bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feasibility of the development of short videos in vocational education based on the context of deep learning
Authors: Wang Dong , Xu Huaizhi , Wang Zhuo 
Published: October 2024

Abstract
The development of information technology has brought new opportunities for the development of vocational education teaching, and short video, as a product of information technology, breaks through the limitations brought by the traditional teaching mode and provides a new thinking direction for the transformation of the teaching mode. This paper proposes a recommendation system for short videos based on deep learning, where the scene and behavioral features of the target short video are first extracted to obtain the joint features. Then, user and video modeling is realized using deep learning technology. Finally, the similarity and preference are calculated by combining the joint features of the target short video and the user short video preference features. The TOP-N with the highest user preference is obtained according to the actual demand. The recommendation list is generated to complete the intelligent recommendation of short videos based on deep learning. The experimental analysis found that the recommendation system has high performance, with a maximum recommendation accuracy of 98.2%, and is capable of fully comprehending the students’ demand preferences for short videos. The use of short videos in course teaching can make a great positive change in students’ learning attitudes, and the short video recommendation algorithm based on deep learning allows students to more accurately find short videos suitable for their learning conditions, which further improves their learning efficiency.
A Multimodal Grammar Study of Film Adaptations of Traditional Literature in the Information Age
Authors: Wang Xueyuan , Dou Peilin 
Published: October 2024

Abstract
By analyzing the multimodal grammar of films, the deeper concepts that the designers want to express can be better conveyed to the audience, and the expectations of the moviegoers can be enhanced. Therefore, based on the analysis of the classic Zhang Delu’s comprehensive theory of multimodal discourse, the article proposes the multimodal discourse analysis framework used in this paper to explore the ideologies implied in the text and images of movie subtitles and the images they construct and the inspirational meanings they draw from. It is found that among the 150 clauses in the corpus analysis, 28% are material processes, 22% are mental processes, 12% are verbal processes, 14.67% are relational processes, 17.33% are behavioral processes, and 4.67% are existential processes. Through multimodal grammar analysis, the discourse tendencies of the traditional culture-adapted movie can be effectively grasped.
Collegiate Collaborative Piano Innovation Education Model Empowered by Artificial Intelligence
Authors: Yang Linxi 
Published: October 2024

Abstract
In the era of big data, the development of piano education and teaching has irreversibly shifted towards an intelligent piano education model. In order to optimize the effectiveness of piano teaching in colleges and universities, this paper proposes a new model of intelligent piano education for college and university cooperation based on the Bayesian net model of automatic piano composition and dynamic gesture recognition and correction of piano playing. Combined with the rules of piano music accompaniment composition, it outlines the method of playing the piano left and right hand. Propose various genres and features and analyze the detection rate of Bayesian net models for different genres. The Camshaft algorithm is used to track and record the features of piano playing, while the DTW algorithm is used to detect and recognize piano fingering. Combine the DTW algorithm for gesture recognition with two constraints, endpoint fixation, and endpoint relaxation, to further test the recognition advantage of the DTW algorithm for piano fingering. The feasibility of the cooperative piano education model for colleges and universities proposed in this paper can be verified by comparing the students’ comprehensive music literacy scores in traditional piano lessons and intelligent piano lessons. The Bayesian net model and DTW algorithm both have a detection rate of more than 90% for piano music genres. Compared to traditional piano lessons, intelligent piano lessons can improve 25.12% of piano knowledge and 31.75% of piano repertoire scores, with the intelligent college cooperative piano education model demonstrating significant results.
Research on the Impact and Regulation Mechanism of Digital Currency on Economic Policy Combining Blockchain and Smart Contract Technology
Authors: Yang Luyi 
Published: October 2024

Abstract
The emergence of digital currency has had a significant impact on economic policy while increasingly and profoundly changing the lives of the public. The main research object of this paper is the digital currency that incorporates blockchain and smart contract technology, and it deeply examines how its generation and development impact economic policy and the corresponding regulatory mechanism. The application of blockchain and smart contract technology is used to analyze the characteristics and performance of digital currency, and the advantages and shortcomings of using digital currency are briefly explained. Based on the analysis of related research and practical application requirements, digital currency is being introduced into the DSGE model, which includes households, manufacturers, and other sectors. Impulse response and variance decomposition are used to numerically simulate the impact of digital currency on economic policy and the effectiveness of the regulatory mechanism. The economic policy transmission efficiency is the lowest in the no digital currency scenario, while it is significantly higher in the scenario of full use of digital currency, with interest rate shocks into the maximum degree of change in output and inflation of 0.059% and 0.121%. The variance of each macroeconomic variable in the economic policy regulation scenario shows a significant downward trend, and digital currency contributes to the overall regulation effect of macroeconomic policy.
An Experimental Study on Knowledge Transfer of Higher Vocational Accounting Majors by Teacher-Apprentice System--Taking Shanwei Institute of Technology as an Example
Authors: Luo Miaohua , Xie Yushan , Xie Peifeng 
Published: October 2024

Abstract
The Ministry of Education has issued relevant documents to promote the development of vocational education one after another, and vocational education has been raised to an unprecedented height, and has been highly valued by all aspects of local and national. This paper proposes a study on teaching higher vocational accounting using the apprenticeship system as a response to the national education policy. From the development history of the master-apprentice system, the necessity of the “master-apprentice system” in the accounting industry is introduced, and the specific application of the “master-apprentice system” in the teaching of higher vocational accounting is outlined. The students of Shanwei Institute of Technology were selected as the research object, and the data were collected by distributing questionnaires, and the independent samples t-test was applied to experimentally analyze the knowledge transfer of accounting majors in Shanwei Institute of Technology. After the teaching intervention, there were significant differences in accounting skills, accounting knowledge, and comprehensive quality of accounting among the students in Group C and Group D, with P<0.05, which indicated that compared with the traditional teaching mode, the teacher-apprentice teaching mode had a more obvious effect on the students’ enhancement. This study confirms the feasibility of the “mentor-apprentice system” in the education of higher vocational accounting majors.
Innovation of Operation Mode of Traditional Enterprises in the Era of Digital Economy--Taking Traditional Cultural Enterprises as an Example
Authors: Zhou Jianbo 
Published: October 2024

Abstract
The essence of a cultural enterprise’s digital transformation is the process by which it metamorphoses into a new type of cultural enterprise by using new digital technologies to improve its core business and product concepts. This paper delves into the need for digital transformation in traditional cultural enterprises and devises a strategy for this transformation. It applies data mining technology to customer relationship management, analyzing customer value using the K-means algorithm and weighted association rules. The CRM system, built using data mining technology, can categorize customers into four types and implement corresponding marketing strategies for customers with different values. Using Company A as an example, we verify the impact of this innovative model on revenue, profit, and operational capacity. After the digital transformation of Company A, the operating profit maintains a fast growth rate, in which the difference in profit between December and November reaches 31,132 yuan, and the gap between the company’s inventory turnover rate and that of the same industry in December is only 0.1%. The digital transformation of traditional cultural enterprises has facilitated the company’s rapid growth.
Optimization Strategy of Strategic Human Resource Management Based on Big Data in Dynamic Environment
Authors: Liu Shun 
Published: October 2024

Abstract
It is very critical for enterprise human resources to be reasonably and effectively utilized, and information-based human resource management greatly improves enterprise production and work efficiency. The study combines big data algorithms to first optimize the human resource demand in HRM based on a multiple regression model, then optimize the employee performance appraisal with multiple objectives, and finally predict the employee leaving based on the SMOTE-SVM model. The SSH technology framework is combined to construct the strategic human resource management optimization system, and the TJ company is used as an example for instance analysis. After testing, the predicted value of human resource demand based on multiple regression basically matches the actual situation of the enterprise, and the maximum prediction error is only 0.55. After optimization through performance appraisal, the abnormal data decreases from 20 to 6. Employees’ marital status, stock option level, and occupational level are all key factors influencing their departure. Through the optimized human resource management optimization strategy, the enterprise can detect the employee’s status in time and bring better profits.
Research on Monitoring Teaching Activities Based on Image Recognition in Educational Management Informatization Construction of Colleges and Universities
Authors: Liu Zhen , Ran Jingtai 
Published: October 2024

Abstract
Teaching activity monitoring is an important part of the development of educational management informationization in colleges and universities. In this paper, we initially integrate the FDRL model with the self-attention mechanism to create the IMEDRL model, a tool for identifying the micro-expression categories of students during teaching activities. Then, the YOLOv5 model is added with the CA attention mechanism, and the loss function is replaced from CIOU to SIOU to get the IM-YOLOv5 model, which is used to detect the behavioral categories of students in teaching activities. Finally, a teaching activity monitoring system was designed based on these two models and used in actual teaching activity monitoring to explore the application effect of the models in the system. The IMEDRL model achieved an average recognition rate of 95.2% and 91.4% on the two public datasets, CK+ and Oulu-CASIA, respectively. The training and testing convergence on the teaching activity video dataset was superior, and the recognition accuracy rate reached 87.48%, demonstrating the model’s strong practical value. IM-YOLOv5 compared to YOLOv5, the FPS is basically the same, the number of parameters only increases by 8.72%, and the mAP0.5 and mAP0.5:0.95 improve by 0.99% and 1.65%, respectively. Among the 16 indicators of the 8 behaviors, 14 of the 8 behaviors of IM-YOLOv5 are higher than those of YOLOv5, confirming the feasibility of this study to improve YOLOv5. The monitoring system of teaching activities designed in this paper has a strong practicality, which is conducive to promoting informationization in education management in colleges and universities.
Research on the Application of Knowledge Mapping Technology in Vocational Skills Training Demand Forecasting and Curriculum Designing
Authors: Sun Liuyuan , Qi Xin 
Published: October 2024

Abstract
Due to the differences in individual qualifications, training duration, etc., the current vocational skills training is less efficient and cannot rationally arrange the training process and course content design etc. In this paper, we have constructed a model for predicting vocational skills training demand using knowledge mapping and knowledge tracking technology. First, a matrix is constructed to obtain the implicit relationship between knowledge. Reduce the influence weights with time intervals according to the theory of the forgetting curve. Modify the alignment scores of the attention mechanism to pay more attention to the relevant knowledge interactions in the sequence determined by the relationship coefficient g. Comparative analysis of the model and practical application reveals that the model in this paper has a better classification effect and better reflects the deeper recursive relationship and structure between knowledge points. When the professional teacher recommends the knowledge point C, the SVD model predicts the training demand of the trainees only 72.33%, while the model of this paper predicts 94.17% accuracy, which is highly compatible with the judgment of the professional teacher and more accurately identifies the training demand of the majority of trainees for the knowledge point C. The model provides a more scientific and reasonable curriculum and also provides a better classification of knowledge points. The model provides a more scientific and reasonable course design idea. Under the gradual course arrangement, the mastery of the knowledge points of the 20 trainees all shows a steady improvement trend. From the highest score of no more than 17 points in the first week to the 16th week at the end of the semester, all the trainees scored more than 70 points, which maximized the training efficiency. This study develops a reasonable course design based on the relationship between knowledge points, which can ensure gradual and interrelated progress and improve training efficiency and quality.
Understanding English Rhetorical Strategies Based on Neurosemantic Analysis
Authors: Tang Jianfu 
Published: October 2024

Abstract
This paper designs and implements a model for analyzing rhetorical strategies of English texts to address practical needs. This paper combines the true word confusion set and the N meta-model to correct word errors. In addition, a discourse parser is constructed based on the theory of rhetorical structure, which analyzes the semantics of the English text from the intra-sentence and inter-sentence perspectives to extract the key information of the discourse. The information is inputted into a memory network that resembles a tree, and the memory units and gating mechanism are employed to obtain semantic structural features. Finally, a hierarchical network model based on the attention mechanism is designed, using a bidirectional long short-term memory network to extract the global dependency information of the text and adding the attention mechanism to make it adaptively select important features related to English rhetorical strategies. Combining learning of a tree-based long short-term memory network and a hierarchical network based on an attention mechanism is used to generate a model for semantic analysis of English text. This paper’s model has the highest accuracy, recall, and F1 value among the mentioned models in the 20newsgroups dataset, with 93.77%, 91.23%, and 90.15%, respectively. The performance on the three rhetorical strategies of explicit metaphor, personification, and analogy is excellent, with classification accuracy above 90%, which will accomplish the task of analyzing and understanding English modification strategies.
Intelligent Strategies and Implementation Effect Evaluation of Urban Old Neighborhood Renovation--Taking Nanjing as an Example
Authors: Xiang Guangfang 
Published: October 2024

Abstract
The transformation of old neighborhoods is related to the quality of life and happiness of neighborhood residents, and the needs of residents must be fully considered in the development of transformation strategies. The problem to be transformed according to the needs is summarized in this paper, and the key indicators of the neighborhood’s spatial pattern are abstracted. Under the guidance of the perception-behavior correlation model, the correlation and regression curves between spatial indicators and behavioral density are estimated, and the optimal value of the correlated behavioral density is calculated to provide data support for intelligent transformation strategies. The CRITIC method and the cloud model are combined in the article to create an evaluation model for the transformation effect. Taking Kangfu Village in Nanjing as an example, the perception-behavior correlation analysis points out that each public space perception index has an extremely significant statistical correlation with residents’ spontaneous activity density and long-time activity density. The best-fit function curves were obtained through further curve estimation regression analysis, and the ideal value intervals were determined for each perception indicator. After the implementation of the retrofit strategy, the expectation value of the owners’ cloud evaluation of the “community intelligent retrofit” is 3.677, and the similarity between the comprehensive evaluation of satisfaction and the comparative satisfaction level reaches 0.905, which makes the evaluation result more satisfactory. There are sufficient reasons to believe that the intelligent transformation strategy proposed in this paper is generally accepted, and the formulation of the strategy based on the perception-behavior correlation is scientific.
Digital Assessment of Curriculum Ideological and Political Education in Higher Vocational Institutions’ Courses Based on Correlation Matrix
Authors: Yang Xingbo 
Published: October 2024

Abstract
The concept of digital education has produced an unnoticeable impact on the teaching of Civics courses in colleges and universities. The study starts from the digital transformation of the curriculum ideological and political education in college courses to analyze and explore, based on the Internet of Things (IoT) technology to collect data from students’ campus card data, combined with the improved K-means clustering algorithm and correlation matrix, to establish a digital assessment model of curriculum ideological and political education based on the correlation matrix. The model’s data mining performance is evaluated through simulation tests. Then, students of a higher vocational college are selected as the research object, and the model is used to realize the construction of student portraits and the cluster analysis of student behavior and to mine the association rules between student behavior and the effect of curriculum ideological and political education. The data fluctuation range of this paper’s method is small and stable, within 0.35~0.50, with better data analysis performance. The model categorizes the sample students into 6 types of consumption regularity behaviors, 8 types of life regularity behaviors and 5 types of study effort level behaviors, and obtains 9 association rules between students’ behaviors and the effectiveness of Civic and Political teaching. The digital evaluation of curriculum ideological and political education can be effectively achieved using the campus card data mining model based on matrix association, as evidenced by the empirical analysis.
Innovative Research on the Teaching Mode of English Translation Course in Colleges and Universities with the Support of Deep Learning
Authors: Zhan Xia 
Published: October 2024

Abstract
Nowadays, teachers have begun to try to carry out teaching innovation, but there is still the phenomenon of copying the traditional course content and teaching form on the online teaching platform. Aiming at these types of teaching problems, this paper proposes research on the innovation of English translation teaching modes based on deep learning. In order to better analyze the effect of positional distance on word vectors, the PW-CBOW model is constructed on the basis of the structure of the CBOW model, and its parameters are optimized using the Adam optimizer. When the input and output are both indeterminate long sequences, a decoder-encoder is used for processing. The machine translation model is jointly constructed by combining the attention mechanism and the bidirectional gated loop unit. The English teaching model that combines the machine translation model is exemplified by using data analysis software. The results present that this paper’s model has +3.15 and +2.12 more BLEU points than the Transformer model and DynamicConv, respectively, and there is a significant difference between this paper’s teaching model and the traditional teaching model in the three dimensions of final grades, stage grades, and project grades, all of which satisfy P<0.05. A comprehensive analysis of the results suggests that this paper’s teaching model has a significant impact on students’ performance. The comprehensive analysis results show that the teaching model of this paper is of great significance to the improvement of student performance and the development of teaching in colleges.
Distributed Robust Control Design for Multi-Intelligent Body Systems Based on KYP Lemma
Authors: Zhang Chao , Tang Qian , Chang Xucheng , Chen Zhiqiang , Dai Jun 
Published: October 2024

Abstract
In this paper, a distributed robust control system is analyzed and investigated, and a series of control applications of the system are discussed using KYP primitives. Reliable H
∞ -state feedback and output feedback controllers are given for linear continuous time, ensuring that the corresponding closed-loop system is asymptotically stable and satisfies full-frequency performance. Simulation examples verify the superiority of the methodology of this paper, and the level of noise rejection is further enhanced as the number h of measured output values used is increased. The optimal performance metrics for finite-frequency are all lower than full-frequency, and the distributed filter outperforms the conventional filter. The closed-loop system has a maximum magnitude of 5.480 and 9.075, which meet the robustness requirement for the 4-8 Hz frequency range. The time-lag system can return to its stable state about 4 seconds after the disturbance, ensuring good, robust stability. The distributed robust control system proposed in this paper achieves good practical application results, and the numerical examples demonstrate the method’s effectiveness.