bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent E-commerce Logistics Supply Chain Management and Scheduling Optimisation
Authors: Zou Wendong , Guo Ruiwei , Kao Lijun
Published: June 2024

In the digital era, logistics supply chain scheduling has become the key to enhancing the competitiveness of e-commerce, and the fine management and scheduling optimization of the e-commerce logistics supply chain is particularly important. Based on the three-layer supply chain scheduling model, the study combines the production cost, transportation cost, and transportation time of each member of the supply chain with other influencing indexes. It establishes a multi-stage supply chain scheduling model (SCISM) with dual-objective. The rotational algorithm in linear programming is used to solve the model after it has been constructed. The effectiveness of the SCISM model for intelligent e-commerce logistics supply chains is explored through algorithmic and arithmetic validation of the SCISM model. The results show that the SCISM model algorithm determines the optimal solution σ∗ = (1,2,4,6,3,5) and the optimal objective generalized function value J(σ∗) = −49328 in only 0.95 seconds, which significantly improves the solution efficiency. Compared to Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO), the SCISM algorithm quickly achieves the global optimal solution with the minimum number of iterations (100).
On Applying Natural Language Processing Technology to Optimize Accuracy of English Interactive Translation Platform
Authors: Zhang Min
Published: June 2024

This research establishes a phrase-based statistical machine translation platform composed of multiple functional modules, including data preprocessing, word alignment, translation model training, ordering model training, language model training, decoding, and parameter optimization. Special emphasis is placed on the quality of bilingual word alignment, utilizing both the IBM model and the statistical collocation model to enhance alignment accuracy. The study reveals that this new platform significantly enhances translation efficiency across diverse corpora, such as legal documents, news articles, and parliamentary records. Notably, the translation of parliamentary records demonstrates a remarkable reduction in average translation time by 27.53% and a decrease in the number of decoding iterations by 56.46% when compared to System1 using the Baseline system. Furthermore, a BLEU-based evaluation indicates that the Baseline system consistently surpasses the comparison system in translating various text types. This study underscores the practical effectiveness of the developed translation platform and its contribution to the advancement of natural language processing technology.
Exploration of the application of optimisation algorithm using stochastic gradient descent method in satellite resource allocation
Authors: Zhao Demin , Xiong Wei , Shi Jinjing
Published: June 2024

With the popularization and development of communication technology, the resource allocation problem of satellites has become a hot research topic. This study proposes a model for satellite resource allocation through the development of a beam mobilization system model and problem modeling. Then, on the basis of the linear stochastic gradient descent method, the classification accuracy of the algorithm is improved by adjusting the algorithm-solving method. Then its weight assignment method is improved to get the improved weighted linear stochastic gradient descent method. Using the optimized weighted linear stochastic gradient descent method to solve the satellite resource allocation problem from the perspective of the original problem, a model based on IWLSGD is designed and tested for performance. Through simulation experiments, the beam allocation service value of the satellite resource allocation model in this paper is 193, which is 4.32% and 3.21% higher than that of LSGD and WLSGD, respectively, and iterative convergence is faster, and its operation time and service value performs the best under different numbers of communication time slots. The system revenue, system access success rate, and system satisfaction under the interference environment between LEO and GEO are maintained at 15208, 0.8~1.0, and 85%, and keep around 7500, 1, and 75% under 5G base station interference. The satellite resource allocation model in this paper can effectively improve the utilization efficiency of communication resources and better adapt to dynamically changing interference scenarios.
Design and motion analysis of a space crank rocker biomimetic aircraft
Authors: Yan Shuai , Mei Xianhui , Zhou Yongjun , Wang Dewang
Published: June 2024

In order to better understand the flight performance and kinematics performance of flapping wing aircraft designed and developed based on the principle of bionics, a space crank rocker mechanism was tried to carry out the virtual prototype design of the bionic aircraft in space motion, and on this basis, its movement characteristics were analyzed. Analyzing the flight mechanisms and scale laws of common flying organisms yields various physical estimation parameters that affect flight characteristics. Flight patterns and motion patterns are analyzed based on the design principles of seabirds, and structural designs and mathematical models are established. Secondly, carry out the design of its flapping and transmission structures, and based on this, carry out the overall structural design and establish a three-dimensional model. Here, the kinematics analysis of its flapping amplitude and angular velocity value is carried out by combining its mechanism motion diagram, and the kinematics simulation results are compared with the theoretical results, which proves the correctness of the theoretical analysis and the feasibility of the design structure.
Applications and Challenges of Computer Vision in Autonomous Driving
Authors: Liu Jiahao , Ren Peng
Published: June 2024

The rapid evolution of autonomous driving technology has spurred deeper exploration into its multifaceted domains. This paper focuses on enhancing the intelligence and safety of autonomous driving systems through the application of computer vision. Specifically, the integration of EIoU loss and Varifocal Loss functions within the YOLOv5 algorithm facilitates the training of higher-quality samples at reduced costs. This enhanced YOLOv5 algorithm is adept at detecting traffic entities and incorporates the Matrix NMS algorithm to further refine the loss function adjustments. Moreover, this study employs the proximal policy optimization algorithm to construct a vehicular behavioral decision model for autonomous driving. This model utilizes cubic polynomial equations to depict lane-changing trajectories and introduces a safety distance rule to mitigate collision risks during autonomous maneuvers. Comparative analyses reveal that the modified YOLOv5 algorithm surpasses its predecessors—YOLOv3 and YOLOv4—in terms of detection accuracy and processing speed. Notably, the improved YOLOv5 algorithm exhibits lower network loss values than its original version, achieving faster and more stable convergence. In practical applications, the algorithm successfully identified and labeled 20,025 intelligent vehicular targets, with initial testing accuracy reaching 96.12%. This accuracy improved to 96.36% following the application of EIoU adaptive tuning to reduce risk-free class loss, and further fine-tuning elevated the accuracy to 96.54%. The integration of computer vision into autonomous driving technology thus holds significant promise for advancing the field.
Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis
Authors: Xiao Yanling
Published: June 2024

With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of tourism management decision support system has become an important issue in the tourism industry. In this paper, we use the plain Bayesian classification algorithm, the improved Apriori association algorithm, and the gray GM(1, N) prediction model to mine and process the tourism big data and combine the 3S technology and the Agent-based knowledge representation technology to realize the construction of the tourism management decision support system based on big data analysis, and make the optimal decision for the planning of tourist attractions and routes. The attraction classification method’s accuracy rate is 72.98%, and its feasibility is high. The integrated error of the adopted GM(1,7) model is only 2.587%, which is smaller than the 3.483% of the GM(1,1) model and the 4.594% of the linear regression model, and the model accuracy is high. The average response time and average TPS of the system are 8.70s and 15.89s, respectively, which generally meet the demand for the system’s processing capability. This study provides a reference for the construction of a decision support system for tourism management.
Design of Basketball Teaching Aid System Based on Artificial Intelligence
Authors: Xu Xiaohu , Wu Haijuan
Published: June 2024

Artificial intelligence has penetrated every aspect of people’s lives with the rapid development of science and technology. In basketball, how to effectively utilize artificial intelligence technology to improve the effectiveness of training has become a problem worth studying. A Mediapipe-based 3D human posture domain adaptive detection method is proposed in the study by combining statistics and human limb proportion simulation. The modified SVM classification algorithm is used to compare and classify the newly acquired key point data information of the system with the standard basketball action key point data information preset by the system to judge whether the basketball action is up to the standard or not, so as to design the basketball teaching aid system based on artificial intelligence. Based on this basis, after testing the recognition performance of the system, its practical application effect is explored. The total basketball scores improved after the experiment (86.26±2.228 for the experimental group and 81.34±9.672 for the control group), but there was no significant difference (P>0.05). The control group was slower than the experimental group by 0.64 seconds and 0.77 seconds overall in the cross-step breakout time with the ball and the same-side step breakout time with the ball, respectively, indicating that the experimental group was more proficient in basketball technology. The system has been tested in various ways to meet the multiple needs of the users, meet the expected standards, and be able to be applied in real-world scenarios.
Analysis of assembly building quality influencing factors based on deep confidence network
Authors: Chen Jin
Published: June 2024

At present, in the assembly building construction practice, the relevant subjects lack the concept of building quality management and awareness of responsibility, and the rights and duties of the subjects of each link are not clear in the division of responsibility for building quality. In this paper, based on the deep confidence network, for the problem that the accuracy of the traditional DBN model will gradually decrease, a genetic algorithm is introduced to optimize the conventional restricted Morzmann machine, and the number of nodes in the hidden layer of the genetic algorithm optimization DBN node number process is obtained after the improvement. The optimal method for planning building quality assessment is selected based on the comparison results of the established DBN algorithm function. Then, the optimal building quality assessment model is constructed. Then design the evaluation index system for quality influencing factors and verify it with structural equations. Finally, the model is used to quantify the degree of influence of assembly building quality. The study concludes that all path coefficients affecting the quality of assembled buildings are greater than 0.5, the P-value is less than 0.001, and the five proposed hypotheses are all valid. In the assessment of the quality of residential projects, the final results of excellent, good, moderate, and qualified accounted for 0.1952, 0.2299, 0.3086, and 0.2663, respectively, and the quality of the project’s construction was evaluated as a good grade. This study provides a new method for improving the awareness of quality responsibility among relevant subjects in the construction industry and guaranteeing the level of building quality.
Research on Automatic Business English Text Translation Technology Based on Intelligent Computing
Authors: Gao Honggang
Published: June 2024

As global interactions and trade expand, the relevance of Business English has similarly increased, enhancing the scope of its application. Consequently, there is a growing interest in the research of automated translation technologies for Business English texts. This paper provides an overview of the translation requirements for Business English texts, taking into account their distinct characteristics. We propose a novel neural network translation model that integrates an attention mechanism within the sequence-to-sequence (seq2seq) framework. This model incorporates Bayesian optimization to exploit the strengths and features of intelligent algorithms effectively. Furthermore, the model is enhanced by Gaussian regression and an acquisition function, enabling it to simultaneously search for optimal architectural configurations. Experimental analyses demonstrate that the model’s accuracy improves with increased training iterations. In comparative tests across various datasets, the seq2seq translation model augmented with Bayesian optimization and the attention mechanism achieved the highest BLEU scores. Specifically, it exhibited an average improvement of 1.1 points over models using Bayesian optimization without the attention mechanism, indicating a substantial enhancement in translation accuracy. Practical applications show consistent evaluation results across different datasets, with negative evaluations for text translations remaining below 10%. The findings underscore the high quality of the translations produced by our model for Business English texts, affirming its effectiveness and applicability in professional settings.
Research on Informatization Construction of Educational Management Innovation Path in Colleges and Universities in the Intelligent Era
Authors: Li Yongxia , Ding Yanyun
Published: June 2024

The prevailing education management systems in colleges and universities are antiquated, adversely impacting not only the administrative efficiency but also the formulation of faculty teaching plans and class schedules, as well as the management of student electives and credit accrual. This paper presents an innovation and optimization of the existing system, wherein education management processes are categorized into seven distinct modules. It delineates the flow and integration of heterogeneous data sources within the system, leveraging heterogeneous database technology as the foundational framework. Subsequently, we redesign heterogeneous computation scheduling and adaptation, enhancing distributed database allocation and query algorithms. The culmination of this research is the development of a novel pathway for the education management information system in higher education institutions. Upon empirical testing of the system proposed in this paper, we explore its practical implications through IPA analysis. User satisfaction is notably high in areas such as A4 invigilation information, A13 subjective grading of routine assessments, A14 querying and feedback on grades, and A15 remediation of failed courses, with performance ratings of 4.77008, 4.73981, 4.8961, and 4.86558 respectively. These indices predominantly reside in the “continue to maintain” and “oversupply” zones, indicating the substantial efficacy of the innovative college management information system introduced herein. This study significantly contributes to the standardization and intellectualization of college administrative processes, proposing new paradigms and methodologies for the integration of educational management and information technology within tertiary institutions.
Study on the Distribution Patterns and Treatment Effectiveness of Water Body Pollution Monitoring by Remote Sensing Technology
Authors: Lin Zhenjing , Jin Nisha , Dong Yarong , Chen Jing , Wang Lidong , Li Zhiwei
Published: June 2024

With the continuous development of science and technology level, remote sensing technology has a wide range of application prospects in water pollution monitoring. The study combines the Grab-Cut image segmentation algorithm, atmospheric radiation correction, and water pollution identification to construct a remote sensing technology-based method for monitoring water pollution. The research is focused on analyzing the water quality distribution pattern of the upper and lower lakes in the sample lake area using the constructed water pollution monitoring method. On this basis, from the four dimensions of water pollution prevention and control, recycled water recycling, ecological restoration and protection, and environmental monitoring, the comprehensive management measures for water pollution are proposed. The pollutant contents of the upper and lower lakes before and after the management are compared to explore the effect of the comprehensive management of water pollution. The results show that COD contributes 86.77% to the water pollution in the sample lake area. The water quality of the upper and lower lakes is more distributed by class V and class III, which account for 36.67% and 43.33%, respectively. The proposed water pollution monitoring method is able to accurately identify and classify the pollution. After the comprehensive treatment, the COD and ammonia nitrogen content of the upper lake decreased by 30.51% and 37.43%, and that of the lower lake decreased by 35.90% and 39.06%. The effect of water pollution treatment was remarkable.
Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling
Authors: Liu Yan
Published: June 2024

This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026<0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Application and Effectiveness Evaluation of Augmented Reality Technology in History Museum Exhibitions
Authors: Yang Shuguang
Published: June 2024

In the context of the rapid evolution of science and technology, the integration of reality technologies has become indispensable for the evolution of history museum exhibitions towards digitization, connectivity, and intelligence. This study details the development of a visual-inertial guidance system, leveraging both the Inertial Measurement Unit (IMU) and Simultaneous Localization and Mapping (SLAM) technologies. To construct a mixed reality tour guide system, the Scale Invariant Feature Transform (SIFT) feature extraction algorithm and tracking registration technology were employed to enhance the system’s exhibit recognition accuracy and tracking capabilities. Additionally, parallel processing techniques were implemented to accelerate the system’s performance, thereby improving operational efficiency while maintaining accuracy and maximizing the potential of augmented reality interactive functions within the history museum setting. The effectiveness of the system was empirically validated through participant evaluations across three dimensions: task completion, design recognition, and knowledge conveyance. Results indicate that all average scores exceeded 9, with 85% of participants acknowledging the enhanced convenience in knowledge acquisition provided by the technology. Furthermore, the coefficients for the guided tour abstract symbol verification were recorded at -0.007, 0.009, and 0.081, respectively. These findings corroborate that the deployment of an augmented reality interactive system and a mixed reality tour system can significantly expand the public service functions and historical-cultural education offerings of history museum exhibitions while also enriching the sensory experiences of attendees.
A Study of the Combination of Semantic Understanding Enhancement Methods and Deep Learning Techniques in English Translation
Authors: Zhou Gang
Published: June 2024

Semantic understanding enhancement methods and deep learning are popular areas of artificial intelligence research and have significant potential in natural language processing. The English translation is one of the typical application scenarios combining these two technologies. In order to thoroughly analyze the information contained in English texts and improve the accuracy of English text translation, this study proposes an unbalanced Bi-LSTM model. Firstly, the BERT model is used to vectorize the original English corpus and extract the preliminary semantic features. Then, the unbalanced Bi-LSTM network is used to increase the weight of the textual information containing important semantics to further improve the effect of the key features on the recognition of the English text and, at the same time, an attention mechanism that introduces the word vectors is used to widen the gap between the key textual information and the non-key information, so as to improve the effect of the English translation. The accuracy of English text translation can be significantly enhanced by comparing the classification effect with various models, as shown by the results. The accuracy of the model can reach over 90% in about 60 pieces of translation training, and the mean square average is only 1.52. Its translation effect has won the recognition of more than 50% of professionals. The model's ability to translate English is evident.
The New Ecology of Chinese Language and Literature in the Digital Age--Cultural Inheritance and Innovation
Authors: Chai Yu
Published: June 2024

Digital media is perpetually evolving, significantly impacting the Chinese language and literature. Concurrently, the rapid pace of modern lifestyles poses challenges to the transmission of cultural heritage within Chinese literary traditions. Accordingly, this study explores the pathways for digital innovation in Chinese literature, focusing on the integration of key virtual technologies to facilitate the cultural inheritance of the Chinese language. This research highlights the application of advanced virtual technologies in preserving and transmitting Chinese linguistic culture. It involves designing an experiential process for cultural inheritance, utilizing these technologies to process graphic and textual information pertinent to the Chinese language and literature. Furthermore, the study innovates cultural transmission methods through the synthesis of virtual 3D scenes and the implementation of human-computer interaction functionalities. An experimental evaluation approach was employed to assess the impact of virtual reality technology on the learning outcomes in Chinese literature and its cultural transmission. The findings indicate that users who interactively engage with the content in a virtual environment achieve a learning effect score that is 0.6528 points higher than those using traditional browsing methods. This underscores the enhanced efficacy of interactive experiences in facilitating the learning of Chinese literature. The regression analysis further demonstrates the significant promotional effects of the number of virtual experiences in Chinese literature (P1) and the rate of user interaction with computers (P2) on the dissemination of Chinese cultural content. The fixed and random effects for the number of virtual experiences are 0.9428 and 0.9784, respectively, while those for user interaction rates are 0.8945 and 0.8835. These results suggest a substantial impact on the broader dissemination and transmission of Chinese language literature, reinforcing the value of integrating virtual reality technologies in cultural education.