bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-driven decision making and production optimization of higher mathematics in industrial science
Authors: Wen Xiaonan , Dong Liwei 
Published: October 2024

Abstract
Since complex industrial chemical systems contain huge process data, these process data will present the operation characteristics and laws, and the use of appropriate methods to analyze the data is one of the feasible directions for fault diagnosis. In this paper, we analyze data from industrial chemical production processes using the support vector machine algorithm as our decision-making approach. Considering the large amount of data generated in industrial chemical systems and its nonlinear characteristics, this study applies Gaussian and non-Gaussian space to obtain the high-dimensional characteristics of the data before putting it into an SVM model. The rotary drying kiln production simulation experimental process is optimized by applying the fault diagnosis method, with the thermal efficiency shown by several tests being close to each other. The method proposed in this paper has obvious advantages in terms of search efficiency for a feasible global optimal solution.
Cultivation Methods and Effectiveness Assessment in Teaching University Language Courses in the Context of the Artificial Intelligence Era
Authors: He Xi , Huang Mingjie , Zou Hongju , Deng Taiping 
Published: October 2024

Abstract
Aiming at the blind spots of university language course learning in the age of artificial intelligence, this paper builds a technical framework of intelligent cultivation method with a university language intelligent question bank, classroom student behavior recognition model, and intelligent teaching evaluation model as the main modules. The knowledge point mastery model is constructed, the knowledge point test question matrix is composed, and the learner similarity calculation is carried out to design the intelligent question bank. Introduce the YOLO algorithm to solve the problem of occlusion and small target detection of students’ behavioral actions, and propose a student behavior recognition model based on the improved YOLOv5. Intelligent selection of language teaching evaluation indexes, adoption of the recursive goal evaluation method, and establishment of the teaching evaluation model. In the evaluation of the cultivation method, HZ University is used as the research site, and experimental and control classes are set up to conduct comparative experiments. The language score of the experimental class is significantly higher than that of the control class by 4.83 points (P<0.05), and the mean values of all dimensions of learning problem-solving ability are higher than those of the control class, which also show significant differences (P<0.05). The experimental class also outperformed the control class in a number of aspects, including course learning level, cooperative communication, pre-study, and classroom learning status.
Machine Learning-Enhanced ORB Matching Using EfficientPS for Error Reduction
Authors: Li Zhanrong , Su Haosheng , Jiang Chao , Han Jiajie 
Published: October 2024

Abstract
The study comes up with a new way to improve the accuracy of image matching in binocular vision systems, especially those that are mounted on vehicles. It combines machine learning with the ORB (Oriented FAST and Rotated BRIEF) image-matching algorithms. Standard ORB matching frequently encounters mismatches in complex and repetitive environments. To minimize false positives in matches, our strategy utilizes the EfficientPS (Efficient Panoptic Segmentations) algorithm, a panoramic segmentation technique that uses machine learning in conjunction with ORB. The procedure begins with the EfficientPS approach, which delivers fine-grained and efficient segmentation of images, assigning semantic category labels and unique identifiers to each pixel. The ORB feature point matching process is refined using semantic data to filter out mismatches between foreground objects and the background effectively. This machine-learning-augmented method significantly decreases the frequency of erroneous matches in intricate settings. Empirical findings from the KITTI dataset demonstrate that in non-targeted environments, the accuracy of our proposed method (0.978) is marginally less than that of LoFTR (0.983). Still, it surpasses other methods when utilizing 50 ORB parameters. In more intricate situations, such as multi-target scenarios with an increased number of ORB parameters (200), our method maintains a high level of accuracy (0.883), outperforming the conventional ORB (0.732) and rivaling the performance of DL-BDLMR (0.790) and ORB-MFD-FPMC (0.835). Our method’s processing time is competitive and slightly higher than the standard ORB, but it improves accuracy. In scenarios without targets and with single targets, our method’s processing time (0.195 seconds and 0.211 seconds, respectively) is greater than that of ORB and ORB-MFD-FPMC. Yet, it is significantly lower than that of LoFTR. In multi-target scenarios, our method’s processing time (0.226 seconds) is considerably better than LoFTR’s (0.445 seconds), effectively balancing processing speed and accuracy. We highlight the efficacy of incorporating machine learning to enhance the robustness of image-matching algorithms in dynamic and complex environments.
Research on the individualized teaching of Chinese in higher vocational colleges based on cloud computing
Authors: Shang Yuanyuan 
Published: October 2024

Abstract
Whether it is the development trend of education in the whole world or the quality education that is being comprehensively promoted in China, more and more attention is paid to the students’ leading position in the entire education process, respect for students’ differences, and the importance of students’ personality development. This paper uses a distributed cloud computing platform to construct a clustering algorithm based on K-means-CE to categorize language learner characteristics based on language learner characteristics combined with the Felder-Silverman style model. Then, the ItemCF algorithm of collaborative filtering recommendation is studied, and the combined similarity calculation method is proposed to deploy the recommendation algorithm to the Hadoop cloud computing platform to realize parallelized calculation and improve the efficiency of the personalization algorithm. The differences in learner types in the information input, information processing, learning attitude, and learning behavior input dimensions of the learning style analysis model constructed in the study will be expressed in the performance (P<0.05). The total cognitive load of the cloud computing-based personalized teaching group of higher vocational Language (M=65.26, SD=8.58) is lower than that of the conventional teaching group (M=66.23, SD=10.09). The personalized teaching method based on cloud computing is better than the conventional teaching in refining the learners’ knowledge structure. The learners’ achievement rate of the target learning level is higher, and the effect of the personalized teaching is more significant.
Artificial Intelligence Enabled Teaching Optimization for General Studies Elective Courses
Authors: Li Bo 
Published: October 2024

Abstract
Network general knowledge course is a meaningful way to cultivate comprehensive quality talents in Chinese higher education based on the modern information technology environment, and the optimization of network general knowledge course teaching is directly related to whether the network general knowledge course can be effectively implemented, which affects the overall effect of talent cultivation. In this paper, starting from the purpose and goal of humanities general education courses, the optimization direction of teaching general education courses using artificial intelligence is clarified. Skeletal sequence feature extraction is used to obtain students’ general education classroom behavior data, and a stackable multimodal attention module consisting of a self-attention layer and a multimodal co-attention layer is designed for feature fusion and action prediction based on multimodal characteristics. After data integration and processing, the HGCL-LG network model is proposed to complete the diagnosis of students’ general knowledge cognition. The trend of interest assessment and multimodal model calculation value is consistent. In contrast, the student’s interest attention in general knowledge classroom above 0.33 accounts for more than 40% of the overall information, and the interest attention is good. Students’ scores on the objective questions of general knowledge are good; the average score range is between 1.5 and 2 points, the degree of dispersion is small, and the mastery of basic knowledge of general knowledge is good.
Construction of a micro-application for full lifecycle data management based on a new-generation electricity consumption information collection system
Authors: Tang Zhu , Yang Tianyu , Liu Heng , Xiao Yuhang , Xu Nan 
Published: October 2024

Abstract
The power consumption information collection system encompasses multiple complex technical relationships, along the data flow chain, numerous data conversion links and processing activities, as well as a multitude of threat exposure surfaces, triggering sources, and uncontrollable factors. This paper proposes a complete lifecycle micro-application management system for the power consumption information collection system, aligning with the objectives and construction programs of the project. The life cycle link of collecting, storing, and sending data about electricity use measures the uncertainty of each piece of information in the binary grid protocol by using the Gini index and information entropy. The characteristics of information data are solved using Bayes’ theorem. By analyzing the users’ behavior patterns, we can prevent them from stealing access rights and other behaviors and dispose of security risks in time. In conjunction with case studies, we conduct simulation experiments to evaluate the power consumption information collection system’s security, complexity, and privacy. In the model without privacy protection, the accuracy rate of member inference attacks is about 68%. This paper’s designed system is more resilient to member inference attacks, with an accuracy rate of less than 50%, demonstrating a superior level of privacy protection for electricity consumption data. The system in this paper uses less time than the other three schemes when the number of users exceeds 2200, peaking at about 700 ms when the number of users reaches 4000.
Research on Civil Engineering Construction Safety Management Methods by Introducing Bayesian Network Modeling
Authors: Tan Jianling , Li Yongli 
Published: October 2024

Abstract
This paper aims to improve the effectiveness of civil engineering construction safety management (CSM)and ensure the safety of civil engineering construction. Firstly, an examination of contemporary CSM methods in the field of civil engineering in China is being undertaken. An innovative Civil Engineering Construction Safety Early Warning System (EWS) based on Bayesian Network (BN) principles addresses the unanticipated emergence of safety incidents during construction endeavors. Finally, a comprehensive evaluation model for civil engineering construction safety has been formulated, using the Bayesian framework to assess the efficacy of the previously mentioned Civil Engineering Construction Safety EWS based on BN. The incidence of safety-related accidents in the Chinese construction industry has consistently declined over the past decade, as the empirical findings indicate. Moreover, compared to international counterparts, the frequency of such incidents in China remains significantly below 600. Additionally, the Civil Engineering Construction Safety EWS created using BN consistently achieves a functional realization score exceeding 75 points, with the highest possible score reaching an impressive 93 points. Notably, through the utilization of the Civil Engineering Construction Safety evaluation model, which integrates Bayesian methodologies, it is discerned that machine tool quality and the placement of construction organization carry a substantial weight, each exceeding 0.3. Remarkably, the indicator of illegal operations is identified as having the highest risk level.
Construction of Electricity Load Forecasting Model Based on Electricity Data Analysis
Authors: He Yue , Zhang Zhi , Chang Yongjuan , Lu Yanyan , Yin Xiaoyu 
Published: October 2024

Abstract
This paper builds a time series prediction model of recurrent neural networks based on time series electricity load forecasting. In this paper, the household electricity consumption record data of some residents in urban area S is taken as the research object, and the laws and characteristics of users’ electricity consumption behavior are analyzed in depth based on the real residential electricity consumption data. External factors such as temperature conditions, holidays, etc. The arithmetic cases are also analyzed using real load data sets. In the short-term continuous electricity data analysis, the smaller the time interval is, the closer its corresponding electricity consumption ratio is to 1. There is a negative correlation between long-term continuous electricity consumption. When the temperature is 30~35oC versus -5~0oC, electricity consumption rises significantly. Comparing and analyzing the time series decomposition-RNN with several models, the time series decomposition-RNN model has the highest fit at 10:00-12:00 and 12:00-14:00, and the result verifies the validity of the model proposed in this paper.
Application and Evaluation of Artificial Intelligence Technology in Collegiate Soccer Sports
Authors: Tang Jingwei 
Published: October 2024

Abstract
This paper discusses the development of school soccer with the help of artificial intelligence. Propose a machine learning-based action feature extraction method for students in school soccer. Obtain action images of students playing soccer and identify the actions of students in school soccer based on the threshold recognition algorithm. The Harris 3D operator is used to establish the potential function of the action sequence, and based on the potential function of the action sequence, the AdaBoost algorithm is used to filter the action feature data of the students in soccer, which is used as the training sample to realize the action feature extraction of the students in soccer. To extract the effective feature values and improve the recognition accuracy of the algorithm, a soccer action recognition model based on SVM was constructed. The feasibility of the DTW scoring method in the field of soccer action recognition has been verified. The SVM algorithm model has the strongest denoising ability, and its feature action recognition rate is maintained between 80% and 90% and the recognition rate of features with large action amplitude is higher, which is suitable for the recognition of soccer actions in this study.
Research on 3D urban landscape digital modeling method based on image processing technology
Authors: Tian Tian 
Published: October 2024

Abstract
The development of digital modeling technology opens up endless possibilities for three-dimensional modeling of urban landscapes. Based on computer vision methods such as stereovision and optical flow, this paper investigates how to carry out three-dimensional digital modeling of urban landscapes. We utilize the method of combining UAV and ground shooting to obtain image data of urban landscapes, then process the collected data with image smoothing, enhancement, edge detection, and segmentation. The Moravec operator and Hough variation are used to extract point and line features from the cityscape image, respectively. The beam leveling algorithm is used to reconstruct the cityscape in three dimensions, and it is optimized by combining the Kalman filter correction. This paper uses the landscape of Li City, Quanzhou City, as the experimental object for digital modeling. A comparative analysis of the three groups of experiments, A, B, and C, shows that the digital modeling technology of urban landscapes, based on image processing technology, achieves the highest degree of restoration of the original image. The errors for landscape elevation and non-elevation are 0.69% and 0.37%, respectively, which are the smallest errors among the three groups of experiments.
The Use of Intelligent Computing in Teaching Model Innovation in Higher Education
Authors: Zheng Min 
Published: October 2024

Abstract
In the field of education and teaching, adaptive learning is generally considered to be superior to non-adaptive learning, and intelligent computing is regarded as a valuable tool in terms of building adaptive learning platforms. In this paper, we intelligently calculate students’ neighbor totals in terms of knowledge point competencies and learning resources through a collaborative filtering recommendation algorithm to get the similarity between students in higher education teaching and generate a learning resource recommendation dataset. Subsequently, an adaptive learning system is constructed based on this collaborative filtering algorithm to assist teaching and promote innovation in higher education teaching modes. The average MAE value (1.90) of the collaborative filtering recommendation algorithm in this paper is lower than that of other algorithms, indicating that the improved collaborative filtering recommendation algorithm in this paper has better prediction accuracy. The experimental study of teaching practice shows that there is a significant difference in the level of students’ posttest learning ability in different teaching modes at the end of the experiment (P=0.002), and the posttest learning anxiety score of students in the group applying innovative teaching modes (M=10.48) is lower than that of the control group. This paper’s adaptive system serves as a guide for the comprehensive, innovative reform of higher education teaching in the future, as well as provides research value and direction for intelligent computing to enter education teaching.
Content Innovation and Strategy Dynamic Adjustment of College Students’ Civic and Political Education Based on the Empowerment of Intelligent Algorithm
Authors: Lu Debing 
Published: October 2024

Abstract
Students’ ideology is mainly developed in the college period, so the ideological and political education work in colleges and universities should be strengthened in order to help students improve their ideological and moral qualities. In this paper, after the introduction of data mining, through the comparison of two algorithms of COBWEB clustering and K-means clustering based on the model method, we chose to use the K-means clustering method to carry out the learning characteristics mining clustering for the students of logistics management majoring in the school of management of a university in Zhengzhou City, Henan Province, and 220 students were selected as the research object. The students were divided into four categories for integration from six dimensions: learning foundation, learning interest, independent learning ability, collaborative learning ability, beginning of Civics learning, and advanced Civics learning. and. then, the purpose of students’ learning Civics education was studied in terms of learning motivation. In all dimensions, students in the first category did well, and 135 students in each category aimed to improve their academic qualifications while learning Civics courses. The innovative path for the content of Civic and Political Education is proposed based on the learning characteristics of the students. 180 students were selected as examples of logistics management class students, divided into experimental and comparison groups, and explored in terms of cultural self-confidence level, academic level, and motivation for learning using the difference analysis method. It was found that after applying the proposed content to the Civic and Political Education classroom, the experimental group and the comparison group showed significant improvement in all dimensions except for learning motivation. It indicates that the content innovation strategy of Civic and Political Education proposed in this paper has a better impact on the teaching effect.
Research on Visual Design and Realization Path of Educational Value of Nature Study Activities in Zhejiang Province under the Perspective of Culture and Tourism Integration
Authors: Guo Wei , Gyu Oh Chi 
Published: October 2024

Abstract
Today, in the information age, digital technological innovation is changing rapidly, and the in-depth development of digitization, networking, and intelligence has revitalized the living heritage of natural heritage and provided a new direction for the development of the field of natural heritage study tours. In this paper, a Monte Carlo rendering method based on deep learning is used to improve the image output quality of the virtual model by analyzing the characteristics of the output image of the natural heritage model. The digital display of nature study activities features Zhejiang Nature Museum as the design object. Its educational value can be demonstrated through virtual characters, storylines, interactive games, and other methods. Through the survey statistics, it can be concluded that the visual design program for nature studies has a better practical application effect. Based on the SOR theory, a model for analyzing research experience satisfaction is established based on the perception transfer of the research place and the research activity scenario. In this research activity, the regression coefficients of the quality of the virtual scenario and the perception of the educational value are the highest, which are 0.238 and 0.226, respectively, indicating that the two have the greatest role in the satisfaction of the students.
The Evolution and Impact of Information Exchange Standards in International Tax Law Based on an Information Theory Perspective
Authors: Huang Yuxiang 
Published: October 2024

Abstract
With the development of economic globalization, international tax problems such as international double taxation and international tax evasion continue to emerge, and global tax governance is receiving increasing attention and focus from the international community. This paper, based on the standard framework of information exchange in international tax law, provides a preliminary analysis of the effectiveness and function of international tax law. The linear transformation of the data input sequence leads to the acquisition of corresponding vector values, and the international tax law topic faceted tree is constructed based on the knowledge extraction results. Using the BERT model, the semantic information text matrix for international tax law is obtained. Extract and mark the relevant elements of the international tax law information exchange standard, combined with element feature marking. The extraction results are analyzed in terms of evolution and outcome: from 2008 to 2019, China signed the Convention and tax information exchange agreements with 33 and 10 countries, respectively, accounting for no more than 50% of the total. Between 0.0178 and 0.0187 is the size of the coefficient of the policy effect of information exchange agreements on cross-border tax avoidance of businesses. This means that it can stop businesses from avoiding cross-border taxes after the agreements go into effect, but it has a pretty small effect.