bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on online interactive teaching mode of college vocal music based on adaptive Kmeans algorithm
Authors: Dong Le 
Published: October 2024

Abstract
In order to improve the comprehensive learning quality of students when teaching university vocal music courses, teachers can create a teacher-student interactive teaching mode with a wide platform, precise business, and many approaches by carrying out interactive teaching. The research introduces the cuckoo algorithm for adaptive optimization on the basis of the traditional K-means algorithm, gives the clustering process of the adaptive K-means algorithm, and constructs an online interactive teaching system to realize the clustering of the characteristics of online interactive behavior of teachers and students. Finally, we use the speech in the vocal music classroom as an example to explore the online interactive teaching characteristics of vocal music. The traditional vocal music classroom is mainly a teacher-driven teaching mode. The frequency of teacher-student interaction is low, the proportion of students’ active questions to students’ active speech is 2.13%, and the teacher is less likely to organize students’ exchanges. In this teaching mode, the teacher encourages the acceptance of the proportion of speech is 16.17%, 75% of the classroom students use the online teaching system is higher than the teacher frequency, followed by the proportion of teachers using the online teaching system and the proportion of using the traditional teaching mode is close to 3:1, based on the online teaching system of the interactive teaching mode of the feedback effect is better.
A Study on the Construction of Mental Health Indicators for College Students Based on Social Media Data Mining and the Evaluation of Their Intervention Effects
Authors: Ji Huaichen 
Published: October 2024

Abstract
The development of social media has brought many tests to the mental health education of college students, and some college students have fallen into network addiction and dependence, which greatly affects their physical and mental health. The article uses microblogging social media as the source of students’ mental health data and preprocesses the data using data de-emphasis and Chinese word separation. It also analyzes the problematic manifestations of students’ mental health in colleges and universities, extracts students’ mental health indicators by using the TF-IDF algorithm, and realizes the recognition of students’ mental health topics by using the BTM model. The CNN-LSTM-ATT model was established by introducing the attention mechanism and LSTM model to assess the mental health status of college students. The data was analyzed in terms of students’ mental health characteristics predictive validation and used to develop intervention strategies for students’ mental health. The text length of students’ mental health is [1,22], which occupies 86.98% of all sentences, and the AUC value corresponding to the BTM model is 0.946, and the prediction accuracy of the CNN-LSTMATT model for the assessment of students’ mental health in colleges and universities can reach up to 97.62%. The social media data can clarify the mental health status of college students and realize the construction of students’ mental health intervention strategies from the dimensions of students’ media literacy and regulatory mechanisms.
Informatization strategy for the management of laboratory safety operation platform for mining majors in colleges and universities
Authors: Jiang Qinghua 
Published: October 2024

Abstract
Constructing an intelligent management platform for laboratory safety informationization for mining majors in colleges and universities is an important way to achieve campus safety in colleges and universities, and safety assessment is the core of this work. This study proposes to establish a laboratory Tripod-Delta model to establish the factors that affect laboratory safety and analyze their weights through a C-OWA operator to provide directions for the design of an intelligent platform. The fuzzy set qualitative comparative analysis method is employed to analyze the conditional grouping of laboratory accidents, given multiple factors that cause laboratory safety accidents. Among the established factors affecting the safety of mining professional laboratories in colleges and universities, according to the weight analysis, it can be seen that colleges and universities need to pay more attention to the influence of safety facilities (15.88%), instrument materials (14.03%), management system (12.35%) and experimental habits (10.6%). And there is a need to pay more attention to the human aspect. The sum of the factors under this aspect is the largest, which is 29.49%. Using the fuzzy set qualitative comparative analysis method for grouping analysis, we get five groupings leading to laboratory accidents, starting from the causal relationship leading to the frequent occurrence of laboratory accidents in colleges and universities, to provide a reference for the construction of laboratory safety management systems in colleges and universities.
Data-driven modeling of capital liquidity changes during financial market crises
Authors: Lu Man 
Published: October 2024

Abstract
Against the backdrop of the ongoing global financial crisis, clarifying the mechanism of the role of the drivers of capital liquidity changes is the basis for maintaining financial stability during financial market crises. This paper proposes the basic hypothesis of capital liquidity changes during a financial market crisis, with reference to related studies. Subsequently, a data-driven model of capital liquidity changes during the financial market crisis is constructed based on the vector autoregressive model, and the main nodes of capital liquidity changes during the financial market crisis are obtained according to the changes in the degrees of freedom of the model. The MCMC method is utilized to assess the model’s smoothness and ensure the stability of the data-driven model of capital liquidity changes. The empirical analysis of the model finds that the positive shock of capital liquidity change on stock growth rate exhibits a positive response, and the stock growth rate explains 0.527% and 1.292% of the capital liquidity change on average during one year of financial market crisis. It has also been found that the arbitrage motive drives capital liquidity more strongly during a financial market crisis compared to the hedging motives. The model proposed in this paper helps to prompt policymakers to monitor the changes in capital mobility and make useful decisions in a timely manner, which in turn maintains the stable operation of the financial market.
Innovation of Teaching Methods of Civic and Political Education in Colleges and Universities with the Assistance of Artificial Intelligence
Authors: Lv Huan 
Published: October 2024

Abstract
Artificial intelligence empowers education and promotes its integration and innovation, enabling the use of learning analytics to accurately diagnose the learning situation and implement precise teaching. In this paper, we have leveraged artificial intelligence to innovate traditional teaching methods, developed a wisdom teaching model specifically for college civic education, and concentrated on the precise application of wisdom teaching in this context. First, it creates student profiles based on academic data and then provides personalized recommendations for learning resources by combining a collaborative filtering algorithm and an improved K-means algorithm. Finally, we analyze the actual impact of the teaching intervention by comparing the student portraits before and after the intervention. When the number of neighbors K takes the same value, the improved cosine similarity calculation method outperforms other calculation methods, and the RMSE, MAE, accuracy, recall, and F1 values are 0.14, 0.24, 0.99, 0.74, and 0.85 when K = 60, respectively, which indicates that the algorithm improvement in this paper is practical. This paper categorizes the sample students into four groups based on their learning behavior and outcomes: potential, excellent, marginal, and hardworking. All these student types have demonstrated some progress following the teaching intervention, confirming the efficacy of this paper’s artificial intelligence-based teaching approach for college civic education. Simultaneously, the created profiles of these student types establish the groundwork for future personalized and accurate instruction.
Research on the Construction of Economic Statistical Model and Application of Data Cleaning Technology in Big Data Environment
Authors: Peng Ziying 
Published: October 2024

Abstract
With the rapid development of big data technology, the number of economic data grows faster, and it is more challenging for people to grasp and calculate economic data. In this paper, an economic statistical model is proposed to optimize data-cleaning technology. The economic data application framework is constructed using Multi-Agents, and the mining of economic data is achieved using Microsoft time series and clustering algorithm. The GMDH algorithm, which is the core of self-organized data mining, is proposed and improved to complete economic data analysis and prediction by using the fitting error or prediction variance criterion as the identification criterion. The wild value identification method based on the regression model is utilized to eliminate potential wild values and carry out data cleaning. Statistical analysis of the economic data of the printing industry in Shanghai, China, reveals that the total assets, total industrial output value, and total profit of the printing industry in Shanghai in 2023 declined by 2.63%, 4.77%, and 5.68%, respectively. External investment and enterprise R & D investment up to 109,440,100 U.S. dollars, 140,301,000 yuan. The overall number of employees declined, and the profit margin on output value decreased by 5.68%. It is predicted that the number of enterprises, total assets, total industrial output value, and total profit of Shanghai’s printing industry will rebound in 2024, and the external direct investment and R&D investment will be appropriately reduced.
Plot Dynamic Adaptation and Character Interaction Design of Artificial Intelligence in Musical Theater Performing Arts
Authors: Wang Yingyueyuan 
Published: October 2024

Abstract
In recent years, artificial intelligence technology has received widespread attention in the field of artistic creation. This paper proposes the use of intelligent software to create a musical work that matches the emotions of the audience when they watch a musical performance so as to mobilize the development of the musical plot and to involve the virtual digital man in the performance of the musical to increase the interactivity of the characters. Both the music automatic generation software and the application of the virtual digital person need to match the rhythm of the performance. Accordingly, this paper proposes a multimodal emotion recognition model based on deep learning, which is applied to recognize the audience’s emotions in real time to create music that matches the plot development and reasonable role interaction. Through simulation experiments, the average recognition rate of multimodal based on decision layer fusion is 84.8%, which is slightly higher than the average recognition rate based on feature fusion and much higher than the average recognition rate of single-modal speech emotion (67.5%) and face expression (78.5%). 76% and 73% of the audience members liked the “music” and “character” elements of the musical performance with AI technology, and 62% of the audience members expressed their desire to watch it again.
Study on Response Strategy and Efficacy Improvement of China-ASEAN Emergency Security Symbiosis Mechanism Based on Data Mining Technology
Authors: Wei Linshan 
Published: October 2024

Abstract
The emergency decision support system proposed in this paper uses data mining technology to analyze and mine a large amount of dynamic operation data and historical operation data in the China-ASEAN natural disaster database, which can be used to analyze the dynamic safety risk and optimize the emergency rescue and disposal plan for China-ASEAN natural disasters. In order to optimize the emergency decision support system, the core algorithm of data mining in the system is improved, an improved sequence analysis algorithm is proposed, and improvement measures are proposed on the basis of the inadequacy of the original Apriori algorithm. Through examples, the data mining algorithm’s specific process of automatically generating response suggestions for security events is demonstrated. The analysis of the data shows that the test results meet the expected results, and the algorithm can effectively make response decisions. The average false detection rate is only 8.05%. In scenarios 2, 3, and 4, all of them have higher emergency response efficiency, respectively, of 0.4445, 0.4616, and 0.5299. The completion time of the emergency response increases as the response level increases.
An Exploration of the Methods of Virtual Reality Technology to Enhance the Tourism Experience of Non-native English Speakers
Authors: Wu Shanshan , Zhang Liang 
Published: October 2024

Abstract
This paper combines virtual reality technology with end-to-end speech recognition technology to design a digital tourism system to satisfy non-native English speakers (tourists) with the advantage of interactive projection technology. VRML technology is utilized to accelerate network interaction and piggyback on the construction of the digital tourism system. Particle swarm-based hybrid collision algorithm and end-to-end English speech recognition algorithm are proposed respectively to construct a real and complete tourism environment and convert English speech to meet the tourism needs of non-native English speakers. The performance advantages of the improved AABB-MPSO algorithm are detected by combining the exploration of the influence of the size of the search space on the search success rate and search time. Analyze the relationship between the number of modeling units and the recognition error rate of the end-to-end English recognition model with a joint CTC and attention mechanism, using spoken speech of free conversation in real scenes. The seven digital tourism experience dimensions of scenic integrity, scenic service quality, scenic authenticity, scenic infrastructure, degree of commercialization, English recognition and conversion, and cultural connotation embodiment are delineated to explore the key factors of tourism physical examination. Non-native English speakers’ evaluation of the digital tourism experience is generally high, and the mean value of each dimension is greater than “3”, which means that the digital tourism system designed in this paper can meet the tourism needs of non-native English speakers.
Strategies for Modern Art Product Morphogenesis and Functional Optimization Based on Computer-Aided Design
Authors: Xu Jing , Zhu Qi 
Published: October 2024

Abstract
The research constructs a computer-aided design-based morphological design system for art products and processes it with a BP neural network and genetic algorithm for optimization. Computer-aided design techniques such as face segmentation, morphological proportion design, homogeneous curve drawing and organization, chamfer setting, and management methods are introduced to design the morphology of art products. By analyzing the error value of each part of the product in the design of sculpture products in this paper’s art product morphology design system, the performance of the product morphology design of this paper’s system is examined. The color characteristics of the art products are deconstructed and analyzed by comparing the hue, brightness, and purity of the designed sculpture samples. The mean square error values of each part of the sculpture product (head, arms, torso, legs) are 0.0046, 0.0019, 0.0073, and 0.0036, respectively, and the overall average mean square error is 0.0044, which makes the art product morphology design model based on computer-aided design of this paper have good accuracy. The values of hue, brightness, and purity of the main color of the sculpture product are relatively stable, with the range of hue being roughly 4~8 and the main range of brightness and purity being 20%~40% and 20%~40%, respectively. The brightness and purity of auxiliary colors and accent colors are both steady, and the purity of auxiliary colors is greater than that of the main color. The accent color has the largest range of hues in the selection of hues.
Using Game Theory to Analyze the Effects of Different News Communication Strategies on Public Opinion Formation
Authors: Zhu Haibo 
Published: October 2024

Abstract
In this paper, the public opinion game model is introduced through the analysis of game theory and news dissemination theory. According to the basic assumptions to form the payment matrix, the mathematical derivation of the benefit expectation function, to get the replication of the main body of the dynamic equations, and its equilibrium point of the stability of the test, so as to complete the construction of the public opinion game model under different news dissemination strategies. The model of this paper is validated and analyzed when the news dissemination to the public economic loss Lg
is higher, leading to the rise of the probability of strict government regulation, prompting the public judgment and self-opinion ability to improve. If the government does not intervene in the news media, the gray gains of the news media Re

2 gradually increase, resulting in the lack of relevant, correct knowledge of the public. In this paper, we study the impact of different news communication strategies on the formation of public opinion and promote the formation of public opinion.
A synergistic study on farmers’ income enhancement and urban-rural financial development under the perspective of common wealth by an intelligent decision support system for land improvement
Authors: Teng Man 
Published: October 2024

Abstract
This paper proposes the general framework and technical process of the intelligent decision support system for land improvement based on a multi-objective artificial immunity algorithm. Based on the analysis of the current situation of land use structure and spatial layout in County S, three land resource quantity structure optimization schemes are selected, and the improvement of agricultural land use efficiency and agricultural output value in the three schemes are evaluated and analyzed. The mechanisms that affect income distribution in urban-rural financial development and land finance are summarized. The results show that after land improvement, the area of cultivated land and grassland in County S decreased by 1,444 ha, and the area of garden land and forest land increased to the greatest extent in the economically prioritized scenario. The size of towns increased to 541 ha, and the size of villages decreased to different degrees and all three land improvement programs had the combined effect of increasing the gross agricultural product and boosting farmers’ incomes. The development of urban and rural finance is aided by land improvement, but the degree of inequality in income distribution is widened.
A study on the dynamics of regional economic differentiation in China under big data environment combined with neural network algorithm
Authors: Wang Jiangnan , Lu Yao , Pan Lina , Zeng Ye , Zhang Dexin 
Published: October 2024

Abstract
China’s economy is in a state of rapid development in today’s digital era, and big data technology has become a key force in promoting industrial upgrading and economic operation. The article first constructs 14 economic operation indexes in 7 aspects to establish the regional economic index system according to each type of regional economy. After researching BP neural networks, a TMBA-BP neural network prediction model based on adaptive t-distribution has been proposed. The prediction model is used to predict the economic development of Liaoning City, and the results show that the TMBABP prediction model is accurate. Finally, the Gini coefficient is combined with the TMBA-BP model to explore the causes of regional economic differentiation in China. In terms of the contribution rate of regional differences, the inter-regional contribution rate is higher than the intra-regional contribution rate. It indicates that in the big data environment, the main reasons for the obvious regional economic differentiation in China are inter-regional differences, followed by intra-regional differences.
Research on deep neural network-based anomaly detection technology in high-dimensional data environment
Authors: Wang Yan 
Published: October 2024

Abstract
With the popularization of information technology, the dramatic growth of data size, the significant rise in data dimensions, the increasing complexity of data types, and the diversity and complexity of the manifestations of anomalies all make anomaly detection more difficult. In this paper, we enhance the structure of the variational self-encoder in deep neural networks to maintain the benefits of anomaly detection technology, which relies on reconstruction error. Utilizing the high reliability of agent supervision to make up for the lack of detection performance of the deep self-encoder when the anomaly data dimension is high, a novel unsupervised anomaly detection model for high-dimensional and high anomaly rate data is proposed. Results from tests on several public datasets show that the UAD-ADC model does better than four other traditional anomaly detection methods in all of the datasets when looking at AUC values. On two datasets, Pendigits and Letter, the method improves by 0.3648 and 0.3004 over the lowest algorithm, and the experimental results confirm its effectiveness.
Information Technology Integration and Teaching Effectiveness Enhancement Strategies in Public Physical Education Programs
Authors: Wu Nengwei 
Published: October 2024

Abstract
By integrating a heart rate monitoring module and a step frequency monitoring module, this study aims to use informatization technology to monitor the exercise level of students in public physical education courses and to adjust the teaching methods of public physical education courses based on informatization feedback. A school had 332 students divided into an experimental and control group for comparative experiments. Based on the results of students’ exercise data monitoring by heart rate oximetry module and acceleration sensor, it was found that the exercise density of students in the experimental group (73.3%) was much higher than that of students in the control group (54.4%), which means that the students’ exercise status was better under the informatization teaching mode. During the experimental period, there were significant differences in the average heart rate and MVPA percentage time between the experimental group and the control group students (p<0.05). The results of the physical fitness test showed that the informatization teaching mode significantly improved the student’s performance in body weight, pull-up, and standing long jump events (p<0.05) and significantly improved the student’s performance in lung capacity, sitting and lying forward bends, 50-meter run, and 1000-meter run events (p<0.01).