Reviewerly/exHarmony huggingface dataset:
Research on the Change and Protection Strategy Innovation of Non-Genetic Inheritance in Sports in the Era of Artificial Intelligence

Abstract In this paper, we study the operation mechanism of the Openpose model under artificial intelligence technology, reconstruct the spatial coordinate system of the skeleton, and do the standardization of the skeleton coordinate system. Analyze the problem of limb angle similarity, sort out the algorithm process for extracting differential posture features, and extract human limb angle features through center-of-gravity regression calculation. A model for the change of non-heritage inheritance methods in sports is constructed using motion-captured interactive images of non-heritage sports dance. There are five innovative ways to use non-heritage protection strategies. The Openpose model was used to capture the kinematic features of sports gestures to analyze the non-genetic inheritance of sports in A place for empirical research. In the study of athletes’ front leg characteristics, the mean value of level 1 athletes was 44.68±21.93 higher than that of level 2 athletes, and the p-value was 0.049&lt;0.05, which was significant. In the empirical analysis of sports NRLs, the comprehensive score of the protection effect of 14 sports NRLs in place A was 3.293, which was better overall. Among the protection effects of different dimensions, the survival conditions dimension has the best protection effect, with a mean value of 4.436. This aspect of survival conditions is handled with the best effect in place A. The government is not as good as the other two dimensions in terms of protection and needs to increase its protection efforts.
The Rationale of Physical Education and Aesthetics in the Context of Big Data and the Value and Significance of its Implementation in Colleges and Universities

Abstract This paper first takes the deep learning theory as the basis of the thesis of sports and aesthetic education, constructs the flipped classroom teaching model of sports and aesthetic education based on the theory, and introduces the Bayesian estimation theory and the structural equation model to apply it to sports and aesthetic education. Secondly, the research object and method are selected for the flipped classroom teaching model of physical education and aesthetic education, and the teaching process of the flipped classroom experiment and the assumptions related to students’ learning satisfaction are given. Finally, the application value of physical education and aesthetics in colleges and universities is analyzed through the convergence of path coefficients in Bayesian structural equation modeling and hypothesis testing. When the number of iterations reaches 100*10 3 times, the H6-H11 path coefficient convergence value interval is [-22.5, 14.2]. The convergence interval fluctuates a lot and shows a more unstable trend than the H1-H5 path coefficient. Students learning satisfaction will have a direct effect on their aesthetic ability and improve their aesthetic awareness, and the path coefficients are 0.629 and 0.524, respectively. Deep learning theory based on sports and aesthetics in colleges and universities under the background of big data can improve students’ learning satisfaction and then promote the enhancement of students’ aesthetic ability and aesthetic awareness.
A Study on the Construction of Cultivating High-Quality Farmers in Higher Vocational Colleges and Universities under the OBE-PDCA Approach

Abstract Rural revitalization, talent first, from the high-quality farmer’s cultivation policy as well as vocational education characteristics research, to achieve high-quality farmers’ talent cultivation. This paper analyzes the status quo of the cultivation of high-quality farmers, constructs the cultivation model of high-quality farmers through the OBE-PDCA method, and constructs the cultivation effect evaluation model of high-quality farmers based on the BP-PCA method by combining BP neural network and principal component analysis. On this basis, the cultivation path of management-type high-quality farmers is taken as an example to explore the effect of OBE-PDCA in constructing the cultivation model of high-quality farmers as well as the cultivation effect of high-quality farmers. The results show that the averages of the cultivation of provincial higher vocational colleges and universities in the four dimensions of PDCA are 16.07, 10.51, 13.48, and 22.58, and the F-values of the overall test are 2.4, 7.8, 4.5, and 4.1, with the correlation coefficients of 0.46, 0.45, and 0.59, respectively. The resulting difference in the weight scores of Δ ≥ 10 is 1 item, 5 ≤ Δ ＜ 10 is 6 items, 0≤ Δ ＜ 5 is 1 item, with good cultivation effects. Item, there is a good cultivation effect. The method constructed in this paper can effectively enhance the cultivation effect of high-quality farmers and promote rural revitalization.
Application of Sustainable Education in Chinese Language Education in the Context of Big Data

Abstract This paper explores all student related data using educational data mining techniques to draw conclusions about their performance and behavior. The main idea and theoretical basis of Random Forest is described, the importance of each feature is calculated using Random Forest based Important Feature Selection Algorithm, and each feature is ranked and the best feature is selected as the effective feature for constructing the performance prediction model. By Light GBM is to further improve the GBDT algorithm and XGBoost algorithm to construct the Light GBM grade prediction model in order to improve the training speed and the prediction ability of the model. In order to verify the feasibility of the constructed model, the application of sustainable education in Chinese language education is tested from various aspects such as model testing as well as learning behavior. The results show that the accuracy of the Ligth GBM grade prediction model increases from 0.68 to 0.918 when the course progress is raised from 10 to 100, i.e., the accuracy of the Ligth GBM grade prediction model in predicting students’ grades gradually increases as the course progresses, so that it can effectively analyze the application of sustainable education in Chinese education.
The Value and Realization of Choral Conducting Talent Cultivation for Music Teachers in the Context of Deep Learning

Abstract Focusing on virtual chorus, this paper analyzes the training process of VR technology and music choral conductor talents. After the construction of the online data platform, the image splicing, image alignment and image fusion algorithms of VR technology are integrated with the Harris corner detection improvement algorithm to realize the virtual chorus in the context of deep learning. The influence of family support and school aesthetic education on various dimensions of psychological needs for talent cultivation was modeled using structural equation modeling. Based on the data of music teachers using VR technology, mediated effect analysis and total effect analysis were conducted to analyze the factors influencing the use of imagery with VR technology. ANOVA was utilized to compare the data on the four dimensions of rhythmic memory, pitch, melodic memory, and listening ability to verify the effectiveness of VR technology on music teachers’ talent cultivation. The total effect analysis showed that the order of the influence of each influencing factor on the intention to use was 0.361&gt;social influence 0.263&gt;perceived usefulness 0.263&gt;perceived ease of use 0.251&gt;computer self-efficacy 0.169&gt;convenience conditions 0.162&gt;job relevance 0.134&gt;simulation 0.075.
Research on the Reform and Practice of Informatization Mode of Adult Higher and Continuing Education Academic Records Management in the Context of Three-Whole Parenting

Abstract Information technology reform is an important way for schools to realize efficient management and information technology should be introduced into the management of student registration files to accelerate the process of informationization of registration file management. This paper builds an informatization database of adult higher continuing education student registration file management based on informatization technology and carries out the design of the main business process of student registration files. The multi-layer stacked LSTM network in the ELMo model is utilized to learn the preprocessing of the text of the school records data, and the CART decision tree algorithm is improved based on the reinforcement learning mechanism in order to improve the classification prediction accuracy of the decision tree in the dataset. In order to verify the feasibility of the school registration file informationization database constructed in this paper, a test analysis is conducted for the application of data mining technology and informationization database. The results show that the check accuracy of the CART decision tree algorithm improved by the reinforcement learning mechanism is around 80%, and the throughput of this paper’s database reaches 4827ops/s when the data writing volume is 1*10 5 GB. When the number of threads reaches 128, the throughput of this paper’s informative database is around 5486ops/s, and when the data volume reaches 12*10 5 entries, the database in this paper the query response time is around 602ms. Under the background of three-round education, it is necessary to fully integrate information technology with the management of school registration files, which will lead to the innovation of the informationization mode of the management of school registration files in adult higher education.
Vocational Education and Industry Integration and Symbiosis of Subjects and Digital Drivers

Abstract Under the new round of scientific and technological revolution and industrial change, the integration of vocational education and industry is an important direction of educational development. In this paper, the structure of the learning path network of higher vocational professional courses is analyzed, and through the analysis of the elements of learning behaviors, the learning behaviors are mined by adopting sequence relations, and the behavioral structure model is established. The assessment of the learning path under the integration of industry and education courses was finished using uncertainty association coefficients and time association coefficients. After building the learning path network for vocational education, the subjective characteristics of students under vocational education were detected using isolated point analysis. The detection results show that the correlation coefficient between the first pair of typical variables is 0.488. The Wilks statistical test results for typical correlation coefficients show that the p-value of the first typical correlation coefficient is 0.098, which is less than 10%. The correlation study of the environment of integration of education and industry and the effect of integration can be simplified to study only the relationship between the first pair of typical variables. By analyzing the main characteristics, the main body for vocational education and industry integration is clarified, which creates a digital-driven impetus for the integration of industry and education.
The Application of Big Data Technology in University English Teaching Mode Innovation and Effect Analysis

Abstract In the dynamic planning of reinforcement learning algorithms based on inverse recursion, this research shows how well Q-learning performs in finding the optimal decision function for accurate English teaching intervention in colleges and universities using data simulation. This simultaneously contrasts the two scenarios of calculating the best decision function independently and in a single stage using the Q-learning method. The correlation analysis between the innovative English teaching model and learners’ learning effectiveness was carried out by demonstrating the great clusters of the subset of English teaching data in colleges and universities. The results show that the correlation coefficients of English innovative teaching mode and learning outcomes are 0.385 and 0.276, respectively, in which H2-P1 and H2-P2 involve the structure of students’ personalized learning of English, whether it is an edge or a node, and the learning behaviors are not completely autonomous processes. This study proposes innovative strategies for improving the quality of university English teaching and laying a good educational foundation for students’ English learning.
A Study on the Lexical Evolution of Yuzhong Dialect Based on Cognitive Mapping and Its Pragmatic Mechanisms

Abstract Based on the method of cognitive mapping, this paper systematically organizes the evolution of Yuzhong dialect vocabulary in different historical stages, revealing its important role in the evolution of the Chinese language. The global statistical information of the corpus is utilized to train the word vectors, which are input into the recurrent neural network as feature vectors to enhance the model’s ability to learn long-distance information. Perplexity is defined as a metric for evaluating the performance of a language model, the inverse of the geometric mean probability assigned to each word by the model given a known sequence of words in the test set. Subsequently, based on this metric, a corresponding model was built to comprehensively evaluate the performance of the language model, which provides insights into the functions and expressions of Yuzhong dialect words in communication. Among them, the number of unsubstituted nouns, such as weather, is 41, and the color of the sky is 67, showing the rhyme and stability of Yuzhong dialect vocabulary in specific domains.
Research on the Innovation of Business English Teaching Mode in Colleges and Universities under the Background of Artificial Intelligence

Abstract This paper examines the application of artificial intelligence technology in business English teaching and artificial intelligence in natural language processing and recognition to receive good results. These two technologies are suitable for business English teaching innovation. At this time, the introduction of the concept of ADDIE is the process of integrating artificial intelligence technology into the business English teaching innovation model. The business English teaching model is designed to utilize the conditional random field expansion model to address the label bias limitations of the entropy model. Add the LSTM model to this basis to handle speech recognition and translation tasks to complete the construction of a business English teaching model based on LSTM-CRF. After speech recognition, detect the existence of grammatical errors in the sentence through the multi-task model, deform the LSTM model, and use the deformed Bi-LSTM to construct the multi-task learning model framework. The objective of controlled experiments and empirical analyses is to investigate the students’ willingness to learn AI Business English and their learning effects. The experiment shows that among the seven items of students’ willingness survey, only the mean value of the hedonic motivation variable is 3.93, and the mean value of the remaining six items is above 4. Students’ willingness to learn AI is high. In the comprehensive learning engagement degree, the mean value of the engagement is 1.457, and the highest value reaches 1.83278, and the students have a high degree of engagement in AI English learning.
A neural network-based prediction model for cultivating skilled personnel in higher vocational education

Abstract In this paper, the neurons in the BP neural network are used to represent the skill-based talent cultivation feature vectors, and the feature vectors are trained on the network to obtain the vector transformation function. On the basis of the vector transformation function, after constructing the technical talent cultivation prediction model by using the backpropagation algorithm, the data indicators are determined according to the requirements of skill-based talent cultivation, and the initial data are normalized by overfitting for the technical talent cultivation prediction results. The empirical research on the synthesis of technically skilled talents in higher vocational education is designed by means of questionnaires, and the data analysis software is used to analyze the examples of skilled talent cultivation in vocational education under the background of big data technology. The results show that from 2015 to 2020, the predicted values of the number of technically skilled talents demanded in a province are 92,130, 105,396, 160,946, 225,045, 232,313 and 216,150 respectively, and the relative error values are less than 0.05 compared with the actual demand values of technically skilled talents in the same period, indicating that based on the BP neural network-based technical talent cultivation prediction model outputs a good fit between the extrapolation test prediction value and the real value. This study guides the cultivation of skilled talents in higher vocational education.
Research on the Evaluation of the Effectiveness of the Construction of First-Class Undergraduate Majors in Colleges and Universities Based on Student Input in the Context of Deep Learning

Abstract This paper uses triangular fuzzy numbers to solve the weights of the evaluation indexes and then applies the improved TOPSIS method to evaluate the effectiveness of the construction of first-class undergraduate majors in colleges and universities. Empirical data were collected through questionnaires and materials related to the declaration of first-class major construction to verify the factors affecting the effectiveness of first-class undergraduate major construction and the comprehensive evaluation. The results show that four indicators, namely student learning outcomes, effective teaching practices, teacher-student interaction levels, and professional curriculum plans, explain 57.52% of the effectiveness of first-class undergraduate major construction. Among the ten universities, the relative closeness of the first-class undergraduate major construction effectiveness to the optimal solution of University D is 0.75221, and the major construction effectiveness is the most significant. The combination of students’ inputs and the effectiveness of first-class undergraduate major construction in universities under the background of deep learning can further enhance the depth of students’ learning for the major and promote the effectiveness of major construction.
The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

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&gt;211 colleges and universities, and general undergraduate schools&gt;211 colleges and universities.
Digital transformation of college teacher education from the perspective of behaviorist psychology: initiatives, effectiveness and pitfalls

Abstract Behaviorist psychology can deeply reflect the perception of digital technology in teacher education in colleges and universities, and the digital transformation of teacher education is an effective path to improve the quality of education. Based on the TAM model, this paper analyzes the factors affecting the digital transformation of teacher education in colleges and universities, selects the relevant variables for research hypotheses, and obtains the relevant data for empirical analysis through questionnaires. In order to deeply analyze the initiatives, effectiveness and hidden worries brought about by the digital transformation of college teacher education from the perspective of behaviorist psychology, the combination of factor analysis and principal component analysis is used to carry out the extraction of common factors, which provides the basis for analyzing the factor loading coefficients of the influencing factors of digital transformation. The validity of the method was assessed using various methods, including descriptive statistics, reliability and validity tests, and structural equation modeling path fitting. The results show that the absolute values of skewness and kurtosis of the survey data in this paper are less than 1, which is in line with normal distribution, and the value of Cronbach’s Alpha coefficient of the reliability test is basically above 0.9, and the standardized factor loading coefficients are greater than 0.6. The direct effect of behavioral imagery on the actual application is 0.448, resulting in a significant difference at the 1% level. The digital transformation of teacher education in higher education needs to pay attention to the relevant applications of external environment and media technology so as to realize the improvement of the quality of teacher education in higher education.
Research on action recognition for college dance teaching based on deep learning

Abstract In this paper, Gaussian modeling is established to perform background subtraction of action feature images of dancers and combined with median filtering to denoise the background of feature images. The cumulative edge feature algorithm extracts relevant features from the dance dataset using the dance itself. Based on this, the directional gradient is introduced to output the extracted dance histogram features and cascade the optical flow histogram feature vectors in all blocks to form the HOF features of the image. The deep learning model is used to design the dance teaching model, and the teaching objectives are determined. The effect of dance teaching is tested through the method of simulation experiments and empirical analysis. The loss value of the model without deep learning is very high, reaching 1.25, and the loss values after convergence of the parametric model using deep learning are 0.85 and 0.95, and the deep learning algorithm has a better optimization effect on the model. The scores of the seven measures of teaching effectiveness are all above 12, and the significance p-value of each indicator is &lt;0.05. The dance teaching effect is significant.