Reviewerly/exHarmony huggingface dataset:
Public Language Diagnostic Study for Migrants: Focusing on immigration and social integration network

This study aims to expand the discussion on public language, which has been made only from the perspective of purifying the Korean language for the Korean language-speaking people, and to attempt to diagnose public language for immigrants staying in Korea. Considering that information provided by public institutions is in Korean except for English, Chinese, and some languages, it is easy to guess the difficulty of lack of information as migrants in Korea. In this study, the public language of the social integration program page of the social integration information network that migrants encounter for their initial settlement in Korea was diagnosed. First, when analyzing the social integration program page based on technical terms or expressions, rarely used words or expressions, and awkward expressions, there were many legal terms that were difficult for migrants to understand, and there was no match between difficult literary expressions and subjects and predicates. Sentences, translation-to-sentences, and foreign language expressions were also noticeable. Next, as a result of analyzing 388 vocabulary, beginner vocabulary took up 18.81%, intermediate vocabulary took up the highest at 43.04%, and advanced took up 21.65%. Intermediate or higher vocabulary accounts for 64.69% of the time, suggesting that it will be difficult for migrants at the elementary and middle level to get information from the site without the help of others. However, migrants should also be considered when discussing public language improvement and policies in the future, as they are subject to the guaranteed “right to know” and “right to equality” as members of Korean society. Through this study, it is hoped that migrants’ access to information can be improved by considering migrants’ positions in improving public languages.
Augmented Reality Technology in Teaching about Physics: A systematic review of opportunities and challenges

The use of augmented reality (AR) allows for the integration of digital information onto our perception of the physical world. In this article, we present a comprehensive review of previously published literature on the implementation of augmented reality in physics education, at the school and the university level. Our review includes an analysis of 96 papers from the Scopus and Eric databases, all of which were published between January 1st, 2012 and January 1st, 2023. We evaluated how AR has been used for facilitating learning about physics. Potential AR-based learning activities for different physics topics have been summarized and opportunities, as well as challenges associated with AR-based learning of physics have been reported. It has been shown that AR technologies may facilitate physics learning by: providing complementary visualizations, optimizing cognitive load, allowing for haptic learning, reducing task completion time and promoting collaborative inquiry. The potential disadvantages of using AR in physics teaching are mainly related to the shortcomings of software and hardware technologies (e.g., camera freeze, visualization delay) and extraneous cognitive load (e.g., paying more attention to secondary details than to constructing target knowledge).
An Outlier Detection Algorithm Based on Probability Density Clustering

Outlier detection for batch and streaming data is an important branch of data mining. However, there are shortcomings for existing algorithms. For batch data, the outlier detection algorithm, only labeling a few data points, is not accurate enough because it uses histogram strategy to generate feature vectors. For streaming data, the outlier detection algorithms are sensitive to data distance, resulting in low accuracy when sparse clusters and dense clusters are close to each other. Moreover, they require tuning of parameters, which takes a lot of time. With this, the manuscript per the authors propose a new outlier detection algorithm, called PDC which use probability density to generate feature vectors to train a lightweight machine learning model that is finally applied to detect outliers. PDC takes advantages of accuracy and insensitivity-to-data-distance of probability density, so it can overcome the aforementioned drawbacks.
Fuzzy Logic based adaptive weighted despeckling method designed considering inter-pixel scattering similarities

Automated interpretation of image data provided by Synthetic Aperture Radar (SAR) using several machine learning and deep learning techniques have become an emerging research area in the computer vision field in recent years. But processing these images without performing speckle noise removal often leads to the production of highly erroneous results. These images are inherently subjected to speckle noise which are formed due to constructive and destructive addition of backscattered electromagnetic waves while mapping them into resolution cells of finite dimensions during image formation. To overcome this limitation, in this work, we have designed an adaptive weighted image despeckling strategy based on fuzzy logic (FL). The considered strategy allows to remove speckle noise from any pixel based on the relative similarities of its scattering properties with each pixel lying in its neighborhood. The despeckling of pixels lying within predefined neighborhood is conducted using a set of 125 new fuzzy inference (FI) rules designed for this purpose. The proposed FL based adaptive weight assignment strategy enhances the efficiency of the designed despeckling technique as it enables it to perform effective speckle noise removal as well as preservation of edges. The effectiveness of this filtering strategy over existing well-known de-speckling filters is demonstrated in the Results section using quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Edge Preservation Index (EPI).
Attack Some while Protecting Others: Selective Attack Strategies for Attacking and Protecting Multiple Concepts

Machine learning models are vulnerable to adversarial attacks. Existing research focuses on attack-only scenarios. In practice, one dataset may be used for learning different concepts, and the attacker may be incentivized to attack some concepts but protect the others. For example, the attacker might tamper a profile image for the "age'' model to predict "young'', while the "attractiveness'' model still predicts "pretty''. In this work, we empirically demonstrate that attacking the classifier for one learning task may negatively impact classifiers learning other tasks on the same data. This raises an interesting research question: is it possible to attack one set of classifiers while protecting the others trained on the same data?
A STUDY OF THE MOTIVATIONAL CRITERION OF TEAM BUILDING IN AN EDUCATIONAL ORGANISATION

The authors of the article present the results of a study aimed at studying the indicators of the motivational criterion of team building. The purpose of the study is to diagnose the motivational criterion of the team formation process. The work is based on the hypothesis, according to which the readiness of a teaching team to implement modern innovative pedagogical projects, interest in the overall results, contributing to further improvement of the quality and effectiveness of the educational process as a whole, are conditioned by a set of internal motives of individuals, expressed in orientation to the content of the activity performed, the desire to achieve certain positive results in it. The main research methods used were source analysis, testing, quantitative and qualitative analysis.
Estonia, an early convert to digital technology

Abstract For over twenty years, Estonia has been betting on technology, particularly in the education sector. A gamble that is paying off.
Sistem Informasi Manajemen Arsip Pada Komisi Pemilihan Umum Provinsi Jambi

The analysis and design of the archives management information system at KPU Jambi Province is a research that produces a design of a records management system that is expected to answer the problems that arise at this time, namely the process of managing records that still use manual methods and the absence of well- managed archival records. This research uses the prototype method with the UML (Unified Modeling Language) approach. This research produces a prototype that can provide an overview of the design of the archives management information system and the UML (Unified Modeling Language) approach as a tool that can facilitate the developer later in making the information system. With this the resulting prototype could be the first step for the Jambi Province KPU in managing records better.
IDENTIFIKASI PENYAKIT JANTUNG MENGGUNAKAN MACHINE LEARNING: STUDI KOMPARATIF

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns that are close to heart disease. Prediction of heart disease is important given the behavior of people who are still prone to risk factors. Conditions where predictions using machine learning for heart disease have not been compared with many using machine learning methods. Predictions of heart disease are needed along with the interrelationships of the variables. This research compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value of 82% in the KNN method with the confusion matrix test, precision, accuracy, re-call, and fi-score. These results can be applied to real case studies of heart disease
Efforts to improve dental and oral health of tunagrahita through interactive counseling at SLBN 1 Pangkep, District Minasa Tene, Sulawesi Selatan

Tunagrahita is a developmental disorder of brain function known as mental retardation. Based on this, it is necessary to give special attention to special schools by conducting socialization and counseling on oral health through education with interactive media with the aim of impro-ving the oral health of children with tuna grahita through interactive counseling and tooth brushing demonstrations at Sekolah Luar Biasa Ne-geri 1 Pangkep, Minasatene District, Pangkep Regency. Socialization and counseling of oral health through education is carried out with in-teractive media. It was found that the level of knowledge, attitudes, actions and poor oral health behavior in 40 students of SLBN 1 Pangkep increased after interactive education, namely 38 students showed moderate results and 2 students showed good results. It was concluded that there were changes after interactive education was carried out on knowledge, attitudes, actions and behavior in children with disabilities.
Pengembangan Aplikasi Pembelajaran Gerbang dan Rangkaian Logika Memanfaatkan Augmented Reality Untuk Siswa SMK

As time goes by, technological developments are getting faster, one of which is mobile technology. In mobile technology in this era, what is often used is the use of augmented reality technology. The following study aims to provide an overview of several steps involved in making augmented reality-based Android learning media applications, gate materials, and a series of computer system lesson logic for vocational level students. In addition, to produce android learning media applications utilizing augmented reality logic circuit material for vocational level students that are practical, effective and valid. In this research, the type is application development. The process of developing this application applies the ADDIE method which is composed of five stages, namely Analysis, Design, Development, Implementation and Evaluation. From this study, the average score of validity of android mobile learning applications using augmented reality gates and logic circuits for vocational students is 4.54 and is included in the very valid section. The response results showed that students responded positively to mobile learning media, augmented reality-based logic circuits. From student responses, 80% of students responded in agreement to each part of the response, and 85% of students obtained scores above the minimum completeness criteria so that the use of logic circuit learning applications using augmented reality is categorized as effective.
A Closer Look At The EFL Students’ Grammatical Errors In Writing Descriptive Text

EFL students found some difficulties in writing descriptive texts because they have to observe the language features, and generic structure of such genre carefully to produce a good paragraph. Students made some errors in their writing and experienced difficulties dealing with the grammatical rules. The objectives of the present research are to identify the errors types that students might encounter in writing descriptive text and to describe what errors are dominantly made. Therefore, a descriptive qualitative research design was applied to describe students’ type of errors as proposed by Dulay (1982) and to figure out errors that dominantly occur in students’ writing, the present study used Ellis’s theory (1994), namely error collection, error identification, error description, and error explanation. The respondents were 30 students (5 males and 25 females) in English Language Education Study Program at Universitas Teknologi Yogyakarta. The research data was taken from students’ works and analyzed qualitatively. The findings revealed that students made four types of errors, ranging from the highest to lowest, namely omission, misformation, addition, and misordering. Of 174 errors, 60 errors dominantly occurred in the omission part, and 51 errors were in the misformation part. Meanwhile, 47 errors occurred in the addition part, and 16 errors occurred in the misordering part.
PENERAPAN ALGORITMA NAIVE BAYES PADA APLIKASI PENELUSURAN ALUMNI (TRACER STUDY) BAGI TAMATAN SMKN 2 KOTA BENGKULU

Alumni are a very valuable asset for an educational institution. The success of educational institutions will be greatly influenced by the success of alumni in the process of getting a job or continuing their education. The more alumni who succeed in their work, the better the rating of the educational institution because many companies employ them or who successfully continue their education to the next level. Vocational High School is considered able to answer the problems of employment and unemployment. Vocational education is part of the education system that prepares a person to be better able to work in one occupational group or field of work than in other occupations. SMKs also assist with re-industrialization through three main groups: training young people for work, training current workers, and returning to the workforce. One of the successes of educational institutions can be determined by how successful graduates are when they are in the community. The ratio of graduates who have worked to those who have not worked, the average waiting period for work, the field of work occupied, the amount of income received when they first worked, and others. One method to obtain appropriate data is to use a tracer study, which is an academic activity to obtain feedback from graduates about the relevance of the educational process that has been undertaken with the ability to improve the quality of life of graduates in the community. Tracer study is a tool to obtain data needed for the development of an educational institution. The process of obtaining tracer study data can use a computer connected to the internet so that the process becomes easier because it can be accessed anytime and anywhere. The data obtained from the study tracer is processed according to the need for the data, one method that can be used is Naïve Bayes. Naïve Bayes algorithm is one of the methods used based on probabilistic reasoning.
Occluded Vehicle Detection with Fusing Motion Information

Vehicle detection is essential for autonomous driving and Intelligent Transportation System (ITS), but it remains the challenge of occlusion. Occlusion leads to incomplete vehicle feature, which affects feature extraction and recognition. In this paper, we propose a vehicle detection algorithm of fusing motion information and adding localization branch for occluded vehicle detection. Firstly, RGB images and corresponding optical flow are input into two branches of the dual-stream network to extract appearance features and motion features. Then, we improve attention mechanism so that the model has outstanding feature extraction capability with fewer parameters when the two features are fused. Finally, the location prediction branch is added to acquire the localization confidence to evaluate the localization accuracy of the bounding box. We multiply the classification confidence and localization confidence to compute the final detection confidence, solving the problem of low correlation between the classification score and the localization accuracy. A series of experiments demonstrate the effectiveness of the algorithm in dealing with occluded vehicle detection, with accuracy of 87.34% and speed of 35.7 FPS, meeting the requirements of practical applications.
An End-to-End TTS Model in Chhattisgarhi, a Low-Resource Indian Language

With the advancement of voice technology, there is a drastic improvement in the realization of Text To Speech (TTS) systems. But the lack of technological support and diversities in the low-resourced Indian languages hinder the development of such systems for the Indian population. SYSPIN is an initiative that aims to develop TTS corpora and AI models in nine Indian languages. The primary motivation behind this work is to set up an initiative to popularize the scope of voice technology for the Chhattisgarhi population. This paper presents 20 h of TTS dataset of 2 speakers in Chhattisgarhi, a low-resourced Indian language with 18 million native speakers. The paper also describes the baseline TTS systems which achieve high subjective scores of 4.38 and 4.46 mean opinion score (MOS) for each speaker.
An ASR Corpus in Chhattisgarhi, a Low Resource Indian Language

RESPIN is a project that aims at the development of a dialect-rich database and some user-friendly voice-technology applications in 9 Indian languages including Chhattisgarhi. The paper elaborates on the entire process of such a low-resource database preparation in a crowd-sourced manner . Through this work we have open-sourced around 250 h of dialect-rich, domain-rich Chhattisgarhi ASR dataset to popularize the scope of voice technology to the Chhattisgarh population. The paper also describes the development of a base model with a WER score of 11.58% on the test set.
Performance evaluation of KPCA pre-imaging methods for speech signal denoising

&lt;p class="Abstract"&gt;Kernel principal component analysis (KPCA) has gained wider interest amongst the researchers in nonlinear dimensionality reduction, data compression, feature extraction, and denoising applications. In KPCA, data from low dimensional input space implicitly mapped to higher dimensional feature space where linear PCA is enforced. However, for denoising application data need to invert back to input space, which is impossible and known as pre-imaging problems. Over recent years, several pre-imaging methods have proposed each with its benefits and disadvantages. In this paper, we evaluated the performance for selected pre-imaging methods for denoising speech signal, whose intelligibility and quality degraded by background noises. Further, we extend our work by comparing the performance of these pre-imaging methods by objective evaluation of denoised speech signal on generated toy examples and NOIZUES database.&lt;/p&gt;
Perbandingan Kinerja K-Nearest Neighbors dan Naive Bayes Untuk Klasifikasi Perilaku Nasabah Pada Pembayaran Kredit Bank

:Credit customers are people who use banking services or other financial services. to use bank money in its business activities, so it expects that the bank's credit can meet business capital needs. To reach information to increase profits and reduce company losses, we need a method that can provide knowledge to support the company's data. Research data can be obtained from processing classification data from credit customer data that are categorized as potential or not potential in the next credit grant. Data processing can be done using machine learning, namely classification techniques. This technique will produce a predictive churn model to determine which customer categories belong to a group. potential smooth or jammed. The Naive Bayes method was chosen because it can produce maximum accuracy with little training data. Meanwhile, the K-Nearest Neighbor method was chosen because it is robust against noise data. The performance of the two methods will be compared, so that it can be seen which method is better in classifying documents. The results obtained show that the Naive Bayes method has better performance with an accuracy rate of 70%, while the K-Nearest Neighbor method has a fairly low accuracy rate of 40%. Thus, it can be seen the accuracy value displayed by applying the classification algorithm. K-Nears Neighbors and Naïve Bayes. Parameter category. which in this study are account numbers, names of debtors, collectibility in the categories: current, DPK (on special mention), substandard, doubtful, loss. Then clarified with a description of the type of loan, collectability of ADK (computer data archive), type of business.&#x0D;
Analisis Perbandingan Nilai Kondisi Jalan Menggunakan Metode SDI dan IRI Dari Alat Survei Roadroid

Good road infrastructure plays an important role in the economic development of a city. Therefore, the government must maintain and improve road conditions yearly to help attract investment and ultimately lead to overall economic growth. Presently, road condition surveys are conducted and assessed using the manual method with the Surface Distress Index (SDI), which is time-consuming and expensive due to the large number of access roads. In order to address this issue, a fast and cost-effective alternative survey approach is required, such as the International Roughness Index (IRI) method using the Roadroid application. Roadroid is an application installed on a smartphone and then attached to a car's windshield to record road conditions while the car is in motion. This study aims to compare the use of the Roadroid application with the manual survey method in terms of time and cost efficiency in identifying road conditions in the city of Palembang, which has 1204 roads. In this study, the Roadroid application results is compared to the road condition assessment results from the SDI methods. The results showed that the time required to obtain road conditions with manual survey and Roadroid application was 37.313 minutes and 4.123 minutes, respectively. The cost required to obtain road conditions was IDR 290.937.000 and IDR 68.006.000 for manual surveys and using the Roadroid application, respectively. Furthermore, the comparison of road condition assessment results between the SDI and IRI methods showed that they have average mean differences of 24.86%, so it can be concluded that the road conditions obtained in the IRI method using Roadroid have high accuracy with the conditions in the SDI method. From the results, it can be concluded that using the Roadroid application to obtain road conditions is more effective and efficient than manual surveys.