bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on English Translation of Intangible Cultural Heritage in the Age of AIGC
Authors: Sun Wulin 
Published: January 2024

Abstract
Cultural dissemination and display methods with the progress of science and technology requirements continue to improve, especially in the AIGC era. The traditional cultural dissemination methods are no longer applicable, and “non-legacy digitization” needs to become more and more urgent. This paper analyzes the neural machine translation method and constructs a back-translation model by synthesizing a corpus and generating parameters. BLEU and other evaluation indexes are used to optimize the factors that affect the computation of synthetic data. An enhanced synthetic corpus is created using the kNN algorithm to construct a monolingual translation memory. Using the semantic information-sharing strategy, generate the parameters of the embedding layer of the non-legacy translation model while introducing the encoding layer to complete the overall construction of the non-legacy translation model. Comparative experiments and empirical analyses are set up to study the heterogeneity of the quality and dissemination effect of English translation of NRLs, and the experiments show that in the first set of translation tasks, the BLEU scores of the model proposed in this paper are 1 and 1.4 higher than those of the wait-k model when the value of k is 5 and 7, and the translation quality of the model proposed in this paper is better. The values of primary and secondary term coefficients of the English translation of non-legacy are 14.974 and -3.071, respectively, in the case of not establishing samples, showing an inverted U-shaped linear relationship.
Application of Big Data-based Information Technology in Business English Courses in Colleges and Universities
Authors: Kang Chunjie , Kang Bo 
Published: January 2024

Abstract
In this paper, after using Cool Edit Pro2.1 software to complete the speech signal acquisition work, the DTW method is used to pre-emphasize the speech signal. Due to the small continuous correlation between adjacent frames, the use of windowing and frame-splitting operation makes the amplitude change of the speech signal smooth and obtains the spectral envelope of the speech signal. The negative impact of MFCC feature parameter extraction is optimized by parameterizing the volume intensity and fundamental trajectory, and the application of the English scoring mechanism in business English education is explored. According to the current situation of business English teaching in colleges and universities, research problems and sampling objects are identified, and statistical analysis is applied to analyze the teaching effect of business English informatization by example. The results show that the speech evaluation system of spoken business English based on DTW does not differ much from the similar results of the scoring of the master’s degree of English majors in our university, and its difference value always stays between 1 and 10. In terms of the simple correlation coefficient and significance level (0.01), the simple correlation coefficient between teaching effectiveness and the use of technological media is 0.385 (P=0.00), which means that there is a significant correlation between both teaching effectiveness and the use of technological media. This study improves the standardization of English pronunciation among students, which is important for the development of business English in colleges and universities.
Research on Economic Trade and Cultural Value of the China-Russia Ten Thousand Mile Tea Route by Integrating Probabilistic Assessment Models
Authors: Ma Lankun 
Published: January 2024

Abstract
According to the relationship between the economy and trade of the tea ceremony in foreign markets, this paper establishes the economic output model of tea in the tea ceremony in China and Russia, and at the same time, considering the spring tea frost disaster, combined with the probability model, the economic loss rate of tea frost is quantitatively evaluated. Through the analysis of social network and semantic network diagram, the semantic related high-frequency words were classified into the concept of tea culture, and the correlation between high-frequency words about tea culture was obtained. According to the comprehensive influence of domestic factors and foreign factors, the index system of influencing factors of tea export value was determined, and the prediction model of China’s tea export value based on CNN-BSO-LSTM algorithm was constructed by using big data technology, and the tea economy and culture from the perspective of Sino-Russian tea ceremony were studied and analyzed. The results showed that the three indexes of CNNBSO-LSTM were 0.9431, 8850212 and 4.112, respectively, which performed better than other models. The CNN-BSOLSTM model predicts that China’s tea exports will reach around RMB 3.5 billion in 2025, up significantly from RMB 2.3 billion in 2022. This study promotes the economic and cultural exchanges between China and Russia, and is of great significance for promoting the rise and development of urban belts along the Belt and Road.
Sustainable Management of Decorative Patterns of Shanghai Shikumen Architectural Houses Based on ROI Optimization
Authors: Sun Zhongping 
Published: January 2024

Abstract
The decorative patterns of Shikumen architectural dwellings in Shanghai are of great investment value, and their cultural benefits can be fully utilized through sustainable operation and management. In this paper, after analyzing the traditional pattern generation method, we combine the semantic generation method with the design of the loss function and network structure of the generative adversarial network and construct the generation algorithm of architectural residential decoration pattern based on semantic perception. The constructed pattern generation algorithm is used to manage the sustainability of architectural residential decoration patterns and optimize the rate of return on the investment of sustainability management through fuzzy theory. The results show that the color similarity, pattern similarity, and organization form similarity are (0.62, 0.91), (0.58,0.89), and (0.59,0.88), respectively, and the return efficiency of the investment in the architectural residential decoration of Shanghai Shikumen architecture before and after the optimization fluctuates between (-0.26, 0.30) and (-0.18, 0.26). The ROI of sustainable business management of decorative patterns in Shanghai Shikumen architectural dwellings in this study can develop stably.
Internet Data-based Media Resource Mining Method for Artistic Talent Cultivation
Authors: Wang Lina 
Published: January 2024

Abstract
The cultivation of innovative artistic talents is the top priority of art education, making full use of Internet media resources to provide talents with high artistic literacy for art development. This paper begins with the integration of media resources in Internet data and builds a framework for the dimensions and processes of media resource knowledge integration. The BERT model combined with the multi-head self-attention mechanism is used for feature extraction of media text resources in Internet data, the signal decomposition of audio media resources is realized through the matching tracking algorithm, and the recommendation of video resources for art talent development is carried out based on the temporal hierarchical attention model. To verify the effectiveness of the algorithms given in this paper, a verification analysis of each algorithm is performed. The results show that when the IR value of audio signal decomposition increases from 1.49 to 10.36, the ROC-AUC value of art cultivation media audio decreases from 0.95±0.02 to 0.93±0.04. After 200 iterations, the video resource recommendation algorithm’s time fluctuation is between 0.21ms and 9.81ms, depending on the users’ behavior sequence. Making full use of technology to deeply mine Internet data can enable the diversification of media resources for art talent training and improve the quality of art talent training.
Exploration of cross-border e-commerce and its logistics supply chain innovation and development path for agricultural exports based on deep learning
Authors: Jin Lijing 
Published: January 2024

Abstract
This paper studies the cross-border e-commerce of agricultural products and its logistics supply chain collaborative management approach, the overall transaction mode and basic content, and proposes a cross-border e-commerce supply chain conceptual model. Aiming at the problems of agricultural product supply chains, a method for predicting agricultural product export prices is proposed. The Prophet algorithm under deep learning is utilized to construct the Prophet agricultural product price prediction model for trend, cycle, and holiday terms. Over the introduction of RNN algorithms and LSEM algorithms to optimize the prediction performance of the model, as well as the gradient explosion. On this basis, GRU neural networks are proposed as an improved model of RNN-LSTM. Prediction comparison experiments are designed to empirically analyze agricultural export price prediction and supply chain logistics risk control, and the results of the empirical analysis show that the vegetable export price predicted by using Prophet algorithm during the period of date 2013/4-2013/9 is 2.975, which differs from the actual price by 0.009 yuan, and the margin of error is in the interval of [-0.091,0.014], which is the smallest variation among the three algorithms, which shows that Prophet model has the best performance. After optimizing the FAPSC risk control coefficient, the risk value of supply chain logistics and transportation was successfully reduced from 0.364 to 0.296, and FAPSC effectively minimized the risk.
Research on the Innovative Path of College English Teaching Based on Deep Reinforcement Learning
Authors: Zhang Yumei , Cheng Hongmei 
Published: January 2024

Abstract
College English is a very flexible and important subject that needs to be constantly innovated. For the shortcomings of traditional teaching methods, this paper constructs a teaching behavior tree based on reinforcement learning. By analyzing the direction of the behavior tree, using the path planning algorithm to propose better teaching strategies, combined with the Q learning algorithm, the teaching theme and the environment of the information are timely updated in the Q value table to update the teaching behavior selection strategy, and ultimately obtain the teaching behavior tree. The reinforcement learning algorithm based on Q -function interacts with the environment to directly train the method of generating the behavior tree to check whether the teaching behavior tree reaches the optimal standard. Combine reinforcement deep learning with English teaching to introduce a new approach to teaching English. The results show that on the diversified evaluation behavior, teachers of 985 colleges scored significantly higher than teachers of 211 colleges, with a score of 3.05 for teachers of 985 colleges and 2.68 for teachers of 211 colleges. Among the two teaching factors, the F-value of the diversified evaluation is 4.601, and the F-value of the personalized teaching is 3.596, which indicates that both of them have a greater influence on the students and they are able to improve the English proficiency of the students. This study provides a new reference direction for college English teaching, which can effectively improve students’ practical skills.
Teaching Integration of Piano and Traditional Music Elements in Colleges and Universities Based on Network Flow Optimization
Authors: Dai Yufei 
Published: January 2024

Abstract
In this paper, from the basis of piano music theory, pitch and twelve equal temperaments are studied, and due to the characteristics of the piano’s construction, there are different timbres and harmonic structures. In order to realize the digitization of music signals for piano teaching, the collected analog signals are converted into digital signals by Fourier transform, the samples are inputted into the neural network model to get the output results, and the loss function based on Markov’s music language model in piano teaching is used to find the partial derivatives of the parameters to realize the maximization of the efficiency of the piano pitch and note recognition. According to the multi-task learning in neural networks and the envelope curve of notes, we construct the automatic music transcription model in piano teaching based on CNN-HMM and use the simulation experiment analysis to empirically analyze the piano teaching based on CNNHMM. The results show that the dual-channel audio results are improved by 2.68% in F1 value, 3.18% in accuracy and 2.61% in recall than the mono audio results. That is, dual-channel audio has richer information than mono audio, which enables the CNN-HNN network to learn richer features and thus improve the pitch and note recognition results. In this paper, the reliability of the teaching quality evaluation results of the CNN-HMM-based quantitative assessment model for facilitating the combination of electronic organ and piano teaching concepts ranges from 78.1% to 85.4%. This study provides guiding value for the development of digital piano teaching, which is of great significance for the innovative reform of piano teaching in colleges and universities.
Research on the Design and Practice of Civics Teaching in College Physical Education Courses Based on VAR Modeling
Authors: Fan Jun , Wang Bing , Wang Yueying 
Published: January 2024

Abstract
In this paper, a penalty function is added in the construction of the VAR model to control the accuracy of the model prediction, and considering the nature of the correlation between the variables, a weight fusion with Laplace penalty is introduced to generate a sparse solution to complete the construction of the vector autoregressive model. The basic types of vector autoregressive models under panel data are divided, the number of parameters to be estimated is determined, the parameters of the vector autoregressive model are estimated by the generalized method of moments estimation, and the parameter fitting effect of the model is improved by the great likelihood parameter estimation method, and the vector autoregressive model for panel data is constructed. Based on the VAR model, we propose a design scheme for the teaching of Civics in college sports courses and analyze the development of sports + Civics and the teaching effect of the courses through simulation and empirical analysis. The results of the study showed that OSI and OPCI rejected the original hypothesis at the significance level of 5% after second-order differencing, at which time both p-values were less than 0.1, and the series was smooth. The elasticity coefficient of the starting value of the sports response path is 0.0342, and the error interval is [0.0564, 0.06], indicating that the future development of the scale of “Sports + Civics” is stable.
Innovative Practice of Physical Education Teaching in Colleges and Universities in the Context of “Digital” Education
Authors: Zheng Lihong , Chen Kesheng 
Published: January 2024

Abstract
This paper takes the experimental design of the flipped classroom teaching mode under digital information technology as the entry point, chooses the appropriate research object and method, and designs the experimental program of flipped classroom teaching in college sports. Since the teaching effect of the flipped classroom teaching mode needs to be supported by students’ movement data, this paper utilizes the least squares distance to match and retrieve the data of students’ movement postures and then combines it with the ANN model to deeply mine the students’ movement data. For the teaching effect of the flipped classroom teaching mode, innovative practices are carried out for physical education teaching in colleges and universities. The results show that when the linear correlation coefficient of the ANN model is 1, the lowest value of validation loss is 2.1*10−4. The flipped classroom teaching mode can effectively enhance students’ physical fitness. The mean value of students in the experimental group in the learning motivation dimension has been improved by 5.44 points compared with the preexperimentation period. The results of the T-test of the sports concern and learning satisfaction are all significant differences at the 1% level. The combination of flipped classrooms and college physical education teaching in a digital environment can improve the effectiveness of physical education teaching and increase students’ interest in physical education learning.
Application of BIM technology in construction project cost refinement control and construction energy consumption control
Authors: Cao Hongmei 
Published: January 2024

Abstract
In order to solve the problem of accurate cost control management in the process of engineering construction, this paper applies BIM technology to the whole process of cost management, covering the whole life cycle of the project. Through efficient sharing of information on the BIM collaborative platform, it carries out unimpeded process work and achieves refined management. Aiming at the complexity of the information characteristics that affect the cost and uncertainty of information transmission, an algorithm fusion scheme based on NCM-FPN has been designed. The TOPSIS method is used to integrate the case feature attributes into the cost case information database. By combining the clustering algorithm with practical management experience, the knowledge base of engineering cost control strategies can be established, and the logical relationship of strategy matching can be determined. The inverse cloud algorithm is used to establish the feature cloud, determine the fuzzy Petri network structure based on generative rules and design the inference algorithm to establish an intelligent model for strategy matching so as to realize the accurate mapping and rapid response from the target case features to the control strategies. Through the analysis of construction energy consumption, it is concluded that the construction energy consumption of different structure buildings is not the same; the construction energy consumption per unit area of brick structure, steel structure residence and frame office building is 0.1659GJ/m2, 0.2706GJ/m2 and 0.3104GJ/m2 respectively, and the largest is the shear-wall structure residence is 0.3381GJ/m2 for the 2.04 times of the brick structure. Based on BIM technology, the cost of dynamic control engineering for construction projects was reduced by 5.4 million. Therefore, BIM is an important technical means to promote the whole process control of project cost, from empirical control to informationization, standardization, and intelligent control.
Integration of Instrumental Elements and Vocal Teaching Based on Music Core Literacy
Authors: Li Hua , Zhang Qingru , Liu Yuhe , Feng Fan 
Published: January 2024

Abstract
The current lack of music core literacy in vocal music teaching needs to be solved, and this paper aims to improve this problem. In the paper, a hybrid attention module is added to the multi-channel of MFCC to extract the acoustic elements of the musical instruments, and after characterizing the musical instruments as a multi-view sequence, visual features and temporal features are fused, and a graph convolution module is added to extract the visual elements of the musical instruments, and the two are fused. After extracting the elements of musical instruments, the development of vocal fusion teaching of the two was analyzed by combining the gray correlation analysis method, and experiments were designed on the basis of which to investigate the extraction effect of the elements of musical instruments as well as the development of vocal fusion pedagogy. The results show that the average accuracy of instrumental elements of the multimodal extraction model is 0.919, which is improved by 0.17 compared with the Baseline classification accuracy, and the extraction accuracy can reach more than 90% for flute, percussion instrument, plucked instrument, wind instrument, suona, xiao, sheng, wind instrument, pipa, and erhu. The gray correlation of the fourth grade ranged from 0.75 to 0.88, with a high improvement in the same grade, and the gray correlation of different grades all ranged from 0.3 to 1.0, and the higher the grade, the better the development.
Exploration of financial tax system in the context of big data
Authors: Ji Ning 
Published: January 2024

Abstract
The financial tax system is related to the actual effectiveness of a country’s modernized economic development and has a close relationship with the country’s economic enhancement. To examine the causal effects between potential variables in the tax system, this paper constructs a measurement model of financial tax system variables using Bayesian structural equations. Parameter estimation for SEM is completed by adding prior information to the sample data and calculating the posterior distribution of relevant parameters using Bayes’ theorem. The correlation between financial taxation and economic development is calculated to indicate whether there is consistency in financial taxation on economic efficiency. Cronbach’s coefficient is used to measure the degree of consistency and stability of each part of the financial tax index system. The results show that the impact value of personal tax is 0.71, 0.37 and 4.18 in provinces C, A and B. Promoting economic development in provinces and municipalities with a better degree of economic development is aided by the current personal income tax sharing ratio. Reducing the proportion of indirect taxes while keeping the macro tax burden unchanged results in a gradual increase of the proportion of personal income tax in total tax revenue from 8.7% to 18.7%. The research in this paper further improves the financial tax system and provides an effective method to improve economic efficiency.
Realistic Challenges and Optimization Approaches of Civic Education in Colleges and Universities in the Context of Big Data
Authors: Lai Chen , Liu Wei 
Published: January 2024

Abstract
Civic and political education with big data should not just be a one-way knowledge inculcation. It is more of a guideline and direction that can guide everyone’s life. This paper gives a detailed description of the realistic challenges facing Civic and Political Education in the context of big data and gives the optimization path of Civic and Political Education in combination with big data technology. The essence of ideological education is to help students establish correct concepts and values of life. This paper uses the PSO algorithm to improve the K-means algorithm, builds a student portrait based on student data, and fully analyzes the behavior related to student life consumption. Then, through the FOA algorithm to optimize the parameters of the SVM algorithm, the student learning data for deep mining in order to achieve the student academic early warning, to help college administrators understand the student learning situation. The purpose of Civic Education is to aid students in establishing correct concepts of life and learning, and this paper conducts corresponding empirical analysis. The results show that the PSOK-means algorithm achieves absolute convergence after 45 iterations, and its MSE value is 2.208, the FOA-SVM algorithm has 0.0979s more computing time than the RF-SVM algorithm after 2000 iterations, and the clustering of students’ portraits results in 4 classes, and the accuracy of academic warning is more than 80%. Reasonable big data technology and the deep integration of the application of political education in colleges and universities can help college administrators understand the development of students so as to formulate a political education program that is more in line with the development of students and to improve the quality of talent cultivation in colleges and universities.