bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combination of GNN and MNL: a new model for dealing with multi-classification tasks
Authors: Li Qiling , Yuan Xiaohui , Xie Jiyang 
Published: December 2024

Abstract
In daily life, tasks such as choosing a mode of transportation in traffic, diagnosing diseases and selecting medications in healthcare, as well as recommending products in e-commerce, can all be fundamentally classified as multi-classification tasks. Currently, effective approaches to solving multi-classification tasks include the behavior modeling-based Integrated Choice and Latent Variable (ICLV) model and the machine learning-based Multinomial Logit Model (MNL). The former slightly outperforms the latter in multi-classification tasks due to its ability to identify latent variables and integrate the selection process. However, if certain shortcomings of the MNL model, such as the assumption of independence from irrelevant alternatives, linearity assumption, and lack of hierarchical structure, can be addressed, MNL could outperform the ICLV model in some datasets. Graph Neural Networks (GNNs), which treat the entire feature set as a graph and consider the relationships between features, break the linearity assumption and offer a more flexible and hierarchical structure. This indicates that GNNs are able to effectively alleviate the limitations of MNL. Therefore, we propose an innovative GNN MNL composite model: first, GNN is employed to efficiently extract features from the dataset, and then the extracted features are used as input to train the MNL model. Finally, the trained MNL is utilized to classify new samples. The model’s accuracy was enhanced by incorporating Generative Adversarial Networks (GANs) for data augmentation during the training process. Through validation on three datasets, including modeChoiceData, we demonstrated that the GNN MNL composite model indeed achieves higher accuracy, confirming its feasibility. Future research could explore the generalizability of the GNN MNL model in other classification domains.
Research on Network Attack Sample Generation and Defence Techniques Based on Generative Adversarial Networks
Authors: Shan Jizhou , Ma Hong , Li Jian 
Published: December 2024

Abstract
Generative Adversarial Networks, as a powerful generative model, show great potential in generating adversarial samples and defending against adversarial attacks. In this paper, using Generative Adversarial Networks (GANs) as the basic framework, we design a network attack sample generation method based on Deep Convolutional Generative Adversarial Networks (DCGANs) and an adversarial sample defence method based on multi-scale GANs, and verify the practicality of the two methods through experiments, respectively. Compared with the three adversarial sample generation methods of AE-CDA, AE-DEEP and AE-ATTACK, the DCGAN-based adversarial sample generation method in this paper can interfere with the detection function of the anomaly detection model more effectively, and has better stability and versatility, and can maintain a relatively stable attack effect on a wide range of models and datasets. On the MNIST dataset, the classification accuracy of the adversarial sample defence method proposed in this paper is only slightly lower than that of the APE-GAN defence method on the JSMA adversarial samples, with a maximum classification accuracy of 98.69%. The maximum classification accuracy reaches 98.69%, and the time consumption is 1.5 s, which is only slightly larger than that of the APE-GAN defence method of 1.2 s. Thus, the time consumption of this paper’s multi-scale GAN-based adversarial sample defense method is smaller or equal to that of other comparative defense methods when systematic errors are ignored. The purpose of this paper is to provide a technical reference on how to eliminate adversarial perturbations using generative adversarial networks.
Experimental study on the wear mechanism of cemented carbide tools under high speed cutting conditions
Authors: Huang Wenjing , Yang Jingfen 
Published: December 2024

Abstract
This paper takes interface friction theory and tool wear theory as the starting point and proposes an experimental study on the wear mechanism of cemented carbide tools under different cutting conditions. Titanium alloy Ti-6Al-4V material is selected as the object of study, and its corresponding tool grade YG8, in addition to setting up two groups of control tools, S30T and YW2, and at the same time to determine the experimental workpiece materials and experimental equipment. In order to more intuitively demonstrate the experimental effect of the wear mechanism under high-speed cutting conditions, a finite element simulation model is constructed, and the material constitutive equations, contact properties, and failure criteria of the model are set to investigate the wear mechanism of cemented carbide tools under high-speed cutting conditions. When the cutting speed is 200m/min, the relationship between the size of the main cutting force of the three tool materials is S30T (407N) > YW2 (348N) > YG8 (301N), and with the increasing cutting speed, the YG8 tool still maintains excellent wear performance. Under the same conditions, the cutting distance of YG8 (L=11000mm) is larger than that of YW2 (L=5700mm). The wear mechanism of cemented carbide tools under high-speed cutting conditions is demonstrated in this paper in an intuitive manner, which has a positive impact on their performance improvement.
Analysis of comprehensive treatment strategies and prognostic factors in spinal fracture combined with spinal cord injury
Authors: Wu Kailin , Jian Huan , Hou Mengfan , Song Yijie , Wu Lulu 
Published: December 2024

Abstract
Spinal cord injury is a kind of serious trauma, mostly caused by traffic accidents, falling from height and other causes of structural changes in the spine, resulting in damage or compression of the spinal cord, causing functional disorders of the organs innervated by the spinal cord, which seriously affects the quality of life of patients. This paper analyzes the main factors of spinal injury, emphasizes the importance of pre-hospital first aid measures, and proposes pre-hospital first aid principles and technical specifications for spinal injuries. By asking for medical history, physical examination and other measures, the damage pattern of the affected vertebrae and neurological function is comprehensively evaluated, and the spinal cord injury is evaluated by combining X-ray, CT, and MRI techniques. From the three perspectives of medication, non-surgical, and surgical procedures, a comprehensive treatment plan is proposed, and surgery is selected to be performed within 24-72 hours of the occurrence. Different treatment methods are selected based on the level of spinal injury. Considering the long-term stability of the spine, a combined anterior and posterior surgical treatment plan was used, and a clinical analysis was performed. The prognostic influencing factors were analyzed around gender and age. The surgery and post-treatment rehabilitation were evaluated, and postoperative complications were dealt with.
Innovative Research on Teaching Mode Integration of Virtual Reality Technology and Audio Signal Processing for Music Education in Colleges and Universities
Authors: Song Ximeng , Zhang Chunbo 
Published: December 2024

Abstract
VR is an important engine in the process of informatization road in the education industry, which has advantages that traditional technology can not reach. In this paper, we actively explore audio signal processing technology and VR technology combined with the teaching of musicology majors in a new teaching method, and apply it to the teaching practice. After pre-processing the audio signal and extracting the acoustic parameters, the LVQ neural network is used to construct a music singing evaluation model based on audio signal processing. Based on this, an innovative design for music teaching mode is proposed in combination with VR technology. Taking the music majors of a university as an example, we carry out the application of the VR music teaching model and analyze its application effect. The evaluation results of the LVQ model are very close to the expert scores, with an average difference of 0.214. This can be better used in the evaluation of student singing in music teaching. The test scores of the sample students improved by 0.768 points overall after the experiment, and all dimensions were significantly different at the 1% level, indicating that the VR music teaching mode can effectively promote the music knowledge skills and satisfaction of students in music teaching, and the reasonable use of the VR music teaching mode can effectively improve the effect of music teaching.
A Study of Students’ Learning Behaviour Patterns in a Digital English Teaching Environment
Authors: Hu Ke , Zhang Liang 
Published: December 2024

Abstract
In this paper, the multimodal data of students’ learning behaviours are classified into multimodal data such as sound, image and text according to the characteristics of data types presented in the digital English teaching environment. According to the data modal characteristics and factor characteristics, the multimodal behavioral data of students’ English learning is preprocessed. In view of the algorithmic advantages of the Deep Mixed Discriminant Restricted Boltzmann Machine (HDRBM) neural network, the accurate learning evaluation annotation of learning behavioural features by HDRBM is established, constituting the learning behavioural feature extraction model based on HDRBM. The optimal setting value of HDRBM algorithm performance is determined through training tests to extract students’ digital English learning behavior features. The English learning performance levels are divided, and the correlation relationship between different learning behaviors and learning performance is examined. The digital English learning behaviours include attention (23.5 points), learning motivation (15.8 points), learning attitude (36.7 points), and learning strategies (24.9 points), among which the English learning motivation scores are low. There was a significant difference in the scores between the group with learning difficulties and the intermediate, good, and excellent groups. Students’ English learning achievement can be improved by adjusting their learning behaviors.
Optimisation of Teaching Space Design and Innovation of Teaching Mode in Trouser Structure and Craftsmanship Course Empowered by Digital Intelligence
Authors: You Lingli , Dai Zhaojue 
Published: December 2024

Abstract
“Numerical Intelligence” teaching is an important content and means of classroom teaching reform in clothing design and craftsmanship. The first part of the study focuses on the use of digital intelligence technology in trouser design. After selecting the characteristic parameters of the trouser structure, the GA algorithm is used to improve the GRNN algorithm, the global optimization of the smooth factor is carried out, the trouser structure generating a model based on GA-GRNN is constructed, and the model is trained and tested in order to analyze its performance. With this background, we explore the combination of numerical intelligence technology and clothing craft course, innovatively propose the teaching mode of numerical intelligence craft course, and design teaching experiments to explore the practical effect of this teaching mode. The GA-optimized GRNN model improves the number of iterations and the adaptability by 63.23% and 0.0131, respectively, and the error between the model test generation results and the actual values is below 0.4, which has a better trouser suit parameter size generation effect. After applying the teaching mode of the digital intellectualization process course, the student’s overall evaluation of the mode is better, and the number of students with good and excellent academic performance has increased by 64.29%. The teaching mode of the digital-intelligent process course can promote students’ learning efficiency and improve the quality of teaching in the process course.
Research on the Realisation Path of Virtual Music Space Construction and Immersive Experience for Small Chamber Orchestra in the Digital Era
Authors: Zhou Wanlu 
Published: December 2024

Abstract
Music spatialisation is one of the means to enrich the performance effect, and the use of digital technology to construct a virtual music space greatly improves the accessibility and availability of the orchestra’s performance. This paper explores the path of virtual music space construction and enhances experience, and proposes a sound field replay technique. A ball microphone array is used to measure the raw ATF, and a sparse decomposition algorithm is used to reduce the number of measurement microphones. The target sound field can be reproduced accurately by solving the loudspeaker excitation signal. Experimental analysis reveals that in the [250,550] Hz interval, the method in this paper has obvious advantages compared with the traditional method, and the mean scores of listeners’ subjective evaluations of physiological immersion, psychological immersion, and emotional immersion are all greater than 4, which indicates that the constructed virtual music space has a better immersive experience, which is conducive to achieving a better performance of small chamber orchestra.
A Study on the Communication Effect of Chinese Traditional Sports Culture on a Global Scale Based on High-Dimensional Data Processing
Authors: Zhu Lei , Fang Jie 
Published: December 2024

Abstract
Against the background of advancing globalisation and rapid development of information technology, international dissemination of Chinese traditional sports culture has become an important way to charm Chinese culture and promote cultural exchanges and mutual understanding. This paper analyzes the technology of international dissemination of traditional sports culture and independently builds a digital, traditional sports museum with a set of cultural data visual platforms. By selecting high-dimensional data features from traditional sports culture, batch gradient descent processing is carried out on cultural data. Based on the logistic regression model of high-dimensional data processing, regression analysis is conducted on the effect of sports culture dissemination. The cultural data visualisation platform designed in this paper can help foreign users understand the origin and development of Chinese traditional sports culture. Through the digital museum, foreign users’ awareness of wushu, taijiquan, qigong, Chinese wrestling, Chinese chess and go, archery, dragon boat, dragon and lion dance, cuju, and acrobatics exceeded 4.0 points. There is a significant difference between the overall impact of digital museums and the occupation of foreign users (P < 0.05). Traditional sports culture dissemination subject, dissemination method, dissemination content and dissemination object can promote the dissemination of culture on a global scale, in which the impact coefficients of the digital museum constructed in this paper, as well as the cultural information visualisation platform, are as high as 0.324 and 0.417, respectively. To sum up, the dissemination of Chinese traditional sports culture on a global scale is strengthened by high-dimensional data processing technology.
Construction and Optimisation of an Intelligent Virtual Teacher System for English Language Education
Authors: Wang Haijuan 
Published: December 2024

Abstract
In this paper, a dialogue teaching system based on the virtual teacher is designed mainly by artificial intelligence and 3D modelling technology, and the design of overall architecture of the system is described in detail, including the design of the functional structure of the system and the design of the software architecture. The emotion calculation technology is applied to the teaching system, and the Bayesian optimization method is used to optimize the emotion calculation. By evaluating and analysing the results of the pilot experiment, the results show that the average speech recognition time of the system is 1.58977, and the average response time is 0.83076, calculated from ten sets of test data. After optimization, the system’s accuracy in recognizing emotions has increased to 90%. The virtual teacher’s dialogue teaching system can be adapted to the teaching needs of multiple learning types and can significantly improve students’ motivation, learning experience, and learning effectiveness.
A Network Analysis Model of Interactive Strategies for Teaching Civics in Colleges and Universities in a Virtual Reality Environment
Authors: Huang Li 
Published: December 2024

Abstract
Taking the ideological and political education course of contemporary colleges and universities as the main research subject, we consider VR technology, a brand new media technology, in improving the interactivity of the traditional Civics and Political Science classroom embedded possibilities as the research direction. A multi-channel virtual reality environment for human-computer interaction is built, and a VR-based interactive teaching system for Civic and Political courses is developed by introducing the B/S 3-tier architecture model. Subsequently, 40 teachers and students applying the VR Civic and Political Science course teaching system were selected to study the teaching interaction behaviours using the social network analysis model, and the student-student interaction behaviours were mined using the cohesive subgroup analysis method. The results show that students prefer watching micro-videos for knowledge learning, the overall online interaction and communication are more active, teachers and teaching assistants have high centrality (both greater than 20), and their timely feedback plays an important role in the motivation of student interaction. In addition, there are more factions composed of different learners, members in the factions interact closely, and there are some members with high faction overlap, which promotes communication and cooperation among subgroups and is the core of the whole network. The study aims to promote the occurrence and development of interactive behaviors within the online learning community, enhance the flow of information, and create a better learning environment for civics.
Optimisation of key algorithms for vision-based SLAM in highly dynamic environments
Authors: Wang Shiqi , Song Shuzhong 
Published: December 2024

Abstract
Highly dynamic environments can cause large deviations in visual SLAM when solving the camera position between two frames, which in turn affects the overall positioning accuracy of the system. For this reason, this paper optimizes the traditional vision SLAM algorithm by focusing on the tracking part of the SLAM algorithm. In the highly dynamic acquired environment, features are extracted through a target detection network that utilizes the multi-scale channel attention module (MS-CAB) and attention feature fusion module (AFFB). The GC-RANSAC algorithm is used to distinguish and remove dynamic feature points, and then the static feature points are utilized for position estimation. Experiments show that this paper’s algorithm has less trajectory error in high dynamic environments, improves the accuracy by more than 94% compared to ORB-SLAM2 in dynamic Walking scenarios, and improves the localisation accuracy of this paper’s algorithm by 90.0%, 86.7%, 98.8%, and 97.5% compared to ORB-SLAM3, with the time spent being only 30.08% of that of DS-SLAM. The experimental findings validate the effectiveness of this paper’s work.
A Study of Machine Vision-Assisted Methods for Cultivating Intercultural Communication Skills in Higher Vocational English Education
Authors: Jiang Shuangshuang 
Published: December 2024

Abstract
Multicultural intermingling has become a major development trend in international society, and in this context, higher vocational English education takes the cultivation of students’ cross-cultural communicative competence as an important educational goal. In this paper, we use the YOFD network in machine vision technology to detect students’ faces in the English teaching classroom and solve the head pose rotation matrix by combining it with the solvePnP function in the OpenCV software library. Combining the results of students’ face and head posture detection, we detect changes in their concentration through their fatigue state and score their classroom concentration. The intercultural communication model proposes a 3-level multi-component competence structure, while the symbiotic texture of human-computer collaborative teaching constructs a human-computer symbiotic English teaching model for intercultural communication. The video data of students’ English classroom behaviors were obtained through a high-speed camera, and a human-computer symbiotic English teaching comparison experiment was designed. The ACC value of the YOFD model for students’ face detection was 95.36%, and the average values of yaw, pitch, and rotation angle errors for head posture detection ranged from 1.98° to 3.27°. The experimental group’s students outperformed the control group in English reading by 9.95 points, and their intercultural communication competence improved across all dimensions by 1.065 to 1.434 points. Relying on machine vision technology to assist English teachers in grasping students’ concentration in the classroom, combined with the human-computer symbiosis English teaching model, can enhance students’ intercultural communicative competence.
Elementary Number Theory Problems. Part XV – Diophantine Equations
Authors: Pąk Karol , Korniłowicz Artur 
Published: December 2024

Summary
This paper formalizes problems 38, 58, 160, 164, 168, 171, 188, 195, 196, and 198 from “250 Problems in Elementary Number Theory” by Wacław Sierpiński.
The Influence of the Code of Ethics and Corporate Social Responsibility on Consumer Buying Behaviour
Authors: Caldeira Rui Nóbrega , Moro Alfonso Infante 
Published: December 2024

Objective: To analyze the impact of Corporate Social Responsibility (CSR) on organizational reputation and assess how codes of ethics influence consumer behavior.

Theoretical Framework: This study examines CSR principles, focusing on aspects like ethical labor practices, community development, and environmental sustainability. It includes an exploration of "Greenwashing," a practice where companies promote sustainable values without significant eco-efficient actions.

Method: A survey was conducted with 230 Portuguese consumers to evaluate their awareness and perceptions of CSR and ethical practices in organizations.

Results and discussion: Findings indicate that CSR and ethical behavior are important to consumers when making purchase decisions. While many participants value CSR, there is limited awareness about Greenwashing among Portuguese consumers, highlighting a need for better consumer education on corporate environmental claims.

Research Implications: This study underscores the importance of transparency and genuine CSR efforts in shaping consumer trust and brand reputation, and highlights the influence of CSR on consumer purchasing behavior.

Originality/value: This research provides insights into Portuguese consumer perspectives on CSR and ethical codes, contributing to understanding the importance of CSR in corporate reputation management. It also offers new data on Greenwashing awareness among consumers.