bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integration Strategies of Information Technology in Mathematics Education and Student Motivation Enhancement
Authors: Yan Yunna 
Published: July 2024

Abstract
Integrating information technology with the curriculum has been closely watched by the educational community, and how well the two are integrated determines the effectiveness of mathematics education. This paper proposes an implementation scheme for mapping mathematical knowledge. Firstly, the Word2vec word vector model is used to acquire data from the original mathematical text, and then the Conditional Random Field model is used to extract the knowledge entities, and the Markov Logic Nets are used to extract the relationships between the knowledge entities. Then, the Path Ranking algorithm is utilized on the basis of entity alignment to realize the knowledge inference of the subject knowledge graph and mine the learning paths in the knowledge graph. Through the experimental and questionnaire methods, it was found that the math scores of the experimental class before the comparison experiment increased by 9.4337 points, which was significantly ahead of the control class. The average number of students who thought knowledge mapping teaching greatly improved their interest in math learning was 46.81%, while 31.29% believed that the new method had a relatively positive impact on their motivation to learn. It shows that knowledge mapping can be an effective method of teaching information technology.
Research on Innovation and Entrepreneurship Education Reform of Electrical Engineering and Automation Professionals Driven by Big Data
Authors: Li Ning , Zhao Ziyu , Yang Dongxu 
Published: July 2024

Abstract
The application of big data in various fields has become one of the important trends in the development of the information age, and in this context, educational reform in the context of big data has also become an important issue in the field of education. In this paper, we design a “four-in-one” innovation and entrepreneurship education model and combine the big data-driven algorithms, including association rules and clustering mining, to realize the driving of the education model. Specifically, the systematic clustering algorithm is used to analyze the students’ classroom performance, explore the association rules between the student’s classroom performance and their course grades, and finally, this paper applies the “four-in-one” education model to the classroom teaching of innovation and entrepreneurship courses to study the practical application effect of the model. The results show that after the cluster analysis of the student’s classroom performance data, three categories of students with different levels of classroom performance were formed, accounting for 49.46%, 41.68%, and 8.86%, respectively. After three applications of the new education model, the teacher’s participation in classroom speech, questioning, and silence decreased by 12.75, 5.47, and 3.54 percentage points, respectively. In contrast, the student’s speech and questioning rates increased from 5.86% and 10.98% to 8.37% and 19.84%, respectively. The “four-in-one” innovation and entrepreneurship education model designed in this paper has good practical application effects.
Research on the Innovation of Hotel Talent Cultivation Mode in the Context of Municipal Industry-Education Integration in the Information Age
Authors: Wei Wei , Li Yi 
Published: July 2024

Abstract
In the context of the information era, the integration of industry and education, as a key direction for future educational reform, imposes higher standards on the quality of talent cultivation in colleges and universities. The study develops and investigates the hotel talent cultivation model and operation mechanism based on industry-education integration, starting from industry-education integration’s guiding ideology and principles. Then, in order to confirm the realistic feasibility of the hotel talent training model, the research object is selected, the relevant questionnaire is designed, and after the reliability test of the questionnaire, the questionnaire is distributed. The data are collected, and then the statistical analysis algorithm, including the difference test, correlation analysis, and regression analysis, is used to verify the examples. The data showed that corporate internship time, corporate internship environment, corporate internship management, corporate internship course planning, on-campus supervisors, and off-campus supervision were all significantly and positively correlated with satisfaction (Sig<0.05). At the same time, the regression equation was satisfaction= 0.2989+0.0459*corporate internship time+0.1329*corporate internship environment+0.0641*corporate internship management+ 0.1034*Enterprise Internship Curriculum Planning + 0.1017*Internship Supervisor + 0.0762* Externship Supervision. This study not only ensures the effectiveness, continuity, and standardization of talent cultivation but also helps to promote innovation in hotel talent cultivation modes in the context of industry-education integration.
Innovative promotion of cultural heritage and high-quality integration of the tourism industry
Authors: Zhang Ya 
Published: July 2024

Abstract
In the context of the high-quality economic development imperative, it is both necessary and urgent to expedite and deepen the integration and interconnection between cultural heritage and the tourism industry. This study constructs an evaluation index system for the integration and development of cultural heritage and tourism by reviewing pertinent literature and computing index weight values utilizing the Analytic Hierarchy Process (AHP). Furthermore, this paper introduces a model that assesses the coupling degree and coordination between these sectors by integrating the index system and analyzing the decoupled levels of coupling coordination. Additionally, a system dynamics model is developed to facilitate innovative enhancement of this integrated development. Empirical results indicate that the integration and development level of cultural heritage and tourism in Longji Town is on an upward trajectory. However, it remains at a nascent stage of coordination with a score of 0.6983. Innovation-driven initiatives have spurred growth across all dimensions within the cultural heritage and tourism subsystems in Longji Town. By 2024, the integration level between these two sectors is projected to reach 0.937, signifying a state of high-quality, coordinated development. This research provides empirical support for the innovative advancement of integrated development between cultural heritage and tourism industries. It offers valuable insights for refining and upgrading the strategy for integrated development.
Research on the Integration Strategy of Technology and Teaching in the Construction of Accounting Smart Classroom in Colleges and Universities
Authors: Li Mingwei , Dong Lize 
Published: July 2024

Abstract
In the “Internet+” era, the smart classroom has emerged as a significant focal point in the research on educational informatization. This advancement represents a synergistic melding of novel technologies and academic practices. Accordingly, this study proposes a smart classroom teaching model tailored for accounting courses in higher education institutions. Within this framework, we have developed a specialized model to evaluate teaching efficacy in such environments. This evaluation model incorporates an enhanced version of the basic YOLOv5s model, which has been modified in three key areas to facilitate the detection of students’ facial expressions during lectures. Furthermore, to accommodate low-resolution images, the model integrates an advanced DRN-F face super-resolution reconstruction algorithm, thereby augmenting the accuracy of face recognition. This paper introduces a composite model for assessing the effectiveness of smart classroom teaching by amalgamating the two refined models. Comparative analysis with traditional manual recognition methods reveals that our model aligns more closely with the actual attentive responses of students when their faces are unobscured. A practical study on the teaching application further substantiates the efficacy of the smart classroom approach. Employing an independent sample t-test, the findings demonstrate a statistically significant difference in the post-test scores between the experimental and control groups, with a Sig. Value of 0.001 (p<0.05). This indicates that the learning achievements of students under the smart classroom model are significantly enhanced compared to those in conventional teaching settings.
A Security Assessment Strategy for Corporate Financial Systems Based on Data Mining Techniques
Authors: Wu Jianhong 
Published: July 2024

Abstract
The security assessment of corporate financial systems has become a popular area of research, although few results have been published so far. This research focuses on integrating the association rule algorithm with Bayesian networks to evaluate the security of corporate financial systems. It primarily employs Bayesian networks to calculate the conditional probability, prior joint probability, and posterior probability of each attribute node in the IDRI-Tree to assess the security of the company’s financial system. Additionally, abnormal data from Company A’s financial system were collected to evaluate the method’s effectiveness. The results indicate that Company A’s financial system has data discrepancy compliance rates of 98.92% and 96.50%, respectively. The security assessment scores for monitoring, evaluation, and assessment in financial system security are the lowest, averaging 1.41, while the scores for payment, service, and support are the highest, averaging 3.32. The financial system security assessment method proposed in this paper demonstrates high practical value and offers a reference for the future establishment and improvement of financial system security assessment methods.
Research on Artificial Intelligence and Piano Playing Technology in Colleges and Universities Based on 5g Big Data Networks
Authors: Wang Yibing 
Published: July 2024

Abstract
By analyzing the importance of fingering in piano performance and the challenges posed by existing techniques, this study develops a 5G and AI-based piano fingering automation system. The system leverages deep learning and big data analysis to generate efficient and scientific fingering suggestions. Experiments demonstrate that the system can recommend correct fingering in 98% of cases, achieving 85% consistency with the fingering of professional pianists. The system effectively improves the efficiency and accuracy of students’ learning. After using the system for one week, 25 students experienced a 30% improvement in their playing skills and a 40% reduction in their error rate. The results indicate that the system holds significant potential for application in piano education at colleges and universities, effectively assisting in both learning and teaching.
Exploration of Agricultural Product Packaging Design Innovation in the Context of Deep Learning
Authors: Hu Songyong 
Published: July 2024

Abstract
Utilizing Internet technology and resources to enhance the packaging design of agricultural products is a critical task for promoting their development in the current era. This study proposes an automatic generation design for agricultural product packaging based on deep learning theory, grounded in the principles of packaging design and market value. To represent the spatial relationship and distribution of color pixels, color moments and color correlation maps are used after obtaining color features of agricultural products through the HSV color space. The grayscale covariance matrix method is employed to get the texture features necessary for the packaging design. Proportion, boundary, pairing, white space, and balance calculations are combined to achieve the layout and typography of feature elements in the agricultural packaging design. The impact of deep learning technology-supported packaging design is analyzed. Data show that in the first group, the average values of the sample group (P001, P002, P004) are 2.98, 3.08, and 2.93, respectively, with an overall average of 2.997, while the overall average of the intelligent group (P003) is 2.96. Overall, deep learning technology-assisted image generation has a positive impact on agricultural product packaging design, contributing to a new level of economic development of farming products.
Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China
Authors: Li Hongbo , Zhou Zhenyong , Du Wei , Lu Lili , Zhang Yang 
Published: July 2024

Abstract
With China’s economic and social development, material living standards have improved, leading to a dramatic increase in the demand for beef and higher requirements for its quality. In this context, molecular breeding technology for beef cattle has gradually become a research hotspot. This paper designs the entire process of knowledge mapping for molecular breeding technology of beef cattle in China, focusing on the molecular breeding knowledge extraction model based on BERT. It also integrates models and algorithms used in knowledge alignment, complementation, and storage to construct the knowledge graph. After testing the performance of the knowledge extraction algorithm, the development history of molecular breeding technology for beef cattle in China was analyzed. The results show that from 2013 to 2023, research related to molecular breeding technology for beef cattle in China exhibited a slow, then rapid growth trend. The terms “molecular breeding” and “beef cattle” appeared most frequently, with 218 and 165 occurrences, respectively, and had the highest centrality. Keywords with high occurrences include “comparative genes” (11.3068), “functional genes” (9.2376), “genome-wide association” (8.3197), and “genome-wide selection” (6.9012), all closely related to the development of molecular breeding technology for beef cattle in China. The prediction of future trends in molecular breeding technology for beef cattle in China is significantly influenced by this study.
Discussion on the Deep Integration of College Students’ Innovation and Entrepreneurship and College Students’ Civic and Political Education under the Background of Data Technology
Authors: Zhao Panpan 
Published: July 2024

Abstract
Currently, most of the studies related to the integration of innovation and entrepreneurship education into the ideological and political work of college students are theoretical discussions, lacking empirical evidence and rarely quantitative analysis. Based on data technology, this paper explores the deep integration of innovation and entrepreneurship with college students’ ideological and political education. After mining the significance of the integration of innovation and entrepreneurship and college students’ civic and political education, the study utilizes the Bayesian theory and face recognition model to assess the effect of the deep integration of civic innovation and entrepreneurship education from the perspective of emotional change. Its algorithmic model, which is mainly based on classification criteria, recognizes students’ classroom expressions according to the classroom expression classification criteria and the emotion score calculation formula so as to judge the effect of students’ classroom listening. Through empirical analysis, all dimensions of ideological and political education self-efficacy and entrepreneurial values of innovation and entrepreneurship are significantly positively correlated at the level of 0.01. The average value of emotion scores of innovation and entrepreneurship education classes integrating ideological and political education is higher than that of traditional innovation and entrepreneurship classes by 0.04 and by 4 more students. The innovative entrepreneurship classroom integrated with Civic and Political Education has achieved good teaching results among college students.
Exploring the Development Status and Stylistic Characteristics of National Wave Art Based on the Background of Cultural and Creative Economy
Authors: Wang Yan 
Published: July 2024

Abstract
In recent years, the national trend has become increasingly popular, and the art of national trend is the beginning of the production field’s skillful utilization of national art, dedication to category innovation, and making the product have a “root” and a “soul”. This paper combines the sparse principal component analysis method and the entropy weight TOPSIS comprehensive evaluation method to analyze the development level of national tide art in the context of the cultural and creative economy. At the same time, the VGG16 deep learning network is used to extract the depth features of style features in the images of Guochao art-related products, and the Softmax function is used to recognize and classify the features of style features of Guochao art to get the main style features of Guochao art industry. The study shows that the added value of Shaanxi Province’s Guochao art industry accounts for 4.14% of the province’s total GDP by 2023. The cultural, leisure, and entertainment service industry is developing rapidly, providing impetus for the development of the national tide art industry. Meanwhile, it is found that the style characteristics of Shaanxi Province’s national tide art industry appear to change with the development of the sector, and the Shanghai Fine Arts Factory style dominates the style characteristics by 2023 (0.359). This paper sets the foundation for the growth of the Guochao art industry and provides a useful reference for identifying style characteristics in the development of Guochao art.
Research on the Integration of Cultivation of Craftsmanship in Vocational Education and Online Platforms in the Context of the New Era
Authors: Wen Furong , Xu Tian , Dang Xuezhen 
Published: July 2024

Abstract
The rapid ascent of online learning has positioned it as a preeminent method of instruction. This rise prompts a critical examination of how online education can be seamlessly integrated with the development of craftsmanship skills, a pressing issue within the educational realm. This study addresses the challenge by proposing a novel learning analytics model tailored for vocational education. The model, which emphasizes process orientation, continual enhancement, and interactive engagement, is strategically applied to three key areas: identification of learning characteristics, analysis of learning behaviors, and scrutiny of online interactions. Such applications aim to bolster data-driven decisions in nurturing craftsmanship within vocational settings. Further, the research adopts the “Learning by Doing” approach to probe the inherent elements necessary for the endogenization of learning on vocational education online platforms with a focus on craftsmanship. The empirical findings reveal that the integration of online learning with variables such as vocational beliefs, virtues, attitudes, and aspirations—quantified by Beta values of 0.185, 0.142, 0.207, and 0.394, respectively— exerts a uniformly positive influence. These results underscore the efficacy of online vocational education in fostering craftsmanship, thereby enhancing the quality of talent development in vocational disciplines.
Research on Collaborative Educational Mechanism of School-Enterprise Cooperation in Higher Vocational Colleges and Universities Based on Deep Learning
Authors: Chen Fei 
Published: July 2024

Abstract
Although China’s higher vocational colleges and universities have achieved substantial results in school-enterprise cooperation, numerous problems have surfaced in practice. This paper analyzes the influence of strategic synergy, organizational synergy, knowledge synergy, and synergy effects within the context of school-enterprise cooperation. It also examines the role of equivalence matching factors in enhancing these synergy effects. Utilizing relevant data on school-enterprise cooperation, a synergistic education model was developed using a multilayer linear model to capture the interaction of latent variables. Subsequently, a performance measurement analysis was conducted to verify the scientific validity and effectiveness of collaborative efforts in higher vocational education. The results indicate that the synergistic performance in higher vocational institutions engaged in school-enterprise cooperation is 0.655, representing an increase of 0.107 compared to institutions without such collaboration. Implementing school-enterprise cooperation significantly enhances the synergistic performance of higher vocational institutions, thereby maximizing the benefits of this collaboration.
A Study of the Application of Semiotics in News Communication Mechanisms in the Digital Era
Authors: Chen Qianli 
Published: July 2024

Abstract
Any news fact is reproduced as news representation in the form of symbols. In this paper, we first analyze the role of symbols in news representation and propose the common components of news symbols such as subject-object, scene, light color, etc. Construct a model for news symbol analysis that is based on clustering and design a method for segmenting news content based on rules and statistics. The symbolic representation is simulated, and the symbolic ideographic similarity is evaluated after the news content space is transformed into the symbolic representation space, and K-means clustering is carried out. The model was used to analyze the symbolic forms of news from two representative news websites and then calculate the 60 best multi-symbol combinations of “Chinese New Year Festival” news content. A questionnaire survey was conducted to test the degree of audience recognition of the symbolic representations of the news imagery matching. The study found that CCTV's “Chinese New Year Festival” news reports have clear thematic representations, rich symbolic imagery, and are easy to understand, with evaluation scores of 4.61, 4.27, 4.16, and 4.08 for color, character expressions, festive atmosphere, and cultural symbols, respectively. The purpose of this study is to explore and innovate ideas while evaluating the application of semiotics in news communication in the digital age.
Analysis and research on China’s carbon trading market and transaction prices based on signal decomposition model and deep learning model
Authors: Wang Yilin 
Published: July 2024

Abstract
With the continuous development of industrialized society, carbon emissions have become a significant global challenge. Carbon trading, as a crucial measure to mitigate carbon emissions, has garnered substantial attention in the context of market prediction analysis. Addressing the nonlinear and nonstationary nature of carbon trading prices, this study proposes a novel prediction model based on signal decomposition and deep learning. A GUR neural network model, integrated with an attention mechanism, is constructed within a deep learning framework. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to address the issue of non-smooth and nonlinear panel data, further enhanced by the Symbiotic Organism Search (SOA) algorithm. This approach culminates in an advanced price prediction model for China’s carbon trading market. Analysis of relevant data from 2014 to 2022 reveals several fluctuations in carbon trading prices, with transaction prices peaking at 68 yuan. The proposed method demonstrates superior performance metrics, with RMSE, MAE, and MAPE values of 0.512, 0.395, and 1.108%, respectively, outperforming other methods. This study offers an effective approach for predicting carbon trading market prices, providing valuable insights for optimizing and managing carbon market trading and development.