Reviewerly/exHarmony huggingface dataset:
Embedding Imputation With Self-Supervised Graph Neural Networks

Embedding learning is essential in various research areas, especially in natural language processing (NLP). However, given the nature of unstructured data and word frequency distribution, general pre-trained embeddings, such as word2vec and GloVe, are often inferior in language tasks for specific domains because of missing or unreliable embedding. In many domain-specific language tasks, pre-existing side information can often be converted to a graph to depict the pair-wise relationship between words. Previous methods use kernel tricks to pre-compute a fixed graph for propagating information across different words and imputing missing representations. These methods require predefining the optimal graph construction strategy before any model training, resulting in an inflexible two-step process. In this paper, we leverage the recent advances in graph neural networks and self-supervision strategy to simultaneously learn a similarity graph and impute missing embeddings in an end-to-end fashion with the overall time complexity well controlled. We undertake extensive experiments to show that the integrated approach performs better than several baseline methods.
Probabilistic Modeling of Assimilate-Contrast Effects in Online Rating Systems

Online rating system serves as an indispensable building block for many web applications. Previous studies showed that due to assimilate-contrast effects, historical ratings could significantly distort users' ratings, leading to low accuracy of product quality estimation and recommendation. To understand assimilate-contrast effects, an “accurate” model is still missing as previous models do not capture important factors like rating recency, selection bias, etc. Furthermore, an analytical framework to characterize product estimation accuracy under assimilate-contrast effects is also missing. This paper aims to fill in this gap. We propose a probabilistic model to quantify the aforementioned important factors on assimilate-contrast effects. We apply stochastic approximation theory to show that when the rating bias satisfies mild contraction conditions, the aggregate rating converges under aggregate opinion heterogeneity. We also apply non-stationary Markov chain theory to show that when the strength of assimilate-contrast satisfies mild stable conditions, the aggregate rating converges under rating recency. We also derive an equation to characterize the converged aggregate ratings. These conditions reveal important insights on how the aforementioned factors influence the convergence and guide the online rating system operator to design appropriate rating aggregation rules and rating displaying strategies. We apply it to rating prediction tasks and product recommendation tasks. Experiment results on four public datasets show that our model can improve the rating prediction and recommendation accuracy over previous models significantly, under various metrics like RMSE, NDCG, etc. We also demonstrate the flexibility of our model by showing that it can be applied to enhance other rating behavior models.
The Effects of Smart Device Use and Learning Competence on Smart Device Utilization Competence - Focusing on 1 Person: 1 Smart Device Demonstration School

The purpose of this study is to verify the effectiveness of one smart device per person in the education in Sejong City. To this end, device use, learning competency, and smart device utilization competency are measured for students at empirical schools. The effects of device use and learning competency on smart device utilization are confirmed by path analysis. As a result, the device use has a statistically significant positive effect on both learning competency and smart device utilization competency, and has a greater impact on learning competency. The method proposed to improve the learner's ability to use smart devices, which is an essential competency for the future society, is as follows. A learning environment rich in human and material resources should be provided, but smart devices should be used under appropriate rules. In addition, it will be necessary to sufficiently develop educational methods that can enhance learning capabilities so that teachers can use them realistically during class.
Introduction of Cut Nyak Dien's Figure in 2D Animation-Based Mobile Applications

The introduction of National Indonesian heroes at this time is still a little using existing technology, where technology is currently developing rapidly. Making prototypes about Indonesian heroes is a form of appreciating, remembering and remembering the existing heroes. Thus, the design of the "Leaftory" Cut Nyak Dien application is expected to make target users with the target of elementary school children ages 7-12 years able to know and remember Indonesian heroes by utilizing online mobile-based technology. The design of this application also pays attention to UI/UX by presenting an attractive appearance in the form of text, graphics and 2D animation to positively impact users. Developers also make trials to target users so that there is feedback from developers to users and vice versa.
Impact of social media marketing on purchasing intentions of luxury brands: the case of millennial consumers in the UAE

The proliferation of social media has created unparalleled opportunities for marketers. This paper studies the effectiveness of social media marketing in driving purchase intentions of luxury brands by exploring the key drivers of luxury brand consumption. Primary research was carried out amongst the millennial generation consumers residing in the United Arab Emirates (UAE), which generates the highest demand for luxury products in the world (Trade Arabia, 2018). To our knowledge, this is the first study to evaluate the social media marketing aspect of luxury brands in the UAE and makes important conceptual and empirical contributions to the existing literature through a multi-dimensional quantitative framework. Our results revealed that social media marketing has been effective in driving purchase intentions of luxury brands through the stimulation of materialism, hedonism, the need to seek social approval, or a combination of these factors. Our study has strong recommendations since consumers were found to be strongly influenced by celebrities and advertisements on social media platforms, which sends signals to marketers to enhance brand knowledge through 'role model' influencers and through innovative interactive features of networking sites.
Scenario-based mobile application design for young dual-language learners: evidence from factor analysis

This article aimed to provide new insights into critical factors to consider when developing a scenario-based mobile application (SBMA) for dual-language learners (DLLs). It is intended to investigate the factorial structure of the SBMA for young students in an elementary school context using exploratory and confirmatory factor analysis (CFA). The convenience sampling method was used to recruit 1,040 in-service teachers to complete the internet-based questionnaire. For half of the respondents, exploratory factor analysis (EFA) was conducted to identify the factors for SBMA design for young DLLs, and CFA was used to ascertain the model fit. The EFA identified five factors from 32 items, and through CFA, the results indicated that the five factors obtained from the EFA were validated: 1) application attribute; 2) lesson content; 3) immersive environment; 4) learning strategy; 5) instructional use. The findings shed light on how SBMA could be designed for DLLs.
DIGITAL LEARNING – ANALYSIS OF KEY PROBLEMS AND THEIR SOLUTIONS

Technologies introduced into education are aimed at reducing routine procedures, providing teachers and students with various means of teaching and learning, which makes education more accessible and convenient. As a result, the educational environment can become limitless in terms of professional communication and accessibility of learning. The article discusses the key problems and difficulties that arise in the process of implementing digital learning. Proposed measures to address the identified problems.
The effect of virtual and augmented reality on teaching crystal structure of materials and student reflections in applications

The purpose of this study is to examine the effect of a teaching material, developed with virtual and augmented reality technologies and used in teaching the contents related to the crystal structures to the engineering students, on the academic achievement of students. This study was conducted using the mixed method, and 23 students studying at an engineering faculty of a state university participated in this study in 2019-2020 Fall Semester. According to the results of the achievement test, the difference between the application scores in the test was significant, and these technologies contributed to the teaching of the crystal structures of the materials. When the data were analysed in terms of students' skills of drawing 3D figures, mathematical processing and explaining the content, it was found that these three skills improved in all students in a positive way.
The impact of electronic health record discontinuity on prediction modeling

Background To determine the impact of electronic health record (EHR)-discontinuity on the performance of prediction models. Methods The study population consisted of patients with a history of cardiovascular (CV) comorbidities identified using US Medicare claims data from 2007 to 2017, linked to EHR from two networks (used as model training and validation set, respectively). We built models predicting one-year risk of mortality, major CV events, and major bleeding events, stratified by high vs. low algorithm-predicted EHR-continuity. The best-performing models for each outcome were chosen among 5 commonly used machine-learning models. We compared model performance by Area under the ROC curve (AUROC) and Area under the precision-recall curve (AUPRC). Results Based on 180,950 in the training and 103,061 in the validation set, we found EHR captured only 21.0–28.1% of all the non-fatal outcomes in the low EHR-continuity cohort but 55.4–66.1% of that in the high EHR-continuity cohort. In the validation set, the best-performing model developed among high EHR-continuity patients had consistently higher AUROC than that based on low-continuity patients: AUROC was 0.849 vs. 0.743 when predicting mortality; AUROC was 0.802 vs. 0.659 predicting the CV events; AUROC was 0.635 vs. 0.567 predicting major bleeding. We observed a similar pattern when using AUPRC as the outcome metric. Conclusions Among patients with CV comorbidities, when predicting mortality, major CV events, and bleeding outcomes, the prediction models developed in datasets with low EHR-continuity consistently had worse performance compared to models developed with high EHR-continuity.
Social-cultural, psychological, linguistic, and world knowledge; EFL students' voices on their speaking difficulties in the border area of Indonesia

This study was conducted to identify students' difficulties with English speaking. This research used a qualitative method involving six students in the fifth semester of the English department at public university in Kalimantan Utara through purposive sampling. Data collection was carried out with in-depth semi-structured interviews. This study showed that students still experienced some difficulties in speaking English. The first factor was sociocultural;1) family engagement, 2) student's environment, and 3) peer engagement. The second factor was psychological; 4) feeling afraid, and 5) shyness. The third factor was linguistic; 6) local dialect, 7) pronunciation, 8) knowledge of grammatical patterns, 9) lack of vocabulary, and 10) fillers. The fourth factor was world knowledge, 11) topical knowledge, 12. limited learning time, and 13) comprehension. This study contributed to the students' reflection on finding their appropriate learning strategies and language exposure. Besides, the teacher needed to develop professionalism to help students reduce their speaking difficulties.
The Behavior of Students with Regard to School Culture in High Schools

Background: The culture of the school is one of the most significant factors to consider when trying to create an efficient learning and working environment for the staff, teachers, and students at the schools. Aim: The purpose of the research was explained to the behavior of students with regard to school culture in high schools. Methods: The study was conducted in Hue, Vietnam, from 2021 to 2022, and it sampled two high schools with a total of 339 students and 55 teachers. Results: The research questionnaire was prepared by teachers and students at the high school level, and it was based on the behavioral manifestations of high school students. According to the instructors and other students, the students have a sense of cultural conduct. This includes respecting adults, including their parents, teachers, and other school officials, as well as dressing in a manner that is acceptable for their age and the situation. Nonetheless, there are still some children who do not dress well, who are not appropriate for the situation, who behave inappropriately toward teachers and other school officials, and who are likely to pick up risky behaviors such as smoking, gambling, and other such activities. Conclusion and Implications: The findings of this study on the behaviors and cultural expressions of high school students have made it possible for educators and teachers to design curriculum that is suitable for their students.&#x0D; &#x0D; Received: 17 April 2023 / Accepted: 20 June 2023 / Published: 5 July 2023
ERNCLS: A Cross-Lingual Text Summarization Model Integrating Entity Relation Extraction Task

The current research on generative cross-lingual summarization models mainly adopts the framework of seq2seq, where entity relation errors are inevitable. How to reduce the occurrence of errors and improve the accuracy of summary generation needs to be addressed urgently. In this regard, we try to add the task of extracting entity relations as an auxiliary task in the Chinese-to-English cross-lingual text summarization model to reduce the entity relation errors in the summaries, and propose two fusion strategies for merging the auxiliary task. In addition, we also adjust the dataset for this task. The experiment proves that it is effective to reduce errors as well as improve the performance of cross-lingual text summarization model by merging the auxiliary task of extracting entity relations in the way of splicing summaries and entity relation triples as real labels of training sets.
A Comparative Study on Global Educational Strategies of UNESCO, UNICEF and GPE

[Purpose]Since the establishment of SDGs in 2015, the discussion on the roles of multilateral agencies has been expanded. They, indeed, have played critical roles in the global crisis of COVID-19 as developing strategies and implementing programs addressing emerging educational needs. Considering the context, the study aims to systematically identify the educational strategies of UNESCO, UNICEF, and GPE.&#x0D; [Methods]To this end, educational strategic reports published by the agencies from 2013 to 2021 were analyzed, and the unique features, purposes, and functions of each multilateral agency were compared using the text-mining analysis method.&#x0D; [Results]Firstly, UNESCO was characterized by its consistency in its educational strategy as the one who initiates global dialogue and influences educational policy at the national level. Furthermore, UNICEF preferred an inter-sectoral approach addressing education as human-right, and GPE was an evidence-driven funding organization emphasizing the global partnership with the public and the private. Secondly, this study found similar educational strategies aimed at cooperating with various stakeholders for the COVID-19 responses, while the partners and ways to collaborate varied. In addition, the educational strategic trends of UNESCO, which proposed fostering global citizens, UNICEF, which emphasized an integrated approach, and GPE, which supported case sharing and learning among partner institutions, were explored in the pandemic crisis.&#x0D; [Conclusion]Based on the results, this study proposed an understanding of each multilateral agency’s approach and implementation mechanisms for the diversification of international education cooperation in Korea.
Development of Mobile Application for Thai Buddha Amulet Classification

In this paper, we use transfer learning technique to train a custom Thai Buddha amulet coin classifier. The trained model is deployed on a server allowing a dedicated mobile application to request for class prediction results. The trained model achieves good accuracy using relatively small training image dataset. It is important to note that the primary objective of this work is not to evaluate the authenticity and quality of an amulet, but rather to create a prototype system that can be used as a tool to classify types of amulets.
Handling Imbalanced Data Sets Using SMOTE and ADASYN to Improve Classification Performance of Ecoli Data Sets

In this digital era, machine learning is a technology that is in demand by organizations and individuals. In the age of data and digital information, the ability to process data efficiently is needed. As the amount of data grows, there are various problems in machine learning. One of them is that with the increasing amount of data, class imbalance is also often found. Class imbalance is a condition where a class dominates another class, in one example case is when the positive value class has less number than the negative class. The class that is less in number is categorized as the minority class, while the class that dominates the dataset is called the majority class. Class imbalance can affect classification performance in a bad way, so handling imbalanced classes is needed to improve classification results. Classification of imbalanced data using Random Forest has satisfactory results, as well as by implementing SMOTE and ADASYN as sampling methods because they are highly popular and easy to implement. The best model produced in this study is the model that applies SMOTE oversampling on a dataset with 10% IR with a balanced accuracy of 98.75%, and the best result when applying ADASYN oversampling is on a dataset with 13% IR and a balanced accuracy of 99.03%.
EnVisioning CoRals: Embodying Coral Reef Inhabitants to Raise Awareness on Climate Changes Impacts on Remote Environments

Climate change has become a significant concern for humanity, and there is a need for innovative tools to increase awareness of this topic. The countries that contribute the most to climate change often do not suffer the consequences of their actions. Additionally, the effects of our actions are not immediately visible in the environment. These factors create a perceived distance between people’s mental representations and the impact of climate change. Virtual Reality (VR) is a promising tool for raising awareness about climate change and promoting pro-environmental behavior. It allows users to explore hardly accessible environments and experience the long-term effects of pollution from a novel perspective.We developed an immersive VR experience that will enable users to explore a coral reef and experience the decay caused by rising sea temperatures over 60 years. To increase nature connectedness and make people more aware of the consequences of human actions, we integrated virtual embodiment into our experience, allowing users to embody one of three reef inhabitants, a coral, a hermit crab, and a turtle. We presented our experience at a dissemination event and collected preliminary data on usability and feedback on the level of users’ embodiment. Although further studies are needed, our preliminary results are promising, as they indicate a high level of user engagement and a notable sense of embodiment in the virtual environment, suggesting that our application has the potential to be an effective tool for promoting awareness of climate change.
Exploring the Impact of Teaching Virtual Museum Design on Undergraduate Art Students’ Interest and Perception of Museums

Virtual museums have gained popularity due to their interactive and immersive media experiences and have been used to train technical staff and develop design strategies. They present unique challenges for traditional museums, especially when attracting younger audiences. To engage younger visitors, museums must innovate and improve their offerings by providing opportunities for active engagement and immersive cultural experiences. Research has shown that teaching virtual museum design to art students positively impacts their perceptions of museums and increases their interest. This thesis aims to design an effective virtual museum design course using the ADDIE model for undergraduate art students, focusing on the creation of virtual museums using the Unity3D engine, and exploring the potential of virtual museums to enhance young people’s attitudes towards museum visits.
Research on Satisfaction Prediction of "Internet + Elderly Care Service" Based on BP Neural Network

The aging in society is accelerating. Improving the happiness index of the elderly in their later years has become an urgent focus of family, society, and country. Based on the existing literature review, in-depth interviews, and unequal probability PPS sampling, we investigated the situation of "Internet + pension service" for 477 elderly people aged 60 and above in Shenyang. A BP neural network model is constructed to predict their satisfaction and evaluate the actual and predicted values of pension service satisfaction in the recent three years with absolute residual comparison. Solutions to the current weak links of pension services are discussed to meet the needs of the elderly and improve the elderly's sense of gain and happiness.
Multi-view graph representation with similarity diffusion for general zero-shot learning

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various parameters, including categories, subjects, and anomalies, in different domains. Nonetheless, most existing ZSL methods require the pre-defined semantics or attributes of particular data environments. Therefore, these methods are difficult to be applied to general data environments, such as ImageNet and other real-world datasets and applications. Recent research has tried to use open knowledge to enhance the ZSL methods to adapt it to an open data environment. However, the performance of these methods is relatively low, namely the accuracy is normally below 10%, which is due to the inadequate semantics that can be used from open knowledge. Moreover, the latest methods suffer from a significant ”semantic gap” problem between the generated features of unseen classes and the real features of seen classes. To this end, this paper proposes a multi-view graph representation with a similarity diffusion model, applying the ZSL tasks to general data environments. This model applies a multi-view graph to enhance the semantics fully and proposes an innovative diffusion method to augment the graph representation. In addition, a feature diffusion method is proposed to augment the multi-view graph representation and bridge the semantic gap to realize zero-shot predicting. The results of numerous experiments in general data environments and on benchmark datasets show that the proposed method can achieve new state-of-the-art results in the field of general zero-shot learning. Furthermore, seven ablation studies analyze the effects of the settings and different modules of the proposed method on its performance in detail and prove the effectiveness of each module.