bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study on Innovative Teaching Strategies of English in Universities Based on VAR Model
Authors: Lyu Ruifeng 
Published: September 2023

Abstract
English is one of the basic courses in college classroom education, but the current model of college English education gradually reveals some problems. For this reason, this paper researches and analyzes the strategies of English innovation education in colleges and universities based on the VAR model. Firstly, this paper constructs a VAR model, on which a TVP-VAR hybrid model is constructed, and four estimation methods of the TVP-VAR model are analyzed in detail. Secondly, we designed the English teaching method based on the TVP-VAR model and the “5C” model of innovative teaching. Based on these models and methods, this paper investigates English majors’ learning status and teachers’ teaching status in several universities in different regions. The analysis shows that the overall attitude of English majors toward learning English is very good, and those who like learning English account for 83.58% of the total number of students surveyed, but 93.6% of the students learn English to deal with exams go to higher education and going abroad, and only 6.4% really like English. Although multimedia in teachers’ teaching methods has exceeded 50% in all four grades, it is still far from matching the information age. Finally, based on the various problems analyzed in the survey, this paper proposes strategies for innovative teaching English in colleges and universities in China.
Analysis of the impact of ADDIE education model based on logistic regression model on teaching contemporary cultural and creative product design
Authors: Huang Yingying 
Published: September 2023

Abstract
Exploring the influence of the ADDIE education model on teaching cultural and creative product design to promote the improvement of the quality of teaching cultural and creative product design. Starting from the ADDIE education model, this paper analyzes the five components of the model and the specific functions of each component. The logistic regression algorithm that constitutes the analysis module of the ADDIE education model is introduced, and a sparse prior factor is introduced to optimize the logistic regression algorithm; then, a sparse logistic regression algorithm is constructed to solve the likelihood function of the weight vector. Finally, the ADDIE education model is used to analyze the indicators of cultural and creative product design teaching in two aspects: the teaching objectives and teaching principles of cultural and creative product design. From the teaching objectives, the influence of cognitive, skill, and emotional objectives accounted for 47.11%, 44.41% and 51.26%, respectively. From the teaching principles, the percentages of uniqueness, practicality, and attractiveness are 51.5%, 43.7%, and 40.3%, respectively. Based on the ADDIE education model can affect the emotional goals and uniqueness in the teaching of cultural and creative product design, and it is necessary to focus on the emotional teaching of product design as well as diversified and differentiated teaching in design teaching in order to enhance better the learners’ interest degree in cultural and creative product design.
Research on practical education model of mental health development of college students in higher education institutions in the context of big data
Authors: Luo Le 
Published: September 2023

Abstract
The advent of the big data era brings opportunities and challenges to the innovation of mental health education for college students in higher education institutions. In this paper, we monitor the mental health of college students in higher education institutions by collecting students’ consumption data, access control data, network data and historical achievement data, and use a convolutional neural network to extract features from behavioral data and then input the feature set into random forest model for training. The recognition effect of the RF algorithm is analyzed by comparing it with other algorithms, and the problems of college students’ mental health education are analyzed based on the correlation of features. The accuracy of identifying mental health problems of college students based on RF analysis of big data reached 0.72, the recall rate reached 0.58, and the F1-Measure was 0.67. For the correlation of different features, the most significant correlation effect was the time spent in the dormitory on rest days and the number of swiping access cards, with the correlation coefficients reaching 0.2719 and -0.2191, respectively. The analysis based on big data can accurately grasp the current situation and problems of mental health education of college students in higher education institutions and promote the comprehensive development of mental health education of college students.
Orientation and triage of preschool students’ interest development based on deep learning model
Authors: Hao Weiren , Kong Cuiwei 
Published: September 2023

Abstract
To study the direction of preschool students’ interest development, this paper proposes to mine and analyze preschool students’ interest development data using a deep learning model. This paper first introduces the basic algorithmic process of deep learning BP neural network model, then uses a genetic algorithm to optimize the traditional BP neural network to get the best performance. The deep learning model is then used to analyze the preschool students’ family income, family structure, interest cultivation direction, gender and age, and interest cultivation orientation and diversion. The lower the family income, the higher the percentage of children choosing interest classes, mostly concentrated in families with income between 2000-8000. In terms of gender, there are also differences in interest cultivation analysis, with boys favoring the cultivation of science and sports abilities such as logical thinking, technology, calculation, and sports, accounting for about 20% more than girls in general, while girls favoring the cultivation of art abilities such as dance, English, reading, and vocal music, with 15-20% more than boys. Deep learning model-based interest development for preschool students can follow the natural choices of young children and provide scientific guidance for interest triage of preschool children.
Assessment of tax-related risks in corporate betting agreements based on vector autoregressive model
Authors: Zhang Yang 
Published: September 2023

Abstract
This paper is based on the vector autoregressive (VAR) model, and the model optimization is completed by determining the lag order to eliminate the autocorrelation in the error term through model comparison. The variables in the tax-related risk of the betting agreement are formed into a vector form, the parameters are estimated by least squares or maximum likelihood estimation, and the interrelationship of the variables is analyzed by impulse response function. The vector autoregressive model was used to evaluate the tax-related risk of the betting agreement of Company M, and 12 factors were obtained. Among them, the factors that have more than 80% influence on tax-related risk are the lack of research on the betting agreement and the backwardness of the theoretical aspects of the handling of the betting agreement, with the influence of 87.89% and 84.98%, respectively. Therefore, the vector autoregressive model can effectively assess the tax-related risks in corporate betting agreements and provide a reference for companies diversifying into restructuring and mergers.
Exploring Innovative Strategies for Teaching English in Higher Education with the Support of Big Data
Authors: Ren Lilin 
Published: September 2023

Abstract
Under the background of the rapid development of big data technology, how to use big data technology to improve the quality and efficiency of English teaching in higher education is currently a hot topic of key concern in society and education circles. This paper is firstly based on the feedback data of English teaching from students and teachers in higher education institutions. The ranking model of fused similarity pre-processes the feedback data to get the data to feature values of English-level similarity. Then the personalized teaching system is constructed based on the data feature values, and the overall teaching framework and ideas of the MOOC personalized teaching system are proposed. The satisfaction of the MOOC personalized teaching system platform in the past five years, from 2018-2022, was analyzed employing a questionnaire, and the results showed that: on the teachers’ side, the overall satisfaction increased from the initial 53.46% to 73.64% year by year. Regarding parents, the overall satisfaction rate increased from 46.72% at the beginning to 65.76% year by year. In terms of students, the satisfaction rate increased from 75.37% at the beginning to 91.23% year by year. Overall, students showed better satisfaction with the English personalized instruction system platform than parents and teachers. This study can lead to pedagogical innovations based on the data feedback on students’ English performance, which can improve teachers’ teaching ability and pedagogical innovations.
Cultural and artistic dissemination of traditional Chinese silk patterns in the 17th-18th centuries in the context of big data
Authors: Wang Taidi , Feng Hui , Sun Ying 
Published: September 2023

Abstract
Silk has evolved a unique silk culture in its long history, and silk patterns have condensed the external aesthetics of silk culture and shown distinctive national characteristics. This paper firstly constructs the cultural communication network of the 17th-18th century Maritime Silk Road through big data technology and uses social network analysis to study the structural characteristics of the cultural and artistic communication network. The MPI multidimensional spatio-temporal communication evolution model is used to delineate the network’s layers further, and exploratory spatial data analysis is introduced to explore the spatio-temporal communication evolution pattern of the network. Finally, in the empirical analysis, 200 years of silk pattern data from 1600-1800 in China were selected to establish four dimensions and 11 indicators data for measurement. A comprehensive MPI multidimensional spatio-temporal propagation evolution model was derived by principal component analysis to derive the contribution of each indicator. The results show that the multidimensional cultural dissemination index of traditional silk pattern culture and art from 1600 to 1800 was 0.7461 and approximated to 0.8, and the dissemination influence was 0.2252, respectively, rising to 97%, indicating that the dissemination of silk pattern culture and art in the 17th-18th centuries was a flourishing trend. The MPI multidimensional spatial and temporal communication evolution model proposed in this paper effectively visualizes and analyzes the communication channels of silk pattern culture and art, which provides a guiding reference for the revival and communication of modern Chinese traditional culture.
Scale and efficiency of China’s radiation industry based on LightGBM model
Authors: Gong Yu , Qu Yunhuan , Xu Ying 
Published: September 2023

Abstract
In the digitalization of big data information, how to use big data and China’s radiation industry combined have become a key topic of social concern. This paper first constructs LightGBM model by gradient boosting tree and deep neural network. Gradient boosting tree optimizes the convergence and stability of LightGBM model, while the deep neural network improves the accuracy and efficiency of the LightGBM model by updating the weights on each edge. Then the radiation safety management method is proposed according to the danger of the radiation industry, the characteristic data of radiation industry scale and efficiency are obtained through research work, the research indexes are determined, and finally, the data analysis of China’s radiation industry scale and efficiency is conducted by using LightGBM model. Take the medical isotope industry as an example: from 2010 to 2020, the number of nuclear medicine industries increased from 857 to 1148, and the deviation rate of prediction was always maintained at 10%. In terms of radiation industry benefits: the annual growth rate of PET diagnostics, SPECT diagnostics and Nuclide Therapy benefit is 56%, 14% and 5.3%, respectively, the radiation industry has great market demand, and the benefit will continue to grow in the future. This study is a comprehensive and accurate analysis of the scale and benefits of China’s radiation industry and has a guiding reference value for the development and research of China’s radiation industry.
Research on the Development of Young Teachers’ Informatization Teaching Ability in Universities Based on Big Data Technology
Authors: Xu Qiongyan 
Published: September 2023

Abstract
This paper presents an incomplete multi categorization algorithm based on relative distance, which instead of simply using the Euclidean distance between class centers to measure the degree of dissimilarity between classes, uses the relative distance between classes to measure the dissimilarity of classes. The entire algorithm strictly follows the principle of splitting the most easily separable classes first, which decreases the accumulation of errors occurring in the binomial tree structure and improves the algorithm’s overall evaluation accuracy. Relevant tests are designed to measure the comprehensive performance of the algorithm. In terms of accuracy, the algorithm in this paper can be 4.14% higher than the best OVR algorithm and 1.63% higher than the best OVO. The experimental results prove that the algorithm is effective and that the overall time consumption in testing time is less than OVR and OVO. The analysis of young teachers’ informatization teaching ability obtained that the main influencing factors were the lack of theoretical guidance for teachers’ teaching, the lack of a common sharing platform for subject teaching resources and the lack of a sound service support system, which influenced the informatization teaching ability to 86%, 83.62% and 84.06%, respectively. Therefore, the results proved that the algorithm is effective, young teachers should focus on theoretical training in teaching informatization, and the school should build an informatization teaching platform to improve the service support system.
Design of energy big data and carbon emission monitoring system based on perceptron model in the context of carbon neutral and carbon peaking
Authors: Hu Yuanyuan , Xie Tianxiang , Chi Ning , Yang Yujie 
Published: September 2023

Abstract
The big energy data and carbon emission monitoring system is designed to collect carbon emission-related data for pollution gas management. This paper constructs a carbon emission monitoring system in the context of carbon neutrality and peaking. A multi-layer perceptron algorithm is introduced based on the principle of perception, and a BP-MLP neural network model is proposed by optimizing the perceptron weights using BP neural network. For the sensors in the carbon emission monitoring system, the node redundancy is processed, and the optimal sensor distribution is proved by using the correlation coefficient. Finally, the evaluation analysis of the carbon monitoring system was carried out in three aspects: relevance coefficient de-redundancy, number of iterations and daily emissions. The results show that when the correlation threshold is 0.8, the sensor distribution of the monitoring system can satisfy the monitoring under various wind conditions, and when the number of iterations is 600, the difference between the real value and the monitored value is only 3.63% and the daily emission peaks at 5.243 mg/m3 at 14:00 a.m. This shows that the carbon emission monitoring system constructed based on the BP-MLP model can effectively collect and analyze carbon emission data. Data collection and analysis, and provide corresponding data support for the management of gas pollution.
A model of online opinion dissemination based on vector autoregressive model of emotional information entropy
Authors: Yin Jingqi , Chang Rui 
Published: September 2023

Abstract
In the context of the digitalization of big data information, to solve the problems of timeliness and accuracy of traditional online opinion dissemination models. This paper firstly proposes a vector autoregressive model in the research problem of online opinion dissemination model and constructs a vector autoregressive model based on multivariate time series and unit root test. Then the emotional information entropy is determined based on the statistical characteristics of the emotional index, and the evaluation index is mainly composed of the current mainstream online public opinion dissemination modes. Finally, data analysis of online opinion dissemination modes based on the vector autoregressive model is conducted, and the dissemination efficiency of different modes is compared based on the analysis results. The results show that in the microblog dissemination mode, online public opinion’s dissemination efficiency remains in the range of 38.02% to 44.81%, and its average dissemination efficiency is 41.51%. In the online short video dissemination mode: the online public opinion dissemination efficiency remains between 32.15% to 35.74%, and its average dissemination efficiency is 33.97%. In the consultation mode, online public opinion’s dissemination efficiency remains within the range of 22.06% to 28.99%, and its average dissemination efficiency is 25.61%. In terms of the average dissemination efficiency, the microblog dissemination mode performs better than the other two. Through a comprehensive and objective study of online public opinion dissemination modes, this study has a guiding reference value for improving online public opinion dissemination to promote the development of online public opinion dissemination in China.
Multimodal construction of human movement dataset under the development of physical dance teaching in colleges and universities
Authors: Liu Feng 
Published: September 2023

Abstract
First, this paper proposes a multimodal fusion-based human motion recognition technique applied to college physical dance teaching, where feature-level or decision-level fusion maps human kinematic semantic information from datasets to classifiers under a single-level stage. Secondly, a multi-level multimodal fusion approach for human motion recognition is proposed to make it more adaptable to practical application scenarios. The depth data is converted into a depth motion projection map at the input side, and the inertial data is converted into a signal image. Finally, all the modalities are trained by a convolutional neural network for extracting features, and the extracted features are fused at the feature level by discriminant correlation analysis. The results show that the multi-level multimodal fusion framework achieves 99.8% and 99.9% recognition accuracy on the two datasets, respectively, with 100% accuracy in the Throw and Catch action recognition and the lowest recognition rate of 95.36% in the Clap action test, with an average recognition rate of 97.89%, which has a high recognition accuracy. The multi-level multimodal fusion model can obtain movement data close to the actual movement data after optimization in low-precision movement data, which provides data support for physical dance teaching and learning.
Reading promotion mode of university libraries in the context of information development
Authors: Liang Yuhua , Wang Jinying 
Published: September 2023

Abstract
This paper mainly proposes a technical route of user profiling based on multi-view clustering and constructs a multidimensional and multi-view reader feature system based on reader behavior data. According to the constructed reader feature system and the defects of the classical K-means algorithm in multi-view clustering, a multi-view dichotomous K-means algorithm based on Marxian distance is proposed. Using the proposed algorithm for multi-view clustering of readers, the user portrait features of readers can be obtained, and finally, a university library user portrait system is designed and implemented. By analyzing the behavior of college students through the user portrait technique, we get that the most preferred book genre of college students is literature accounting for 71.45%, followed by science and history with 57.64% and 51.37%, respectively. The most popular form of reading promotion among college students is classical film and television exhibitions, accounting for 63.73%, followed by lectures by celebrities and experts, accounting for 60.91%. The highest influence on college students going to the library is too many spare time activities, accounting for 64.38%, and not choosing books, accounting for 36.95%. These data can help libraries understand readers’ needs and provide help for reading accurate recommendations and services based on readers’ user profiles.
Design of online art education system based on 3D virtual simulation technology
Authors: He Shuijing 
Published: September 2023

Abstract
At present, the call for information reform in education teaching is increasing, and art education is also facing the demand for online. This paper analyzes the demand for online art education according to the characteristics of existing online education, proposes an online education system based on virtual simulation technology for the special needs of art education, and studies the structure distribution of learning resources, teaching organization and online teaching practice under 3D virtual simulation scenarios respectively. At the same time, a virtual simulation explanation robot is added using Unity3D technology to realize online learning interaction in art education. In the continuity test of the system, the system throughput per second accounted for 42.31% of the maximum throughput on average, with the lowest accounting for only 28.65% and the highest accounting for 56.98%, and the operating efficiency of the system met the teaching needs of art education. In the concurrency test, the success rate of system interface calls remained above 80% as the number of concurrent users increased. In practice, the overall satisfaction rate of students with the art online education system reached 88.3%. The system in this paper can enhance the interest in art teaching and provide a feasible new path for the development of art education in the information era.
Diversified Integration of Civics Teaching Resources in Universities Based on Discrete Regression Model
Authors: Gan Lu 
Published: September 2023

Abstract
Diversified integration, rationalized allocation and high-efficiency utilization of various practical teaching resources are the prerequisite and foundation for realizing the goal of monolithic education of Civic and Political Science teaching. This paper proposes a local two-way adaptive degree function of a complex network of Civic and Political Science teaching resources cluster and constructs a discrete regression-based resource diversification integration model based on the adaptive degree function by considering the characteristics of a complex network of Civic and Political Science teaching resources cluster with local worldliness (cluster structure), complexity, incomplete and asymmetric information obtained. The research results show that the discrete regression model Cronbach’s α coefficient value is 0.903, and the cumulative explained variance reaches 81.69%, which can effectively and highly fit the integration of diverse resources of Civics teaching. And compared with the traditional BA integration model, the discrete regression model increases the integration speed by 36.21% and the breadth by 41.39%. The discrete regression-based resource diversification integration model proposed in this paper effectively improves the level of practical teaching of Civics in colleges and universities and provides some reference and help for related researchers.