bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the Digital Transformation of Enterprise Finance Based on the Finance Sharing Model
Authors: Chen Shuyang , Yu Xian , Liang Jinhua , Feng Cuicui , Yang Xiaoyang 
Published: July 2024

Abstract
Blockchain is one of the key technologies for the digital transformation of corporate finance because it satisfies the requirements of enterprise financial sharing with its features of contract execution, openness and transparency, decentralized distributed ledger, traceability, and non-tampering. In order to support business digital transformation under the financial sharing model, blockchain technology is used in this study, along with an analysis of the internal and external factors influencing enterprise financial digital transformation. In order to guarantee data security and enhance the processing efficiency of the financial sharing center, the Byzantine fault-tolerant RAFT algorithm is adopted as the private chain’s consensus algorithm, and PBFT is adopted as the federated chain’s consensus algorithm. These decisions are based on the compatible architecture of blockchain technology and the characteristics of financial sharing. The financial sharing center’s effects are assessed in relation to Enterprise A’s blockchain-based financial activity application system. According to Enterprise A’s cash flow statistics, paid employee compensation fell in 2018 from 2.943 billion to 2.864 billion. The audit and accounting industry may streamline procedures, expedite processing, lower labor and time expenses, and achieve favorable outcomes by utilizing the financial shared service model under the blockchain paradigm.
A Study on the Cultivation of Multicultural Literacy in College English Teaching Based on the Construction of Cognitive Maps
Authors: Chai Qi 
Published: July 2024

Abstract
Using the CNN network model and attention mechanism, this paper completes the construction of the cognitive graph pattern layer based on Protégé programming, crawls the relevant data from the dimensions of language ability, learning ability, thinking, and cultural character of multicultural literacy in English teaching, extracts the entity-relationship of multicultural literacy in English teaching, stores it in the Neo4j corpus, and ultimately completes the construction of the cognitive graph for the cultivation of multicultural literacy in English teaching. Following simulated experiments to evaluate the algorithm’s performance, a case study was carried out to foster English multicultural literacy from the standpoint of the cognitive graph. The information demonstrates that the convolution technique and an attention mechanism work well together to increase relationship extraction efficiency. Further evidence that the cultivation of multicultural literacy in English teaching focuses on language ability, learning ability, thinking character, and cultural character in order to quickly improve students’ English multicultural literacy comes from the keywords with high word frequency in English multicultural literacy: “language ability (388, 0.899)”, “learning ability (185, 0.449)”, “thinking character” (114, 0.38), and “cultural character” (105, 0.335).
Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice
Authors: Chu Li 
Published: July 2024

Abstract
Taking cultural ecology as the carrier of tourism resources, it fully embodies the significance of the synergistic development of cultural tourism ecology for the innovation of the cultural tourism model. The article establishes a cultural tourism ecological synergy model by integrating cultural ecology and cultural tourism under the guidance of the theory of the life cycle of tourist places and industrial innovation. Taking the nine provinces of the Yellow River as the research object, its cultural tourism data are collected and pre-processed, cultural tourism keywords are extracted by the TextRank algorithm, and the LDA model is established to obtain cultural tourism themes. The Skip-gram model was combined with the GCN network to establish a cultural tourism emotional image perception model, and the Moran index and nearest-neighbor index were introduced to analyze the spatial structure and regional evolution of cultural tourism attractions. The keywords of cultural tourism are mainly embodied in ecology and culture, which all appear more than 1,000 times, and the percentage of tourists’ most concerned cultural tourism themes is 22.15%, and most of the nearest neighbor indices of the spatial structure of the attractions at different levels are less than 1, which is cohesive. The Moran index of tourists’ footprints in 2022 is 0.106, the footprint coverage rate is 0.26 percentage points higher than that in 2012, and tourists’ positive emotional evaluation of cultural tourism destinations reaches 80%. Based on cultural ecology, realizing the synergistic development of cultural tourism and ecology is the way to innovate cultural tourism.
Deep Learning-Based Image Style Transformation Research on Landscape Paintings of Wei, Jin and North-South Dynasties
Authors: Li Shuangqi 
Published: July 2024

Abstract
In this paper, the convolutional neural network is applied to the processing of stylistic features in the images of landscape paintings of Wei Jin and North and South Dynasties. After convolution and pooling activation, rich and less parametric feature maps are generated to lay a solid foundation for generative adversarial networks. Meanwhile, in order to ensure the rigor of the image dataset of landscape painting images under scene transformation, an improvement is proposed on the basis of traditional GAN technology, and a function represents the improved CycleGAN model. The experimental dataset has been constructed, and the loss function, network training, and hyperparameters have been designed for the model. The visual and conversion outcomes of the model-generated images are evaluated through simulation experiments. The migration of all landscape painting style textures is within the range of (0.5,2.4) at a gray level of 8. The migration styles increase as the gray level increases, and the data between groups does not overlap. In the subjective evaluation of the landscape painting style transformation generated by the model used in this paper, the score of the generated image is 3.8135 points, while the number of high scores is more than 65% of the votes. The model constructed in this paper has a better effect on the transformation of the image style of the Wei, Jin, and North and South Dynasties, and it can satisfy people’s visual needs.
Enrichment and Expansion of Classical Dance Teaching Content under the Perspective of Innovative Education
Authors: Liu Zhuo 
Published: July 2024

Abstract
This paper proposes a collaborative filtering-based learning resource recommendation algorithm to help learners find interesting resources in a huge amount of teaching resources and improve learning efficiency and learning interest so that the classical dance teaching content can be further enriched and expanded. By improving the K-means clustering algorithm, the article realizes the clustering of students’ classical dance teaching content preferences and further proposes a collaborative filtering algorithm based on ratings and attribute preferences of classical dance teaching resources, which takes into account the relationship between users’ needs and the attributes of the resources in order to complete the personalized recommendation of classical dance teaching resources for students. The article finally takes a dance teaching classroom as an example for empirical research. In the two groups of classical dance performance scoring a single total average score, the experimental group’s performance of dance movements is better than that of the control group, the average score of the experimental group’s pre-test is higher than the average score of the control group’s pre-test by 8±1.8 points, and the average score of the experimental group’s post-test is higher than the average score of the control group’s post-test by 9±2 points.
A Study on the Utilization of Polyphonic Folk Songs in College Choral Teaching in the Context of Big Data
Authors: Xia Fan 
Published: July 2024

Abstract
Introducing polyphonic folk songs into the choral teaching classroom in colleges and universities not only helps to carry forward the excellent traditional culture but also promotes the students’ emotional education and physical and mental health development. This paper analyzes the status quo and dilemma of the use of polyphonic folk songs in college choral teaching and puts forward the model of music choral intelligent tutoring system (ITS) and the method of spectral tone recognition as countermeasures. A collaborative filtering algorithm is used to construct the learning resource recommendation algorithm in the music chorus intelligent guided learning system model, which is based on the ITS system structure. The YIN algorithm is used to extract the audio, and the pitch template matching is accomplished with the help of the improved DTW algorithm to complete the choral ability assessment. An improved normal harmonic algorithm has been proposed to preprocess the audio signal and achieve spectral tone recognition for multi-part folk songs in the fundamental frequency extraction stage of spectral tone recognition. In a comprehensive university in Sichuan Province, China, the teaching practice of polyphonic folk song chorus was carried out. The students’ scores in the dimensions of learning interest and learning confidence increased by 0.976 and 0.698 on average after the experiment. The p-values were all less than 0.05, showing significant differences.
Research on data-driven multi-sensory design path based on Chinese gardens under the perspective of AI big data
Authors: Xie Na , Xia Zhongjun , Zhou Wenyu 
Published: July 2024

Abstract
In order to promote the development of the Chinese gardening field, this paper proposes a data-driven, multi-sensory design for Chinese gardens. The multisensory design of Chinese gardens, which combines data-driven technology and VR technology, is explored from two perspectives: the visual and auditory senses. It is found that the design easily leads to the loss of the natural beauty of the garden. For this reason, the layout optimization model is constructed using the improved PSO algorithm, and the corresponding analysis method is applied to analyze the effect of the multi-sensory design of the garden. The results show that the model in this paper is better than the other two control models in terms of weighted mean (4.078) and convergence time (0–4 ms). It was also found that the basis for visitors to make experience visual satisfaction evaluations was basically whether the floral landscape was beautiful (39.18%) and whether the field of view was open (23.27%), and the auditory experience design evaluation scores were higher for acacia forests (5.436), red-leafed ridges (4.091), snapdragon forests (3.872), and metasequoia forests (3.657) in the gardens. The research in this paper reveals the focus of multi-sensory design in Chinese gardens, which can provide theoretical references for research in the field of Chinese garden design.
Research on Practical Innovation of Elderly Education Service System in the Era of Artificial Intelligence
Authors: Li Xiao 
Published: July 2024

Abstract
The trend of our country into an aging society is becoming more and more obvious, and the elderly education service has become the focus of social attention. The integration of artificial intelligence into elderly education is one of the inspiring ideas in which the personalized recommendation algorithm can recommend educational resources according to the characteristics of older people, and it has the prospect of application. In this paper, we first provide a CTransD-GAT recommendation model based on a knowledge graph, which improves traditional problems such as data sparsity by setting weight preference and feature aggregation. A dynamic preference-capturing method is proposed based on contextual interaction to capture the variable user learning interests more accurately and flexibly. This paper examines the practical utility of personalized recommendation methods for educational resources based on these two improved techniques. The post-test mean score of each knowledge module test of the experimental group is improved by 1.83 points compared with the pre-test, 11.46 points improve the score of teaching ability, and the scores of perceived usefulness, ease of use, and intention to use are 3.82, 3.89, and 3.97, respectively. It shows that the improved educational resource recommendation model has an excellent effect on improving knowledge structure and teaching ability, and it is characterized by simplicity and ease of use.
Artificial Intelligence-Assisted Training and Skill Enhancement Strategies for Athletes
Authors: Lin Li , Wang Pengshan 
Published: July 2024

Abstract
The continuous progress of science and technology promotes the continuous development of artificial intelligence, and digital intelligence empowerment has gradually become a focus topic of widespread social concern. Considering that traditional sports training is difficult to meet the higher requirements put forward by athletes on training methods, training load, and training equipment, this study combines artificial intelligence technology to explore the path of skill enhancement. In this paper, based on the skeletal key points for athlete training action recognition, the improved DHRNet network is optimized for the detection of skeletal key points and then for the complexity of the athlete’s training state, the occlusion of the skeletal points and the repair of motion information are carried out. On this basis, we construct an artificial intelligence-assisted training system composed of a video image input function system, action recognition function system, and behavior analysis function system. After completing the design, the core module of the system, the motion recognition system, was tested. The average recognition success rates of basic and sport movements were between 90.8~95.6% and 84.5~96.5%, respectively. The overall recognition correctness rate remained above 90% in the actual application test, which can meet the training needs of athletes. The average score for all abilities of the athletes using the AI-supported system is 85.242, which is 6.21 higher than that of the traditional training method. The AI-assisted system is of great help to athletes’ training, and it also enables the early realization of intelligent training.
Study on the Path of Rural Tourism for Rural Revitalization under the Threshold of Value Co-Creation
Authors: Liu Lingyu 
Published: July 2024

Abstract
Rural tourism presents multifaceted development and community participation as the core of rural tourism development also serves as a solid force for rural revitalization. Under the concept of value co-creation, community residents are involved in the entire process of rural tourism development, and their role is more significant. Studying the participation of community residents in rural tourism development is of great significance. In this paper, we construct a dynamic model of the rural tourism value chain using value co-creation and elaborate on the co-creation processes involved in the entire value creation process. Combined with the sample data to analyze the overall situation of rural tourism development, we collect data on the willingness of community residents to participate and construct a regression model of the factors influencing the willingness of community residents to participate in rural tourism. With the conclusion of the influencing factors analysis, we further analyze the motivation and behavior of community residents’ participation in the development of rural tourism and propose relevant strategies to optimize the path of rural revitalization with respect to the overall level of community participation. The correlation analysis of the influencing factors shows that the correlation coefficients of residents’ ability to participate, residents’ motivation to participate, residents’ attitude to participate, and the degree of community participation in rural tourism are 0.686, 0.785, and 0.706, respectively, all indicating positive correlation. Community residents’ participation in conservation behavior and responsibility and obligation motivation have a significant positive correlation. The correlation coefficient is 0.623, and the significance is 0.000, which indicates that community residents’ participation occupies an important position in the whole rural tourism development community, and the innovative community participation mechanism can significantly accelerate the development of rural revitalization.
Emotional Characterization of Rural Recreation Tourists Based on Big Data
Authors: Ma Jin 
Published: July 2024

Abstract
In recent years, people’s consumption of the health industry has increased dramatically. The tourism industry will focus on the development of rural tourism and recreation tourism, as a new form of rural recreation tourism is also regarded as one of the leading directions of recreation tourism. This paper focuses on the study of tourists’ emotion visualization model based on big data, which first screens the textual topic words of rural recreation tourism based on the method of information gain and chi-square test and classifies different recreation topics after calculating the eigenvalues of the screened topic words based on IF-IDF. On this basis, the recuperation theme texts of different themes are preprocessed, while the emotional features embedded in the texts are extracted and empowered, and the emotional tendency of tourists towards rural recuperation tourism is explored based on the support vector machine model. Taking Kunming City as an example, among the positive emotional features of tourists towards Kunming’s rural recreation tourism, the highest frequency is “good”, with a frequency of 9,153, and most of them are praises for Kunming’s “scenery”. The words “average”, “disappointment”, “regret,” and “poor” are the negative emotions generated by the failure of certain scenic spots to meet the expectations of tourists. The study confirms the existence of time-emotional tendency differences and emotional preference demand differences among tourists in order to enrich the ideas and application value of this research field and bring more practical significance to tourist destinations.
Improvement of Employment and Entrepreneurship Ability of College Students in Higher Vocational Colleges and Universities under the New Situation of Artificial Intelligence
Authors: Huang Ri’an 
Published: July 2024

Abstract
This paper first established a multiple linear regression model and estimated the model parameters using the least squares estimation method. Then, it analyzes the employment and entrepreneurship abilities of college students in higher vocational colleges and universities and analyzes the differences in the level of employment and entrepreneurship ability of students in different grades. Then, the impact of artificial intelligence technology on employment and entrepreneurship ability was analyzed in combination with relevant measurement indexes at home and abroad, and finally, based on the results of the analysis, the strategies for improving the employment and entrepreneurship ability of college students in higher vocational colleges were proposed from multiple aspects. The results show that for every 1% increase in the level of AI technology, the total number of people employed in the labor employment market rises by 0.48, and the employment income rises by 6.56. This study has certain reference significance for balancing the “positive effect” and “negative effect” brought by the development of artificial intelligence on labor and employment, and provides realistic and feasible basis for the introduction of employment policy and talent development policy adapted to the development of artificial intelligence.
A Model for Assessing the Impact of Intelligent Algorithms on the Translation Quality of Literary Works in Cross-Cultural Communication
Authors: Li Xue 
Published: July 2024

Abstract
In recent years, the accuracy and linguistic fluency of translation based on intelligent algorithms have been relatively close to that of human beings, and the application of intelligent algorithms has gradually become a research hotspot in the academic world. In this paper, it is found through relevant research that semantic retention, cultural appropriateness, linguistic fluency, content comprehensiveness and text readability are important factors in the application of intelligent algorithms for the translation of literary works for cross-cultural communication. To explore the degree of influence of these factors on the translation quality of literary works, this paper analyzes them using a multiple linear regression model. The first step in building the mathematical model for multiple linear regression is to construct it. Then, the formulas for calculating the goodness-of-fit and significance test of multiple linear regression equations are introduced. Finally, the regression model was tested using residual analysis and multicollinearity. The regression results show that the quality of literary translation = 0.165+0.124*Semantic retention + 0.356*Cultural appropriateness + 0.254*Linguistic fluency - 0.001*Content comprehensiveness + 0.256*Text accessibility. According to the results of the analysis, the impact of content comprehensiveness on the translation quality of literary works is not significant, and cultural appropriateness has the greatest impact on the translation quality of literary works.
Study on the Dissemination Mode and Influence of Literature on Rural Revitalization Topics in the New Media Environment
Authors: Lin Yejin , Pan Weiwei 
Published: July 2024

Abstract
The literature on rural revitalization serves as both a record and a witness to the development of rural revitalization in China. This paper outlines rural revitalization literature’s communication mode in the new media environment and proposes a model for its We propose IEOC, an important node group mining algorithm based on information entropy and overlap coefficient, to construct the communication influence model. We also introduce information entropy calculation nodes, measure the overlap of influence using overlap coefficients, and propose a floating probability cascade model, TFIC, to construct the communication influence model of rural revitalization literature combined with the model for analyzing the communication influence of rural revitalization literature, it reveals a moderately low total word frequency of high-frequency words (2332). Out of the 12 rural revitalization literature works under study, only Moon Beach has a communication influence index of more than 1. of more than 1. Compared to other mainstream literature, rural revitalization literature has a communication power of only 1785 and 18369, with fewer high-communication works and high-influence contents. The number of high-impact works and contents is relatively small. Overseas dissemination is characterized by a higher number of Chinese editions than foreign language editions, and there are also shortcomings in translation.