bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on the Development Prospects of China-Japan Trade in the Context of Global Value Chains
Authors: Li Bowen , Li Xianrui 
Published: January 2024

Abstract
This paper proposes a value-added trade measurement model after a specific analysis of the extent of China and Japan’s participation in GVCs, the mode of participation and the status of division of labor, and the characteristics of the participation structure. Combined with the ratio of value-added to total exports, China is compared with major trading countries to study China’s level of value-added in GVCs, trade characteristics, and the proportion of China’s industry-wide development. Combined with the WIOD data, based on the model proposed in this paper, the bilateral trade interests of China and Japan, the bilateral export value, and the trade interest rate of both sides are measured from 2000 to 2014. The classification method of HS commodity code is used to extract the trade data of important time nodes between China and Japan and analyze the development of the structure of commodity trade between China and Japan. It measured that in 2014, the trade interest rate of China to Japan was 81.95%, and the export of 172.911 billion dollars contained 141.707 billion dollars of trade interest. Japan’s trade benefit rate to China was 73.94%, with $95.615 billion of trade benefits included in $129.314 billion of exports. It shows that as of 2014, China has replaced Japan in the bilateral trade between China and Japan to occupy a relatively favorable position, indicating that the international division of labor between China and Japan has gradually shifted from a vertical division of labor to a horizontal division of labor.
Digitization of Civics in College Physical Education Courses Based on the Correlation Matrix
Authors: Liu Jing , Si Jingmei 
Published: January 2024

Abstract
In this paper, after the extraction of teaching resource features by mutual information method, the classification of Civics teaching resources is realized by constructing a decision tree classifier. According to the current situation of Civics education in university sports courses, the set segmentation correlation function is determined, the contribution of each factor is combined using the correlation matrix, the correlation coefficient weights are calculated, and the design of the Civics multimodal corpus is accomplished based on big data technology. Nine universities in Guangzhou City were selected as the objects of this study, and the research data were obtained through questionnaire surveys of physical education teachers in these nine schools. SPSS26.0 and Origin 2019 software were used to study the Civics and Politics of Physical Education Courses in Colleges and Universities. The results show that the convergence speed of the objective function of the algorithm in this paper and the classification accuracy has been improved substantially, compared with other algorithms, by about 0.3 to 0.7, i.e., it shows that the algorithm in this paper has a good performance of classification of the course Sijian. The scores of national sentiment and sports core literacy are 0.76 and 0.76, respectively, with a rank of medium (less than 0.79), and the score of physical and mental health is 0.82, with a rank of high (greater than or equal to 0.8). That is, the public physical education (volleyball) class has a relatively satisfactory effect on the cultivation of students’ physical and mental health, while there is still room for progress in the cultivation effect of family and national sentiment and sports core literacy. This study supplements, to a certain extent, the lack of an evaluation index system for the effect of sports courses’ ideology and nurturing and promotes the better development of public sports courses’ ideology.
Research and Application of Performance Pay Platform Construction for College Teaching Staff Counting and Multi-scale Feature Fusion Networks
Authors: Yuan Weiwei 
Published: January 2024

Abstract
In this paper, a multi-scale feature fusion network is constructed by combining U-Net and Densebox algorithms, and feature extraction is performed by the deep residual network (ResNet). Taking colleges and universities as an example, a teacher reputation incentive model combining a multi-scale feature fusion network is established based on marginal productivity. The multi-scale feature fusion network is proposed to be used to realize the employee performance measurement level and personal appraisal system in the salary performance appraisal platform for teachers in colleges and universities. The appraisal test analysis of the teacher team’s performance and salary was carried out with the teacher team of School G as an example. The results show that the starting salary points of B1, C1, and D1 of the teacher team of school G are 2250, 1650, and 1260, respectively, in which the median difference in salary value between the highest grade B1 (9618) and the lowest grade I2 (784) is 8834 yuan. The performance pay management platform constructed in this paper effectively provides incentives and promotes the high-quality development of the university teaching team through strict assessment and fair distribution.
The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data
Authors: Zhu Xiaoguang 
Published: January 2024

Abstract
This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose personalized teaching based on a genetic algorithm. Genetic algorithms are used to extract learner characteristics, and content-based recommendation algorithms are applied to match ability characteristics, goal characteristics and learning object characteristics to achieve personalized course teaching. The crossover probability, variation probability, selection operator and crossover operator of the genetic algorithm are determined, and simulation experiments are designed to analyze the optimization effect of its parameters. Setting three levels of colleges and universities, with the connotation of the objectives and level positioning of English and American literature teaching, i.e., reading experience, reading knowledge, reading method, and the cultivation of cultural connotation and humanistic sentiment as factors, a one-way ANOVA was carried out to examine whether the teaching objectives of different levels of colleges and universities differed significantly in personalized teaching. In terms of the highest level of the teaching objectives of English and American literature, i.e., the cultivation of humanistic sentiment, F=6.607, Sig=0.002, 985 colleges>211 colleges and universities, and general undergraduate schools>211 colleges and universities.
The Logical Mechanism and Practical Progress of Digital Economy Contributing to Rural Revitalization in the Context of Big Data
Authors: Liu Wenju , Song Shuang , Yao Ximin , Miao Yuqing 
Published: January 2024

Abstract
This paper proposes spatial data mining and clustering analysis techniques based on big data and uses four distance similarity calculation methods, namely, Euclidean distance, Manhattan distance, Ming’s distance, and Ma’s distance, to constrain spatial clustering similarity metrics. Combine the K-means algorithm and FCM clustering algorithm to calculate the center value of fuzzy clustering, use the additive synthesis method to solve the combination weights, and construct the combination weighted distance, fuzzy C-mean algorithm model. Research on the mechanism and path of digital economy-enabled rural revitalization through the C-FCM model, and propose three correlation hypotheses for this paper. Empirical experiments are conducted to verify the authenticity of the hypotheses proposed in this paper and the use effect of the constructed model. The experimental results show that the Moran index range of digital economy and rural revitalization is [0.06, 0.12], [0.05, 0.16], respectively, and there is a significant aggregation phenomenon of the two under the geographic matrix. In the analysis of the spatial spillover effect, the values of spatial autoregressive coefficients of rural industrial revitalization are all >0, with significance p>0.05. The digital economy has a positive effect on rural revitalization in neighboring areas through the spatial spillover effect. In the clustering sample extraction test, the accuracy is most stable when the number of samples is 2200, and the accuracy rate is as high as 79.66%.
Innovation of Mental Health Education Path in Colleges and Universities under the Perspective of “Internet +”
Authors: Chen Lingjing 
Published: January 2024

Abstract
The traditional mental health education model has made it difficult to cope with the increasingly serious psychological problems of college students. In this paper, under the perspective of “Internet+”, based on the social network of college students, we constructed a propagation dynamics model of depression in colleges and universities and utilized the heterogeneous mean field to detect the propagation status of students’ depression. After calculating the comprehensive centrality degree of students with depression, the community mining of peer psychological adjustment is completed. The C-P similarity of students under the mental health perspective was defined by the ordering of the comprehensive centrality degree and the friend recommendation results were obtained by the distance measure of similarity. In the network of students without depressed mood, after a period of oscillation initially, the end state can be basically reached within 1000 steps, and the infected nodes reach 1000. When the depressed mood factor is small, such as S=0.02 and S=0.04, it has been able to prevent the transformation of depressed mood to the state of all 0, and the proportion occupied by positive and negative moods in the network is basically stable. Internet technology effectively improves the problem of identifying students with psychological abnormalities and implementing targeted mental health education.
The situation and transformation of sports governance in national colleges and universities under the background of double reduction
Authors: Fu Xiaolong 
Published: January 2024

Abstract
This paper aims to improve the quality of sports development by using particle swarm optimization (PSO) based long and short-term memory network (LSTM) and support vector machine (SVM) based sports performance prediction model (SPPM) to realize the shift to information technology (IT)-based sports governance in the context of the “Double Reduction”. The effects of LSTM and PSO-LSTM are evaluated by testing individual exercise physiological data like heart rate and blood oxygen values. Test the effectiveness of particle swarm optimization neural network’s sports performance prediction model with sample data and conduct comparative experiments with multiple linear regression, genetic algorithm optimization BP neural network, and firefly optimization BP neural network, respectively. Analyzing the characteristics of the extracted samples and performing a paired-sample t-test on the sample size revealed the gap between service perceptions and expectations. The analysis shows that the overall expectation mean of parents is 4.482, and the actual perceived mean is 3.966. All the indexes fail to reach the ideal level of parents, which indicates that the delayed sports service proposed by the institutional sports needs to be further adjusted.
Optimization Analysis of the Path of Ideological and Political Construction in Higher Vocational English Courses Based on Intelligent Data Analysis
Authors: Kong Liping 
Published: January 2024

Abstract
Incorporating the elements of curriculum Ideological and Politics in English teaching is an important way to enhance the effect of nurturing, and this paper explores its integration. In the paper, the intelligent data mining process for the construction of higher vocational English curriculum Ideological and Politics is constructed, and the related data are integrated based on the rough set method to explore the set relationship between the construction of intelligent data. In addition, combined with the PSO algorithm, the PSOBR algorithm is constructed on the basis of the PSORSFS algorithm of PSO’s rough set feature selection for the optimization of the path of construction of the English curriculum Ideological Politics. On this basis, relevant data on English majors in higher vocational colleges and universities were collected to carry out empirical research on the path of Ideological and Political construction of English courses in higher vocational colleges and universities. The results show that the main distribution of GPA of the three groups of students based on rough set classification is (0.60,0.80), (0.36,0.50), (0.24,0.44), respectively, and the distribution of the construction of higher vocational English courses is not balanced. The average construction effect after optimization has been improved by 0.30, and the highest construction effect has been improved by 0.42, which makes the optimization effect obvious. The optimization effect of the path of English course ideological and political construction through the formation of a professional teaching team, enrichment of teaching methods, and other means is obvious.
Research on Physical Education Management System in Higher Education Institutions in the Context of Deep Learning
Authors: Zhang Fan 
Published: January 2024

Abstract
This paper focuses on the management of physical movement sports disciplines and combines relevant algorithms to design an artificial intelligence-based physical education management system framework. In terms of related algorithms, the first is for the research of character recognition algorithms. Considering the intra-class differences between individual movements, the LR-based feature extraction algorithm is proposed. Secondly, for the research of the action evaluation algorithm, the statistical value, DTW parameter action time series difference and correlation coefficient based on the statistical value and DTW parameter are used as the features, combined with SVM-based feature extraction algorithm for perceiving the action differences. Finally, with the help of deep neural networks for movement analysis, we can improve students’ movement techniques during training. In the course schedule supported by the system, the proportion of moderate-intensity exercise time is high, basically, more than 40%, which meets the WHO requirements for high-intensity exercise time in physical education classes. The post-course RPE feedback from students was spread out between 12 and 15, and the highest post-course RPE value was 14.82<unk>1.38, which was evaluated within a reasonable range of intervals. The teaching management system in this paper can meet the requirements of physical training and teaching, and provide quantifiable basis and management tools for the informatization and precise teaching of physical movement subjects.
Research Progress of Weld Tracking Image Processing Technology Based on Deep Learning Theory
Authors: Shen Zilei , Du Yongqiang 
Published: January 2024

Abstract
In this paper, a convolutional neural network is used to localize the weld seam feature points with noise interference in complex welding environments. A priori frames are introduced into the feature point extraction network, combined with position prediction and confidence prediction, to improve the accuracy and anti-interference ability of the weld tracking system. To improve welding efficiency by utilizing the continuity of weld tracking, the weld tracking network is designed based on the twin structure. The weld detection network designs the first frame to locate the key position of the bevel and inputs into the weld tracking network as a template, and the weld tracking network completes the automatic tracking of the subsequent welds. At the same time, the network introduces a hybrid domain attention mechanism, which makes full use of the weld feature channel dependence and spatial location relationship and puts more attention near the inflection point of the weld laser line to ensure the accuracy of weld tracking. The research results show that the extraction error of weld seam feature points based on the convolutional neural network is within 17, which is much lower than that of the grayscale center of gravity method and Steger's algorithm. In the weld tracking experiments under the workpiece tilting state, the average value of the absolute error of the tracking trajectory in the X-axis direction is not more than 0.7 mm, and the maximum value is less than 1.15 mm. The absolute tracking error in the Z-axis direction is relatively low, with an average of 0.638 mm and a maximum of 1.573 mm. Therefore, the weld-tracking image processing technique proposed in this paper has strong anti-noise interference capabilities and high localization accuracy. And high accuracy in localization.
The Integration of Traditional Music Culture in Modern Informational Music Teaching
Authors: Hou Changzhi 
Published: January 2024

Abstract
In this paper, in the process of music teaching, the music notes characterized in the time domain are Fourier transformed to the frequency domain, and the resulting notes in the frequency domain are analyzed and processed to obtain the inverted spectral domain features of the notes. On the basis of the cepstrum features, the music recognition model based on the depth confidence network is constructed, and after the training of the depth confidence network, the overfitting phenomenon often occurs for the depth confidence network, and the optimization is carried out by embedding the Dropout method between the implicit layers. From the perspective of music recognition and informationized music teaching, the research on traditional music culture integration of informationized music teaching is designed, and statistical analysis and simulation analysis methods are constructed. The results show that better recognition performance than using music features from different convolutional layers can be obtained using Deep Confidence Networks, with values of 77.5%, 78.8%, and 78.2%, respectively, and that music recognition research based on Deep Confidence Networks is able to better explore and pass on traditional music culture. In the linear regression analysis between the factors of incorporating folk songs into music teaching, the Sig. F and Sig values are 0, which are smaller than the significance level of 0.01 and 0.05, indicating that there is a significant relationship between the factors of students’ gender, age, whether they like folk songs, and the channels of exposure to folk songs and whether folk songs are incorporated into the music teaching program of colleges and universities. This study takes folk songs as representative of traditional music culture, raises people’s awareness of the value of folk songs, and enhances their understanding of the importance of music culture inheritance.
Consumer behavior prediction and marketing strategy optimization based on big data analysis
Authors: Zhang Zifeng 
Published: January 2024

Abstract
A large amount of user data on network platforms contains rich treasures, and the mining and development of user consumption behavior in combination with big data provide new possibilities for enterprise precision marketing. In this paper, a consumption behavior prediction model is constructed based on context-aware data and support vector machine classification algorithm, and interactive information collection is used to collect contextual data of user consumption behavior and consumption behavior cycle data. Combined with the support vector machine classification algorithm, the collected behavioral consumption data is divided into hyperplanes, and for nonlinear data, the method of placing the corresponding behavioral data in hyperplanes in the feature space is proposed. The selection of consumption behavior features is obtained using the min-max normalization process, and behavior prediction is carried out on this basis. The results show that the context-aware behavioral prediction model in this paper has the highest R-value, F1 value and NDCG among all models in Top-10, which are 0.796, 0.645 and 0.878, respectively. The two-stage prediction method in the behavioral prediction model can achieve a 98.15% data capture rate, which can accurately obtain the number of these users so as to formulate an accurate marketing strategy.
Design of Internet Platform for English Teaching in Colleges and Universities under the Background of Big Data
Authors: Dong Xiaoyan 
Published: January 2024

Abstract
This paper constructs an English teaching platform based on a speech recognition algorithm and Internet technology, adopts a three-tier architecture to design the web application, and combines the server and browser technology to realize the operation of data interaction with the SQL database. A rectangular bandpass filter is selected for narrow-band information extraction by introducing the sinc function to improve the feature extraction efficiency of speech signals at each frequency. CTC is used to obtain input feature-to-state mapping to solve the sequence problem in speech recognition and achieve end-to-end speech recognition. To achieve the final audio recognition results, the speech decoding algorithm is employed to select the optimal speech output. The results show that in the listening score, the score of the experimental group is 9, and the score of the control is 6. The completion rate of the topics among students who studied on the English teaching platform was higher than that among students who were taught in ordinary English. The completion rate is 0.82 for English questions of grammar type and 0.98 for English questions of listening type, and the average number of passes is the least number of passes, and the number of times is 2.
Selection of corporate strategic and operational decisions based on the strategic orientation of informatization: an experimental study
Authors: Li Jing 
Published: January 2024

Abstract
This paper firstly divides the enterprise informatization strategy into three objectives, namely cost (C), time (T) and performance (P), so as to form a triangle with mutual constraints and equilibrium. Secondly, based on demand effect curve fitting, establish the product sales revenue effect function to find the product price that maximizes sales revenue. And under the cumulative effect of the multi-period business decision-making scheme, the traditional Markov process model is improved to form a multi-period business decision-making optimization method based on the Markov process. Finally, using Enterprise A as a study case in the horizontal expansion stage, simulation and sensitivity analysis of the enterprise’s business decision-making scheme are performed. The results show that in the 8-year operation cycle, as the target profit margin increases from 0.05 to 0.20, the cumulative profit level decreases from 11.43 to 8.38. Enterprises can select the scheme that obtains the optimal comprehensive ability in one aspect according to their own internal and external environments as the final implementation scheme.