Reviewerly/exHarmony huggingface dataset:
Measurement of the Integration of Education and Industry in Guangdong, Hong Kong and Macao Greater Bay Area under the Background of Big Data and Analysis of Influencing Factors

Abstract In this paper, the Herfindahl coefficient (HHI), intuitionistic fuzzy hierarchical analysis (IFAHP) and binary semantic evaluation methods are used to quantitatively measure the degree of industry-academia integration in Guangdong, Hong Kong and Macao and to explore the level hierarchy of the degree of industry-academia integration. The factors influencing the behavior of industry-academia cooperation between enterprises and higher vocational colleges were analyzed by selecting the employment of H, A and X institutions in the past five years. The results show that the level of industry-academia integration in the Greater Bay Area is 0.6833, and the obstacles in communication channels between enterprises and higher education institutions are the biggest influencing factors. The industrial structure deviation is 28.9, which indicates that there is a discrepancy between the university specialty settings and market demand in the region. The optimization countermeasures proposed in this paper have a positive significance in promoting the connection between local higher education and industry.
Exploration of Interdisciplinary Teaching Innovation Mode for Environmental Design Majors Based on Information Integration Technology

Abstract This paper constructs an interdisciplinary, innovative teaching model for environmental design majors based on information fusion technology. Based on sensors to collect students’ expression characteristics, interest characteristics, attention characteristics and other information, and three-dimensional modeling of students’ state, the trajectory multimodal data generated from students’ interactions with the teaching facilities in the process of learning are correlated with the externally manifested learning activities and the implicit learning characteristics. Subsequently, the extracted data was merged and analyzed, and feedback was provided to enhance teaching effectiveness. The results show that the information fusion model has a recall rate of more than 0.9, a checking accuracy rate of 0.87-0.93, and a student behavior recognition rate of 85%-91%, which are all better than other models. After using the innovative teaching method, the coefficient of student feedback evaluation increased from 0.4 to 0.88, which indicates the effectiveness of this paper’s model in student behavior identification and feedback.
Intelligent big data visual analytics based on deep learning

Abstract In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network, and used the generative adversarial network to calculate the probability distribution of data. Then, the advantages of deep learning in intelligent big data visualization and analysis are explored from the dimensions of data preprocessing, dimension anchor layout, coordinate expansion and data analysis. Finally, the efficiency of the deep learning model is compared with that of other algorithms using indicators such as accuracy and recall, and the feasibility of this paper’s method is verified by empirical analysis using intelligent transportation data as an example. The results show that the model in this paper achieves an accuracy rate of 95.5%, the loss rate is stable at 0.2% to 0.4%, and the average running time is maintained at 20ms, which are all better than other models. The predicted and real values of traffic data for the Deep-STCL model using deep learning basically match, indicating that the deep learning model has obvious advantages in data visualization and analysis.
A personalized recommendation algorithm for online teaching resources of vocal music based on graph neural network

Abstract In the development process of education informatization, realizing personalized learning is the urgent demand of learners. In this paper, we construct an online teaching resources recommendation algorithm (VGNN) based on a graph neural network (GNN). The project-based collaborative filtering algorithm is used to find highly similar teaching resource materials using Pearson’s correlation coefficient in order to personalize the recommendation of online teaching resources. The personalized recommendation algorithm for online teaching resources was tested and analyzed using the online teaching course Fundamentals of Vocal Music Theory as a case study. The results show that the course “Fundamentals of Vocal Music Theory” is divided into 4 clusters with clear boundary contours under the graph neural network algorithm, and the number of knowledge points in the 4 clusters is 2308, 655, 513, and 97, respectively. After learning the personalized recommended content from the 4 clusters of knowledge points, the average rate of loss of points for each of them decreases by about 0.3. The algorithm effectively solves the accurate classification of vocal knowledge and implements personalized recommendations for educational resources.
Research on Teaching Resource Management in Colleges and Universities Based on Multiple Data Chain Networks

Abstract In this paper, we first take the datalink network as the entry point and construct a university teaching resource management model based on a multivariate datalink network. A Link-22 data chain time synchronization algorithm with KF-RTPT is proposed to address the actual existence and unavoidable time deviation based on accuracy and timestamp acquisition errors. Meanwhile, a time slot allocation and optimization algorithm designed to maximize message transmission benefits is being developed to optimize the allocation of node time slot requirements. Finally, the constructed teaching resource management model is empirically analyzed from three perspectives: error optimization, dynamic time slot allocation, and model application effect. According to the findings, the KF-RTPT algorithm increases motion error efficiency by 3.83 in low-speed motion states and 8.16 in high-speed motion states when compared to the conventional RTT method. This effectively eliminates motion error and boosts the effectiveness of teaching and managing resources in colleges and universities.
Research on Distributed Virtual Reality Technology Based on VIRTOOLS in the Context of Big Data

Abstract In this paper, virtual reality development software Virtools is used as the main tool for scene rendering in distributed virtual reality systems. The nearest distance-based control strategy is designed to achieve synchronous interaction between nodes by utilizing consistency control and prediction algorithms for nodes in the same cluster. Optimize the virtual scene by using texture mapping, detail level, and dynamic loading techniques. Propose a distributed parallel drawing method based on image plane segmentation to accelerate image drawing. The analysis and testing of the multiuser virtual reality roaming system in this paper is completed from four aspects: offline training model evaluation amount roaming test, lock performance test, and operation sequence error correction test. Among the analyzed data, the average lock time overhead is 123us, which has no obvious connection with the number of nodes, the number of locking times, and the execution of operations. According to each lock overhead of 123us, the number of concurrencies allowed per second is 8563, which fully meets the requirements of the system at this stage.
Research on the Design of Digital Teaching Resource Management and Sharing Platform in Colleges and Universities Based on Cloud Storage

Abstract In this paper, after analyzing the structural system of the digital resource sharing model in colleges and universities, a shared storage model based on cloud storage is designed, and a digital teaching resource management and sharing platform for colleges and universities based on cloud storage is constructed on this basis. In order to realize accurate sharing, the sharing behaviors of teaching resources are symbolically illustrated, double behavior aggregation is performed, and a multidimensional preference personalized recommendation algorithm, i.e., DB-CGAT model, is designed to construct the personalized sharing module of teaching resources that integrates the behavioral graph. In addition, three types of experiments are conducted on the personalized sharing module of the platform to test its sharing performance and overall performance. The results show that the accuracy of this model is reduced by 0.04 compared with the traditional algorithm; the 90 percent time of login transaction is 2.44, and the time of query transaction is 1.46, which is all up to the standard value. All the action actions are green, and the number of clicks and throughputs in 5~30ss are around 120. Universities can benefit from the digital teaching resource management and sharing platform designed in this paper, which can improve teaching efficiency.
Virtual Speech System Based on Sensing Technology and Teaching Management in Universities

Abstract In this paper, digital speech is compressed using discrete Fourier transform, discrete cosine transform, and improved discrete cosine transform, and compressed sensing technology is proposed. Based on the compressed sensing technology, the frequency domain parameter compression algorithm and the speech coding and decoding algorithm are designed, and the interactive 3D virtual speech system design is completed through the pre-processing of the speech system, the extraction of speech features and the design of speech control commands. The virtual voice system designed in this paper is introduced in the teaching management mode of colleges and universities, and the main functions of the system include four major sections: notification management, online Q&amp;A, virtual voice system interaction, and teaching resource management. The virtual voice system built using sensing technology is simulated and tested, and the practical application effect of the system is studied through empirical analysis. The experimental results show that the amplitude of the sound recorded by the compression sensor in the voice sensing experiment is more concentrated, the range is concentrated between [-0.025,0.025], and the detected voice is smaller and more effective than the amplitude recorded by the cell phone. Students were mainly satisfied and very satisfied with the four system functions designed in this paper, and in terms of the online Q&amp;A function, only one student expressed great dissatisfaction and the total number of satisfied people was 119, and the students were highly satisfied with the teaching management of the system designed in this study.
Research on the Integration Path and Practice of AI Intelligent Technology and English Distance Education

Abstract This paper introduces AI intelligence technology in English distance education and establishes a learning analytics model that includes information collection and acquisition, data storage, cleaning, integration, analysis, visualization, and action. Each component corresponds to a series of specific technologies and processes, and generates information such as learner portrait modeling profiling learning behaviors, using data mining to provide personalized learning solutions for learning. The assessment system is constructed from three perspectives: contextual, cognitive, and technological, which ensures accurate teaching for the personalized needs of English learners. It has been verified that the contextual interaction effect on students’ performance accounts for 78% of the total sample in the English distance education system after applying AI intelligent technology. Students in the experimental class showed positive interest in the distance education system 47.44±7.15, which was significantly higher than that of the control class 29.65±8.21. This integration model provides a new development direction for English distance education, and also provides strong support for personalized teaching and continuous optimization of the educational process.
Exploration of Innovative Practical Path of Party Building Work in Colleges and Universities Based on Cognitive Mapping Construction

Abstract This paper combines knowledge meta-extraction and knowledge fusion to construct a cognitive mapping model of party building work in colleges and universities, and the system contains four subsystems: data resource management, mapping ontology modeling, mapping construction, and mapping application. The content of the cognitive mapping is further optimized through the continuous collection, supplementation and improvement of subject knowledge data and the deduction, inference, induction and integration of party-building work using the cognitive mapping data model. After cognitive arbitrariness, the P-values of party-building organizational strength, intelligent party-building, branch activities, and organizational security are all less than 0.5, with significant positive effects. In the work dimension, the scores can reach 97 and 95. The number of participants in party-building organizational activities exceeded 500 after the innovation and even reached a maximum of 900. The results of the study can provide specific practical guidance for party-building work in colleges and universities, which can help them achieve better results in party-building work.
Scalability and Performance Evaluation of Graph Database Systems: A Comparative Study of Neo4j, JanusGraph, Memgraph, NebulaGraph, and TigerGraph

This paper presents a comprehensive analysis and evaluation of the performance and scalability characteristics of graph databases. The study focuses on the leading graph databases, namely Neo4j, JanusGraph, MemGraph, Nebula-Graph, and TigerGraph, as ranked by DB-Engines. In the face of the ever-increasing daily data volumes, traditional database management systems (DBMS) are struggling to cope with the growth, necessitating the exploration of alternative solutions. Graph databases, along with other types such as document and key-value databases, have emerged as promising alternatives that may offer more efficient handling of data-related challenges faced by DBMS.The objective of this research is to evaluate and compare the performance and scalability of these graph databases using various metrics, including query response time, data loading time, and memory usage. Through our experimentation, we have obtained compelling results. Among the top-five graph databases considered, Neo4j has exhibited superior performance, especially when handling larger datasets. It has consistently outperformed the other engines, showcasing its potential as a robust and efficient solution for managing graph-based data.
Construction of Online Interactive Teaching Platform for College English Based on Multilayer Perceptual Machine Modeling

Abstract Most of the existing English online teaching platforms are just digital replicas of offline teaching, which cannot fully utilize the advantages of information technology. Based on the learning theory of constructivism, this paper builds the functional architecture of a personalized English interactive teaching platform and completes the design of the main functional modules. According to the quantitative results of the platform’s user proposition requirements, a matrix description method for English test papers is proposed, and an algorithm is designed to realize the automatic generation of test papers. Meanwhile, the resource recommendation mechanism of the English platform is optimized using a multilayer perceptual machine. Unstructured English learning resources are processed based on the word vector representation method, and for the problem of insufficient processing of nonlinear data in the DN-CBR model, multilayer training is carried out using the MLP to improve the perceptual degree of this part of the data, and the design goals are verified through the test of the teaching platform. The correlation coefficient between self-assessment difficulty and topic difficulty is 0.368, and the correlation coefficient between self-assessment difficulty and positive error is 0.163, indicating that there is a moderate correlation between learners’ self-assessment difficulty and the difficulty of the system topics and between learners’ self-assessment difficulty and positive error. The MLP-based pushed questions’ difficulty is adapted to the learner’s personalization level by dynamically revising it based on their learning process data.
Evaluating the Effectiveness of the Practical and Applied Integration Teaching System of College English Based on Decision Tree and Rule Association Analysis

Abstract In this paper, the basic framework of data mining is first proposed, and the data mining model based on the ID3 algorithm decision tree is constructed by analyzing the information gain of the attributes to divide each subset recursively, and the model is utilized to mine the data related to the teaching of practical application of English in the university effectively. Then, the attributes of support, confidence, and interest from the Apriori rule association algorithm are utilized to measure students’ evaluation of English teaching. Finally, the applicability of this paper’s algorithm was evaluated by indicators such as classification accuracy and computing speed, and the effectiveness of the English practical application integrated teaching system was verified in terms of the dimensions of students’ English literacy, learning interest, and classroom problems. The results show that the mean values of students’ English basic knowledge, English application ability, English core skills, and English awareness attitude are 4.02, 4.15, 4.06, and 3.99, respectively, and the students’ learning interest has been improved by at least 50%, indicating that the teaching effect is good.
Quantum-Secure Hybrid Blockchain System for DID-Based Verifiable Random Function with NTRU Linkable Ring Signature

In this study, we present a secure smart contract-based Verifiable Random Function (VRF) model, addressing the shortcomings of existing systems. As quantum computing emerges, conventional public key cryptography faces potential vulnerabilities. To enhance our VRF’s robustness, we employ post-quantum Ring-LWE encryption for generating pseudorandom sequences and a NTRU lattice-based linkable ring signature scheme. Given the computational intensity of this approach and associated on-chain gas costs, we propose a hybrid architecture of VRF system where both on-chain and off-chain can communicate in a scalable and secure way. Our decentralized VRF employs multi-party computation (MPC) with blockchain-based decentralized identifiers (DID), ensuring the collective efforts of enhanced randomness and security. We show the security and privacy advantages of our proposed VRF model with the approximated estimation of overall temporal and spatial complexities. We also evaluate our VRF MPC model’s entropy and outline its Solidity smart contract integration. This research also provides a method to produce and verify the VRF output’s proof, optimal for scenarios necessitating randomness and validation. Lastly, using NIST SP800-22 test suite for randomness, we demonstrate the commendable result with a 97.73% overall pass rate on 11 standard tests and 0.5459 of average 𝑝-value for the total 176 tests.
The Innovation of “Six Steps” Teaching Model of College English under Data Chain Mode

Abstract In this paper, under the data chain model, the form of envelope modulation is first utilized to identify the lexical feature quantity precision based on the horizontal energy clustering point of lexical distribution. Then, the reconstruction of the teaching model is accomplished by clustering assessment of modulation means and optimal experience through subtractive clustering. Finally, the multivariate array parameter distribution method is used to realize the node optimization deployment design of the data chain system, and at the same time, the teaching quality weight coefficients are obtained according to the overlap probability and the energy domain detection probability. After applying the six-step teaching model, the passing rate of the third level is about 40%, and the scoring rate of the experimental group is higher than that of the control group by 1.64%. When the vocabulary frequency is 200HZ, the P-value is 0.0039 less than 0.05 effect is significant, which can enhance the students’ subjective initiative, and the English teaching mode realizes a better innovative effect.
Construction of college music information teaching mode under the background of Internet

Abstract The traditional music teaching method in the informationization era has been difficult to adapt to the needs of modern teaching and must be reformed in the direction of informationization. In this paper, based on the closure, inflection point and outer enclosing box features of the stroke line element, the recognition of handwritten notes is carried out from the three categories of straight line segments, folded line segments and quadratic curves. Meanwhile, for the binarized music score image, the multi-directional LBP features for spectral line detection are improved, and the computation method of multi-scale spectral line detection LBP features is established. The Manhattan distance is used to evaluate and select the features, which are inputted into XGBoost for classification and recognition training based on the statistical distribution characteristics of the features. Note recognition and spectral line recognition are applied to college music teaching, and the effectiveness of teaching is explored. In the rhythm-recognition path, the recognition teaching based on multi-scale and multi-directional LBP features led to an increase in students’ mastery of the musical score by 2.8 and in the phrasing and segmentation path by 3.5. Informational teaching led to a deepening of students’ mastery of the notes and musical scores.
Research on Artificial Intelligence Data Based on Image Texture Characterization and Aerobics Teaching Mode in Colleges and Universities

Abstract In this paper, we first studied the action recognition method based on image texture features, established a human body model, and realized the extraction of skeletal data and joint calibration. Based on the skeleton information, the aerobic movements are recognized, the difference between the aerobics movement sequence and the standard movement sequence is calculated, and the movements are scored. Then, the database for aerobic movements was established, and an AI-assistant aerobics teaching model was developed. Finally, the effect of the teaching method was analyzed through indicators of recognition effect, skill impact, and students’ learning interests. The results show that the accuracy of the system’s action recognition reaches 95%, and the overall response time is between 5.2-6.4s, with high real-time performance. And it has a significant effect on students’ movement participation, active interest, and independent learning behavior, with p-values of 0.013, 0.041, and 0.036, respectively, p&lt;0.05. This study promotes the development and innovation of aerobics, which can scientifically adjust training countermeasures and enhance the skill level of aerobics athletes.