Reviewerly/exHarmony huggingface dataset:
Use of Multi-Temporal Sar Satellite Images to Detect Hazards to Infrastructures Induced by Postseismic Surface Deformation: A Case Study of 2016 Meinong Earthquake in Southwestern Taiwan

Over the past two decades, the development of InSAR techniques has apparently accelerated. Both conventional InSAR and multi-temporal InSAR (MTI) are successfully approved for monitoring surface deformation during earthquake events. This study takes the advantage of MTI technique to target on postseismic surface deformation, which is often overlooked while comparing with the coseismic surface ruptures and displacements. Although the advancement of the MTI technique has significantly improved the accuracy of postseismic surface deformation analysis, it has not yet been fully utilized as required data source for the hazard potential assessment. Neither the MTI-derived displacement information of postseismic deformation has not been directly transferred into the hazard potential index of public infrastructure, nor considered in the classification of emergency status. A rapid assessment of the hazard potential of infrastructure induced by postseismic displacements is of practical importance to societal security. We therefore processed multi-temporal images obtained by the Sentinel-1 satellite using persistent scatterer InSAR (PSI). With the constraints of continuous global navigation satellite system (GNSS) data, we also estimated 2D (E-W and vertical) postseismic deformation rates for the three years following the 2016 Meinong earthquake. The resulted 2D rates were converted into the annual deformation tolerance ratios (ADTR) for infrastructure inspection. Furthermore, by using a statistical method, the threshold of ADTR was established to highlight the infrastructure segments with high hazard potential. Along the high-hazard potential segments, we zoomed into the imagery pixel unit for local scale monitoring and conducted time-series variation analysis to characterize the spatiotemporal behavior of the fault systems after the 2016 seismic event. Our results indicated that the postseismic deformation rates in the E-W and vertical directions are 1.5 and 2–3 times higher, respectively, than of the interseismic period. Also founds are the multi-faults occurred the linkage relationship and controlled the characteristics of postseismic surface movements. For the hazard potential assessment of the high-speed railway infrastructures, the high-hazard potential segment in the E-W direction was located in Kaohsiung with the ADTR value between 5 and 7.5‰. On the other hand, the high-hazard potential segment in the vertical direction was located in Tainan with the ADTR value between 22–33‰. The time-series variation analysis of the high-hazard potential segments revealed that the hazard potential gradually decreased after the end of 2017, and the reginal stress adjustability of tectonic activities might terminate around mid-2018. The earthquake cycle began to transform from the postseismic to the interseismic period hereafter. Thus, the ADTR and its time-series analysis can effectively appraise the hazard potential of the infrastructure during the postseismic period.
Development of Chatbot Retrieving Fact-Based Information Using Knowledge Graph

Chatbot assists users by providing useful responses and not just a conversational system functionalities. The advanced Chatbots such as Siri and Alexa are the results of evolution of different response generation and NLU techniques that have arrived since 1960s. Usually, chatbots are designed to address domain-specific queries; for instance, a medical chatbot requires the user to provide his/her symptoms; in the corporate world, the chatbots designed are mainly for addressing the FAQs asked by their clients/customers. However, state-of-the-art technologies are emerging, and knowledge graph is one of them. The idea of using knowledge graphs is that the data stored in them is linked. The proposed-chatbot addresses the problem of answering factoid questions by retrieving information from knowledge graphs. Initially, the neural machine translation approach was used; however, due to its limitations, keyword extraction approach was adopted for the proposed chatbot. In order to compare the proposed chatbot system with DBpedia metrics, the F-measure quality parameter were used for determining the overall performance of chatbots.
Report scientific conference of the Institute for Digitalisation of Education of the NAES of Ukraine " Digital transformation of education in Ukraine under martial law": collection of materials

The collection contains the materials of the Report Scientific Conference of the Institute for Digitalisation of Education of the NAES of Ukraine " Digital transformation of education in Ukraine under martial law", which took place on February 24, 2023. The reports of the participants of the conference defined the modern directions of development of digital technologies of open education and science, described the theoretical and practical aspects of designing and using modern learning tools in a computer-oriented environment, in particular, the use of cloud and immersive technologies. The collection is addressed to scientific and scientific-pedagogical workers, post-graduate students, students of higher education institutions, everyone who is interested in the use of information and digital technologies in teaching, scientific and scientific-pedagogical activities. The materials were printed in the author's editorial office, tested during the discussion at the conference.
Recognizing Facial Expression Despite Occlusion Using Deep Belief Networks

Facial expression recognition is challenging in several scenarios including occlusions, noise, cultural differences, and limited training data. Facial expression recognition is challenging in occlusions because the occluded regions can contain important facial features that are necessary for the accurate recognition of emotions. This can lead to misclassifications or reduced accuracy in the recognition of facial expressions.In this article, I prposed deep belief network to recognize expressions despite occlusion. First, I used stratified split to ensure that the distribution of classes in the training and test sets was similar to the overall distribution in the dataset. Next, for scaling, I implemented Lanczos interpolation, which uses a weighted average of nearby pixels in the original image to determine the pixel value in the scaled image. For feature extraction, I used the Hopfield network.Then, I implemented the proposed DBN training, which utilizes Persistent Contrastive Divergence (PCD) for training Restricted Boltzmann Machines (RBMs). The backpropagation algorithm is then used to propagate the error backwards through the network. To fine-tune the network, I used DenseNet, a CNN architecture that introduces the concept of dense connections between layers, where each layer is connected to every other layer in a feed-forward fashion.The proposed method was evaluated on both wild and in-lab datasets, and the experimental results showed that the recognition accuracy of the proposed method outperformed state-of-the-art methods for randomly selected occluded images in the wild datasets.
Deep Multiview Adaptive Clustering With Semantic Invariance

Multiview clustering has attracted significant attention in various fields, due to the superiority in mining patterns of multiview data. However, previous methods are still confronted with two challenges. First, they do not fully consider the semantic invariance of multiview data in aggregating complementary information, degrading semantic robustness of fusion representations. Second, they rely on predefined clustering strategies to mine patterns, lacking adequate explorations of data structures. To address the challenges, deep multiview adaptive clustering via semantic invariance (DMAC-SI) is proposed, which learns an adaptive clustering strategy on semantics-robust fusion representations to fully explore structures in mining patterns. Specifically, a mirror fusion architecture is devised to explore interview invariance and intrainstance invariance hidden in multiview data, which captures invariant semantics of complementary information to learn semantics-robust fusion representations. Then, a Markov decision process of multiview data partitions is proposed within the reinforcement learning framework, which learns an adaptive clustering strategy on semantics-robust fusion representations to guarantee the structure explorations in mining patterns. The two components seamlessly collaborate in an end-to-end manner to accurately partition multiview data. Finally, extensive experiment results on five benchmark datasets demonstrate that DMAC-SI outperforms the state-of-the-art methods.
Study on the Integrated Validation of College Major Ability Diagnosis Tool: Focusing on the Department of Chemical Engineering

This study is to verify the unified approach validity of the major competency diagnosis tool for the Department of Chemical Engineering at A University in the metropolitan area. For this purpose, the Rasch measurement model is applied and analyzed. As a result of the validity verification based on the test contents, both the internal and external fit and the correlation coefficient of the point lag measurement are good. As a result of validation based on reality, 83.23% of the subjects show an item response form suitable for the Rasch model. As a result of validation based on the internal structure, it is found to support a dimension of the item. As a result of validation based on the reaction process, it is found to be good. As a result of validation based on generalization, no gender-specific discrimination function items are found in the tool. As a result of estimating item and subject reliability, the R reliability, separation index, and stratification index for subjects and items all show high internal consistency.
Research on the Development and Effect Verification of Blended Learning-based Korean Teaching Model: Focus on the “Intermediate Korean2” course at G University in China

This study aims to develop a Korean language-teaching model using blended learning and verify its effect through experiments. By probing into the teaching model and investigating the actual needs of teachers and learners, it proposes a teaching model suitable for Chinese learners. To verify the effect, 29 Chinese learners were taught using the blended learning model for 17 weeks, while 29 learners of similar levels were selected as statistical groups for traditional teaching. The test scores of the two compared groups indicate the blended learning course has greatly improved the listening and speaking abilities. Questionnaires and interviews show that the blended teaching mode has a positive effect on the learning enthusiasm, initiative, and autonomous learning ability.
The Effect of Text Coverage of Known Expression Items on Korean Reading Comprehension

The purpose of this study is to examine the effect of the text coverage of expression items known to Korean learners on reading comprehension. To this end, 51 foreign students were evaluated for reading comprehension of texts representing the different text coverage of expression items. The target expression items were modified to be meaningless expression items. After the reading comprehension evaluation, interviews were conducted. As a result, as the text coverage of the known expression items decreased, the reading comprehension score of the participants also decreased, but it was confirmed that there was no statistically significant difference. In addition, through the results of the post-interview, it could be assumed that the inference of meaning for the target expression item also affects reading comprehension. Although this study has several limitations, it will be meaningful in that it is an early study that looking at the relationship between the text coverage of expression items and Korean reading comprehension.
A Case Study on University Freshmen's Satisfaction with Introductory Software Education Classes

This study aims to examine the satisfaction and learning experiences of students in humanities, arts, and science and engineering disciplines who participated in a mandatory introductory software(SW) course within their university curriculum. A self-report questionnaire was employed to assess student satisfaction, while sentiment analysis and N-gram analysis techniques were applied to investigate students’ experiences throughout the course. The outcomes from the study are as follows. First, high levels of satisfaction concerning both SW education and its implementation were observed across all majors, with science and engineering students exhibiting the highest level of satisfaction. Second, a predominantly positive perception of SW education emerged, influenced by factors such as course content, instructor, and course implementation. Conversely, negative perceptions were also identified, with course content, implementation, and task-related aspects contributing to these views. Finally, based on the study’s results, recommendations for enhancing satisfaction in future online-based SW education programs are proposed.
Analysis of the role of emotional intelligence in the formation of identity in different European cultures

The objectives of the article are to determine the peculiarities of the theoretical, ideological and practical-functional factors that determine the cultural characteristics of the global European society and individual communities of the European region. The methodological basis of the study was a combination of general scientific, scientific-cultural, and philosophical methods. The results of the study show that emotional intelligence is a mechanism for implementing the fundamental principle of European society in variedade Concordia and providing functions of adaptation, integration, as well as pragmatism, and balancing socio-cultural dimensions. Consequently, emotional intelligence appears to be an important component that determines the peculiarities of the formation of the cultural identity of European communities.
The Effects of Playfulness and Creative Personality on Play Teaching Efficacy of Pre-Service Early Childhood Teachers: The Mediating Effects of Play Belief

The purpose of this study is to verify the mediating effects of play belief in the effects of playfulness and creative personality of pre-service early childhood teachers on play teaching efficacy. For this purpose, 256 pre-service early childhood teachers attending the Department of Early Childhood Education at University in Gyeonggi-do were collected and analyzed through an online survey. The research results are as follows. First, there was a statistically considerable correlation between playfulness, creative personality, play belief, and play teaching efficacy of pre-service early childhood teachers. Second, the playfulness and creative personality of pre-early childhood teachers had an effect on the efficacy of play teaching, and all of the play beliefs showed partial mediating effects. This means that the direct effect of playfulness and creative personality and the indirect effect through play belief exists simultaneously in the play teaching efficacy of pre-early childhood teachers. This results are meaningful in expanding the theoretical basis through verification of the mediating role of play beliefs along with the importance of playfulness and creative personality to improve the effectiveness of play teaching in the future.
A Study on the Early Career Patterns of Wage &amp; Salary Workers with Vocational High School Graduates

이 연구는 직업계고를 졸업한 임금근로자(이하 직업계고졸 임금근로자)의 노동시장 진입 후 초기 경력경로를 유형화함으로써 직업계고 졸업자의 다양한 경력이동 양상을 탐색하기 위해 수행되었다. 이 연구에서는 한국교육고용패널(KEEP) 1차 및 2차 웨이브 자료를 활용하여 2005년 졸업자와 2018년 졸업자의 경력경로를 각각 분석했으며, 각 표본의 임금 변화, 직종 이동, 일자리 이동에 대한 집단중심다중추세 분석을 실시하여 유형을 구분했다. 연구 결과, 2005년 직업계고졸 임금근로자의 초기 경력경로 유형은 첫 일자리 유지형, 직무 변화형, 지속적 이동형, 이직 후 유지형 등 4개 유형으로 구분되었으며, 2018년 직업계고졸 임금근로자의 경우 저임금 일자리 이동형, 첫 일자리 유지형, 직무 이동형 등 3가지 유형이 도출되었다. 이러한 초기 경력경로 유형은 시기와 상관없이 고용 안정성의 높고 낮음에 따라 구분되는 경향을 보였으며, 과거와 최근 모두 초기 경력단계에서 첫 일자리를 유지하는 유형이 주요 유형 중 하나로 나타났다는 점에서 선취업 후학습 등 고졸 취업 활성화 정책이 어느 정도의 성과가 있었음을 보여 준다. 다만 경력이동을 경험하는 직업계고졸 임금근로자의 노동시장 내 안정성은 과거보다 양극화가 심해진 경향이 나타나기도 했다. 이 연구에서는 이러한 결과를 토대로 후속 연구와 직업계고 졸업자의 경력개발 지원을 위한 제언을 제시했다.This study aimed to explore various aspects of the career movement of vocational high school graduates by categorizing their early career paths after entering the labor market. To this end, we analyzed the career patterns of 2005 and 2018 graduates using the Korea Education and Employment Panel(KEEP) 's first and second-wave data with group-based multi-trajectory modeling. We found the initial career path of 2005 graduates to be four patterns: ‘first job retention type,’ ‘job change type,’ ‘continuous mobility type,’ and ‘retention type after a job change.’ For 2018 graduates, we derived three initial career patterns: ‘low-wage job mobility type,’ ‘first job retention type,’ and ‘job mobility type.’ These early career patterns tended to be classified according to high and low levels of employment stability, regardless of the period. The results show that the policy to promote high school graduate employment has had some success. However, the stability in the labor market of wage & salary workers with vocational high school graduates experiencing career mobility tends to be more polarized than in the past. Based on these results, we presented suggestions for follow-up research and career development support for vocational high school graduates.
If You Can’t Measure it You Can’t Manage it - Quantitative Analysis of Cyber Risk Prediction and Mitigation

Cyber breach incidents have increased dramatically during COVID-19 pandemic and keep a cyclical trend there after. Data breach incidents result in severe financial loss and reputational damage to business, government, healthcare and educational institutions. Compared to sufficient amount of cyber risk investigation in economic and IT system domain, seldom investigations of cyber risk have been made in quantitative perspective, In order to fill this gap, we propose a Bayesian generalized linear mixed model to analyze data breach incidents chronology since 2001. Our model captures the dependency between frequency and severity of cyber losses, and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time trend effects are taken into consideration when investigating breach frequencies. A statistical predictive model is generated under actuarial mathematics frame, with flexible input available such as location and organization types. Predictions and implications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed and illustrated. Our results show that both geological location and business type play significant roles in measuring cyber risks. The outcomes of our predictive analytics provide numerical currency loss level that can be utilized by various kinds of organizations and design their risk mitigation strategies.
Factors Affecting Digital Literacy of Specialized Vocational High School Students

이 연구의 목적은 특성화고등학교 학생의 디지털 리터러시 영향요인을 구명하는 데 있었다. 이 연구의 모집단은 특성화고등학교에 재학 중인 3학년 학생이며, 지역과 계열 비율을 고려하여 층화표집하였다. 본조사는 온라인 조사를 통해 실시되었으며, 불성실 응답과 이상치를 제외하고 총 406부의 자료가 최종 분석에 활용되었다. 연구 방법의 경우 R 4.2.1 프로그램을 이용하여 기술통계와 위계적 회귀분석을 실시하였으며, 통계적 유의 수준은 0.05로 설정하였다. 연구 결과를 요약하면 다음과 같다. 첫째, 특성화고등학교 학생의 디지털 리터러시는 보통보다 높은 수준으로 나타났으나 하위요인 중 컴퓨팅 사고는 보통보다 낮은 수준으로 나타났다. 둘째, 개인, 가정 및 학교 특성 변인 중 학습전략, 개인혁신성, ICT 자기효능감, 가정 내 정보교육관심도, 교수실재감, 프로그래밍 교육 경험 중 ‘있고, 2년 이상’이 디지털 리터러시에 유의한 정적인 영향을 미치는 것으로 나타났다. 개인, 가정 및 학교 특성 변인의 설명량은 각각 46.1%, 1.2%, 4.8%로 나타났다. 연구 제언은 다음과 같다. 첫째, 마이스터고등학교 학생으로 연구 대상을 확장하여 두 집단을 비교하는 등의 추가적인 실증연구가 필요하다. 둘째, 특성화고등학교 학생의 디지털 리터러시 영향요인 간에 구조적 인과관계를 구명할 필요가 있다. 셋째, 특성화고등학교 학생의 컴퓨팅 사고 수준을 높이기 위한 교육적 개입 및 지원이 필요하다.The purpose of this study was to investigate the factors affecting digital literacy of specialized high school students. The population of this study was third-grade students attending specialized high school students. Stratified sampling was performed in consideration of region and school major. This survey was conducted through an online survey system, and the final responses of 406 people were used. The R 4.2.1 program was used for descriptive statistics and hierarchical regression analysis, and the p-value was set to 0.05. The main findings of this study are as follows, first, The digital literacy of specialized high school students was found to be somewhat high, however, among the sub-factors, computational thinking was lower than usual. Second, among the factors affecting digital literacy of specialized high school students, learning strategy, personal innovation, ICT self-efficacy, home information education interest, teaching presence, and programming education experience ‘more than two year’ were found to have a significant positive effect on digital literacy. The explanatory amount of personal, family, and school characteristic variables was 46.1%, 1.2%, and 4.8% respectively. The research suggestions are as follows. First, additional empirical studies such as comparing the two groups by expanding the research subject to Meister High school students are needed. Second, it is necessary to investigate the structural causal relationship between the factors influencing digital literacy and Third, educational intervention and support are needed to increase the level for computational thinking.
The Anthropological Approach to Education and Modern Training and Learning Methodology

The paper deals with the anthropological approach to education in particular to teaching theory through the prism of the correlation between the mentioned approach and modern teaching and learning methodology. The purpose is to establish and trace the correlation between anthropological approach to education and modern teaching and learning methodology &#x0D; The tasks are: to find out the essence of the anthropology in relation to pedagogy; to reveal and describe teaching and learning models based on anthropological approach to teaching theory; to figure out effective instruction methods, which can be applied within the designated teaching and learning models. The methods of the investigation are analysis and syntheses of the related scientific materials aimed at collecting and presenting the results as well as drawing the conclusion. &#x0D; The results revealed three different understandings of the essence of the studied phenomenon, one of which (as a methodological core and a modern paradigm of the complex of pedagogical sciences with practical orientation of education to the person as its main goal and value) is associated with our investigation. Among the correspondent teaching and learning models we revealed and described personality-oriented learning model, student-centered learning model and a model based on individual approach to teaching. A number of effective instruction methods (as: problem-based learning, case study, inquiry-based learning, discovery-based learning, role playing game, game learning, different forms of dialogic learning, team learning and others) was figured out. &#x0D; The conclusion is that in modern educational system anthropological approach is one of the central as knowledge about human, his nature and the laws of his development allows designing the educational process.