Reviewerly/exHarmony huggingface dataset:
Productivity of Online Learning Media in Education Management

Introduction/Main Objectives The productivity of online learning media for the implementation of learning has increased intensely since the implementation of the corona lockdown. This presents a challenge to improve learning by developing media so that students have more time to study independently during the lockdown period.: Background Problems Researchers compared the productivity of various learning media such as text, video, and video-text combinations.: Novelty: Statistical analysis using Mann Whitney showed that there were significant differences in knowledge retention between the text and video groups as well as between the video and text-video combination groups. Research Methods: This study was conducted using a quasi-experimental post-test method by comparing participants' retention before the intervention. Finding/Results: The study involved 60 participants who were randomly divided into 3 groups, text (guidebook), audiovisual (video), and text-video combination. Video and text prepared by Widyaiswara. The results showed that the average scores were 35.1 (text group), 58.1 (video group), and 85.2 (text-video combination group). Conclusion: use of multimedia can increase knowledge retention in the absorption of the material.
DISTANCE LEARNING TODAY//Social Sciences: Achievements and Prospects Journal 1(14), 2022 (ISSN 2733-3825)/ Chief Editor Vitalii Vl. Klevtsov /OEAPS OU - Tallinn, Estonia (Open European Academy of Public Sciences): Berlin, Germany, 30.04.2022: OEAPS OU., 2022 - P. 39-42.

The modern education system, which is being implemented in our country today, implies an increase in the amount of time for independent learning. In the educational process, the role of information educational technologies is increasing, and the leading positions are given to electronic textbooks. The main conditions for a smooth and effective transition to an online education system are obvious: maximum coverage of the territory with high-speed broadband Internet, provision of universities with computer equipment and the availability of educational Internet platforms.
A Study into Patient Similarity through Representation Learning from Medical Records

Abstract Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines.
AN ANALYSIS OF TEACHERS’ STRATEGY IN TEACHING ENGLISH VOCABULARY DURING COVID-19

The main objective of teaching English is to explores and describes the strategies used by the teacher, problem faced by the teacher, and problem solved by the teacher in teaching vocabulary. A descriptive qualitative conducted to analyze the data of the study. The data were taken from interview, observation and document with participant are the English teacher of SMP IT IMISc Labuapi. Furthermore, this study focuses on the process of teaching vocabulary to the first-grade students in SMP IT IMISc Labuapi. Based on the class observation, interview, and document, this study shows that there are some strategies conducted by the teachers, i.e.: (1) memorization, (2) synonym/antonym, (3) translating, and (4) fill in the blank. The problems faced by the teacher are: (1) classroom management, (2) student’s motivation and (3) student’s laziness. The problems solved by the teacher are: (1) she called the students who make noisy and asked them some questions about the material or she ordered the student who was noisy to go out, (2) the teacher gave advice and motivation about the importance of English subject and the importance to improve the vocabulary. Besides, the teacher also implemented another strategy by giving score to the active students. The students also can consult the difficult materials with the teacher out of class, (3) the teacher gave new vocabulary that wrote in the whiteboard every meeting to improve their vocabulary. The strategies used by the teacher are good because the strategies can make the students more active. Besides that, the strategies used by the teacher can make the students interested in following the class.
ADDRESSING DIFFICULTIES IN TEACHING MATHEMATICS TO STUDENTS WITH MATHEMATICAL LEARNING DISABILITIES

The essence and reason for the inability to master mathematics are described as a lack of working memory. This paper describes two main approaches to solving the problem of teaching mathematics to students with learning difficulties in mathematics (MLD): (1) training working memory and (2) reducing the load on working memory in the instructional process. It was found that the results of the first approach are ambiguous: Training working memory leads to its improvement, which is confirmed by the test results but may not lead to improvement of the mathematical learning process associated with the student’s working memory. This justifies the primacy of the second approach. Both previously known methods for reducing the load on working memory in mathematics instruction are presented. A computer-based mathematics learning system developed by the author aims at automating basic computational skills (arithmetics, trigonometry, geometry). It is explained how to work with the developed computer-assisted learning system, which is based on the method of interval repetitions, and empirical data on the results of the system implementation are given.
DEPELOVING STUDENTS’ LISTENING COMPREHENSION BY APPLYING BOTTOM-UP TECHNIQUE

The aim of this study was to depelove students’ listening comprehension by applying Bottom-Up technique. This study is a classroom action research which has done in three cycles. The subject of the study was the eight grade of the students of MTs. Darul Aminin NW Aikmual Lombok Tengah. The study was conducted by applying Bottom-Up technique which consists of three main stages. There are word processing, phrase processing, and comprehension. The writer observed students’ depelovement in listening comprehension by collecting data through field notes, observation checklist and listening test. Field note and observation checklist were used to gather the students’ attitude in learning process. The data of listening was collected through listening test and it was assessed through scoring rubric. The result showed that students’ problems in understanding the contents of listening and vocabulary had been solved by applying Bottom-Up technique. In the third cycle, the students mean score was 83.7. It increased in the second cycle to 82.1, and 76.7 in first cycle. As the conclusion, the technique was able to be applied in depeloving students’ listening comprehension. As a technique in teaching and learning process, the writer recommends the teacher to apply Bottom-Up technique.
New Virtual Environment Based on Python Programming

Python is an amazing language for large and complicated programming ventures. This language uses codes that are simple to use or modify for any software programmer. In this paper, one annoying issue during the project expansion was described. Once a package is updated to the new version, this package is no longer compatible with previous projects, causing trouble when working on the program. To solve this problem, Python supports the programmer with a function called virtual environment. This function can construct an independent environment for any venture to avoid conflict. Various steps have been explained to create a virtual environment in Python. Different examples have then shown the benefits of Python language to building shapes, animations, and interactions
AN ANALYSIS OF STUDENTS’ ERROR IN TRANSLATING INDONESIAN SENTENCES INTO ENGLISH

This research was an error analysis of the first semester students of Sumbawa University of Technology. The aims of the research were to find out what types of error that the students committed and what error that they committed the most. The research was a descriptive study, where the population of the research was all first semester students, while the sample was Electrical Engineering Class which consists of 34 students. The technique data collection was by giving the students translation tests. The data was analyzed by identifying, classifying, and describing the errors. Based on the result of analysis, it was found that (1) there were four types of error that students committed, they were omission, addition, misordering and misformation error: (2) type of error that students committed the most was omission, and the least error was miordering.
Ethical Leadership and Organizational Safety Performance in Abu Dhabi National Oil Company in the United Arab Emirates

This scientific research study investigated the effect of ethical leadership on organizational safety performance in Abu Dhabi National Oil Company (ADNOC) in the United Arab Emirates (UAE). The specific scientific objective of the study was: To establish the contribution of ethical leadership on organizational safety performance in Abu Dhabi National Oil Company in the United Arab Emirates (UAE). The research study employed survey and descriptive research designs in order to determine relations among the research scientific study variables using the quantitative approach. The scholar also employed the positivism paradigm so as to traverse through her research procedure. The independent variable (IV) was ethical leadership and the dependant variable (DV) was company safety performance (OP) among the human capital of ADNOC in the UAE. The parent population was 300 employees of ADNOC in the UAE. The sample size was 250 respondents obtained while making use of table made by Morgan &amp; Kreijcie (1970). The scholar summarized data with use of descriptive statistics including means, standard deviation and inferential techniques like structural equation analysis and structural path analysis. The findings were among others: Ethical leadership positively predicts company safety performance. It was concluded that cooperation among company leaders and the general emploees leads to superior performance with the required safety measures.Recommendations of the organised research study included: organisational leaders should put more emphasis on sensitisation campaigns among ADNOC employees in order to promote safety programs within the company.
Real-Time Face Detection Attendance Management System

The issue of human error, inaccurate and time-consuming of manual attendance system gradually magnified the inefficiency and obsolescence of the system. An automatic attendance management system with face detection will increase the efficiency, accuracy, and security of a system in attendance taking task. Real-Time Face Detection Attendance Management System is concerned in finding human faces through real-time with the attendance taken and saved as reference for future. Face detection system is implemented by using Haar Cascades Classifiers for the object detection. Attendance system is managed with a database management system called MySQL using SQL programming language. Real-Time Face Detection Attendance Management System able to detect front face and manage the attendance in real-time. A text box will display for the attendee to enter their name for the purpose of attendance taken when face is detected. The attendance list and image collection in the database will auto increment and update in MySQL. The accuracy of Real-Time Face Detection Attendance Management System is shown with the comparison between different front face condition in different illumination condition. The result is tested with 4 sets of the attendee’s face in different illumination and different front face condition. 94.44% accuracy of the face detection system is shown from the result taken by the system. The image acquisition for every attendee is collected and saved in the right folder. The attendance is taken with the attendee’s name, date and time in attendance database and image is save into face database. Some technology such as anti-spoofing measures, mask detection and deep learning concept are recommended to be added into the system for future improvement
Mathematical Theory of Bayesian Statistics for Unknown Information Source

In statistical inference, uncertainty is unknown and all models are wrong. That is to say, a person who makes a statistical model and a prior distribution is simultaneously aware that both are fictional candidates. To study such cases, statistical measures have been constructed, such as cross validation, information criteria, and marginal likelihood, however, their mathematical properties have not yet been completely clarified when statistical models are under- and over- parametrized. We introduce a place of mathematical theory of Bayesian statistics for unknown uncertainty, which clarifies general properties of cross validation, information criteria, and marginal likelihood, even if an unknown data-generating process is unrealizable by a model or even if the posterior distribution cannot be approximated by any normal distribution. Hence it gives a helpful standpoint for a person who cannot believe in any specific model and prior. This paper consists of three parts. The first is a new result, whereas the second and third are well-known previous results with new experiments. We show there exists a more precise estimator of the generalization loss than leave-one-out cross validation, there exists a more accurate approximation of marginal likelihood than BIC, and the optimal hyperparameters for generalization loss and marginal likelihood are different.
A Study on the Inaugural Address of the President of Korea: Focusing on feature values in Textmining

Unlike artificial languages, natural languages are difficult for computers to process directly. Therefore, it is necessary to process it as structured data. In the end, by giving weight to the sample text, the frequency of appearance in the document was weighted.&#x0D; The inaugural address data was entered through information filtering of text mining, and it was possible to obtain the result of the characteristic value according to the relation to the main word.&#x0D; This study analyzed the vocabulary of the inaugural speeches of past presidents of the Republic of Korea. After refining words through morpheme analysis, we found vocabulary frequency and characteristic values, and the data was visualized in wordcloud. This study did not perform a fragmentary analysis of the existing lexical analysis. This study presented feature values and word clouds. Therefore, it was possible to extract objective keywords representing the characteristics of the keywords.
Passing the Torch: Continuation of the South Carolina Libraries Journal

In this article, The South Carolina Libraries Editorial Board discusses the history and rebuilding of the South Carolina Libraries journal, the official publication of the South Carolina Library Association (SCLA). As the journal adjusted in response to community needs, changes in the Editorial Board, the publication cycle, and peer review process were made to expand and enhance the functionality, appearance, and content of the journal. The Editorial Board reviews strategies for library community members – including practitioners, research faculty, and LIS students – to become involved as a part of the publication through authoring, book reviewing, peer reviewing, or artwork for future issues.
New Bounds on the Size of Permutation Codes With Minimum Kendall T-distance of Three

&lt;p&gt;We study $P(n,3)$, the size of the largest subset of the set of all permutations $S_n$ with minimum Kendall $\tau$-distance $3$. Using a combination of group theory and integer programming, we reduced the upper bound of $P(p,3)$ from $(p-1)!-1$ to $(p-1)!-\lceil\frac{p} 3}\rceil+2\leq (p-1)!-2$ for all primes $p\geq 11$. In special cases where $n$ is equal to $6,7,11,13,14,15$ and $17$ we reduced the upper bound of $P(n,3)$ by $3,3,9,11,1,1$ and $4$, respectively.&lt;/p&gt;
SISTEM INFORMASI PENJUALAN BUKU BEKAS DI KOTA BANDAR LAMPUNG BERBASIS WEB

This study aims to create an information system that functions to sell used books in the city of Bandar Lampung. Currently the used book sales system still relies on media interaction with direct consumers and is still centered on a particular location. By creating a web-based used book sales information system in the city of Bandar Lampung, it can facilitate customer transactions in purchasing used books online. In this research the system design uses UML (Unifield Modeling Language), PHP (Hypertext Preprocessor) programming language, MYSOL as a database and using Windows as an operating system. Information systems are created by applying the Extreme Programming Method to determine the steps in making a system. System functional testing uses the Black Box Testing method in the functional test scenario. Accuracy testing uses 20 test cases that exist in the information system, then performed related to the system output with actual events. From the test results using 20 test cases, the results obtained are in accordance with the system output so that the test results are categorized as very good.
MEDIA COMPETENCE FORMATION OF STUDENTS IN THE PROCESS OF TEACHING THEORETICAL DISCIPLINES

The article highlights the problem of formation of media competence of a modern student in the process of studying theoretical disciplines. Media competence is seen as a set of knowledge, skills, abilities, facilitating the using, critical analysis, evaluation, creation and transmission of media texts. It is proved that a graduate of a higher education establishment must use traditional and modern information technologies to meet the level of a modern specialist. In the development of modern society, a modern teacher must have the skills to use media resources in his work, follow the media information about existing media products for educational purposes, possess the ability to use it effectively in the educational process. Options for the use of media products in the educational process through the study of theoretical disciplines are considered. The reasons for the need to introduce media education into the educational process are determined. The main provisions of the Concept of Media Education in Ukraine are analyzed.
Does Minority Case Sampling Improve Performance with Imbalanced Outcomes in Psychological Research?

In psychological research, class imbalance in binary outcome variables is a common occurrence, particularly in clinical variables (e.g., suicide outcomes). Class imbalance can present a number of difficulties for inference and prediction, prompting the development of a number of strategies that perform data augmentation through random sampling from just the positive cases, or from both the positive and negative cases. Through evaluation in benchmark datasets from computer science, these methods have shown marked improvements in predictive performance when the outcome is imbalanced. However, questions remain regarding generalizability to psychological data. To study this, we implemented a simulation study that tests a number of popular sampling strategies implemented in easy-to-use software, as well as in an empirical example focusing on the prediction of suicidal thoughts. In general, we found that while one sampling strategy demonstrated far worse performance even in comparison to no sampling, the other sampling methods performed similarly, evidencing slight improvements over no sampling. Further, we evaluated the sampling strategies across different forms of cross-validation, model fit metrics, and machine learning algorithms.
Simplifying the Control of Mobile Robots through Image-based Behaviors

The development and commissioning of mobile robots is usually a time-consuming and cost-intensive undertaking. Today, systems are realized primarily through the use of complex software and hardware architectures, which only partially meet modern requirements for mobile robots. The core discipline that a mobile robot must fulfill is the correct perception of its environment. To achieve this, various technologies are used that are useful for detecting obstacles or navigating targets. An increasingly attractive technology in this domain are artificial neural networks. Existing literature focuses on either improving the performance of existing systems or on training for very specialized applications. In the field of mobile robotics, the focus is usually on realizing a specific task, while little attention is paid to generalizability, cost and energy constraints. To fill this gap, this paper investigates the possibility of reducing the setup for a mobile robot application to a minimum while still enabling complex behaviors. For the implementation, we take a biological inspired approach and investigate the usability of artificial neural networks, in this case YOLOv4, in a mobile robot application. In particular, we examine whether the currently available technologies meet todays requirements.
Explainability in Automated Training and Feedback Systems

Automated training systems (ATS) for a range of skills are an emerging artificial intelligence (AI) technology that aims to deliver automatic assessment and feedback in the absence of human expert input. However, most current systemsare not designed to explain the assessment to help trainees improve their motivation and performance. We propose that explanations on the assessments and predictions made by these automated systems can act as feedback providing tangible informational insights, and increasing the trainee’s effectiveness and performance. Explainable AI (XAI) methods promoting human understanding of the AI models’ decisions have the potential to satisfy these informational needs of the feedback. However, explainable ATS must adhere to the main elements of feedback seen in traditional manual methods (e.g., actionable, clear impact, withexamples). This paper discusses the validity of the explanation as feedback and its challenges in designing and implementing explainable ATS.