bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study on the Innovation of Traditional Drama Performance Forms by Intelligent Media Based on AI-Assisted Analysis Framework
Authors: Cai Yingdi , Kim Hae Yoon , Dong Jinpu 
Published: June 2024

Abstract
This paper investigates the impact of contemporary entertainment preferences among youth, who increasingly favor novel forms over traditional theater arts, driven by the rapid pace of modern life. Specifically, it explores how action recognition technology can transform theatrical performance forms through the lens of intelligent media. Utilizing the inertial sensor MPU9250, the study mathematically captures the action characteristics of theatrical performances using quaternion algebra. This process involves preprocessing steps that set the stage for coordinate system transformations. Furthermore, the research constructs a database of theatrical morphology by classifying and storing morphological features derived from a human 3D skeleton dataset. The effectiveness of motion recognition technology in enhancing theatrical performances is then empirically tested and analyzed. The results reveal that this technology successfully identifies morphological movements in traditional theater with accuracies of 74.42%, 73.77%, and 64.45%, respectively. An additional pedagogical assessment highlights the significant role of motion recognition technology in educational contexts. In particular, the study notes a substantial difference in the ‘imagination’ dimension scores between control and experimental groups. The control group scored an average of 6.67, whereas the experimental group achieved 8.07, indicating enhanced engagement and cognitive stimulation through the application of motion recognition. These findings underscore the potential of such technology to offer theoretical and practical insights for the revitalization and progressive development of traditional theatrical performance forms.
Optimization of Communication Mode for Ethnic Sports and Cultural Development in the Age of Artificial Intelligence
Authors: Chen Yuqing , Yu Jing , Chang Lei 
Published: June 2024

Abstract
With the development of artificial intelligence technology, the field of cultural communication is also gradually becoming widely used in artificial intelligence-related technologies. Aiming at the phenomenon of lagging development of national sports culture, this paper proposes a technical framework for optimizing the communication mode of national sports culture development with data collection and accurate information-pushing technology as the core. In order to collect data, the social network is transformed into a network graph model, and the Markov chain Monte Carlo method is used to ensure random and probabilistic sampling. The accuracy of prediction results is improved by establishing user profiles, using support vector machines, and combining the real-time computing framework to develop an accurate information delivery model. In the analysis of the development level of China’s wushu culture communication, it can be learned that the total reading volume of wushu culture-related topics on the microblogging platform totaled about 4.81 billion, the total discussion volume was 431,000, and the video playback volume of the related issues on the Jieyin platform reached 534 million times. The average value of the overall evaluation of the soft power of wushu culture is 3.83, which is close to the “good” evaluation level. Combined with intelligent technologies such as data collection and precise information pushing, the communication of Wushu culture has achieved better results.
Research on visualization monitoring technology of vulnerable high-voltage electrical equipment in substation based on BP artificial neural network
Authors: Cheng Lan , Hu Xianmin 
Published: June 2024

Abstract
This study develops a visualization monitoring system for substation equipment operational status, utilizing mobile monitoring technologies. The system architecture integrates a core functional module aligned with comprehensive system requirements, enhanced by a BP neural network to optimize the server’s data mining capabilities. The research focuses on the analysis of typical faults in crucial substation electrical equipment, applying a Fourier algorithm for preprocessing the fault data. Employing the diagnostic principles of the BP neural network, the study designs a thermal fault diagnosis process for the substation apparatus. Experimental scenarios were established to evaluate the BP neural network’s performance by comparing three linear regression sample values. The practical application of the BP neural network model was assessed through integration with substation field data. Cross-validation of the field data indicates that the fault location algorithm accurately identifies 11 types of faults from 85 alarm signals in the secondary condition monitoring of substations, achieving a reliability of 98% or higher, which underscores its high applicability and operational feasibility.
An investigation on the application of Ridge regression model in the optimization of virtual practice teaching innovation path of Civics and Politics courses in colleges and universities
Authors: He Guizhen , Zhang Jianing 
Published: June 2024

Abstract
Civic and political affairs course is a key course for colleges and universities to implement the fundamental task of cultivating people with moral character, and the rapid development of network technology and the information age has had a direct impact on the Civic and political affairs course in colleges and universities. This paper puts forward the optimization path of virtual practice teaching for Civic and Political Science courses in colleges and universities from three aspects: establishing a virtual simulation practice teaching website for Civic and Political Science courses in colleges and universities, constructing a virtual simulation practice teaching information resource base for Civic and Political Science courses and optimizing the content system of virtual simulation practice teaching for Civic and Political Science courses. In order to verify the effect of virtual practice teaching, this paper proposes a new determination method as well as a Ridge regression model for the selection of Ridge parameter K in the Ridge trace method by analyzing the existence of Ridge parameter K and the excellence of Ridge estimation. The Ridge regression model was used to examine the impact of virtual teaching on college Civics and Politics classes and found that the standardized regression coefficient beta for the virtual teaching environment is 0.113, showing significant performance. For the virtual practice teaching of Civic and Political Science classes in colleges and universities, the better the virtual teaching environment is, the better the classroom learning effect.
Research on the Application of Virtual Reality Technology in Motor Skills Training
Authors: Tang Yinchun 
Published: June 2024

Abstract
The emerging efficacy of Virtual Reality (VR) technologies in sports training is explored in this study. We developed a VR-based motor skill training model utilizing a markerless optical motion capture system. This system employs a domain adaptive detection method in the 3D human posture realm, enabling precise identification of motor action postural feature points in students. Subsequently, the model incorporates a human dynamic motion tracking function that minimizes computational errors in motion capture data, facilitating the implementation of multi-feature fusion-based virtual digital human body motion capture technology. An empirical analysis was conducted using H College University as the case study to evaluate the effectiveness of virtual motor skill training. The findings demonstrate that the average accuracy of the proposed method for recognizing body gestures achieved a significant level of 0.834, enabling more accurate identification of students’ motor action characteristics. Moreover, the post-test results of the VR class showed marked improvements, ranging from 0.56 to 15.56 points over traditional lecture classes and 0.43 to 10.33 points over video teaching classes. These substantial variances not only underline the superiority of the VR-based training model in enhancing academic interest and physical fitness but also in advancing motor skills among learners. The study accentuates VR’s potential to augment motor skills and foresees its expansive application in future motor training domains.
Research on the optimization of interior design of architectural space considering user perception
Authors: Wang Long , Wang Yiming 
Published: June 2024

Abstract
This paper integrates the concept of user perception into the presentation of architectural interior spaces. It posits that by employing virtual presentation modes, interior design products are fully demonstrated to users, enhancing their perception of the available information and thereby facilitating the optimization of interior design. The research systematically extracts the influencing factors of user perception by conducting a thorough analysis and synthesis of the literature pertinent to architectural interiors. Additionally, this study combines findings from a questionnaire survey with methods of correlation and regression analysis to further substantiate the identified factors influencing user perception. On this basis, combined with the establishment of a refined model based on real data sources and an indoor modeling method based on tree hierarchy, an indoor 3D virtual scene is constructed with the help of Unity3D software to realize the optimization of architectural space interior design. Overall style of architectural interior space = −2.304 + 0.381*furniture + 0.274*soft furnishing color + 0.205*hard furnishing material + 0.165*lamps + 0.183*hard furnishing color + 0.106*accessories + 0.124*soft furnishing material + 0.115*fabric. Senior and graduate users of higher education had the same attitude towards “personalization” of interior design (both scored 4.33, close to “approved”), while faculty members had the lowest rating (2.6). This study can serve as a guide for the presentation of architectural interiors.
Research on Multimodal English Teaching Methods and Practices Leading to Intelligent Generation
Authors: Yao Xiaochao 
Published: June 2024

Abstract
In this paper, we first establish a multimodal knowledge graph and process the course teaching video using by PaddleOCR algorithm to distinguish and understand the teaching text. This paper also improves the traditional classroom behavior recognition method by using the skeletal covariance matrix extraction strategy to aggregate the skeletal sequence features while combining the courseware information features to jointly achieve the recognition of classroom behavior. Based on the above algorithms, this paper summarizes the English multimodal teaching system based on intelligent learning, which improves traditional English teaching methods in terms of learning environment, teaching method, and mode. Finally, this paper conducted an experimental test on the effects of a multimodal English teaching method. The results show that the class scores of the class applying the multimodal English teaching method have been cumulatively improved by 22.06 points, and the rankings have been improved from 4 or 5 to 1 or 2 among the six tested classes. Students’ satisfaction with the pedagogy increased from an initial score of 3.4 to 4.5. The results show that multimodal English teaching promotes the change of English classrooms in the smart era.
An Analysis on English Teachers’ Effective Classroom Discourse and Its Interactive Model Innovation with the Assistance of Artificial Intelligence
Authors: Zhong Yongjun 
Published: June 2024

Abstract
The rapid evolution of technology catalyzes innovation in English teaching methodologies, rendering traditional approaches—predominantly reliant on singular language modalities—insufficient for modern educational demands. This paper introduces a multimodal English teaching model anchored in multimodal discourse analysis and interaction theory to address these evolving needs. Utilizing a dual threshold extraction algorithm for chunked cumulative frame difference, the model effectively identifies semantic key frames during multimodal English instruction. Additionally, it incorporates a composite convolutional neural network that merges spatio-temporal features to delineate the dynamics of the teaching videos, further enhanced by the LK optical flow method. The integration of a pre-interactive LSTM decoder facilitates the fusion of video and textual features, culminating in the construction of an annotated English teaching video model. This model was applied to analyze the classroom discourse of high school sophomores’ English teachers in X city. Analysis revealed that the duration of classroom discourse varied between 14 to 19 minutes per session, with the introductory segment alone accounting for 307.54 seconds—12.81% of the total lesson duration—of purely linguistic content. Furthermore, the average number of questions posed by teachers per lesson was 73.17, indicating a high reliance on verifying discourse strategies, which constituted 92.22% of the discourse. This technological approach to analyzing classroom discourse provides novel insights and valuable references for refining English teaching strategies, demonstrating the effectiveness of integrating advanced computational techniques in educational settings.
Multimedia Interactive Technology for Improving Language Perception in English Teaching and Learning
Authors: Zou Cuiping 
Published: June 2024

Abstract
Based on Direct Show software, this paper acquires English teaching videos and stores them in the corresponding files. As the position of the teaching projection screen may be different in height, it will lead to the phenomenon that the captured image will be tilted, and it is necessary to use the affine transformation and perspective transformation methods to realize the correction of the teaching projection image. In order to promote the general application of multimedia interactive technology in the teaching process, dynamic gesture tracking with Kalzmann filtering is utilized to construct a non-contact human-computer interaction system, which is applied to the process of English teaching, thus designing an English teaching model incorporating multimedia interactive technology and analyzing the effect of the model. It was found that the mean score of the experimental group’s pre-experimental evaluation was 19.225, and the mean score for the post-experimental assessment was 23.087, t=2.274, P (two-sided)=0.018<0.05 at the 0.05 significance level, which is a significant difference. It shows that compared with traditional English teaching methods, the teaching mode that integrates multimedia interactive technology can effectively improve the English language perception ability of college students in China.
Long Text Summarization and Key Information Extraction in a Multi-Task Learning Framework
Authors: Lu Ming , Chen Rongfa 
Published: June 2024

Abstract
In the context of the rapid advancement of big data and artificial intelligence, there has been an unprecedented surge in text-based information. This proliferation necessitates the development of efficient and accurate techniques for text summarization. This paper addresses this need by articulating the challenges associated with text summarization and key information extraction. We introduce a novel model that integrates multi-task learning with an attention mechanism to enhance the summarization and extraction of long texts. Furthermore, we establish a loss function for the model, calibrated against the discrepancy observed during the training phase. Empirical evaluations were conducted through simulated experiments after pre-processing the data via the proposed extraction model. These evaluations indicate that the model achieves optimal performance in the iterative training range of 55 to 65. When benchmarked against comparative models, our model demonstrates superior performance in extracting long text summaries and key information, evidenced by the metrics on the Daily Mail dataset (mean scores: 40.19, 16.42, 35.48) and the Gigaword dataset (mean scores: 34.38, 16.21, 31.38). Overall, the model developed in this study proves to be highly effective and practical in extracting long text summaries and key information, thereby significantly enhancing the efficiency of processing textual data.
Application of 3D Printing Technology in Customized Garment Production
Authors: Sun Ning , Mu Xichan , Zhao Kaiming 
Published: June 2024

Abstract
In this paper, human body images are acquired by DSLR camera shooting, and the photos are grayscaled based on the HIS model. Then, the different operations weaken the similar parts between the images, and the other parts of the photos are highlighted. The image is separated from the background by image binarization. After the contour detection of the image, the pixel coordinates of the contour points in the image are deposited into a dynamic array by the size judgment of the pixel values. The subsequent size calculation is called to make a pad. According to the human body, clothing size automation measurement has been derived from the human body characteristics of the size, the use of three-dimensional printing technology to produce clothing to meet consumer demand, and three-dimensional printing technology to support the production of customized clothing under the experimental analysis. It is verified that the data difference between the actual size of the subject’s ready-made clothes and the size of the clothes produced based on 3D printing technology is kept within ±0.48cm. The virtual fitting pressure is less than the comfort threshold of women’s upper body pressure (2.75kpa), which indicates that the clothes produced by this research will not cause physiological discomfort to the human body and, at the same time, meet the consumer’s personalized clothing customization needs.
An Analysis of the Efficacy of L2-MT Mixed Modeling in Second Language Acquisition - A Cognitive Ability Perspective
Authors: Tian Mengxin 
Published: June 2024

Abstract
Cognitive ability provides a new perspective for second language acquisition research, but there are still many problems to be solved. Therefore, after fully exploring the concept of cognitive synergy, this paper proposes two research methods: qualitative multimodal interaction and hybridization of cognitive synergy. The deep neural network algorithm is interpreted layer by layer, and the model is applied to the construction of bilingual language models. Based on this, the L2-MT hybrid model is introduced, and the generalization performance of the bilingual language model is improved using the joint approach. The model’s speech recognition function for monolingual multi-task learning is used to aid learners in learning a second language. The effectiveness of the L2-MT hybrid model in second language acquisition was tested through statistical correlation analysis. Under this model, the attention of the second language learners fluctuated twice in the first half of the learning process, with the fluctuation range between 80% and 100%, and finally stabilized at about 70%. The subjects were more concentrated in the second language acquisition process. In the second language achievement comparison experiment, class A performed better than class B in terms of speaking, writing, translation, and total achievement. The difference between the two classes is 7.83, 5.002, and 3.6, respectively. Meanwhile, the p-value is 0, which is less than 0.01, and there is a significant difference between the two classes. The L2-MT model has a higher value of application in second language acquisition, which provides a better method for learners.
The application and innovation of erhu music in modern popular music
Authors: Tong Meng , Ji Chen 
Published: June 2024

Abstract
This study introduces a sophisticated music recognition classification system designed to preprocess musical samples from various instruments and extract cepstrum coefficients. These coefficients are subsequently utilized to train the tuning parameters of a deep confidence network. Once trained, the model is employed to predict the types of instruments used in test samples. Additionally, this paper explores the innovative application of a digital sound effector to process audio from the erhu and popular music, aiming to achieve a harmonious integration of frequency and pitch, thereby facilitating both reference and innovation of the erhu within the realm of popular music. The analysis delineates that within the digital sound effector, the pitch accumulation plugin and the stirring effect plugin exert a more pronounced influence on the fusion of erhu sounds with pop music compared to the comb filter plugin. The integration of frequencies is predominantly observed in two key segments of the composition. The first segment, spanning from 1’30” to 3’25”, showcases the fusion within bars 21-85 of the erhu piece “Chu Song”. The second segment, occurring from 4’68” to 7’49”, employs equalizer and delay effects to blend electronic music elements with the erhu, enhancing the piece’s contemporary relevance and appeal. This innovative approach underscores the potential of digital effects in bridging traditional Chinese musical instruments with modern music genres.
Research on the Influencing Factors of Cultivating Interdisciplinary Talents by College English in the Context of Free Trade Port Based on Knowledge Graph
Authors: Wu Shuzhen 
Published: June 2024

Abstract
This paper proposes a named entity recognition method based on multitask pointer networks, which uses Transformer and BERT to extract features, thus greatly increasing the fault tolerance of the model. Sequence annotation is realized through the conditional random field model, which can recognize complex nested vocabularies, therefore establishing a knowledge graph related to the Free Trade Port (FTP), constructing the framework of influencing factors for the cultivation of interdisciplinary talents of non-English majors in higher education and preparing a questionnaire on influencing factors. The results show that after 2019 and 2020, the number of articles issued and cited about FTP has demonstrated significant growth. The keywords related to the research hotspots of FTP were clustered, and 14 effective clusters were obtained. The intention to cultivate interdisciplinary talents of non-English majors is positively correlated with students’ knowledge, students’ personality traits, and the economic environment of FTP (P<0.01), with Pearson coefficients of 0.534, 0.287, and 0.335, respectively. To cultivate interdisciplinary talents, the pedagogical principle should be developed, which involves enhancing curriculum, increasing practical experiences, and innovating instructional methods.