, obtain the optimal hyperparameter configuration value based on the Leaky ReLU activation function to initialize the weights of the method, and construct the teaching evaluation methods of RG-UNNS and RG-CNNS, and at the same time, we use the coefficient of determination, the average absolute error, and the average error as the evaluation indexes to evaluate the effectiveness of the constructed methods. Evaluation indexes are used to assess the effectiveness of the constructed methods. In this paper, using the constructed teaching evaluation methods to evaluate the teaching effect of the course ideology and politics in the course of Tourism Management, 84.6% of the students were affected by the course ideology and politics education in the classroom of Tourism Management, and 42.35% of the students strongly agreed that this classroom improved their ability to link the theory to the practice, teamwork ability, independent learning ability, and professional literacy level.
Reform Method of Music Teaching Theory System in Colleges and Universities Based on Deep Learning Analysis Technology
Authors: Man Xiangyi 
Published: February 2024

Abstract
The traditional music teaching theory system is usually based on test scores as the only evaluation index, which is difficult to reflect the learning dynamics of students in real-time, thus making it difficult for teachers to strengthen the teaching of students’ weak knowledge points in real-time. To address this problem, this paper uses the DKVMN knowledge tracking model, combined with the recommendation algorithm to design a music theory analysis and recommendation model for college students based on deep learning analysis technology. In it, the Softmax function is utilized as the activation function of the output layer of the LSTM network, and the weight vector is increased to enhance the recurrence probability of students’ unfamiliar knowledge points. After the model design was completed, it was used in the music program of a teacher training school, and the overall effect of the use, the students’ personalized diagnostic reports, and the operation of the model were analyzed and explored separately. From 54.51% during the first test to a maximum of 81.42%, the accuracy rate progressed gradually. For the 7th time after the music theory knowledge level test, the experimental group of students scored 81.23, which was significantly higher than the score of the control group of students 74.25. The p-value tested was 0.004, which indicates a significant difference between the levels of the two groups.
Towards Chinese-style Modernization: Digital Economy Helps Liaoxi - An Innovative Path for High-quality Development of Beijing-Tianjin-Hebei Logistics Enterprises
Authors: Duan Lijun 
Published: February 2024

Abstract
An in-depth study of the boosting role of the digital economy is crucial to promoting the high-quality development of the economy in the Liaoxi-Beijing-Tianjin-Hebei region. This paper uses the gray correlation method and global entropy value method to evaluate the weights of the constructed index system, analyze and get the 4 main influencing indicators on the high-quality development of the logistics industry in the Liaoxi-Beijing-Tianjin-Hebei region, and construct the innovation path. By analyzing the development level of the four indicators and the financial performance of logistics enterprises in 14 cities, it is found that the economic open development level of logistics enterprises in Beijing can reach 1.0 in 2021 and that the open development level of Fuxin City grows rapidly from 0.3 to 0.7 within 8 years, while the total return on assets of logistics enterprises increases by 0.8% within 7 years, so that the innovation path constructed in this paper can strongly promote the transformation of logistics enterprises in Liaoxi-Beijing-Tianjin-Hebei region to high-quality development.
Research on the Innovation and Development Strategy of Stage Art Design in the Context of the New Era
Authors: Lv Hang 
Published: February 2024

Abstract
People’s aesthetic concepts have changed a lot in the modern context, and the relevant requirements for stage art design are also constantly improving. The purpose of this paper is to propose a strategy for the innovative development of virtual stages using augmented reality technology. Specifically, the process involves extracting stage movement frame data through the virtual environment and processing stage mechanical movement data. Combined with the stage in the actual operation of the situation, the use of linear functions to optimize the stage data and the data correction at the same time reduce the virtual stage in the movement parameters of the error. The immersive stage is constructed using augmented reality technology, and the elements of color, lighting, and music are added to the stage. Sound presentation, interactive performance, and satisfaction analysis are the three perspectives that examine the impact of stage art design. In the sound presentation, the difference between the top panels of the three heights under full stage conditions is between 5 and 7 dB, with an average value of 5.7583 dB, and the sound presentation is closer to the ideal state after full stage. Nearly 50% of the audience thought that the stage engagement was better, giving a high satisfaction score of 5. The use of augmented reality technology has enhanced the choreography’s interactivity.
Research on the application of visual illusion in the field of art and design
Authors: Qi Lu , Wang Genbing 
Published: February 2024

Abstract
The visual illusion is complementary to the art design techniques, which can be partially alienated according to this psychological characteristic of human beings and increase the interest in art design works. In this paper, after designing visual art images, the original local binary pattern is optimized to obtain an improved directional LBP algorithm to improve the accuracy and comprehensiveness of feature extraction. The K-means algorithm is used to cluster the colors extracted using the color space method into a color feature vector in the visual illusion art image. The empirical analysis is carried out from three aspects: feature vector similarity of visual illusion art images, image color clustering, and the application of visual illusion in the field of art design. The results show that: in the application example of clothing art design, the mean value of line spacing Z1=0.4cm is significantly larger than the line spacing Z2=0.8cm and smaller than the line spacing Z3=1.2cm in the line width Y1=0.4cm, which is larger than 1.25 and smaller than 0.09, respectively. Z3 is significantly larger than Z2, i.e., Z3>Z1>Z2. The mean value of Z2 (M=1.25) on the line width of 0.8cm is significantly greater than Z3 (M=0.8), indicating that lines with narrow width and wide spacing have the best slimming effect. The application of visual illusion in the field of art design can strengthen the effect of visual communication, enhance the interest of works, and improve the creativity and connotation of works.
A Study on Lexical Disambiguation in English Translation Based on Twin Neural Networks
Authors: Cui Cui 
Published: February 2024

Abstract
To solve the problem of the lack of effective algorithmic models to improve the accuracy of lexical disambiguation in English translation, this paper constructs a twin network lexical disambiguation model based on the characteristics of twin networks, and studies the construction process from the original corpus to the input sample pairs. The Stacked-LSTM algorithm is utilized to align the input Chinese and English corpus and expand the dataset. To achieve disambiguation, the input sample similarity is calculated after training the twin neural network, which extracts corpus features using BiLSTM Attention. After comparing the disambiguation experiments of various algorithms, the model of this algorithm can effectively calculate the similarity of the input samples and achieve the disambiguation accuracy of 68.23% for English vocabulary translation, and 87.0% for vocabulary segmentation of complex English sentences or articles. This shows that the model of this algorithm has good performance for disambiguating English translations.
The design of teaching system for university English digital course based on deep learning model
Authors: Fu Li 
Published: February 2024

Abstract
In this paper, the dependency relationship between different knowledge points is constructed by statistically counting the correct rate of answering knowledge points at adjacent times, finding the prior knowledge of each knowledge point, and the prior of the prior. The dependencies of each knowledge point are stored as key-value pairs and added into the loss function of the model as constraints, thus completing the construction of the deep learning knowledge tracking model. Aiming at the shortcomings of the deep learning knowledge tracking model, we propose to improve the DT-BKT learning knowledge tracking model and use it to design the teaching system of the university English digital course and analyze the teaching system of the university English digital course by using simulation experiments. The results show that the shortest time for acquiring the digital materials of college English is 12.35s, which is 52.87s shorter than that of the conventional English teaching resources information management system, indicating that the system in this paper is more suitable for the development and innovation of college English teaching in colleges and universities. This study can mobilize students’ willingness to participate in English learning in all aspects and has a promoting effect on the development of university English digital course teaching in colleges and universities.
Exploration of a deep learning-based mechanism for predicting the work competence of community caregivers
Authors: Huang Juan , Li Li , Zhang Lifang , Qiao Xindi , You Feng 
Published: February 2024

Abstract
To predict the workability of community nursing staff and provide corresponding training strategies based on the results. In this study, a nursing staff workability prediction model based on R-GCN-GRU was constructed. In the process of community nursing staff workability feature extraction, the attention mechanism is introduced, combined with the degree of association between the captured nodes of the R-GCN network and the long-term memory capacity of the GRU network, and the model optimization is carried out using the cross-entropy loss function. Finally, the workability of community caregivers in a city in Guangdong Province was predicted to verify the accuracy of the model from multiple perspectives. The results showed that clinical handling ability, keen observation ability, and communication ability were more valued by most caregivers, and their selection rates all reached 98.4%. On the other hand, clinical research, organizational management, and innovation abilities were relatively low. In the ability prediction of individual characteristics, the highest income personnel’s working ability was second only to the lowest salary personnel reaching 44.61±6.03. The working ability of older age and higher-position nursing staff, and nursing staff with more than 25 years of service reached 45.62±6.14, 48.30±5.22, and 45.86±5.52, respectively.
Readability Assessment of Chinese Linguistic Texts Based on Dependent Syntactic Networks
Authors: Zhao Jun 
Published: February 2024

Abstract
Based on Chinese linguistic text features, this paper classifies dependent syntactic networks into two types: supervised and unsupervised, and researches to illustrate the inter-conversion relationship and difference between the two. Based on traditional linguistic features, the orthogonal features in linguistic features are fused with the neural network features extracted from the pre-trained model utilizing feature projection to complete the construction of the Chinese linguistics text readability assessment model, and the Chinese linguistics text readability assessment model is empirically analyzed. The results show that the accuracy of the six classification algorithms takes the range of 0.379-0.648 when only the baseline model is used, which is much lower than the performance of the corresponding classification models on the feature set constructed in this study, confirming that the algorithms in this paper can better fulfill the task of automatically assessing the readability of Chinese linguistics texts. The research results in this paper can be applied to educational scenarios to help teachers select reading materials of appropriate difficulty for learners.
Research on the Design and Application of Online Learning Activities for Chinese-foreign Cooperative Universities under the Perspective of Deep Learning
Authors: Xiang Yu , He Yingjie 
Published: February 2024

Abstract
After clarifying the development characteristics of Sino-foreign cooperative education in a depth perspective, this paper designs a framework of online learning activities for Sino-foreign cooperative education in combination with the components of online learning activities. Correlation analysis and cluster analysis are used to explore the learners’ knowledge preparation of online learning activities and learners’ behaviors are mined to explore the correlation between behavioral characteristics and learning effects. Regression analysis is conducted with the interactive learning dimension as the independent variable and the learning outcome as the dependent variable to construct a multiple regression model of students’ interactive learning on the learning outcome of online learning activities, and to analyze the influence of online learning autonomy and interactivity on the learning outcome. It also explores the relationship between online learning activity design, teaching quality, faculty, and student satisfaction during internship practice and overall satisfaction. The data show that personal background variables and online learning autonomy explain 0.408 of the variance in learning outcomes, which is an increase of 0.390 in the strength of explanation over that of only personal background factors. The substantial increase in the R-square of the model suggests that the level of autonomy of students in the online environment is an important factor influencing the variance in learning outcomes. The more autonomy the students have in their online learning, the more competent they become in the online learning activities of Sino-foreign cooperative universities.
The Auxiliary Utility of Big Data Intelligent Translation in English Writing
Authors: Zhang Lihua 
Published: February 2024

Abstract
With the popularization of big data technology, intelligent translation technology gradually rises and is applied in daily life, and this paper explores its utility in English writing. English writing can benefit from the auxiliary utility of Big Data intelligent translation The grammatical error correction model for intelligent English translation is constructed based on statistical classification and deep classification to optimize the English translation system. Through the performance testing of the intelligent translation system, the performance of the algorithm model of this paper is improved by 3.03, 2.64, and 8.55 compared with CYK, CYK-PU, and the shift-and-return algorithm, based on which the intelligent translation system is applied to English writing for the evaluation and analysis of the effect, and the reliability coefficients of the three scales are greater than 0.9 through the reliability and validity test and correlation analysis. Finally, a practical analysis of the auxiliary utility of intelligent translation in English writing is conducted. Finally, the auxiliary utility of intelligent translation in English writing is practically analyzed, and in the final achievement test, its various ability test scores and comprehensive achievement scores are all greater than 0.9. An intelligent translation system can help cultivate and improve English writing ability, which enhances the realistic reference significance of intelligent translation to aid English writing.