bluuebunny/crossref_metadata_2025_split huggingface dataset:
Legal Challenges and Responses to Artificial Intelligence-Assisted Decision-Making in the International Economic Law System
Authors: Zhang Xiaojuan 
Published: August 2024

Abstract
Legal judgment prediction is becoming a research hotspot in the legal field as an important artificial intelligence-assisted decision-making tool in legal case management, which is able to predict judgment results. In this paper, data from the 2018 China Law Research Cup competition is gathered, and the dataset is preprocessed in the context of international economic law. Then, a multi-task model for legal verdict prediction is proposed, and the training optimization and prediction of the model are designed using CNN, RNN, and LSTM as the semantic coding layer. The model proposed in this paper achieves a significant improvement of 8% and 6% in the accuracy of the model in the prediction of the charging task and the legal sentence task, respectively. In case outcome prediction, the accuracy of the model proposed in this paper is improved by 14.6% on average compared to the feature model-based modeling approach.
Research on Optimization Strategies for Accuracy of English Automatic Language Translation under Machine Learning Framework
Authors: Liu Disha 
Published: August 2024

Abstract
The mainstream machine translation model Transformer is completely based on the self-attention mechanism for translation operation, but there are still some problems, such as not being able to combine the syntactic structure information of the natural language for translation, which leads to problems such as mistranslation and omission. In this paper, for the problem that the position encoding obtained by traditional RNN and attention mechanism machine translation models using a fixed formula does not contain contextual information, the source language sequences containing contextual positional information are obtained by introducing a bidirectional long-short-term memory network and a tree-shaped long-short-term memory network, trained horizontally and vertically, respectively, and the self-attention mechanism is used in Tree-LSTM for the prediction of the contribution of the decision that The relative position information between words is preserved to the maximum extent, and finally, the Bi-Tree-LSTM translation model based on positional encoding optimization is constructed. The performance of the model is tested on four datasets: general, legal, business, film, and television, and the BLEU value of the model translation is analyzed under low data resources and increased sentence length, and then a 4000-sentence long English text is translated to check the wrong sentences and analyze the translation quality. It was found that the BLEU values of this paper’s model are 33.5, 35.2, 31.7, and 34.4 in the four types of text tests, which are the highest among the models. The BLEU of this paper’s model at 5K data volume has been as high as 26.14 points, which is 2.72 points higher than the highest score of the rest of the machine translation models at 50K data volume. The BLEU scores for 8-18 word sentences consistently remain above 45, and the peak performance is superior to that of other models. 4000 sentences of English long text translation, the total number of error sentences is 54, accounting for 1.39% of the whole text, which is lower than that of the Transformer model’s 7.15%, and the performance is in line with the expectation of the optimization design. This paper provides a new idea and useful exploration for optimizing English machine translation accuracy.
Research on Deep Learning Technology to Enhance the Efficiency of Teaching Interaction in College English Classrooms
Authors: Lin Ling , Chen Rulin , Huang Cheng 
Published: August 2024

Abstract
Accompanied by the significant progress of deep learning in various fields, target detection, an important branch in the field of deep learning, is gradually being applied in educational scenarios. In this paper, using the GCT-YOLOv5 algorithm and the Lasso-LARs algorithm, we studied how to improve the interactive efficiency of college English classroom teaching Research first combines the YOLOv5 model with the GCT unit, constructs the GCT-YOLOv5 target detection model for students’ classroom behaviors, and compares it with Fast-RCNN, SSD, YOLOv3, YOLOv4, and YOLOv5 in the public dataset and the student classroom behavior dataset for comparison experiments, respectively. Then, the LASSO-LARS algorithm is used to construct the classroom behavior scoring model, adjust classroom teaching interactions according to the scoring results, and analyze the effect of the adjustment. On the classroom behavior dataset, the GCT-YOLOv5 model is 98.81%, second only to Fast-RCNN, and improves 0.65% relative to the original model YOLOv5. Its inference time compared to YOLOv5 inference time increased by only 0.13h, shorter than other models. The GCT-YOLOv5 model in this paper has better applicability and timeliness in general. Teaching interaction adjustment based on the results of classroom behavior analysis can improve students’ classroom performance as well as their academic performance, i.e., enhance the efficiency of teaching interaction and provide a way of thinking about how to monitor learners’ learning status in a smart classroom.
Data-driven Cultural and Creative Industry Development Trend Forecast
Authors: Fan Xiujuan 
Published: August 2024

Abstract
Under the deep integration of emerging technologies and industries, big data + cultural and creative industry is not only a simple industrial superposition but also the cooperation and complementarity of natural sciences, humanities, and social sciences. In this paper, the development of intelligent cultural and creative industries is predicted by the gray model, then the residuals are corrected, and finally, the residuals are corrected again by the Markov chain to improve the prediction accuracy. The modified GM(1,1) model and the improved Markov chain are used to predict and analyze the development trend of the data-driven cultural and creative industry, and the actual total output is not much different from the predicted output, which indicates that the method constructed in this paper can reasonably predict the results. According to the prediction method designed in this paper, the development of intelligent cultural and creative industries from 2024 to 2033 is predicted, and from 2029 to 2033, the maximum total output is predicted to be 28.02×107 yuan, 32.77×107 yuan, 38.33×107 yuan, 44.83×107 yuan, and 52.43×107 yuan, respectively, and China’s intelligent cultural and creative industry will enter a stage of rapid development.
Cluster optimization and algorithm design for machine vision in industrial robot control systems
Authors: Guo Linyang , Yang Runxian , Tang Mingjun , Ma Xiaoyan , Wang Lixia 
Published: August 2024

Abstract
Machine vision technology improves the ability to detect the environment of industrial robots, which contributes to the improvement of the collaboration efficiency of swarm robots. This paper proposes a roadmap extraction algorithm that utilizes the improved Mean Shift algorithm to extract the roadmap information from images acquired by binocular cameras. Subsequently, the IEO algorithm with K-Means++ is used to optimize the task allocation of the swarm robots. The experiments show that the average error of this paper’s algorithm’s road sign extraction is 0.025m, the ratio of full scene and homing reaches 90.6%, and the results of the scheduling algorithm under the three kinds of task volume are 59.89, 773.08, and 2704.67. The efficiency of scheduling task completion in dispensing experiments is 9.56% higher than that of the comparative algorithms. The experiment proves that the algorithm proposed in this paper has good performance and practical effects on optimizing the industrial robot control system.
Construction and Evaluation of an Intelligent Analysis System for Interactive Teaching in Physical Education Classrooms
Authors: Xiao Donglin , Wang Rong , Wei Yuqin 
Published: August 2024

Abstract
With the continuous deepening and progress of the research in the physical education discipline, the school and the state put the landing point on the integration of information technology and the development of the discipline. In this paper, we design the system framework and perform data module design, on the basis of which we conduct detection research on teacher-student interaction behavior. The article employs an intelligent analysis system based on the YOLOv5 model to establish a model of teacher-student interaction behavior. This model is based on the movement and facial observations of both teachers and students in a physical education classroom. The intelligent analysis system, based on the YOLOv5 model and independent t-test, observed the behavior of teacher-student interaction based on the ratio of teacher-student verbal interaction and analyzed it accordingly. In teacher- and student-type speech, the ratio of teacher-direct influence speech is 45.62%, which is higher than indirect influence speech, while the ratio of student-passive speech is 5.54%, which is higher than student-active speech. Finally, the interactive intelligent system was evaluated for teaching and learning to understand students’ feedback on the intelligent analyzing system based on the YOLOv5 model from two parts: physical education test scores and students’ satisfaction surveys. In the physical education classroom, the teacher primarily uses their own words and interacts with the students less frequently. However, the system has the potential to significantly enhance students’ performance in Physical Education, with the majority of students reporting improvements in their PE skills and other areas. In terms of participation in the physical education classroom, 32 students thought that there was a “great improvement,” and 22 students thought that there was an “improvement,” for a total percentage of 98.18%, which is a significant effect.
An Analysis of the Application of Motion Capture Technology in the Creation of Contemporary Dance with the Integration of Fibonacci Sequences
Authors: Xu Weisheng , Li Yanyan 
Published: August 2024

Abstract
This paper combines the existing development of Fibonacci sequence aesthetics in art creation for the aesthetic planning of the Fibonacci sequence on music rhythm, proposes to design a new modern dance creation method by combining the diversified rotation methods of the Fibonacci sequence, and builds a process framework for generating Fibonacci sequence dance score based on motion capture. The wavelet transform is employed to determine the start of the dance movement, and the gradient descent algorithm is utilized to analyze the time coordinates and refine the dance movement. Recognize the segmented elemental action sequences using the DTW method, propose three basic actions from the dataset, and test the recognition rate of the DTW algorithm for each action. Generate a Fibonacci sequence dance score by combining human gestures. Combined with the research scoring, analyze the artistic talent of the Fibonacci sequence-generated dance. According to the data results, it can be seen that the action feature vector of the two-handed waving image is P=[0.8968, 0.9925, 0.9124, 1.0243, 1.0198, 1.2438, 1.4596, 1.3698, 1.5868, 1.6596, 1.5029] with 98.67% of the two-handed waving recognition rate (with a standard deviation of 0.25). The evaluation of Fibonacci sequence generation dance by research users tends to be moderate.
A Study of Using VRTECH Virtual Reality Technology in Physical Education Teaching to Improve Students’ Learning Interests
Authors: Yang Qingbin 
Published: August 2024

Abstract
Based on the combination of literature related to virtual reality technology, physical education learning interest, and the impact of virtual reality technology on physical education learning interest, a blended teaching mode design with VRTECH technology as the dominant technology and virtual reality technology as the supplementary technology is carried out. Then, the accuracy of the influence of the blended physical education teaching mode designed based on VRTECH technology on students’ interest in physical education learning is examined, and table tennis class is taken as an example for instance analysis. The results show that the COSELM algorithm has the highest accuracy, and COSELM in able to adapt quickly in an unsteady environment. The T-test results of physical education learning interest in each dimension before and after the teaching experiment showed that the physical education learning interest in the experimental group would be significantly increased under the assisted teaching of VRTECH technology (T=2.15, P=0.032). The T-test results of the five dimensions of students in the -experimental group under the blended teaching mode were -4.25, −3.07, −2.11, −4.45, and −4.99, and the significance of the dimensions was <0.01 except for independent and inquiry learning. The blended teaching mode based on the VRTECH technology can give the students a novel teaching environment, and it can effectively enhance the student’s interest in physical education learning, thus improving the new perspective of students’ interest in physical education learning and providing certain theoretical basis and practical experience for the integration of virtual reality technology into physical education teaching.
Research on Reconstruction and Cultural Inheritance of Ancient Literature under Digital Expression
Authors: Luo Jing 
Published: August 2024

Abstract
It is one of the ascendant research topics to study ancient literature by means of digital humanities technology, to construct the historical scene of literature, to restore the writer’s creative prosperity and to excavate the era value of ancient books. The digital reconstruction of ancient books has greatly impacted the preservation and inheritance of ancient literary resources. As an important carrier of ancient literature, ancient books have many texts that need to be identified, and it isn’t easy to disseminate in the information age due to their text form. This paper uses computer image processing methods to handle broken ancient text and obtain a more clearly visible digital form. At the same time, a method for document correction based on neural networks is proposed for certain texts that have occlusions and distortions. In several sets of experiments, it is shown that the document correction F1 value of the model in this paper is significantly ahead of the mainstream model with 0.903. Applying the digital platform for ancient books to the member groups of the Association for the Study of Ancient Literature, 81.5% of the respondents strongly agree with Q7, “I think the digital platform for ancient books is very conducive to cultural inheritance”, and the total of disagreeing and strongly disagreeing only accounts for 5%. It can be assumed that the ancient books digital platform designed in this paper and its document correction and recognition function have been widely recognized, and the ancient books digital platform can be applied to the digitization of ancient literature documents, daily reading, and storage of texts, text recognition, etc., contributing to the automation of ancient literature research, book storage and dissemination, and electronic reading. Based on this, the future database of ancient books should not only be a query and retrieval tool but also help scholars to make multidimensional statistics, comparisons, and analyses so as to generate new knowledge and ideas.
The Dilemma and Countermeasures of Foreign Language Teachers’ Career Development in Universities under the Era of Big Data
Authors: Tan Xiaochuang 
Published: August 2024

Abstract
This paper discusses the dilemma of foreign language teachers’ career development and the new ecology of English teaching under the environment of education informatization in the era of big data. Through the questionnaire survey of X colleges and universities, we screen the indicators of the influencing factors of teachers’ informatization teaching ability, verify the influencing mechanism of teachers’ informatization teaching ability through the results analyzed by multilayer linear modeling method, and put forward the corresponding countermeasures for the improvement of teaching ability and career development on the basis of this. Using structural equation modeling to test and analyze the influencing factors of the informatization teaching effect, applying informatization teaching to practice, it is analyzed that teachers’ informatization teaching ability is significantly influenced by the number of multimedia classrooms, campus network bandwidth, digital resources, and other factors (P<0.01). Therefore, specific reference suggestions were made for information literacy training and enriching the school’s digital environment. The degree of informatization has a significant effect on teaching effectiveness (P<0.05) and can significantly improve students’ English listening (P=0.004) and speaking (P=0.007) scores.
Decision-making Model Construction of Enterprise Marketing Outlet Resource Allocation by Integrating Fuzzy Logic
Authors: Ma Xiaoyan , Xue Jian 
Published: August 2024

Abstract
In the tide of modern business competition, how to efficiently utilize limited resources to obtain the best economic benefits is the core issue that determines the survival and development of enterprises. This paper integrates fuzzy logic theory with resource allocation and proposes the direction of channel integration of marketing resources. The concept of affiliation degree is introduced in fuzzy logic to describe the relationship between things and form the fuzzy relationship in the fuzzy set, which indicates the degree of association between elements in the fuzzy set. The linguistic rule representation method is used to implement the control strategy for decision-making in enterprise marketing outlets. Improve the construction of the optimization model for enterprise outlets’ marketing resources by establishing a two-layer planning model. The research enterprise’s outlet marketing resources allocation situation is examined by combining actual and simulation, based on the principle of outlet marketing resources allocation. From the point of view of outlet affiliation, the central region with a “suitable” resource allocation level includes Hunan Province, with an affiliation degree of 0.4655, and the affiliation degrees of some western regions and northeastern regions range from 0.3896 to 0.5663. The fuzzy logic model generates six resource allocation strategies, and they are tested one by one using simulation experiments to determine the best allocation strategy.
Analysis of the behavior of structures under the effect of progressive rupture of a cavity
Authors: Boualleg Bilel , Bouacha Nadjet 
Published: August 2024

Abstract
The ground movements related to the presence of old underground cavities are often damaging to structures and infrastructures. Considering these ground movements in calculations will prevent considerable human loss and material damage. Many areas, both in Algeria and in abroad, are prone to instability caused by ground rupture and the phenomenon of sinkhole progression. The objectives of this work are first to numerically simulate the process of cavity collapse and second to analyze the impact of cavity properties on structure stability. A finite element model was established to analyze the influence of several cavity parameters (dimensions, volume, and spacing). Validation of the model relied on comparing numerical results with experimental data from scientific research, as well as those from analytical approaches. Adequate correlation was achieved. The study allowed deriving mathematical equations relating to several parameters, including cavity dimensions and position in the soil, soil characteristics, and footing width. These results will be considered to reduce the risk of surface structure instability.
The effect of dexmedetomidine on hemodynamic parameters during neuro-oncological surgery
Authors: Rumiantceva M. V. , Tsentsiper L. M. , Kondratyev A. N. 
Published: August 2024

Introduction. The use of alpha 2-adrenoagonist dexmedetomidine, as an adjuvant for anesthesiological aid in neuro-oncological surgery, allows to create and maintain stable hemodynamics throughout the entire surgical intervention. The observed hemodynamic effects depend on the dose of dexmedetomidine.The objective was to evaluate the effect of the highly selective alpha2-adrenoagonist dexmedetomidine on hemodynamic parameters during anesthesia in surgeries for the removal of a brain tumor.Materials and methods. The study included 92 patients operated on for a brain tumor. The division into groups was carried out depending on the dosage of dexmedetomidine and the position on the operating table. In group 1 (n = 58), dexmedetomidine was used, which was administered during the first 30–40 minutes at a rate of 1.4 ± 0.4 mkg/kg/h, and after introductory anesthesia at a maintenance dosage of 0.4 ± 0.2 mkg/kg/h. In group 2 (n = 34), dexmedetomidine was administered during the first 30–40 minutes at a rate of 0.7 ± 0.1 mkg/kg/h, and after induction of anesthesia with a maintenance dosage of 0.2 ± 0.1 mkg/kg/h. Subgroup A – patients operated in a sitting position, subgroup B – patients operated in a lying position.Results. More stable hemodynamics and mild parasympathicotonia were noted at all stages of surgery when dexmedetomidine was used at an initial dose of 0.7 ± 0.1 mkg/kg/h and a maintenance dose of 0.2 ± 0.1 mkg/kg/h. Conclusion. The study revealed that the optimal dosage of dexmedtomidine to achieve a central sympathomodulating effect is 0.7 ± 0.1 mkg/kg/h in the initial stages and 0.2 ± 0.1 mkg/kg/h in the maintenance.
The effect of the steroid profile on the severity and the outcome of treatment of newborns with congenital malformations
Authors: Ivanov D. O. , Fomin S. A. , Pshenisnov K. V. , Aleksandrovich Yu. S. , Ditkovskaya L. V. , Fomin A. M. 
Published: August 2024

Introduction. Adrenal insufficiency is often found in critical conditions of the neonatal period and has nonspecific symptoms, which greatly complicates its diagnosis. The objective was to study the hormonal status of newborns with congenital malformations (CM according to the outcome of treatment in the intensive care unit.Materials and methods. Study design – observational, dynamic retrospective comparative study. 60 newborns were examined: 26 healthy and 34 with CM. Depending on the severity of the condition, children with CM were divided into two groups. Group I included patients in an extremely severe condition, and in group II – in a stable condition. We studied the concentration of 17-hydroxypregnenolone, aldosterone, dehydroepiandrosterone, cortisol, cortisone, corticosterone, progesterone, testosterone, estradiol and 17-hydroxyprogesterone upon admission to the ICU, immediately after surgery and on the first day after it.Results. In all children with CM, the concentration of 17-hydroxypregnenolone, 17-hydroxyprogesterone, progesterone and dehydroepiandrosterone before surgery was significantly lower compared to healthy newborns, while in group I, it was minimal. After surgery, the concentration of cortisol precursors remained low, but its level increased significantly, especially in children of group I. A risk factor for lethal outcome in newborns with CM needing treatment in the ICU is the 17-OH-pregnenolone/dehydroepiandrosterone ratio, both at the time of admission to the ICU (AUC = 0.821; sensitivity = 68.97; specificity = 100; p = 0.0002; cut-off < 1,52); and on the first day after surgery (AUC = 0.807; sensitivity = 79.3; specificity = 80; р = 0.025; cut-off = 0).Conclusion. The increase in the concentration of cortisol against the background of low level of hormone precursors and the reduction of the 17-OH-pregnenolone/dehydroepiandrosterone ratio in the first day after surgery is a marker of an adverse course of the postoperative period and outcome.