Reviewerly/exHarmony huggingface dataset:
Perceptions of Tutors on Learner Behaviors Utilizing Computer Programs: Focusing on Off-Task Behaviors of Students with Dyslexia

본 연구에서는 컴퓨터 프로그램을 활용한 교수에서 목표 시각화 유무와 보상 시각화 유무가 학생이 보이는 비과업 행동(자리이탈, 불쑥 말하기, 소음, 기타행동)의 관찰 횟수와 튜터가 보고하는 비과업 행동의 강도에 어떤 영향을 미치는지 살펴보고자 하였다. 이를 위해 25명의 초등학교 1~6학년 학생을 대상으로 한글 파닉스 교수 프로그램을 활용하는 교수학습 환경에서 목표 시각화 유무와 보상 시각화 유무에 따라 활동 중 학생들이 보이는 비과업 행동의 횟수와 강도에 대한 자료를 수집하였다. 수집된 자료를 바탕으로 이원분산분석(2-way ANOVA)을 활용하여 분석을 실시하였다. 먼저 행동의 횟수를 살펴보면 비과업 행동 중 자리이탈, 불쑥 말하기, 소음의 경우 목표 시각화 유무와 보상 시각화 유무에 따른 차이가 발견되지 않았다. 다음으로 강도에서는 비과업 행동 중 소음의 경우 목표 시각화가 제공되었을 때 제공되지 않았을 때보다 튜터가 보고하는 강도는 더 높은 것으로 확인되었다. 또한 불쑥 말하기의 강도에서는 두 독립변인의 상호 작용과 각각의 주효과가 유의한 것으로 나타났다. 구체적으로 목표 시각화 적용 유무에 따른 불쑥 말하기 강도의 차이는 보상 시각화가 있을 때에만 그 차이가 있는 것으로 확인되었다. 본 연구를 통해 컴퓨터 프로그램을 활용하여 교수를 제공하는 과정에서 교수자가 학습자의 행동에 대해 보이는 인식에 대해서 확인할 수 있었으며, 이와 관련하여 교수자 교육의 필요성에 대한 시사점을 도출하였다. This study examined how the presence of a visualized performance goal and reward displayed on computer programs affects tutors’ perception on the frequency and intensity of off-task behaviors (such as ‘out of seat’, ‘talking out’, ‘making noise’, and ‘’other) of students with learning difficulties. For this purpose, we collected data on the frequency and intensity of off-task behaviors based on the presence or absence of the visualized performance goal and rewards in a computer-based Korean phonics learning program. Tutors’ behavior ratings were compared to the ratings of a trained observer. This experiment was conducted while students with dyslexia received a series of Korean phonics instruction using a computer program in a 1:1 clinic setting. The results showed that regarding the behavior intensity, tutors reported higher scores on the ‘making noise’ when the performance goal was visually presented compared to when there was no performance goal presented. More importantly, tutors perceived that students showed more intense “talking-out” behaviors when the visualized performance goal and visualized reward were presented together. However, the trained observer reported that there was no significant difference in the intensity on students’ “talking out” behaviors when the performance goal and rewards were presented on the screen compared to when the goal and rewards were not presented. This study implies that the perceptions of educators regarding students’ negative behaviors during a computer-based instruction need to be delt with caution. The results also shed light on the need for related teacher training to promote positive interactions during computer-based instructions.
Item analysis of reading comprehension questions for English proficiency test using Rasch model

The need to take English as a foreign language proficiency test (known as TOEFL [Test of English Language Proficiency]) has been gaining popularity in Indonesia. The increasing demands for such a test and its expensive cost have reinforced many institutions to develop TOEFL instruments and administer the test internally. However, constructing a test instrument is a complex process that makes conducting item analysis become more challenging. Meanwhile, item analysis is crucial to assess the items’ quality. Therefore, this study reported the results of statistically analyzing 20 questions of TOEFL reading comprehension that were analyzed in terms of the test reliability, the item and person fit, and the items’ difficulty level. Thirty-eight members of the English Department Students’ Association of a state university in West Java participated in this study by taking the reading test. The data were analyzed using the Rasch model by utilizing the Quest program. The results showed that four items (36.8%) did not fulfill the ideal criteria of a valid test because they were too easy and too difficult to be given to the target test takers; thus, they needed to be discarded. Meanwhile, 16 items (63.2%) are of good quality and can be used immediately in the proficiency test, especially to measure reading comprehension skills, because they have fulfilled the standard requirements for a valid test. The findings have provided insight into the importance of item analysis in validating test instruments to improve the test quality for future administrations.
CONVIQT: Contrastive Video Quality Estimator

Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Distortion type identification and degradation level determination is employed as an auxiliary task to train a deep learning model containing a deep Convolutional Neural Network (CNN) that extracts spatial features, as well as a recurrent unit that captures temporal information. The model is trained using a contrastive loss and we therefore refer to this training framework and resulting model as CONtrastive VIdeo Quality EstimaTor (CONVIQT). During testing, the weights of the trained model are frozen, and a linear regressor maps the learned features to quality scores in a no-reference (NR) setting. We conduct comprehensive evaluations of the proposed model against leading algorithms on multiple VQA databases containing wide ranges of spatial and temporal distortions. We analyze the correlations between model predictions and ground-truth quality ratings, and show that CONVIQT achieves competitive performance when compared to state-of-the-art NR-VQA models, even though it is not trained on those databases. Our ablation experiments demonstrate that the learned representations are highly robust and generalize well across synthetic and realistic distortions. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
Interactive Learning Media Application For The Introduction Of Human Needs In Children Aged

Early childhood education is a form of education that focuses on establishing the basis for significant physical growth and development, such as body growth and motor development. Children begin to develop the ability to think, learn, remember and understand basic concepts. This research aims to produce an animated application for the introduction of human needs in children for their congnitive development. In the design process using Flowchart diagram to create an overview of how the process in the interactive learning media application. Storyboard is used for an overview of the appearance and workings of interactive learning media applications. There are several choices of materials such as food, drink, clothing, health, hygiene and safety. All materials are equipped with sound images and attractive displays. Flowchart is used to display the workflow, from the start to the stage of selecting interactive learning media application materials. The application contains material that already covers the curriculum used by PAUD schools in general. Interactive learning media applications help early childhood in learning about human needs.
Dynamic Resilience in Credit Card Fraud Detection: The Adaptive Accuracy Weighted Ensemble Approach

This paper introduces the Adaptive Accuracy Weighted Ensemble (AAWE) algorithm, strategically devised to fortify credit card fraud detection against the constant threat of concept drift. Centered on the foundational Multilayer Perceptron (MLP) classifier, AAWE seamlessly incorporates dynamic weighting mechanisms to skillfully acclimate to the ever-shifting landscape of fraudulent activities. This innovative progression substantiates AAWE's capacity to substantially augment the precision of credit card fraud detection, demonstrating adeptness in negotiating the intricate challenges presented by perpetually evolving fraudulent strategies. In the realm of credit card fraud detection, wherein subtle deviations in fraud methodologies can yield profound consequences, AAWE's intrinsic adaptability emerges as a pivotal asset. The algorithm's ability to promptly respond to concept drift nuances becomes a critical edge. With empirical evidence showcasing its commendable accuracy rates, AAWE emerges as a compelling remedy to the exigent concept drift quandary within credit card fraud detection. This research sheds a compelling light on AAWE's potential to deliver robust and pinpoint-accurate fraud detection outcomes, solidifying its stature as an invaluable instrument for real-world scenarios where timely accommodation to emerging fraud patterns is of paramount significance.
Sentiment Analysis of Comments on Instagram Posts of Indonesia's 2024 Presidential Candidates Using The Support Vector Machine Method

The rising number of Instagram user affecting higher number of comments appear on post especially Instagram accounts of Indonesia's 2024 presidential candidates that made it difficult to understand the public sentiment towards presidential candidate. Therefore, this research aims to classify Indonesian sentiment on Instagram comments of 2024 Indonesian presidential candidates using the Support Vector Machine method. The classified sentiment is divided into three classes, namely positive, negative, and neutral. The results shows that Sentiment Analysis of Comments on Instagram Posts of Indonesia's 2024 Presidential Candidates Using The Support Vector Machine Method has a good accuracy value of 89.41%. This results also obtain recall and precision values of 89% and 87% respectively.
CLUSTERING ANALYSIS OF ADMISSION OF NEW STUDENTS USING K-MEANS CLUSTERING AND K-MEDOIDS ALGORITHMS TO INCREASE CAMPUS MARKETING POTENTIAL

Acceptance of new students is a very important activity for a high school or university. The admissions data has not been utilized by the campus in making strategic decisions, marketing potential, and considering invitations through academic admissions. So, to assist in processing the new student admissions data, in this study the design and analysis of new student admissions data was carried out using stages in data mining. The clustering method approach can be applied in analyzing the potential level of PMB quality produced by utilizing the PMB recording dataset for the 2023 period. 86 data records. The K-Means and K-Medoids algorithm models that are applied have results that show a new insight, namely grouping based on 2 clusters, cluster 1 (C0) is a pass category while cluster 2 (C1) has not been determined. The results of the K-Medoids algorithm which has cluster 1 (C0) 60 results, cluster 2 (C1) has 26 results is a potential pass of 60 and has not yet been determined 26 of the data tested 86 while the results of the K-Means cluster 1 algorithm (C0) 40 , cluster 2 ( C1 ) 46 is a potential pass consisting of 40 and 46 undetermined data from the 86 datasets tested. Testing using the RapidMiner Studio application can also produce similar insights, namely each cluster has Davies Bouldin Index or DBI results from each K-Means and K-Medoids algorithm. K-Means has a Davies Bouldin Index result of -0.533 while K-Medoids has a Davies Bouldin Index result of -0.877
Dominant metacognitive competencies of modern schoolchildren and the problems of their formation: theoretical and empirical substantiation

Introduction. In accordance with the modern requirements of the state, the number of normative results of general education includes personal, interdisciplinary and subject results. The possibility of full-fledged formation, development, education of personal qualities, knowledge and skills, set as the expected results of education, is largely determined by the abilities, cognitive activity of the students themselves. In turn, the subjective position of schoolchildren in the educational process, their cognitive activity, the ability to selforganization and self-development require improvement and manifestation of the formed metacognitive competencies. The purpose of the study: theoretical definition and empirical substantiation of the set of dominant metacognitive competencies, as well as key pedagogical tasks in the field of their formation in modern schoolchildren. Materials and methods. The main method of research is the method of expert evaluation. A group of experts from 50 teachers of general education organizations and higher educational institutions was formed to conduct it. The results of the study. The category of metacognitive competencies includes a set of metacognitive knowledge (knowledge about oneself, about tasks, actions or interaction strategies, about the impact of knowledge on the final result of cognition), metacognitive strategies (mastered intellectual actions related to setting goals, planning one's cognitive activity, monitoring and evaluating its effectiveness, detecting intellectual difficulties), characteristics of metacognitive thinking (premeditation, planning, purposefulness, perspective, cognitive orientation). The expert assessment made it possible to include the following characteristics in the dominant metacognitive competencies: creative thinking, critical thinking, reflexive thinking. Experts' assessment of teachers' readiness to organize and implement the process of formation of dominant metacognitive competencies in schoolchildren showed that more than 60% of the responses indicated complete or partial theoretical, motivational and practical unpreparedness. Conclusion. The results obtained are the basis for designing a system of training future teachers to form dominant metacognitive competencies, which determines the practical significance and illustrates the future prospects of the study.
Implementasi Komik Digital dalam Meningkatkan Kemampuan Siswa Membaca Descriptive Text di MI Oku Timur

The learning process will be effective when supported by various planning, including the use and utilization of instructional media. The objective of this research is to determine the influence of using digital comic media on the intensification of reading abilities in descriptive English texts among students at MI Subulussalam in OKU Timur. The method used is an experimental approach, with data collection through oral tests based on topics within comic images. Based on the research results, it can be concluded that the implementation of Digital Comic instructional media has an impact on the ability to read descriptive texts compared to before the implementation of digital comic instructional media. This is evident from the difference between the pre-test and post-test results.
RADAR: Building a FAIR and Community Tailored Research Data Repository

The research data repository RADAR is designed to support the secure management, archiving, publication and dissemination of digital research data from completed scientific studies and projects. Developed as a collaborative project funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (2013-2016), the system is operated by FIZ Karlsruhe - Leibniz Institute for Information Infrastructure - and currently serves as a generic cloud service for about 20 universities and non-university research institutions. Since its launch, RADAR has witnessed significant changes in the landscape of research data repositories and the evolving needs of researchers, research communities and institutions. In our presentation within the “Enabling RDM” Track, we will show how RADAR is responding to these dynamic changes. In order to create a sufficiently large user base for the sustainable operation of the system, we have moved RADAR away from its previous single focus on a discipline-agnostic cloud service and towards a demand-driven functional optimisation. In 2021, we introduced an additional operating model for institutions (RADAR Local), where we operate a separate RADAR instance locally at the institution site exclusively using the institutional IT-infrastructure. In 2022 we opened up RADAR to new target groups with community-specific service offerings, in particular in the context of the National Research Data Infrastructure (NFDI). Beside the expansion of the functional scope, our ongoing development work focuses also on strengthening the system's support for the FAIR principles [1] and the concepts of FAIR Digital Objects (FDO) [2] and Schema.org. Our presentation will outline recent RADAR developments and achievements as well as future plans thus providing solutions and synergy potential for the scientific community and for other service providers.
Organizing Scholarly Knowledge in the Open Research Knowledge Graph

The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge. Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data. The ORKG includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research and their findings. It facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data, reducing duplication and enabling more efficient use of resources. As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.
NFDI4DS Gateway and Portal

©NFDI4DataScience (NFDI4DS) is a consortium to support researchers in all stages of the research data lifecycle to conduct their research in line with the FAIR principles. The developed infrastructure targets researchers from a wide range of disciplines in data science and AI. We present the ideas of the NFDI4DS gateway and the NFDI4DS portal. Two approaches to navigate digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) from various NFDI4DS resources such as the ORKG, the DBLP database, and other research knowledge graphs (KGs). Transparency, reproducibility, and fairness will be fostered by a step-wise integration of existing and newly developed services into the overall system. With this paper, we want to engage with the community and understand the needs and challenges of researchers in various disciplines regarding data science and AI. Therefore, we will discuss the currently developed prototypes and outline our plans for future development steps.
An efficient approach to secure smart contract of Ethereum blockchain using hybrid security analysis approach

The technology behind blockchain is quickly becoming one of the most crucial innovations in recent years. The Smart contracts are digital agreements, made in between two untrusted parties. Smart contracts are self-executable small piece of code that gets executed due to some predefined triggering conditions. Smart contracts store cryptocurrencies as their balances and deal in cryptocurrencies on network transactions. Because of this, smart contracts are constantly open to the possibility of being attacked. A single security vulnerability can make the smart contract very much insecure. The immutability property of the blockchain ensures that, once a smart contract has been placed on the blockchain, cannot be modified in any way. So, the smart contract must be analyzed for any kind of security vulnerability before its deployment on the blockchain. Existing analysis approaches detect vulnerabilities with high false positive rates. Our proposed approach analyses the smart contracts using a hybrid combination of pattern matching and symbolic execution, which produces results with a low false positive rate. We have performed a comparative analysis of our proposed approach to prove its efficiency with the existing research approaches on a data set of 453 smart contracts with tagged vulnerabilities.
Comparative Study of Models Trained on Synthetic Data for Ukrainian Grammatical Error Correction

The task of Grammatical Error Correction (GEC) has been extensively studied for the English language. However, its application to low-resource languages, such as Ukrainian, remains an open challenge. In this paper, we develop sequence tagging and neural machine translation models for the Ukrainian language as well as a set of algorithmic correction rules to augment those systems. We also develop synthetic data generation techniques for the Ukrainian language to create high-quality human-like errors. Finally, we determine the best combination of synthetically generated data to augment the existing UA-GEC corpus and achieve the state-of-the-art results of 0.663 F0.5 score on the newly established UA-GEC benchmark. The code and trained models will be made publicly available on GitHub and HuggingFace.
Topic Ontologies for Arguments

Many computational argumentation tasks, such as stance classification, are topic-dependent: The effectiveness of approaches to these tasks depends largely on whether they are trained with arguments on the same topics as those on which they are tested. The key question is: What are these training topics? To answer this question, we take the first step of mapping the argumentation landscape with The Argument Ontology (TAO). TAO draws on three authoritative sources for argument topics: the World Economic Forum, Wikipedia’s list of controversial topics, and Debatepedia. By comparing the topics in our ontology with those in 59 argument corpora, we perform the first comprehensive assessment of their topic coverage. While TAO already covers most of the corpus topics, the corpus topics barely cover all the topics in TAO. This points to a new goal for corpus construction to achieve a broad topic coverage and thus better generalizability of computational argumentation approaches.
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3% 4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS.