Reviewerly/exHarmony huggingface dataset:
The system of continuous design education in Ukraine in the context of higher education modernization

Continuous education is characterized by a constantly evolving complex of innovative pedagogical knowledge presented in the context of modernizing higher education in Ukraine. It has determined the relevance of the proposed research. The article aims to establish the effectiveness of introducing a range of educational components of continuous design education into pedagogical courses, perceived as innovations in education, to identify the problems of motivation and integration of the idea of continuous design education into the professional activities of educators. Several planning aspects of education modernization have been identified (attitude of educators towards innovation, anxious use of innovation in design education, external difficulties in introducing innovation in continuous education, innovative pedagogical practices, and readiness for modernization of design education in the higher education space). The study conducted comprehensive research, applying the method of pedagogical experiment. Surveys, observation, and experimental methods were used to diagnose the effectiveness of modernizing design education among educators in this field. The central hypothesis is that continuous courses in design education and self-education activities related to innovations in university education actively contribute to the popularization of continuous education in creative sectors of the national economy and anticipate their integration into the teaching activities of higher education staff. This research proves that programs of continuous training of specialists in design education and educational courses associated with innovation are effective and make it possible to use innovation in their professional practice. Further perspective is to investigate the development of continuous education in the territory of Ukrainian higher education.
Multi-level Noise Filtering and Preference Propagation Enhanced Knowledge Graph Recommendation

Knowledge Graph (KG) can provide semantic information about items, which can be used to mitigate the sparsity problem in recommendation systems. In recent years, the trend in knowledge-aware recommendation methods has been to leverage Graph Neural Networks (GNNs) to aggregate node information in KG. However, many of these methods focus on mining the item knowledge association on KG, but ignore the potential item auxiliary information in user’s history interaction outside KG. Furthermore, these methods equally aggregate all neighbor entities of the item on KG, which will inevitably introduce irrelevant entity-interaction behaviors. To address these issues, we propose a novel model, called Multi-level Noise Filtering and Preference Propagation Enhanced Recommendation (MNFP). Technically, we employ self-attention mechanisms to model the user’s interaction sequence to mine the item’s auxiliary information. Then, we design a twin-tower preference propagation mechanism that iteratively expands item auxiliary information on KG. Additionally, we propose a multi-level noise filtering mechanism. By learning the relationship consistency between the item and its neighbor entities, the model can guide the item to selectively link highly related neighbors in preference propagation, thus reducing the introduction of noise. We evaluate MNFP on three real-world datasets: MovieLens-1M, Last.FM and Book-Crossing. Results show that MNFP significantly outperforms state-of-the-art methods on AUC and F1.
Improving Adversarial Robustness via Channel and Depth Compatibility

Several deep neural networks are vulnerable to adversarial samples that are imperceptible to humans. To address this challenge, a range of techniques have been proposed to design more robust model architectures. However, previous research has primarily focused on identifying atomic structures that are more resilient, while our work focuses on adapting the model in two spatial dimensions: width and depth. In this paper, we present a multi-objective neural architecture search (NAS) method that searches for optimal widths for different layers in spatial dimensions, referred to as DW-Net. We also propose a novel adversarial sample generation technique for one-shot that enhances search space diversity and promotes search efficiency. Our experimental results demonstrate that the proposed optimal neural architecture outperforms state-of-the-art NAS-based networks widely used in the literature in terms of adversarial accuracy, under different adversarial attacks and for different-sized tasks.
Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network

The main purpose of knowledge graph embedding (KGE) is to complete the missing part of triplets. By learning from existing models, we found that adding graph convolutional networks (GCN) to KGE models achieved good performance. These models mainly use the local structural characteristics of data. However, their main goal is to learn an aggregator rather than a feature vector for each node. To address the problems with existing models, we proposed a knowledge graph link prediction model based on attentional relational graph convolutional networks (ARGCN). First, we capture the relationship attribute features between nodes by adding an attention mechanism and extract important node attribute features for visualization. Then, through a relational graph convolutional neural network, we capture the associated attributes between nodes and extract the feature information that can best represent nodes. Finally, we use DistMult as the scoring function. Our experiments show that our proposed is effective on standard FB15k-237 and WN18RR datasets. Compared with other models, it has relative improvements in Hits@1, Hits@3 and Hits@10.
Dual-Granularity Contrastive Learning for Session-Based Recommendation

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel dual-granularity CL framework. Specifically, two extra augmentation views with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data, with which we can explore connections of items on latent factor independently and generate factor-level embeddings. At item-level, the star graph is deployed as the augmentation method. By setting an additional satellite node, non-adjacent nodes can establish additional connections through satellite nodes instead of reducing the connections of the original graph, so data sparsity can be avoided. Compare the learned embeddings of these two views with the learned embeddings of the original view to achieve CL at two granularities. Finally, the item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods.
Document-Level Relation Extraction with Relational Reasoning and Heterogeneous Graph Neural Networks

Document-level relation extraction aims to identify the relations between the entities in an unstructured text and represents them in a structured way for downstream tasks such as knowledge graphs and question answering. In recent years, graph neural network-based methods have made significant progress in relation extraction. However, these methods usually require extracting all the entities in the document first, then a classifier is used to analyze the relations between the entities regardless of whether they have any relation. This wastes a lot of time analyzing the relations of irrelevant entity pairs and reduces the classifier’s attention to relevant entity pairs. To address this issue, this paper proposes a relation extraction module that integrates Relational Reasoning and Heterogeneous Graph neural Networks (RRHGN). The method finds a meta-path for each entity pair in a document and uses multi-hop reasoning to analyze the entities on the meta-path to determine whether there is a strong reasoning path between the entity pair. The relational reasoning module built into the method makes the classifier focus more on the relevant entity pairs in the document, thus reducing the task burden of the classifier and improving the accuracy of entity relation extraction. Experimental results on the large-scale document-level relation extraction dataset DocRED show that the proposed method achieves significant performance improvement compared with existing methods.
ParaNet:Parallel Networks with Pre-trained Models for Text Classification

The application of linguistic knowledge derived from pre-trained language models has demonstrated considerable potential in text classification tasks. Despite this, effectively learning the distance between samples and different labels for supervised learning tasks remains a practical challenge. In this study, we propose a novel approach, termed Parallel Networks with Pre-trained Models (ParaNet), which learns distance information between input samples and different labels within the same space. Specifically, ParaNet utilizes a Parallel Networks network architecture comprising two distinct Transformer Encoders to extract sample features and label features separately. By fine-tuning the network parameters, ParaNet can achieve the closest possible distance between the sample and its corresponding label, while simultaneously achieving the farthest possible distance between the sample and a label that does not belong to it. To fully exploit label information, the model leverages the semantic knowledge of the pre-trained model by adding templates to the labels. Our experimental analysis of eight benchmark text classification datasets demonstrates that ParaNet significantly improves classification accuracy, with an average accuracy rate increase from 89.1% to 89.64%.
M$$^2$$HGCL: Multi-scale Meta-path Integrated Heterogeneous Graph Contrastive Learning

Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks. Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required. Most existing methods for heterogeneous graph contrastive learning are implemented by transforming heterogeneous graphs into homogeneous graphs, which may lead to ramifications that the valuable information carried by non-target nodes is undermined thereby exacerbating the performance of contrastive learning models. Additionally, current heterogeneous graph contrastive learning methods are mainly based on initial meta-paths given by the dataset, yet according to our deep-going exploration, we derive empirical conclusions: only initial meta-paths cannot contain sufficiently discriminative information; and various types of meta-paths can effectively promote the performance of heterogeneous graph contrastive learning methods. To this end, we propose a new multi-scale meta-path integrated heterogeneous graph contrastive learning (M $$^2$$ HGCL) model, which discards the conventional heterogeneity-homogeneity transformation and performs the graph contrastive learning in a joint manner. Specifically, we expand the meta-paths and jointly aggregate the direct neighbor information, the initial meta-path neighbor information and the expanded meta-path neighbor information to sufficiently capture discriminative information. A specific positive sampling strategy is further imposed to remedy the intrinsic deficiency of contrastive learning, i.e., the hard negative sample sampling issue. Through extensive experiments on three real-world datasets, we demonstrate that M $$^2$$ HGCL outperforms the current state-of-the-art baseline models.
Lecturer’s Personal and Social Competence Toward Students’ Writing Proficiency in EFL Classroom

The competency of a lecturer is required for a successful learning process that leads to increased student accomplishment. The present study investigates (1) the profile of students' writing skills at the Port Shipping Management Department of Polytechnic of Makassar Merchant Marine (PIP) through a writing test, and (2) the influence of lecturers' personal and social competence on students' writing proficiency in English as a Foreign Language (EFL) classroom settings using qualitative data. The study employed purposive sampling to select 3 lecturers and 30 second-semester students from the Port Shipping Management Department of PIP. Quantitative data were collected through a writing test administered to the 30 students, while qualitative data were gathered using semi-structured interviews with the 5 lecturers, focus group discussions with the students, and classroom observations. The findings revealed various levels of writing proficiency among PIP students and highlighted the positive impact of lecturers' personal and social competence on students' writing proficiency. The better the personal influence and social competence of the lecturers, the better the students' writing capabilities are. The study emphasizes the need for continued development of lecturers' personal and social competence to foster a supportive and engaging learning environment in maritime education and training.&#x0D;
Indonesian EFL Learners’ Digital Reading Habits

Reading has long been one of the most crucial skills individuals should master to function effectively in society. Due to the revolutionary penetration of information and communication technology (ICT) into almost all aspects of human life, reading has transformed from paper-based to screen-based. Thus, facilitating students to cultivate digital reading should be one of the educational priorities. This study aims at exploring the digital reading habits of students majoring in English as a foreign language (EFL) in Indonesian context. Employing a mixed-methods design, quantitative and qualitative data were collected using an online questionnaire including close-ended and open-ended questions from 79 students of the English Language Education Study Program of Universitas Kristen Indonesia, Jakarta. The results show that a majority of the participants prefer to read in English in digital reading mode due to several reasons. Their most favorable tool for reading is smartphones. Additionally, a majority of them prefer to read at home as far as they have good access to the internet. Finally, based on the analysis of the motivational and de-motivational factors of reading, the participants’ preference towards digital reading has a stronger motivational foundation than print reading.
Prediction of El Niño La Niña &amp; Indian Ocean Dipole Phenomena Using the ARIMA Model by Prospective Teachers in Indonesia

The high number of losses and casualties experienced by the Indonesian people is caused by various factors. One of the factors lies in the people who lack knowledge in dealing with climate phenomena. Student-teacher candidates must take an important role in solving climate problems. One solution is to predict climate phenomena. This study uses the ARIMA model to predict the El Niño La Niña &amp; Indian Ocean Dipole phenomenon. The research method is the ARIMA model prediction steps using student worksheets. (1) know the model by determining ACF and PACF. (2) parameter model estimation determines ARIMA on predetermined ACF and PACF. The ARIMA parameter model is the slightest Mean Squared Error (MSE) level. (3) the diagnostic model of the smallest MSE. (4) a measure of predictive accuracy. And (5) Argumentation. Results In the IOD calculation, the correlation between the three-month prediction period is 0.92 and for six months is 0.96. This is the same as in the Nińo 3.4 correlation data. The value is close to 1, which is very accurate and can be used. The predicted value of the IOD index was expected. The resulting values ​​ranged from 0.01 to 0.17. This means that students are proficient in making climate predictions. Because students are enthusiastic and passionate about studying climate, it is a new science
Collaborative Development Mechanism of Vocational Education and Open Education Based on Advantageous Correlation in the Context of High-Quality Development

Abstract High-quality development is the goal of the collaborative development of vocational education and open education, and this paper constructs a quality evaluation index system for collaborative development starting from the characteristics of vocational education and open education. The subjective and objective weights of each index are obtained by using the G1 method and the CRITIC method, respectively. The combined weights are solved by multiplicative normalization, and the synergistic development of vocational education and open education is comprehensively evaluated based on the gray correlation degree. To verify the effectiveness of the evaluation index system and explore the mechanism of synergistic development, an empirical analysis was conducted. The results show that the weight of the open education index is 0.3491, the correlation coefficient of the evaluation indexes is more than 0.7, and the factor with the strongest influence is the influence of education with a correlation value of 0.973, which indicates that the mechanism of collaborative development between vocational education and open education needs to pay attention to the influence of education, so as to promote the high-quality development of both of them.
Reform and Innovation of Dance Choreography Teaching Based on Virtual Environment Technology

Abstract This paper firstly researches the reform and innovation of higher vocational dance choreography teaching under the background of new media and puts forward the reform and innovation suggestions of higher vocational dance choreography teaching according to the structure and thinking steps of higher vocational dance choreography teaching. Then, research on dance choreography teaching based on virtual environment technology, using quaternion interpolation to define the distance between dance action frames in dance choreography teaching video, selecting public frames of dance choreography teaching video based on the definition of dance action, articulating the keyframes of dance action according to splicing the dance action sequences and analyzing the research on innovation of higher vocational dance choreography teaching. The results showed that in posture, the P-value of students’ shoulder, torso, sitting, standing, walking, and overall posture were less than 0.01, with highly significant differences. In terms of students’ aesthetics, the P value of students’ aesthetic sensibility was 0.026, P&lt;0.05, aesthetic appreciation P value was 0.051&gt;0.05, and aesthetic creativity P=0.063&gt;0.05, which shows that students in the control class got a significant improvement in aesthetic sensibility. This study plays a certain role in inspiring and leading generalization for the teaching of dance choreography.
Digital Teaching Model of College English Based on Accurate Recommendation Model

Abstract This paper analyzes the technology of university English digital teaching formation in the context of the Internet, designs a university English resource base according to the characteristics of digital teaching resources, the degree of integration, and designs an autonomous teaching platform for university English based on the English resource base. On this basis, combined with the matrix decomposition algorithm, the knowledge point association matrix integrating multiple factors was constructed to design the English resource recommendation algorithm based on the matrix decomposition of the improved hidden semantic model. In addition, the Jmeter testing tool and SPSS tool were utilized to analyze the performance of the system and the application effect of the teaching model. The results show that the overall recommendation accuracy is also above 0.92, the request anomaly rate is 0% when the number of threads is 300, the response times are all kept within 0.1s, and the overall English learning effect is improved by about 0.3 on average. The digital teaching mode of university English in the context of the Internet can greatly enhance the impact of English teaching.
Research on the Innovation of Path of Nurturing Education for Ethnic Minority Students in Service Colleges and Universities in the Context of Big Data

Abstract This paper firstly discusses the special name of ethnic minority students’ formation education in colleges and universities under the background of big data, analyzes the influencing factors of formation education, and puts forward the 1535 innovative education model of formation education in colleges and universities. Secondly, based on the innovative education model, the evaluation index system of nurturing education was constructed, the triangular fuzzy number was introduced to optimize the hierarchical analysis method, and the weights of the indexes were determined by using TNFAHP. Finally, an empirical analysis was carried out to assess the effectiveness of the 1535 education model in nurturing education and the evaluation index system. The results show that the path of ethnic minority students in colleges and universities can be coordinated through the development of living habits, learning habits, and civilized habits, and the comprehensive evaluation score of the Adult Education 1535 education model is 80.57 points. This shows that the 1535 education model can effectively promote the formation of education for ethnic minority students and improve their study habits, civilized quality, and life etiquette.
The Application of Big Data Technology in Educational Management in Higher Education

Abstract In this paper, we first constructed a college education management system based on big data technology, clarified the system layout, analyzed the functional requirements of different personnel roles, and used the B/S development structure and mode to complete the layout and construction of hardware. Then, the evaluation model is established by using factor analysis and principal component analysis to calculate the eigenvalue, variance contribution rate and comprehensive score of each factor, and the evaluation index system is established. Finally, the effect of digital education management in colleges and universities is evaluated in terms of reliability, responsiveness, assurance and tangibility, and overall perceived quality. The scores of all dimensions are higher than 4, the student’s expectations are all in the range of 5-6.5, and the real perception is in the range of 4-5.5. Teachers’ expectations were in the range of 5-6 points, and their true perceptions were in the range of 4-5 points. Teachers and students are satisfied with the effectiveness of school management. This study has significance for educational reform as well as management upgrading in colleges and universities.