bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of robotic calligraphy copying techniques based on linear regression models
Authors: Jiang Jianlong , Huang Kun 
Published: September 2023

Abstract
A new robotic calligraphy system is constructed to address the problem of robotic calligraphy systems that only consider robotic writing in the straight state of the brush pen, enabling robots to write Chinese characters in the tilted state. A linear regression algorithm-based stroke model suitable for robotic brush writing is developed. Then a genetic algorithm is used to obtain trajectory information from the stroke images, and rules for starting, walking, and closing strokes are added to generate virtual stroke images. Finally, the B spline algorithm was used to realize the robot path planning and coordinate transformation to complete the pose calculation and perform the actual writing. The results show that the width of the pairs of strokes deepens as the stroke depth increases and the stroke speed decreases, corresponding to six sets of experimental speeds from 20mm/s to 120mm/s at stroke depths of 3mm and 7mm, respectively. From this, it can be seen that the relative error of most points is smaller than 10%, and the maximum relative error reaches 79.08%. The higher error was due to its lower benchmark value after comparison, which led to a huge error. In particular, as the independent variable increases in the experiment, making the benchmark value increase, its relative error decreases significantly, indicating the accuracy of the fitted model derived. It can be seen that the linear regression model-based robotic calligraphy copying technique helps to improve the robot’s purely repetitive trajectory in the writing process, adding its writing style to the original one and making it closer to humans.
Design and implementation of a process-aware accounting information system to improve business process management
Authors: Song Fenxian 
Published: September 2023

Abstract
As one of the core components of enterprise information system, accounting information systems is facing double pressure from the enterprise management environment and the development of accounting discipline, and the functional application-oriented accounting information system is unable to meet the needs of modern enterprise management. To improve the accounting efficiency of enterprises, this paper proposes a process-aware accounting information system based on data mining to improve business process management, hoping to provide some reference values for the design of the accounting information system of enterprises. Based on this paper, the advantages and disadvantages of typical accounting information system construction methods are analyzed and compared, and the feasibility of process-aware accounting information systems for improving business process management is analyzed in terms of enterprise efficiency, accounting cost, accounting time, risk, and control quality. The feasibility of improving the process-aware accounting information system for business process management is analyzed in terms of enterprise efficiency, accounting cost, accounting time, risk, and control quality. The results show that the process-aware accounting information system with improved business process management is economically and effectively feasible, saves time, reduces accounting risks, and improves management control quality of the enterprise. The process-aware accounting information system with improved business process management reduces the accounting cost of enterprise Y. The accounting cost before is between 100,000 and 200,000, while the accounting cost after using the new system is between 80,000 and 100,000. Company Y’s accounting behavior, logic, and convenience satisfaction increased by at least 10-20% with the new system. So, in general, the process-aware accounting information system for improving business process management helps companies to grow and manage.
A digital teaching model for jurisprudence courses based on a linear regression model
Authors: Luo Jing 
Published: September 2023

Abstract
To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.
Under the background of ideological and political education, the path optimization of college students’ consumption outlook education based on AdaBoost model
Authors: Liu Lili , Bai Qianfan 
Published: September 2023

Abstract
To better help college students establish correct scientific consumption concepts, this paper constructs an analysis model of college students’ consumption views based on the AdaBoost model in augmented learning. Firstly, the consumption data of college students are input into the analysis model and analyzed, and the weights of each sample are initialized. Next, a suitable weak regression model is selected, the corresponding number of iterations is determined, and the current sample weight distribution is updated according to the weak regressor weight coefficients. Using root mean square error adjustment and determining the threshold value, iterative operations are performed. Finally, a strong regression model is derived from analyzing the main factors influencing college students’ consumption perceptions. To verify that the proposed optimization path can help college students establish correct consumption concepts, simulation experiments are designed in this paper. The results show that after relying on peer education to penetrate scientific consumption in the context of Civic Education, some students’ remaining monthly living expenses increased from 300 yuan to 500 yuan. After strengthening self-education to encourage the practice of scientific consumption, the percentage of monthly living expenses stored by male students increased from 5% to 16%. Thus, based on the AdaBoost model, we can derive the factors that affect college students’ consumption to optimize the education path of college students’ consumption concept in a targeted way.
Design of reduced-current overcurrent protection circuit based on GBDT model
Authors: Wen Zhixian 
Published: September 2023

Abstract
To address the problems of slow response and insufficient protection sensitivity of existing overcurrent protection techniques, this paper verifies the feasibility of the design of reduced-current overcurrent protection circuits by using the GBDT model. Firstly, the regression tree is used as a circuit learner for iteration, and the CART algorithm is applied to calculate circuit splitting characteristics and optimal splitting points to obtain the gain of splitting. Then the line loss rate is calculated using the differential evolution method to derive the objective function of the minimized line and the optimal value of the circuit. Finally, the mean value is extracted by calculating the optimal line’s statistical characteristics, the regression tree nodes are calculated using the GBDT model, and the root means the square error is used to verify the effectiveness of the circuit protection design proposed in this paper. The experimental results show that the overcurrent protection value of the circuit sample is around 1.35A and the current limit protection time is around 13ms by verifying the current reduction protection function and indication function of the circuit in the full temperature range. This shows that the GBDT model enables the reduced-current overcurrent protection circuit to perform timely and accurate overcurrent detection actions, and the circuit can be reliably protected under different overcurrent conditions.
Analysis of Liability for Defective Capital Contribution of Company Shareholders Based on Discrete Regression Algorithm
Authors: Wang Weixia 
Published: September 2023

Abstract
From the generation of defective capital contributions to the transfer of transactions, a series of civil disputes and liability responsibilities still need to be explored for a better solution. In this paper, based on the current research status of the defective capital contribution assessment model of enterprise shareholders, we combine the XG Boost model with good classification ability and the logistic regression model with good interpretability and construct a discrete regression (XG Boost-Logistic) model for the evaluation of the defective capital contribution of enterprises. Combined with the data of 123 shareholders’ capital contributions from enterprises’ financial audit reports, the XG Boost discrete model, the Logistic regression model, and the XG Boost-Logistic evaluation combination model were used to conduct empirical analysis and comparative experimental analysis with the evaluation indexes of the model. The research results show that the accuracy rate based on the XG Boost-Logistic evaluation combination model is 87.39%; the efficiency of liability assessment is improved by 7.35% compared with the XG Boost model and 12.38% compared with the Logistic regression model. XG Boost-Logistic evaluation combination model can effectively improve the liability prediction of capital contribution defects classification accuracy and provide a good explanation of shareholder liability classification at the same time and can help companies to avoid financial risks plays a key role.
Optimization of talent training path of broadcasting and hosting art based on matrix model
Authors: Zhang Guoyan , Zhao Yajun 
Published: September 2023

Abstract
This study explores how to optimize the path of talent cultivation in broadcasting and hosting arts. In this paper, the accuracy matrix is established according to the knowledge network graph theory. The accuracy matrix is estimated by the great likelihood function and solved using the Glasso algorithm to construct the accuracy matrix, network model. The matrix model is used to analyze the influencing factors of the career development of broadcast hosts and the relevance of the employment of broadcast host graduates to their majors. In the development path of the broadcasting hosts, the top three influencing factors are academic level, resilience, and expression ability, accounting for 20.69%, 18.77%, and 13.41%, respectively. Regarding professional relevance, only 5.88% of broadcast hosts have a relevance of 0.9 or above in their majors, while only 15.35% of broadcast graduates are engaged in industries with a relevance of 0.9 or above in their majors. The cultivation of talents in broadcasting and hosting art should focus on students’ professional skills and personalities, and always look at the world with a developing vision and seek a way out with an innovative attitude.
Analysis of Folklore Sports Tourism Resources Development and Protection Strategies under the Management of Rural Revitalization Strategy in the Background of Big Data
Authors: Ma Yin , Lyu Chenchen 
Published: September 2023

Abstract
Under the background of big data, how to better promote the development and protection of folklore sports tourism resources under the management of rural revitalization strategy is the important research direction of this paper. This paper is based on the data mining method of clustering analysis under big data technology, which is realized by constructing a clustering feature tree, i.e., a CF tree. Next, formal clustering under unguided data mining is introduced. Combined with the folk sports tourism market in Langxia town, the development of folk sports tourism in Langxia town under the management of rural revitalization strategy is analyzed to lay the foundation for further folk sports tourism resources development and protection strategies. The analysis results show that 45.8% of the tourists participating in rural folk sports tourism are male and 54.2% are female; in terms of age composition, tourists aged 18-59 account for 93.5% of the total; in terms of income level, the middle and high-end tourist population accounts for 32%; in terms of education, 65% of the tourists’ cultural level is undergraduate or specialist. Regarding the characteristics of tourists’ travel behavior, the mode of travel is mainly self-driving; the largest proportion of tourists choose to travel with their families, and the per capita consumption expenditure is below 200 yuan. Under the management of the rural revitalization strategy, the main target groups of folklore characteristic sports tourist attractions should be women, middle-aged and middle-aged people, with higher education levels, and higher incomes.
A Modern Communication Path for Traditional Chinese Cultural Design Concepts Based on AdaBoost Model
Authors: Wu Shuangyi , Meng Sheng 
Published: September 2023

Abstract
Clarifying the integration and communication path of Chinese traditional cultural design concepts and modern art design is beneficial to inherit Chinese traditional culture and innovate modern art design. This paper proposes an SD-AdaBoost classification model based on the AdaBoost algorithm, optimizes the algorithm’s design using the sample density method, and conducts a comparative performance analysis for the model. The SD-AdaBoost classification model is used to mine two research items, application path, and dissemination mode, for the fusion of Chinese traditional cultural concepts and modern art design communication, and relevant data analysis is presented. As far as the application carriers are concerned, they embody different values, but all reflect traditional Chinese cultural ideas. Regarding dissemination methods, social media, recommendation engines, and advertising are better ways to disseminate traditional Chinese cultural ideas and modern art design, accounting for 46.96%, 47.92%, and 45.47%, respectively. Based on the AdaBoost model, we can analyze the current dissemination path of Chinese traditional culture design ideas and grasp how we can better integrate and develop Chinese traditional culture and modern art and design ideas.
Application of genetic algorithm in model music composition innovation
Authors: Xu Delei , Xu Haoming 
Published: September 2023

Abstract
The genetic algorithm-assisted musical composition combines evolutionary computational techniques and artistic composition. In this article, the evolution of musical compositions based on traditional genetic algorithm compositions is combined with interactive genetic algorithm techniques, mainly in model music innovation, using a genetic algorithm to generate an initial population with real number encoding of all individuals. The article focuses on studying the fitness function used to automatically evaluate the automatically generated melodies, perform evolutionary operations on the generated melodies, and generate high-quality model music melodies based on the treatment of evolutionary operations. The evaluation time of the generated melodies by the improved model is reduced from 4 minutes to 0.002 seconds, which makes the composer much more efficient in performing model music composition. The evaluation data of 16 pieces before and after the genetic manipulation were also compared, and it was found that the system evaluated the music after the genetic manipulation significantly higher than the music without the genetic manipulation. The different forms of musical compositions created by the composing system developed based on the technical research of computer genetic algorithm-assisted model music innovation are also a useful supplement to composers’ creations and open a new chapter for the field of artificial intelligence.
Design of a literature-based poetry appreciation system based on perceptron model
Authors: Yang Xunhua , Wang Shuhao 
Published: September 2023

Abstract
With the continuous promotion and popularity of the digitalization of big data information, it has gradually penetrated various industries and played an increasingly important role in people’s lives. In this paper, the design of a literary poetry appreciation system is studied using a perceptron model, focusing on the construction of a literary poetry appreciation system through a generalized multi-layer perceptron model structure and the evaluation of metrics on the poetry appreciation system dataset using the GBP training algorithm and genetic training method. In the generalized multilevel perceptron model, the data indicators are classified into four categories: poetry music, poetry sound effects, poetry vocals, and other types, and the accuracy rates of the four categories of indicators are analyzed. The analysis shows that the accuracy rate of all four categories of indicators is between 92% and 97%, indicating the feasibility of the poetry appreciation system. The generalized multilevel perceptron model was used to analyze and evaluate the students from the ability perspective. It can be seen that the students’ data showed only a small and insignificant improvement in the dimensions of literacy, comprehension, and analysis, while the scores in the dimensions of reasoning, strategy, and expression improved significantly. It provides a good research perspective for perceptual machine model-assisted teaching and cultivating students’ quality from a competence perspective, which is of historical importance for developing and transmitting Chinese literature.
Electromagnetic properties and structure of superconducting materials based on AHP-FCE model
Authors: Feng Qing 
Published: September 2023

Abstract
In this paper, the electromagnetic properties and structural characteristics of superconducting materials are studied based on AHP hierarchical analysis and FCE fuzzy comprehensive evaluation method. A sub-analytical model is established using hierarchical analysis and a weight set is established using FCE fuzzy comprehensive evaluation method, respectively, and a comprehensive evaluation model is established to study the average magnetization intensity and vortex of samples with different pore size types and sizes under the same conditions. Then the superconducting current situation at special locations is studied, focusing on summarizing the structural characteristics of most typical superconductors. The number of vortices in the superconducting square tube gradually decreases as the pore size increases. The maximum absolute value of its average magnetization intensity decreases with the increase of the aperture diameter, while its penetrating magnetic field increases subsequently. The superconducting square tube remains in the superconducting state until the magnetic field reaches a certain value. Most of the superconducting material structures are hierarchical crystal structures, such as MgB2, a simple binary and substance of simple hexagonal crystal system, and AlB2 type simple hexagonal structure with theoretical density of 2.605g/cm3 and dot constants of a=30.84nm and c=35.24nm, which belong to the P6/mmm space group. Superconductors not only have zero resistance properties at critical temperature, but also have electromagnetic properties that conventional conductors do not have at all under certain conditions, so it is important to study the properties of superconducting materials.
Information dissemination of intangible cultural heritage based on random forest model
Authors: Huang Zhaoyan 
Published: September 2023

Abstract
To effectively protect and rationally exploit intangible cultural heritage, it is an issue worthy of deep consideration by experts in related fields nowadays. This paper verifies the optimality of the random forest algorithm for informative voting weighting of ICH and compares learning about accuracy in advance with that of the traditional random forest model. The development mode of implementing communication in formless legacy knowledge with historical characteristics existing various communication structures carriers is analyzed, and the computerized communication of ICH in new media vision is explored and studied. Users mainly learn about ICH through new media channels, accounting for 78.3%, while only 11.3% learn about ICH through traditional media channels. In addition, among the new media channels for the dissemination of ICH, short video platforms are the main source of dissemination, accounting for 67.9%; WeChat platforms and Sina Weibo account for 51.5% and 46.9%, respectively. Therefore, only by implanting the content of ICH culture in the existing material new media carriers and forming a cultural experience of virtual and real integration and interaction can we better promote the display and dissemination of ICH informatization.
Remote fault detection and location of power fiber optic cable based on a logistic regression model
Authors: Wang Xin , Liang Gang , Cu Limin , Li Qing , Hu Changyue 
Published: September 2023

Abstract
The current fiber optic communication network presents the characteristics of network scale, complexity, and efficiency, and the use of traditional means for the maintenance of power fiber optic cable has been greatly limited. In this paper, based on the characteristics of simple structure, convenient construction, strong randomness, strong sequence autocorrelation, and good iterative performance of chaos theory in a logistic regression model, a logistic-based remote fault detection and location system for power fiber optic cables is proposed. The fault location test is carried out through with TMS200 series fiber optic cable automatic monitoring management system and GIS method. The fault location error of the logistic-based fault detection method is less than ±3m, that of the GIS method is less than ±10m, and that of the TMS200 series cable automatic monitoring and management system is less than ±15. The comparison shows that this paper’s fault detection and location system is more accurate and can help maintenance personnel have a faster and more comprehensive understanding of the line situation. Realize the rapid repair work of faults, which provides great help to maintain the fiber optic cable lines.