Reviewerly/exHarmony huggingface dataset:
Research on the synergistic development and operation mechanism of vocational education and innovative development concepts in universities based on a separate equilibrium game model.

Abstract Using a separating equilibrium game model, this paper defines the ratio of cooperators to total individuals for the level of school-enterprise cooperation. We use Bayes’ rule to modify the strategy type and solve the vocational education discrete problem by describing the set of six tuples in the separating equilibrium game model under incomplete information. Transforming the vectors in the symmetric positive definite matrix into complementary linear vectors, it is found that the level of cooperation in the split equilibrium game model increases by about 45%. The reorganization, of course, teaching content in higher education according to the split equilibrium game model can effectively mobilize the learning enthusiasm of most students and provide new ideas for the innovative development of the higher education business.
The use of discourse fuzzy strategies in movie lines based on random forest model

Abstract In this paper, a random forest model is built using movie lines, each regression tree's prediction values are aggregated, and the final average is used as the prediction result. The fuzzy comprehensive evaluation decision is taken, and it is based on the fuzzy transformation principle and the maximum affiliation principle. The single-level fuzzy comprehensive evaluation can effectively cope with various fuzzy and uncertain information, which makes the comprehensive evaluation results more stable and reliable. The research results show that the training accuracy of the fuzzy comprehensive evaluation method is 96% and the testing accuracy is as high as 97%, and the proposed research can provide new ideas and research methods for the fields of film linguistics and text analysis.
A study on the application of digital technology in improving the quality and effectiveness of dance teaching

Abstract In this paper, the dance teaching digitization process and virtual character dance movement dataset are designed, combined with the Vicon motion capture system, neural network fusion shape technology and dance movement recognition algorithm to realize dance teaching digitization. The traditional K-modes algorithm is improved from three aspects: determining the number of clustering families, the determination of the initial clustering center, and the clustering distance metric to construct a digital dance teaching quality evaluation model. After the application analysis, it was concluded that the difference between the students of the experimental group and the students of the conventional group of dance movements was P&lt;0.05, and the learning situation of the experimental group showed an upward trend, the learning enthusiasm increased by 0.21, the learning efficiency increased by 0.23, and the clustering index were higher than 0.9. Dance teaching based on digital technology can improve the quality and effect of the teaching.
An Exploration of the Reform of English Informatisation Teaching in Colleges and Universities Based on Deep Learning Model and Microteaching Mode

Abstract In this paper, we use the cross-layer connectivity of residual networks in deep learning to convert convolutional and fully connected layers into sparse connections and cluster sparse matrices into relatively dense subspaces. Extracted features are used to perform target class prediction and regression of target coordinates using a target detection algorithm to meet the demand for real-time target detection. The model's use resulted in a head-up rate of 83.57% in the classroom, with the least serious students at 0.8 and above. Deep learning technology can enhance students' learning experience in English classrooms by providing personalized learning and a deep learning environment.
An analysis of the adaptability of Confucianism Civic Education in colleges and universities based on the construction of cognitive mapping

Abstract To extract the main structural features, we first use the CRF algorithm to annotate different knowledge feature vectors based on cognitive mapping in this paper. To mine the correlation between the main structures and fuse them, the G-DINA technique is employed to set the threshold value. The response function is utilized to diagnose the cognitive parameters of students and accurately quantify their cognitive state. According to the results, the educational platform has an average coverage rate of 78%. The average cognitive index scores of the students were 75, and 92% were content with the teaching effectiveness of the platform. The intelligent Civic Education Platform constructed based on cognitive mapping integrates Confucianism and culture to achieve two-way development.
Research on the Reform of Film and Television Production Courses and Their Student Achievement Evaluation in Colleges and Universities in Shanxi Province Based on Random Forest Algorithm

Abstract This paper classifies the data of the original data set by random forest algorithm, selects the nodes in the attribute space for iteration, and gets the number of decision trees in the random forest. Based on the decision tree, the information gain rate in the sample set of student achievement is calculated, the spatial distance matrix of the sample set is defined, and the centroids of each cluster of the matrix indicators are separated to get the evaluation results of indicators in the student achievement evaluation reform as superior. The indicator weights of student learning achievement are evaluated through five assessment indexes, in which the teacher rating weight is the highest 10. It shows that active use of the Internet is conducive to cultivating and delivering excellent film and television production professionals to society.
Formation of value orientations among female students to physical education and sports

Relevance: The article examines the relationship between the way of life and the formation of value orientations, taking into account stress resistance to the conditions of study of students of higher education. The level of formation of the value attitude of female students of the first and fourth years to their health and their relationship to the lifestyle was determined.&#x0D; Results: It was determined that the majority of girls of both the first and fourth years generally follow the rules of a healthy lifestyle, but at the same time they do not follow the rules of rational nutrition. Despite maintaining a healthy lifestyle, fourth-year female students have a low level of physical activity and have doubts whether their lifestyle is satisfactory.&#x0D; In the course of the study, it was established that among the terminal values of first-year students, freedom, social vocation, and lack of financial difficulties predominate, while among fourth-year students, the leading place is occupied by: financially secure life, family life, interesting work. It is shown that the breadth of knowledge, the ability to insist on one's point of view, the ability not to back down in the face of difficulties prevail among the instrumental values of first-year students, while the fourth-year students are led by efficiency in business, courage in defending their views; self control. At the same time, discipline, rationalism and sincerity are the least important for girls.&#x0D; The level of stress resistance of female students of different courses of study was determined. Thus, the vast majority of first-year students have an average level of stress resistance, and among fourth-year students, girls with reduced and very low levels of stress resistance predominate.
A study on pronunciation assessment of English learners based on temporal classification algorithm

Abstract This paper utilizes a time-series classification algorithm to classify samples, and the selected datasets are utilized to calculate the distance of time-series samples. The information gain is calculated using the information obtained, and the information entropy is determined collectively for the node datasets to generate time series features. The convolution operation is used to obtain the formal representation of the time series classification, and the extracted English pronunciation features are adaptively matched. The evaluation results were determined using a multilayer wavelet feature scale transformation method, and English learners achieved scores of 6 and above in all three tests using the time-series classification algorithm. To master standard English pronunciation, English learners should use the temporal classification algorithm.
A study on the literary elements of children’s literature classics and their influence on reading experience based on principal component analysis

Abstract The dimensional statistics approach of principal component analysis is utilized in this paper to transform a set of variable observations using orthogonal transformation to convert the data to a new coordinate. The original observations data are summed linearly by selecting appropriate values through cross-validation and calculating the spatial data of works with literary elements. Determine a sufficient number of basis functions selected mainly based on the fluctuation of time variables. The number of students who believe that reading classic works enhances scientific literacy was 92.56%, with the highest contribution of linguistic and literary elements found to be 92.56%. The uniqueness and richness of children's literature can enhance character-building and overall core literacy of students, as indicated by this.
The Influence of E-Learning Training on Teacher Performance with Motivation as an Intervening Variable

Education is a central factor in the quality of human resources. The quality of teachers in Indonesia is still relatively low. Low quality has an impact on low teacher performance. The solution to this issue is for teachers to attend training programs organized by both the government and private institutions. This research is ex post facto research. The research design utilizes a questionnaire and documentation as data collection instruments. The research population consists of elementary school teachers who participated in e-learning training at e-guru academy Batch 1, totaling 200 participants. The sampling technique employed in this research is simple random sampling, resulting in a total of 70 research samples. The research results indicate that e-learning training has a positive and significant impact on motivation with a contribution of 29.2%. E-learning training significantly influences performance with a contribution of 22.8%, and motivation has a positive and significant impact on performance by 50.4%. However, the influence of e-learning training on teacher performance through motivation has been found to be non-significant. Keywords: E-learning training, motivation, teacher performance
Shelved–Retrieved Method for Weakly Balanced Constrained Clustering Problems

Clustering problems are prevalent in areas such as transport and partitioning. Owing to the demand for centralized storage and limited resources, a complex variant of this problem has emerged, also referred to as the weakly balanced constrained clustering (WBCC) problem. Clusters must satisfy constraints regarding cluster weights and connectivity. However, existing methods fail to guarantee cluster connectivity in diverse scenarios, thereby resulting in additional transportation costs. In response to the aforementioned limitations, this study introduces a shelved–retrieved method. This method embeds adjacent relationships during power diagram construction to ensure cluster connectivity. Using the shelved–retrieved method, connected clusters are generated and iteratively adjusted to determine the optimal solutions. Further, experiments are conducted on three synthetic datasets, each with three objective functions, and the results are compared to those obtained using other techniques. Our method successfully generates clusters that satisfy the constraints imposed by the WBCC problem and consistently outperforms other techniques in terms of the evaluation measures.
Application of AR technology in the study of controlled structure in basketball training

Abstract To calculate the standard deviation of each attribute in the training control data, the normalized results of the standard deviation coefficient vector of basketball sports metrics are used as weights. Lagrange’s equation is used to analyze the behavioral trajectories of both players to enhance the offensive and defensive transitions of individual players. The weights used to calculate the standard deviation of each attribute of the training control data are based on the normalized results of the standard deviation coefficient vector of basketball sports metrics. It was found that the compression efficiency in the basketball sports teaching video with AR technology was 98.65%, the dominance ratio of offense-defense transition was 1.089, and the variance estimation of the disturbance term was 0.4621. Scientific training with AR technology can adjust training ideas, theories, and methods in real-time to realize the sports ideas of winning the standard and nurturing people.
Higher education management work based on information fusion technology for the development of innovation ability of college students

Abstract This paper explores the optimization methods of higher education management work to develop innovation competence. Focusing on classroom management, a method for assessing learning engagement based on information fusion technology is proposed. The facial images are localized based on Haar classification, and CNN is used to determine the learning emotion. Then, the fatigue level and learning concentration are detected based on eye state recognition and face posture estimation, respectively. The assessment of learning engagement is complete when the multidimensional information of classroom learning is fused. Among them, the better-performing face position decision layer features (F1=76.25%) and the facial expression decision layer features (F1=79.12%) are spliced and fused to increase the classification accuracy. The evaluation of learning engagement aids in improving the quality of classroom management and optimizing educational management of universities.
Construction of Student Information Management System for Universities Based on Cloud Computing Platform

Abstract The first step in this paper is to establish an integrated education information management platform that utilizes cloud computing as the scheduling center, considering the characteristics of cloud computing. The hierarchical structure of the student information management system and each business function module is designed using the B/S three-layer architecture. The fitness function evaluates individual merit based on K-means teaching data to ensure the genetic algorithm’s population diversity. The analysis model of university teaching data is constructed using the penalty function of chromosomes. The final test results show that when the network data transmission time reaches the 60s, the average network data traffic consumed is 260Byte, which indicates that the constructed student information management system has wide applicability.
Lightweight semantic segmentation network with configurable context and small object attention

The current semantic segmentation algorithms suffer from encoding feature distortion and small object feature loss. Context information exchange can effectively address the feature distortion problem, but it has the issue of fixed spatial range. Maintaining the input feature resolution can reduce the loss of small object information but would slow down the network's operation speed. To tackle these problems, we propose a lightweight semantic segmentation network with configurable context and small object attention (CCSONet). CCSONet includes a long-short distance configurable context feature enhancement module (LSCFEM) and a small object attention decoding module (SOADM). The LSCFEM differs from the regular context exchange module by configuring long and short-range relevant features for the current feature, providing a broader and more flexible spatial range. The SOADM enhances the features of small objects by establishing correlations among objects of the same category, avoiding the introduction of redundancy issues caused by high-resolution features. On the Cityscapes and Camvid datasets, our network achieves the accuracy of 76.9 mIoU and 73.1 mIoU, respectively, while maintaining speeds of 87 FPS and 138 FPS. It outperforms other lightweight semantic segmentation algorithms in terms of accuracy.
Students' Acceptance of Technological Devices for E-Learning During Covid-19 Pandemic in Malaysian Higher Education

During the pandemic, the majority of teaching and learning activities have transitioned to electronic platforms, utilizing technological devices. E-learning has emerged as a prominent tool for facilitating and managing these educational processes. Through e-learning, instructors can effectively handle student records, course materials, grading, and communication. This research focuses on examining students' acceptance of technological devices for e-learning in the context of higher education in Malaysia during the pandemic. The primary objective is to investigate students' acceptance and ongoing use of technological devices to support teaching and learning. The extended Technology Acceptance Model (TAM) is employed as the theoretical framework for descriptive assessment of students' acceptance. The study's findings reveal that students exhibit a positive attitude towards utilizing technological devices for e-learning during the pandemic.
<scp>ERSChain</scp>: Towards secure and flexible educational resource sharing using consortium blockchain and revocable ciphertext‐policy attribute‐based encryption

Summary Sharing high‐quality educational resources has become an effective way to promote educational equity. The traditional educational resource sharing platforms using centralized storage architecture have security issues. Recently, many studies use blockchain to achieve secure sharing of educational resources. However, the existing blockchain‐based educational resource sharing schemes only use blockchain as a storage tool, and have issues such as low sharing efficiency, without considering copyright security, and lack of a trusted sharing environment, which prevents the large‐scale sharing of educational resources. In response, we propose ERSChain, a novel blockchain‐based educational resources sharing solution. First, we put forward a hybrid storage method that keeps the hash value of resource in the blockchain and stores the encrypted resource in the off‐chain, which can alleviate the storage and computing pressure brought by massive educational resources while ensuring the integrity of resources. Second, we construct an efficient revocable ciphertext‐policy attribute‐based encryption algorithm to implement flexible access control and an outsourced decryption algorithm to achieve greater efficiency. Obtaining access to educational resources is possible when user's attributes meet the access policy and the user's identity does not exist in the revocation list. Third, we put forward a credit mechanism to adjust the user's credibility and a credit‐based consensus mechanism to maintain the trusted sharing environment. Finally, security analysis and plentiful of experiments demonstrate that our proposed ERSChain achieves security assurance, has better applicability than similar works, and enables large‐scale sharing of educational resources.
UTILIZATION OF THE CERTAINTY FACTOR METHOD TO DIAGNOSE EYE DISEASES

In Indonesia, the number of people with eye disease increases every year. The blindness rate of the population in Indonesia is around 1.2% of the total population. People with this eye disease have problems ranging from mild to blind spots. The main causes of blindness are cataracts, corneal disorders, glaucoma, refractive errors, dry eyes, retinal disorders and nutritional disorders. Conjunctivitis, macular degeneration, diabetic retinopathy and other diseases that affect the eyes. As vision, it is necessary to keep the function of the eye from decreasing. As a person ages, the accommodation ability of the eye also decreases. The causes include sitting too long in front of a computer or staring at a cellphone screen for too long, reading a book at a distance that is not within a normal healthy distance, or the presence of dirty air and solar radiation.
Is the counterfeit currency mechanism strictly enforced by banks and regulatory authorities?

Purpose Counterfeit currency ranges from a low-quality colour scanner/printer notes to high-quality counterfeits produced by hostile powers. Detecting counterfeit money notes is a national priority due to its huge negative economic impact. However, no automatic mechanism exists for identifying the source of counterfeit notes, which is a more sophisticated and critical problem. This paper aims to evaluate the procedure or mechanism which are followed by banks for fake not reporting and the effectiveness of this mechanism. Design/methodology/approach For doing this research, primary data has been collected from bank staff (particularly front desk staff like cashier/teller). A very simple and short questionnaire has been prepared where the researcher just wants to explore that up to what extent bank staff is aware about the clauses of the counterfeit currency note (CCN). Findings The result shows that in approximately 38% of cases, bank staff return the fake notes to the customer without reporting, and approximately 57%–58% are unaware of the fake currency notes reporting to the nodal bank office and police. Practical implications Banks themselves have to take stringent actions to ensure that none of the CCNs circulate again in the economy if caught by bank officials. All the flaws in a system should be effectively monitored along with covering the loopholes in the system like lack of training, time-to-time sensitisation of front desk employees, providing protection to employees in case of any kind of threat from customer and reducing the burden of retaining the customer if a customer is at guilty. Originality/value This is the original work done by the researchers; in fact, the researchers were able to find only a couple of studies related to this kind of in-depth analysis in the literature. This study is done to provide feedback to the authorities on how the system is manipulated for organisational and self-interest.
A DeturNet-Based Method for Recovering Images Degraded by Atmospheric Turbulence

Atmospheric turbulence is one of the main issues causing image blurring, dithering, and other degradation problems when detecting targets over long distances. Due to the randomness of turbulence, degraded images are hard to restore directly using traditional methods. With the rapid development of deep learning, blurred images can be restored correctly and directly by establishing a nonlinear mapping relationship between the degraded and initial objects based on neural networks. These data-driven end-to-end neural networks offer advantages in turbulence image reconstruction due to their real-time properties and simplified optical systems. In this paper, inspired by the connection between the turbulence phase diagram characteristics and the attentional mechanisms for neural networks, we propose a new deep neural network called DeturNet to enhance the network’s performance and improve the quality of image reconstruction results. DeturNet employs global information aggregation operations and amplifies notable cross-dimensional reception regions, thereby contributing to the recovery of turbulence-degraded images.