Reviewerly/exHarmony huggingface dataset:
Practice of Teaching Creative Writing with Text in a High-quality Context for College Students Based on Predictive Analysis Ability of Big Data

INTRODUCTION: With the advent of the digital age, big data has become essential to all walks of life. In digital education, it has become an indispensable part, which can cover the students' learning progress, learning hours, practice scores, course information, classroom interaction information and so on, which makes the teaching process fully covered and a one-stop learning experience. Big data is essential to promote China's education to the scientific, systematic, informatization and customization continue to move forward.&#x0D; OBJECTIVES: To further improve the writing ability of college students in China, the systematic learning of big data is integrated into the daily teaching and life of the university so that college students can better embrace this significant data era.&#x0D; METHODS: This paper discusses the analysis of the current situation of college students' accompanying writing, the problems and improvement methods of college students' accompanying creative writing, the systematic elaboration of big data, and the method of predictive analysis of big data to explore the practice of teaching college students' accompanying creative writing in a high-quality context based on the ability of predictive analysis of big data.&#x0D; RESULTS: With the advent of the digital age, big data has become an essential part of various fields. As an even more indispensable part of the digital education field, it can cover students' learning progress, learning time, practice results, course information, classroom interaction information, Etc., indeed covering the entire education program for a one-stop learning experience.&#x0D; CONCLUSION: For language teachers in colleges and universities, improving the ability of predictive analytics under big data and then integrating it into the teaching practice to cultivate college students with high-quality creative writing skills based on the ability of predictive analytics of big data is the current priority, and is also the goal of colleges and universities to cultivate talents.
Synergistic Integration of Blockchain and Machine Learning: A Path to a Decentralized Intelligent Future

The integration of blockchain and machine learning has emerged as a promising paradigm that can revolutionize various industries and applications. Blockchain’s decentralized and immutable nature, coupled with the analytical capabilities of machine learning, presents new opportunities for secure and transparent data sharing, collaborative model training, and intelligent decision-making. This research paper explores the concept of synergistic integration of blockchain and machine learning, providing an overview of the underlying technologies, related work, and existing frameworks. It proposes a novel Decentralized Intelli- gent Learning Network (DILN) framework that combines the strengths of both technologies to create a decentralized and efficient ecosystem for collaborative machine learning applications. The paper presents case studies in healthcare, finance, supply chain management, IoT, and academic research to showcase the potential impact of this integration. Furthermore, it discusses technical approaches, challenges, and ethical considerations to address in the deployment of decentralized intelligent systems. The research paper concludes by encourag- ing further research and development in the field to unlock the full potential of this transformative technology.
The Comparison of Distance Methods on The On-Demand Ambulance Application

The on-demand ambulance is an application service that can be used to connect hospital ambulances or NGOs with people who need ambulance services. In this application, there is a geolocation feature service that is implemented with the distance calculation method, so that the feature gets the optimal distance with high accuracy. There are 3 distance search methods used in testing the distance search method, namely the haversine method, the Euclidean method, and the spherical triangle law of the cosine method. This study was conducted by utilizing data collected from the Google Maps API and obtained an accuracy of 100% for the haversine and spherical triangle law of cosine methods, while the accuracy was 89.95% for the Euclidean method. &#x0D; &#x0D; Keywords—Distance Method; Haversine; Euclidean, Spherical Triangle Law of Cosine
The WYSIWYG Publication

I was fascinated as I skimmed over a publication detailing a network analysis of crystalline materials.&lt;sup&gt;1&lt;/sup&gt; It’s “Open Access”, meaning I don’t have to pay to read the text and figures. There was one figure that had neat data-driven graphics for two separate materials, one stacked on top of the other. I wondered what the graphics would look like for some of the other 22,600 materials considered by the study.
Reuse of Conceptual Models

As part of her introduction to ontology enginering,&lt;sup&gt;1&lt;/sup&gt; Prof. Maria Keet has a slide depicting ontology as a layer apart from conceptual data models: &lt;em&gt;Conceptual data models vs. ontologies.&lt;/em&gt; [source] I like this visualization of various project-specific conceptual models and their associated implementations in databases and codebases.
A Study on the secondary preservice teachers' recognition of the metaverse and metaverse applied education

본 연구의 목적은 메타버스 및 메타버스 적용교육에 대한 중등예비교사의 인식 수준을 분석하여 교육연구자 및 학교교육 관계자가 근거로 활용할 수 있는 연구 및 실천 방향을 탐색하는 것이다. 이와 같은 연구의 목적을 달성하기 위해서, 66명의 중등예비교사를 대상을 설문조사를 실시하였고, 설문조사의 결과는 학습자의 배경변인(전공계열, 출생연도)에 따라 카이제곱(χ2) 검정을 수행하여 집단 간 인식수준 차이가 있는가를 분석하였다. 분석결과, 설문에 참여한 중등예비교사는 모두 1980년도 이후의 출생자로 모두 MZ 세대로 분류되었으며, 이들의 배경변인에 따른 인식수준의 차이는 나타나지 않았다. 전공계열과 출생연도에 관계없이 나타난 연구결과는 다음과 같다. 첫째, 메타버스에 대하여 전혀 모른다고 대답한 응답자는 없었으며(0%), 응답자의 대부분이 어느 정도 알고 있거나(77.3%), 아주 잘 알고 있다고(7.6%) 응답하였다. 둘째, 메타버스의 활용과 관련해서는, 대부분의 응답자가 일과 학습을 효과적으로 수행할 수 있는 공간으로(34.8%) 생산과 소비활동이 가능한 현실세계의 확장 공간(27.3%)이라고 응답하였다. 셋째, 중등교육에서의 메타버스 적용교육에 대한 의견으로는 대부분이 긍정적이라고 응답하였다(69.7%). 넷째, 메타버스 적용 상황에 대해서는 응답자의 47퍼센트가 학교교육의 보완적 교육의 공간으로 활용 가능하다고 응답하였고, 보완적 공간이 아닌 독립적 공간으로 활용될 수 있다는 응답도 전체 37.9퍼센트로 나타났다. 결론에서, 이와 같은 연구결과가 주는 시사점과 후속연구를 위한 제언이 논의되었다.The aim of this study is to analyze the level of pre-service secondary teachers' recognition of metaverse and metaverse applied education to explore research and practice directions that can be used as evidence by education researchers and school education stakeholders. In order to achieve the purpose of this study, a survey was conducted on 66 pre-service secondary teachers, and the results of the survey were analyzed using a chi-square (χ2) test according to the background variables (major, year of birth) of the learners. As results, all of the pre-service secondary teachers who participated in the survey were born after 1980 and were all classified as the MZ generation, and there was no difference in recognition level according to their background variables. Regardless of major and year of birth, the results of analysis are as follows. First, there were no respondents who answered that they did not know anything about the metaverse (0%), and most of the respondents answered that they knew to some extent (77.3%) or knew very well (7.6%). Second, regarding the utilization of the metaverse, most respondents answered that it is a space where work and study can be carried out effectively (34.8%) and an expansion space in the real world where production and consumption activities are possible (27.3%). Third, as for the opinions on metaverse application education in secondary education, most responded positively (69.7%). Fourth, 47% of the respondents answered that it could be used as a supplementary space for school education, and 37.9% of the respondents said that it could be used as an independent space rather than a supplementary space. In the conclusion, the implications of these research results and suggestions for follow-up studies were discussed.
Linked Data is an Ecosystem of Portal Abstractions

In an episode of the CoRecursive podcast&lt;sup&gt;1&lt;/sup&gt;, Sam Ritchie uses the phrase “portal abstraction” to describe how the use of a particular term can open a portal – a gateway – to a world of relevant prior art. He discusses issues in analytics. One issue is distributing summative calculations over data both as batches and in real-time, specialized for “big” and “fast” data, respectively.
Evaluating Performances of Attention-Based Merge Architecture Models for Image Captioning in Indian Languages

Image captioning is a growing topic of research in which numerous advancements have been made in the past few years. Deep learning methods have been used extensively for generating textual descriptions of image data. In addition, attention-based image captioning mechanisms have also been proposed, which give state-ofthe- art results in image captioning. However, many applications and analyses of these methodologies have not been made in the case of languages from the Indian subcontinent. This paper presents attention-based merge architecture models to achieve accurate captions of images in four Indian languages- Marathi, Kannada, Malayalam, and Tamil. The widely known Flickr8K dataset was used for this project. Pre-trained Convolutional Neural Network (CNN) models and language decoder attention models were implemented, which serve as the components of the mergearchitecture proposed here. Finally, the accuracy of the generated captions was compared against the gold captions using Bilingual Evaluation Understudy (BLEU) as an evaluation metric. It was observed that the merge architectures consisting of InceptionV3 give the best results for the languages we test on, the scores discussed in the paper. Highest BLEU-1 scores obtained for each language were: 0.4939 for Marathi, 0.4557 for Kannada, 0.5082 for Malayalam, and 0.5201 for Tamil. Our proposed architectures gave much higher scores than other architectures implemented for these languages.
Web-Based Application for Malaria Parasite Detection Using Thin-Blood Smear Images

Malaria is an infectious disease caused by the Plasmodium parasite. In 2019, there were 229 million cases of malaria with a death toll of 400.900. Malaria cases increased in 2020 to 241 million people with the death toll reaching 627,000. Malaria diagnosis which is carried out by observing the patient’s blood sample requires experts and if it is not done correctly, misdiagnosis can occur. Deep Learning can be used to help diagnose Malaria by classifying thin blood smear images. In this study, transfer learning techniques were used on the Convolutional Neural Network to speed up the model training process and get high accuracy. The architecture used for Transfer Learning is EfficientNetB0. The training model is embedded in a pythonbased web application which is then deployed on the Google App Engine platform. This is done so that it can be used by experts to help diagnose. The training model has a training accuracy of 0.9664, a training loss of 0.0937, a validation accuracy of 0.9734, and a validation loss of 0.0816. Prediction results on test data have an accuracy of 96.8% and an F1- score value of 0.968.
Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application

Since the emergence of the Covid-19 virus, the Indonesian government urged people to study, work, and worship or work from home. The social restriction policy has changed people's behavior which requires physical distance in social interaction. The government developed an application to minimize the spread of Covid-19, namely the PeduliLindungi application. The PeduliLindungi application is a tracking application to prevent the spread of Covid-19. The government's policy of implementing the PeduliLindungi application during Covid-19 aroused pros and cons from the public. The volume of PeduliLindungi application review data on Google Play was increasing, so manual analysis could not be done. New analytical approaches needed to be carried out, such as sentiment analysis. This research aimed to analyze user reviews of the PeduilLindungi application using classification methods, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The methods used were Synthetic Minority Oversampling Technique (SMOTE), Random Forest, SVM, and Naïve Bayes. SMOTE was used to balance user review data on the PeduliLindungi application. After the data had been balanced, classification was carried out. The results of this study showed that the Random Forest method with SMOTE got better accuracy than the SVM and Naive Bayes methods, which was 96.3% based on the division of training and testing data using 10-fold cross-validation. Thus, using the SMOTE method could improve the accuracy of classification methods in classifying opinions of user satisfaction with the PeduliLindungi application.
The "20 Queries" Heuristic

A scientific database cannot be everything to everyone. Jim Gray came up with the “20 queries” heuristic. What are the 20 most important questions the researchers want the data system to answer?&lt;sup&gt;1&lt;/sup&gt; Five questions are not enough to see a broader pattern, and 100 questions would dilute focus. Also, the relative information in queries ranked by importance is likely to be logarithmic – a “long tail” distribution.
Reliable Data and Knowledge Graphs

What makes data reliable? A recent article&lt;sup&gt;1&lt;/sup&gt; outlines five properties. It’s “clean” – formatted uniformly, conforming to certain rules/schema, etc.
The Prediction Factors of the Career Development Competencies of High School Students

본 연구는 고등학교 학생들의 진로개발역량에서 이질적인 유형이 존재하는지 확인하고, 각 잠재계층 구분에 영향을 미치는 요인들을 탐색하는 데 목적이 있다. 이를 위하여 교육부･한국직업능력연구원의 ‘2022년 초･중등 진로교육 현황조사’를 사용하였으며, 고등학생 7,1245명(남학생 3,853명(54.1%), 여학생 3,271명(45.9%))을 대상으로 하였다. 잠재프로파일분석을 사용하여 잠재계층을 구분하였고 다항 로지스틱 회귀분석을 사용하여 영향요인을 탐색하였다. 진로개발역량에서의 잠재계층을 확인한 결과 3개의 이질적인 집단으로 분류되었으며, 3개의 잠재계층을 구분하는 예측요인을 알아본 결과 개인수준 요인에서는 성별, 진로활동 참여 만족도, 진로체험 참여도움도, 창업가정신 함양 및 창업체험 교육참여 만족도, 진로정보 필요성, 부모와의 대화, 진로정보 인지도, 희망진로 준비도, 학습동기 그리고 자기주도학습이, 학교수준 요인에서는 진로전담교사가 인식한 창업가정신 함양 및 창업체험 교육의 적절성이 잠재계층 구분을 예측하는 요인으로 도출되었다. 끝으로 본 연구의 시사점과 후속연구를 위한 제언을 논의하였다.The purpose of the study is to estimate the latent classes of high school career development competencies and individual and school factors determining these latent profiles. To this end, we used data from the “2022 Career Education Status Survey” of 7,124 students, including (3,8534 male and 3,271 female students). Based on the latent profile modeling analysis, we identified three distinct latent classes of students' career development competencies. These classes were named ‘low-level group,’ ‘intermediate-level group,’ and ‘high-level group.’ Second, multinominal logistic regression analysis revealed that gender, career activity participation satisfaction, career experience participation assistance, entrepreneurship development and start-up experience education participation satisfaction, career information need, conversation with parents, career information awareness, desired career preparation, learning motivation, and self-directed learning were significant predictors of the latent profiles at the individual-level. Also, perception of the appropriateness of entrepreneurship cultivation and start-up experience education recognized by career teachers was derived as predictive factors that distinguish the potential class was significant predictors of the latent profiles at the school-level. Implications of this study and suggestions for follow-up research were discussed.
NHT'23

NHT is a continuing workshop series associated with the ACM Hypertext conference for over a decade. The workshop acts as forum of discussion for the narrative systems community within the wider audience of the Hypertext conference. The workshop runs both presentations from authors of accepted short research papers, and invited talks to provide a venue for important discussions of issues facing and opportunities for members of the narrative and hypertext community. This year the workshop aims to specifically target the timely issue of "Mixed Reality Narrative Hypertext" while maintaining an open venue for wider relevant work.
Enhancing Students’ Learning Outcomes through MALL in English Intensive Program for Railway Mechanical Technology

This study attempts to investigate students’ learning outcomes in English Intensive Program through Mobile Assisted Language Learning (MALL) for Railway Mechanical Technology. The program was designed as a TOEFL preparation class that utilized Google Form, Quizizz, and Kahoot! As game-based learning (MALL platform). It was a case study on the 3rd year students of the Indonesian Railway Polytechnic (N=46). The observation was conducted during 20 meetings (40 hours). The data were collected through pre-test and post-test, then analyzed using descriptive statistics, paired t-test, Pearson r correlation, and Cohen d effect size. The passing grade score determined by the college (480) was also an additional consideration analysis. The results indicate significant impacts and a large effect on students’ learning outcomes after joining the English Intensive Program through MALL. Nonetheless, the English Intensive Program implementing MALL had a moderate correlation and was less effective in enhancing students’ learning outcomes. These findings shed light on the benefits and constraints of implementing MALL in the English Intensive Program.
STRATEGI OPTIMALISASI LAYANAN E-RESOURCES PERPUSTAKAAN NASIONAL REPUBLIK INDONESIA DI MASA PANDEMI COVID-19

This study discusses the strategy for optimizing e-Resources services for the National Library of Indonesia (Perpusnas) during the COVID-19 pandemic. During the COVID-19 pandemic, Perpusnas stated that all online library services had increased three times. Perpusnas provides free open access services to local and international publications through the e-Resources portal. However, during 2020, statistical data shows a decrease in visitors to the e-Resources portal. The decrease in visitors to the National Library of Indonesia's e-Resources services is not proportional to the increase in all online services from the National Library of Indonesia. Therefore, the research problem is how the National Library of Indonesia's strategy in optimizing e-Resources services during the COVID-19 pandemic. The purpose of this research is to find an effective strategy for optimizing e-Resources services during the COVID-19 pandemic. This research uses a qualitative approach with a case study method, where data collection was carried out by online interviews with the National Library librarians and online observations of e-Resources digital collection services. The results show that the National Library of Indonesia's strategies in optimizing the use of e-Resources includes online user guidance, collaboration with universities, promotion on social media, and the Ask a Librarian service.
FAIR Principles for Research Software Released

Cross-posted on 21 June 2022. &lt;strong&gt;FAIR Principles for Research Software Released&lt;/strong&gt; &lt;em&gt;June 21, 2022&lt;/em&gt; FAIR4RS Working Group Steering Committee We are pleased to announce the release of version 1.0 of the FAIR Principles for Research Software (FAIR4RS Principles), after about 2 years of work by over 500 people.
Digitalization of the modern educational process in a risky environment

The use of digital technology is a major requirement in many professional fields, particularly in education. Teachers now understand more and more the benefits that come with the proper use of digital technologies in the organization of modern learning processes. New trends require teachers to be digitally literate. Therefore, there is a need for teachers to develop their own skills in using information technologies. The majority of digital literacy skills are related to the use of modern computer equipment, working with the Internet and others. However, the most important features of digital education are the teacher’s ability to freely use information and communication technologies to organize the learning process at all stages of education. However, certain risks may arise in the digitalization of modern education. The paper provides a typology of risks of digitalization of modern educational process. Sociological research was also conducted: a survey of students of the humanities university LSPU n.a. P.P. Semyonov-Tyan-Shansky, content analysis of the expert interview of the general director of VCIOM V.V. Fedorov, and an interview with Voronin Kim (head of of online course “PowerPoint Ninja”).
Evaluation of Distributional Semantic Models

This chapter focuses on the evaluation of distributional semantic models (DSMs). Distributional semantics has usually favored intrinsic methods that test DSMs for their ability to model various kinds of semantic similarity and relatedness. Recently, extrinsic evaluation has also become very popular: the distributional vectors are fed into a downstream NLP task and are evaluated with the system’s performance. The goal of this chapter is twofold: (i) to present the most common evaluation methods in distributional semantics, and (ii) to carry out a large-scale comparison between the static DSMs reviewed in Part II. First, we discuss the notion of semantic similarity, which is central in distributional semantics. Then, we present the major tasks for intrinsic and extrinsic evaluation, and we analyze the performance of a representative group of static DSMs on several semantic tasks. Finally, we explore the differences of the semantic spaces produced by these models with Representational Similarity Analysis.
Analysis of Factors Affecting User Inclination to use Virtual Education Exhibitions in the Post Pandemic Covid-19 Era: Case Study in Indonesia

Social distancing policies during the COVID-19 period have opened a space for interventions in the use of digital technology that was previously rejected by the public. Currently, most of the community activities have been carried out online, including those related to education, such as hosting virtual exhibitions with an educational purpose. Virtual exhibitions (VE) are online exhibitions that display information on specific topics. However, to the best of our knowledge, research on VE in the educational context is still lacking. This study aims to identify the variables that affect users' intentions to attend the virtual exhibition frequently. The channel expansion theory, flow theory, technology acceptance model, and expectation confirmation theory serve as the foundation for the research model that is constructed. The model was tested using data from 321 respondents who had visited VE, then analyzed using Structural Equation Modeling (SEM) with the aid of the SmartPLS application version 3.3.3. The findings show that 11 factors significantly influence the intention to continue using VE, while the other 2 factors have no significant influence. Thus, perceived usefulness has a significant effect on satisfaction and continued use intention to use VE, but not on perceived enjoyment. Furthermore, the perceived ease of use of VE has a significant impact on perceived usefulness and perceived enjoyment. However, media richness does not significantly affect perceived enjoyment. It's just that media richness influences perceived ease of use significantly. Confirmation also has a significant impact on perceived enjoyment, perceived usefulness, and satisfaction. Perceived enjoyment has a significant effect on satisfaction and continued use intention, and satisfaction has a significant effect on continued use intention to use VE. The findings are useful for VE providers and developers in developing a transformation strategy to increase the intention to use VE in every educational exhibition.