Reviewerly/exHarmony huggingface dataset:
Synchronisation Model of Campus-Industry Partnership Through Smart Expert System Hybrid Advisory for Industrial Internship Students and Teaching Assistance in the Era of Independent Learning

The objectives of this research include (1) developing smart expert system innovation integrated hybrid advisory; and (2) Testing the attractiveness and feasibility of the hybrid advisory integrated smart expert system innovation prototype. The method in this research is in the form of research and development (Re-search and Development). Where the method includes a preliminary study; model design; development of model prototypes; model prototype testing; model prototype revision; validated model prototype results; model prototype experimentation; tested models; and dissemination. all state universities in East Java (students,equipped with lecturers, officials in PT and stakeholders). The research instrument used questionnaires, documents, structured surveys, observations, and in-depth interviews. The results of this study include: (1) the innovations developed have synchronous and asynchronous online guidance features, submit reports and daily assignments, student progress reports, live chat with supervisors and instructors, daily attendance, to review report results; and (2) the Smart Expert System Integrated Hybrid Advisory innovation developed has valid results without revision with an average score of 90.1 (user benefit aspect), an average score of 90.5 (application display aspect), an average score of 93 ,8 (newness of information aspect), average score of 97.6 (content aspect), average score of 90.6 (user-friendliness aspect), average score of 93.8 (competency achievement aspect).
Kemampuan Membaca Pemahaman Siswa Sekolah Dasar Negeri 18 Anduring Kota Padang

The background of this research is that there are still many fifth grade students whose learning outcomes are low, especially in the learning aspect of reading comprehension. Fifth grade students still have difficulties in understanding reading texts. This study wanted to find out and analyze students' reading comprehension skills, reasons for difficulties and alternative solutions to overcome problems in reading comprehension in fifth grade students at SD Negeri 18 Anduring, Padang City. This research approach uses qualitative research subjects 8 students of class V SD. Data obtained through observation, interviews, tests, and documentation. The results of this study indicate that fifth grade students at SD Negeri 18 Anduring, Padang City have sufficient ability in literal comprehension skills and are classified as lacking in interpretive understanding, critical understanding, and creative understanding. Factors causing the difficulty of reading comprehension in fifth grade students of SD Negeri 18 Anduring, Padang City, namely interests and activities in pre-reading, reading, post-reading activities and differences in students' abilities, facilities and infrastructure and school environment and family upbringing. Solutions that can be implemented are changing students' habits to read books, providing facilities and infrastructure, and teachers can apply models, strategies and learning methods that are appropriate for students.
SISTEM INFORMASI AKUNTANSI PENERIMAAN DAN PENGELUARAN KAS PADA WISATA NAKNY FUNLAND

One of the tourist attractions from Deli Serdang, North Sumatra, which recently went viral is Nakny Funland. Nakny Funland is frequently visited by local tourists and tourists from outside the region, however currently Nakny Funland still uses the manual method in its system, namely by recording a book system in the process of recording ticket sales and financial reports so there is a high probability that errors can occur in entering data, errors in calculations. data on tickets sold, errors in making financial reports, and can hamper the search process for the data needed. Therefore, we need an accounting information system that can produce faster and more accurate information. The author will create a ticket sales accounting information system at Nakny Funland Tourism using Microsoft Visual Basic 2010 as software and MySQL as database to process the data. The purpose of creating this system is to assist the performance of officers and persons in charge at Nakny Funland Tourism in making the process of recording ticket sales and financial reports easier, faster, and more accurate, and can minimize errors in making records.
SISTEM INFORMASI KEHADIRAN SISWA MENGGUNAKAN QR KODE BERBASIS ANDROID Studi Kasus SD Negeri 105270

This research develops a QR code-based student attendance information system at SD Negeri 105270. This system uses an Android application with a QR code for student attendance, aiming to increase the efficiency and accuracy of recording student attendance and improve the quality of education services. The research method includes application development using Android Studio and QR code integration. The teacher displays the QR code on their cell phone, and students scan the code using the attendance app on their cell phones. The results showed that this system speeds up the attendance process, reduces recording errors, and improves the security of student attendance data. This research provides a reference for other schools to implement a similar system with a QR code. It is hoped that this system can increase the efficiency and accuracy of student attendance as well as the quality of education services in schools.
WOOD: Wasserstein-Based Out-of-Distribution Detection

The training and test data for deep-neural-network-based classifiers are usually assumed to be sampled from the same distribution. When part of the test samples are drawn from a distribution that is sufficiently far away from that of the training samples (a.k.a. out-of-distribution (OOD) samples), the trained neural network has a tendency to make high-confidence predictions for these OOD samples. Detection of the OOD samples is critical when training a neural network used for image classification, object detection, etc. It can enhance the classifier's robustness to irrelevant inputs, and improve the system's resilience and security under different forms of attacks. Detection of OOD samples has three main challenges: (i) the proposed OOD detection method should be compatible with various architectures of classifiers (e.g., DenseNet, ResNet) without significantly increasing the model complexity and requirements on computational resources; (ii) the OOD samples may come from multiple distributions, whose class labels are commonly unavailable; (iii) a score function needs to be defined to effectively separate OOD samples from in-distribution (InD) samples. To overcome these challenges, we propose a Wasserstein-based out-of-distribution detection (WOOD) method. The basic idea is to define a Wasserstein-based score that evaluates the dissimilarity between a test sample and the distribution of InD samples. An optimization problem is then formulated and solved based on the proposed score function. The statistical learning bound of the proposed method is investigated to guarantee that the loss value achieved by the empirical optimizer approximates the global optimum. The comparison study results demonstrate that the proposed WOOD consistently outperforms other existing OOD detection methods.
Correction: Fati et al. Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction. Mathematics 2023, 11, 3567

In the original paper [...]
Aralin Baybayin: Intelligent Tutoring System for Baybayin Scripts with Character Recognition

Aralin Baybayin is an Intelligent Tutoring System (ITS) designed to help those interested in learning the Baybayin Script, such as historians, foreigners, and fellow Filipinos. It provides an efficient and convenient way to learn how to read and write the Baybayin Script. The proponents assessed the quality of the software and system product using ISO 25010 and the motivational experience of users with 130 respondents consisting of IT and Baybayin Experts and third year students studying Bachelor of Science in Information Technology at Polytechnic University of the Philippines. The proponents are able to deliver a learning motivational experience for the users with a verbal interpretation of agree. The system was also able to utilize organized, progressive, and interactive lessons for the users. Aralin Baybayin also met the user's acceptance through the use of ISO 25010 with a verbal interpretation of agree. For optical character recognition, through training the datasets Aralin Baybayin has proved that CNN models are suitable for Baybayin character recognition with a result of final training accuracy of 99.16% and testing accuracy 98.60%. An accuracy rate of 91.55% was achieved by accurately transliterating 412 out of 450 words. The researchers conclude that Aralin Baybayin is an effective tool for promoting more efficient and interactive learning of the Baybayin Script using the Intelligent Tutoring System (ITS).
Rozwijanie kompetencji mówienia w specjalistycznym języku technicznym. Przykład scenariusza zajęć z języka włoskiego

"The subject matter of the article is identifying didactic solutions aimed at developing oral communication skills in teaching specialized technical Italian language. The author provides the definition of specialized language competence, as well as theories on how to develop it through a proper methodological approach. She also attempts to answer the question of how to plan the teaching process so that it enables the effective development of all linguistic competencies and skills. The author also presents a lesson plan for a class devoted to specialized technical Italian language, the focus of which is car construction. This class was conducted at the Institute of Education and Communication Research at the Silesian University of Technology (currently the Department of Applied Linguistics at the Faculty of Organization and Management of the Silesian University of Technology) as part of the course “Technical Language C (Italian)”."
Exploring YouTube Comments to Understand Public Sentiment on COVID-19 Vaccines through Deep Learning-based Sentiment Analysis

COVID-19 was first found in China in 2019. Since then, it has quickly spread around the world, which has led to a lot of news stories and social media posts about the pandemic. YouTube, a popular video-sharing website, has become a valuable source of information on COVID-19 and other topics. However, it can be difficult to extract useful insights from the vast array of user comments that accompany these videos. One potential method for understanding public sentiment is to use sentiment analysis, which involves classifying text as positive, negative, or neutral. In this study, the dataset of over 44,000 YouTube comments related to COVID-19 vaccines was used, which was filtered to a total of 16,073 comments for analysis. The data was cleaned and organised using NeatText and then processed using GloVe word embedding, a technique for establishing statistical relationships between words. Based on the experiment, the performances of three different types of deep learning techniques: recurrent neural networks (RNN), gated recurrent units (GRU) and long short-term memory (LSTM) are compared in accurately classifying the sentiment of the comments. The study found that the GRU had the highest accuracy of 80.19%, followed by the LSTM with 79.00% accuracy, and the RNN with 67.15% accuracy.
A Comparative Study of Stakeholder Engagement in the Dual Education System: A Case of Germany, the United States and Kazakhstan

Stakeholder engagement in the dual education system is one of the key factors in developing a sustainable educational system. In this study, the authors aim to explore the role and responsibilities of the key stakeholders and the level of their engagementin the dual education system. The authors aim to contribute to stakeholder engagement in dual education by identifying, assessing, and prioritizing project stakeholders’ interests and influence on the education system. The paper used a case study approachand conducted semi-structured interviews with representatives of vocational schools in Kazakhstan. The case study was conducted by comparing the results of the project stakeholder analysis in the dual education systems of Germany and the United States to Kazakhstan. The semi-structured interviews were conducted with 15 experienced educators from different vocational schools in Kazakhstan. The author's analysis reveals that significant changes are necessary to the legislation in Kazakhstan to improve the dual education system.
A study on emotional goal-setting in imaginative expression through art activities: Focusing on elementary school students’ expressions of imaginary companions

Objectives The purposes of this study were to reveal elementary school students' emotional goal-setting through imaginative expression in art activities and to discuss the educational implications of emotional goal-setting in el-ementary art.&#x0D; Methods A total of 103 elementary school students (53 males, 50 females) participated in this study, comprising 48 students (46.6%) from the 2nd grade and 55 students (53.4%) from the 6th grade. The research data consisted of drawings and questionnaires in which elementary school students expressed their imaginary companions. The analysis method used was content analysis based on the emotional goal analysis criteria in imaginary expressions through art activities established for this study. T-test and cross-analysis were also performed to determine dif-ferences by grade.&#x0D; Results Four emotional goal settings were revealed in elementary school students' expressions of imaginary companions through art activities: emotion maintenance, emotion repair, self-protection, and free fantasy. Elementary school students mainly set emotion maintenance and emotion repair as emotional goals in their imag-inary expressions and rarely set self-protection and free fantasy. In expressing imaginary companions, 2nd grad-ers focused on emotion maintenance as an emotional goal, while 6th graders emphasized emotion repair in addi-tion to emotion maintenance.&#x0D; Conclusions This study contributes to identify emotional goal-settings by elementary school students in imagi-native expressions through art activities and suggests that emotional goal-setting in imaginative expressions has educational effects such as visualization of emotion, emotional understanding, and emotional regulation.
The Impact of the Social Classroom Contract on Students with Learning Disabilities

This study explores how social classroom contracts impact students with learning disabilities. The way that students behave and interact has a direct bearing on how they are taught and learned. The research uses a quantitative method to identify the elements influencing the formation of classroom social contracts, explore how they relate to the student code of conduct, and emphasize the beneficial effects of classroom social contracts for students with learning disabilities. The research shows that giving students a choice and a voice in establishing a social contract in the classroom empowers them to take charge of their education and recognize their potential to make a difference. The student code of conduct, a set of rules and expectations for behaviour in the school setting, is also strongly tied to the classroom social contract. Additionally, classroom social contracts give students a stronger feeling of independence and accountability, promoting a socially conscious and active learning environment. This study emphasizes the transformative potential of social classroom contracts for students with learning disabilities with the possibility of increasing their attendance, engagement, and achievement. Keywords: Achievement, Attendance, Code of Conduct, Student Engagement, Voice, and Choice.
Cybersecurity Workforce Readiness Recommender System Using NLP

Increasing the knowledge of cybersecurity and the number of experts in the field is important due to the vast number of cyber threats annually. Cyber threats are trending towards being more complicated, especially for human intelligence. Our day to day quality of life is affected by various threats and attacks in the cyberspace. Using the well-known National Initiative for Cybersecurity Education framework as a guide, we propose CyberReco - Cybersecurity Workforce Readiness Recommender System, an artificial intelligence model that attempts to address readiness gaps needed to prepare users lacking in some cybersecurity knowledge, skills, and abilities to be workforce-ready. CyberReco is built using natural language processing. We first present a hierarchical-based framework that is composed of four components including, text preparation and normalization, keywords extraction and processing, similarity scores and skills computation, and a recommender component. Further, we present several natural language processing-based algorithms that implement the modules in these components. Preliminary results from our experiments suggest that CyberReco is able to recommend relevant contents to users.
A Study on a Convex Polygon That Cannot Be Formed by Seven-Piece Puzzles

Objectives It is known that at most 20 convex polygons can be formed using all seven pieces of the puzzle, but this limit is imposed by the omission of the premise that the puzzle is a square divided into seven pieces. This study analyzes a convex polygon that is conjectured to be impossible to create using the puzzle and then uses the puzzle to prove that it cannot actually be constructed as the convex polygon.&#x0D; Methods The proof is based on the assumption that the given convex polygon cannot be formed using sev-en-piece puzzles. It proceeds in two different ways, depending on how the pieces are arranged within the square shape.&#x0D; Results We accomplished this in two ways: either by placing in the pieces having the largest area or by initially arranging the pieces on an angle and the diagonal of a square. Consequently, we have proved that the convex poly-gon cannot be formed by seven-piece puzzles.&#x0D; Conclusions The actual number of convex polygons that can be formed by seven-piece puzzles was 19, not 20. This study is significant because it provides a corollary for the number of convex polygons that can be constructed using all the pieces of the puzzle.
EFEKTIVITAS PENGGUNAAN MEDIA VIDEO ANIMASI TERHADAP HASIL BELAJAR DAN AKTIVITAS SISWA PADA MATERI VIRUS DI SMAN 01 KUBU KALIMANTAN BARAT

Animated video media can make students interested in learning. The objectives of this research are, 1) To determine the differences in learning outcomes and learning activities of class class X student activities between classes that use animated video media and classes that use powerpoint media. The research sample was all students in class X Mia 1 and Mia 2. Data were collected using the test method in the form of multiple-choice questions and observation sheets. Data analysis uses quantitative descriptive analysis techniques. Based on the research results, it was obtained using the t-test there was a significant difference in student activity in class of 0.048, and in learning outcomes there was a significant difference of 0.000. Based on the Effect Size results, activity results were obtained at 0.51 and student learning outcomes at 0.50, including in the medium category.
User interface analysis on shopee website using heuiristic method

Human and computer interaction is a science that focuses on planning and designing a system so that users can interact and work together according to the needs of the users themselves in the most effective way at every step of their work. The role of the user interface greatly influences the usability of an application or system. Heuristic Evaluation (HE) is considered the best usability evaluation method compared to others because it can find severe problems that other methods cannot detect. Therefore researchers will analyze the user interface on the Shopee website using the HE method. User Interface Analysis on Websites with the HE Method through online questionnaire distribution, from the results of online questionnaire distribution, it can be concluded that the website has deficiencies in terms of auxiliary features and documentation to assist users in using the system. Declared less because it has the lowest rcount (Total) value of 0.587 compared to other counts. Suggestions for research on Shopee user satisfaction analysis using the HE method, the authors convey suggestions that need to be improved in future research, namely that it is necessary to conduct research on Shopee user satisfaction using the HE method in a broader scope.
Digitized Higher Education: Digital Transformation in Education from a Bibliometric Perspective

Higher education institutions should give high priority to keeping up to date by studying how the business environment reflects the technological and social developments brought about by digitalization. This will enable future generations to take advantage of technological inventions for research and teaching. The implementation of the digital transformation approaches brought by Industry 4.0 in higher education has led to the creation of a technologically-supported education area in higher learning. In the educational dimension of this digital transformation, there is a need to organize complex relationships between existing structures and technologically supported structures. The aim of this article is to examine the literature of digital transformation in education and to identify the current state of affairs. In this context, filtering was carried out using the keywords “digital transformation” and “education” in the Web of Science database. A bibliometric data analysis was carried out through VOSviewer by downloading the data set obtained as a result of the filtering. The analysis found that the pandemic had an impact on the increase in the number of publications in literature, and the most common type of text was the article. In addition, the most broadcasting country is Russia, and the most commonly used keywords in terms of conceptual unity are digital transformation, higher education, education, digitization and COVID-19.
Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel

The COVID-19 pandemic has resulted in significant changes in the education sector. The government issued a policy so that learning must be carried out online from home. This policy became a polemic for teachers and students so that pro and con opinions emerged on social media, especially Twitter. Sentiment analysis of public opinion is an interesting study. Standard classification algorithms such as k-Nearest neighbours, naïve bayes, decision tree, random forest, and support vector machine (SVM) can categorize these opinions in a short time with good accuracy. Many studies show that SVM is more accurate than all other classification methods. SVM works using kernels, including Linear, Polynomial and Radial Basis Functions (RBF) where each kernel requires different parameters. The linear kernel only requires one parameter, namely c (Cost). The RBF kernel requires 2 parameters, c and ɣ (gamma) while the Polynomial kernel uses 2 parameters, c and degrees. SVM does not have default values for these parameters and are based on experience and experimentation. The wider the range of parameters, the more likely the classifier obtains the optimal values. This study tries some parameters values of SVM kernels for text classification based on sentiment. Testing using 5-fold cross validation and confusion matrix show that SVM with a linear kernel provides the best performance with an accuracy of above 84%.