Reviewerly/exHarmony huggingface dataset:
Pelaksanaan Kegiatan Literasi Sekolah untuk Membentuk Pembiasaan Gemar Membaca dalam Situasi Pandemi Covid-19 Pada Siswa SD Negeri 13 Kebondalem Kabupaten Pemalang

During the pandemic, literacy programs in schools were hampered and had difficulty continuing to be implemented. Under these conditions, teachers' efforts to regenerate students' interest in reading during the pandemic are urgently needed so that they can continue to form a generation that has a strong character in knowledge and broad-mindedness. This type of research is qualitative with a qualitative descriptive observation design. The form of research used is a descriptive data method. The population of this study were students of class IV, V and VI SDN13 Kebondalem Pemalang. The sample taken was 30 students using observation, interview, questionnaire, and documentation techniques. The data of this study were obtained through the results of a questionnaire from 4 indicators of reading pleasure that had been made and interviews. Based on the results of the analysis of the data obtained, the indicator of "reading pleasure" is high, "awareness of the benefits of reading" is high, "reading frequency" is quite low for students, and "the quantity of reading resources for students" is high. It can be concluded that the literacy activities carried out at SDN 13 Kebondalem Pemalang have been running well and the habit of reading is formed.
Pengaruh Media Gambar Terhadap Hasil Belajar Bahasa Indonesia Pada Pembelajaran Puisi Pada Siswa di Sekolah

This study aims (1) to find out the description of the use of image media in poetry learning in class IX E UPTD SMPN 14 Tanralili, (2) to determine the results of learning Indonesian in poetry learning in class IX E UPTD SMPN 14 Tanralili (3) To determine knowing the effect of picture media in improving Indonesian language learning outcomes in poetry learning for class IX E UPTD SMPN 14 Tanralili students. The data collection techniques of this research are (1) tests which include pretest, treatment, and posttest, and (2) documentation. The data analysis technique in this study used the t-test. The results of this study indicate that the pretest was categorized as very low, obtained 9 students who completed poetry learning by 29.03% who got a score of 70 and above (meets KKM) and 22 students who did not complete 70.97% who got value of 70 and below (does not meet the KKM). Meanwhile, after giving or using the image media, the results of the final test (posttest) in the high category were obtained, 28 students who completed poetry learning were 90.32% who scored 70 and above and 3 students who had not completed 9.68% got value 70 and below.
Penerapan Metode Survey, Question, Read, Recite, And Review (SQ3R) Untuk Meningkatkan Keterampilan Membaca Siswa Kelas III SD Negeri Panaikang Kabupaten Gowa

This study aims to determine the application of the Survey, Question, Read, Recite, And Review (SQ3R) Method to improve the reading skills of grade III students of SD Negeri Panaikang, Gowa Regency. This research is a class action study. This research was carried out in class III of SD Negeri Panaikang Gowa Regency in the even semester of the 2021/2022 academic year. The data analysis techniques carried out are qualitatively and quantitatively. Qualitatively, the data were analyzed from the results of observations of teacher performance and student learning activities. Meanwhile, as qualitative, the data is analyzed from the results of tests carried out at the end of each cycle. The results showed that in the first cycle the teacher's teaching activity was in the sufficient category and in the second cycle increased to a good category. And students' reading skills also improved, in the first cycle it was in the sufficient category and in the second cycle it increased to a good category. From the results of the study, concluded that the application of the Survey, Question, Read, Recite, And Review (SQ3R) Method can improve the reading skills of grade III students of SD Negeri Panaikang, Gowa Regency has been successful.
Analisis Learning Obstacles Pada Materi Simetri Lipat dan Simetri Putar Untuk Kelas III Sekolah Dasar

This study aims to analyze the types and causes of learning obstacles experienced by elementary school class students on the material of folding symmetry and rotational symmetry. The approach used is a qualitative approach with descriptive research methods. Data collection techniques used in this study were tests, interviews, observation, and documentation. The results showed that there were three learning obstacle factors experienced by the third grade on the material of folding symmetry and rotational symmetry, namely 77% of students experienced ontogenical obstacles, 81% of students experienced didactical obstacles, and 100% of students experienced epistemological obstacles. The cause of students experiencing ontogenical obstacles is the lack of student interest in participating in learning, lack of readiness to accept learning, both psychological and material readiness. The learning presented by the teacher is too difficult and the students do not understand the prerequisite material. The causes of students experiencing didactical obstacie are due to the lack of teacher readiness in the learning process, limited material delivery, and the teacher does not provide practice questions. The cause of students experiencing epistemological obstacles is because the teacher does not give non-routine questions and there is a jump in the prerequisite material.
Pengembangan Media Pembelajaran RUMPI (Rumah Pintar) Berbasis Android Kelas IV Sekolah Dasar

This research focuses on developing an innovative and interactive android-based learning media to increase learning motivation and make learning more interest. The purpose of this study is to describe the characteristics of the Android-based RUMPI (Smart House) learning media and to determine the level of validity and practicality of the Android-based RUMPI (Smart House) learning media in the IV grade of elementary school. This type of research used Research and Development (RnD) using the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). This research is conducted in three elementary schools: SDN Bangunharjo, SDN Miroto, and SDN Pandean Lamper 05. The data in this study was obtained by observation through observation, interviews, and questionnaires. The validation results from the two material experts showed an average percentage of 100% and the validation results from the two media experts showed an average percentage of 100%. The results of the practitioner's assessment obtained a percentage of 96%. Student response questionnaires obtained a percentage of 99%. It can be concluded that the learning media developed are included in the categories of "very good" and "very feasible" to be used in the IV grade of elementary school learning.
A Comprehensive Analysis of Automated Threat Modeling Solution Company: Threat Modeler Software, Inc.

Purpose: Effective Security Threat modeling in an enterprise depends on the efficient tools used for modeling. The Threat modeling tool market has multiple players that provide platforms to automate the Threat modeling process in enterprises. Threat Modeler Software, Inc. is one such platform provider company. The paper aims to explore the company and its platform's features. Design/Methodology/Approach: This paper explores the documentation available on Threat Modeler Software, Inc. to understand the features, working principles, and company information. Features are further explored by performing hands-on Threat modeling using the trial edition of the platform. SWOC analysis of the company is performed to analyze the factors affecting the company as a Threat modeling platform provider. Findings/Result: Threat Modeler Software, Inc.’s platform has innovative features that enable automated Threat modeling. SWOC analysis has identified some of the challenges that the company has. The competitor list showed commercial and open-source competitors in the race to create Threat modeling platforms. The innovative culture of the Threat Modeler Software, Inc. must continue to provide new features making the Threat modeling experience unique. Originality/Value: This paper studies Threat Modeler platform's architecture and explores important features of the platform. Capabilities of the features and their importance is studied. SWOC analysis is performed to identify factors affecting the company. A list of different threat modeling platforms is built to understand the current competitors for Threat Modeler Software, Inc. Paper Type: Case Study.
METHODS OF USE STAGE RECEPTIONS IN PROCESS OF LABOR TRAINING

In the article to substantiate the integral system of methods of personality development by means of theatrical pedagogy in labor education of primary school pupils. To achieve this goal at different stages of scientific research used the method of systematization of theoretical and empirical data to design methods for the systematic use of acting in the labor of primary school pupils. The aim of the study was to find suitable for existing concepts of personality development ways to use theatrical skills in labor education and upbringing of pupils, methods of their systematic use, which would clarify educational outcomes and the ratio of variables in the description of components of personality development. The main strategy of research of methods of systematic use of acting, which would correspond to conceptual approaches to personality development was the formulation of working definitions in the form of descriptions of goals, objectives, principles, content, methods and forms of organization of formation and development of components. in the lessons of labor training. The results of the study testify to the achievement of its goal and the solution of the tasks to substantiate the methods of systematic use of acting in lessons of labor training, aimed at: development of pupils (the method of mandatory participation of all pupils in collective creativity pupils skillfully demonstrated by the teacher in the lesson of labor actions); development of properties of cognitive mental processes of the pupil’s personality (methods of physical actions, creation of creative well-being, historical parallels, change of role positions, effective analysis); use of personal qualities of each pupil in mastering the educational material (sketch method, methods of stage exercises of pupils in memory of physical actions, stylistic improvisational tasks-dramatizations); acquisition by students of the ability to transfer the model of communication through the use of artistic skills for role-playing images (step-by-step method of teaching students artistic skills); conditionality of the results of educational influences by the level of the pupil's own activity (group sketch improvisations based on the works of classics of painting). Key words: theater pedagogy, labor training, acting techniques.
Machine Learning: A Solution Approach for Complex Problems

Abstract—Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information.It is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto- tagging, recommender system, and many more. Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own.Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. This paper provides over all views of Machine Learning. Keywords—machine learning, artificial intelligence, data,learning,algorithm.
R/bayesvl-class.R

Man pages for bayesvlVisually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'bayesvlBayesVL package for Bayesian statistical analyses in Rbayesvl-classClass 'bayesvl': object class of bayesvl modelgraphsUtilities to manipulate graphsLegends345Legends345 dataplotsPlot utilities for bayesvl objectsstanBuild RStan models from directed acyclic graphvl2bnbnlearn interface for bayesvl objectsbayesvl documentation built on May 24, 2019, 5:09 p.m. Man pages for bayesvlVisually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'bayesvlBayesVL package for Bayesian statistical analyses in Rbayesvl-classClass 'bayesvl': object class of bayesvl modelgraphsUtilities to manipulate graphsLegends345Legends345 dataplotsPlot utilities for bayesvl objectsstanBuild RStan models from directed acyclic graphvl2bnbnlearn interface for bayesvl objectsbayesvl documentation built on May 24, 2019, 5:09 p.m.
Deterministic three-photon down-conversion by a passive ultrastrong cavity-QED system

In ultra- and deep-strong cavity quantum electrodynamics (QED) systems, many intriguing phenomena that do not conserve the excitation number are expected to occur. In this study, we theoretically analyze the optical response of an ultrastrong cavity-QED system in which an atom is coupled to the fundamental and third harmonic modes of a cavity, and report the possibility of deterministic three-photon down-conversion of itinerant photons upon reflection at the cavity. In the conventional parametric down-conversion, a strong input field is needed because of the smallness of the transition matrix elements of the higher order processes. However, if we use an atom-cavity system in an unprecedentedly strong-coupling region, even a weak field in the linear-response regime is sufficient to cause this rare event involving the fourth order transitions.
Randomly initialized EM algorithm for two-component Gaussian mixture achieves near optimality in $O(\sqrt{n})$ iterations

We analyze the classical EM algorithm for parameter estimation in the symmetric two-component Gaussian mixtures in $d$ dimensions. We show that, even in the absence of any separation between components, provided that the sample size satisfies $n=\Omega(d \log^3 d)$, the randomly initialized EM algorithm converges to an estimate in at most $O(\sqrt{n})$ iterations with high probability, which is at most $O((\frac{d \log^3 n}{n})^{1/4})$ in Euclidean distance from the true parameter and within logarithmic factors of the minimax rate of $(\frac{d}{n})^{1/4}$. Both the nonparametric statistical rate and the sublinear convergence rate are direct consequences of the zero Fisher information in the worst case. Refined pointwise guarantees beyond worst-case analysis and convergence to the MLE are also shown under mild conditions. This improves the previous result of Balakrishnan et al \cite{BWY17} which requires strong conditions on both the separation of the components and the quality of the initialization, and that of Daskalakis et al \cite{DTZ17} which requires sample splitting and restarting the EM iteration.
IMPROVING THE PERFORMANCE OF SUPPORT VECTOR MACHINE WITH FORWARD SELECTION FOR PREDICTION OF CHRONIC KIDNEY DISEASE

Chronic kidney disease is a disorder that affects the kidneys and arises due to various factors. Chronic kidney disease, usually develops slowly and is chronic. For prevention and control, proper treatment is needed, so that detection of this disease can play a very important role. This study aims to determine the level of accuracy in predicting chronic kidney disease through SVM based on forward selection and to determine the performance of Feature Selection which is applied to the SVM method in solving problems in chronic kidney disease. This research was conducted an experiment on the SVM method using various kinds of kernels and it was seen that SVM with the dot kernel was 98.50% with AUC 1,000 which was superior to the polynominal kernel and RBF. However, when the experiment was carried out again by applying FS to SVM, it was found that SVM+FS with the RBF kernel outperformed the other kernels by 99.75% with AUC 1,000. So it can be concluded that the Forward Selection of SVM has succeeded in improving its performance, especially in this case, namely the prediction of chronic kidney disease
IDENTIFY CHOLESTEROL DISEASE RISK LEVELS USING MULTIPLE LINEAR REGRESSION ALGORITHMS

Cholesterol is one of the fat compounds found in the bloodstream that are necessary for the formation of several hormones and new cell walls in the liver. Normal cholesterol levels in the human body are in the range of &lt; 200 mg / dL. If cholesterol levels in the blood are abnormal or excessive, it can result in dangerous diseases such as heart disease or stroke. In this study, cholesterol disease prediction will be carried out using models formed from linear regression methods, so that the results of this study can be used as a reference for early prevention of cholesterol disease and become a means of decision making. Linear regression is one of the prediction methods in data mining that can be used to find out how dependent variables/criteria can be predicted through independent variables or predictor variables individually. In this study by utilizing some data of patients with cholesterol disease that has been stored in the database using several attributes, namely age, BMI, glucose, and cholesterol. So by applying a linear regression algorithm can be done a prediction in the identification of cholesterol diseases based on functional relationships on the attributes in the data. The results of this study showed an RMSE value of 0.347 with a standard deviation of /- 0.000. This shows that the model resulting from linear regression algorithms with the above cases is quite accurate.
Automatic Security Assessment of GitHub Actions Workflows

The demand for quick and reliable DevOps operations pushed distributors of repository platforms to implement workflows. Workflows allow automating code management operations directly on the repository hosting the software. However, this feature also introduces security issues that directly affect the repository, its content, and all the software supply chains in which the hosted code is involved in. Hence, an attack exploiting vulnerable workflows can affect disruptively large software ecosystems. To empirically assess the importance of this problem, in this paper, we focus on the de-facto main distributor (i.e., GitHub), and we developed a security assessment methodology for GitHub Actions workflows, which are widely adopted in software supply chains. We implemented the methodology in a tool (GHAST) and applied it on 50 open-source projects. The experimental results are worrisome as they allowed identifying a total of 24,905 security issues (all reported to the corresponding stakeholders), thereby indicating that the problem is open and demands further research and investigation.
ACTUAL PROBLEMS OF PHILOSOPHY OF EDUCATION IN MODERN ERA

This article is devoted to the study of a system of relations between philosophy and education, studying the philosophy of education as an urgent scientific direction. Problems are established, relevant for a modern sociocultural situation: informatization and digitalization of education, the search for the standard of the educated person, dominance of the principle of linearity in the educational paradigm. The decision of these issues may be because of the close interaction of philosophy and education, the development of the first sectorial philosophy directions - the philosophy of education.
Hospital Information System for Decision Making: A Qualitative Study

Abstract: Background: Information technology applications play a major role in improving the services provided to patients in healthcare settings. Hospital Information System HIS is expected to facilitate information management and decision making, thus enhancing patient services. Purpose of this study is to explore the obstacles that healthcare practitioners in PKU Muhammadiyah Bantul Hospital faces during using HIS for decision making. Method: This research is a qualitative study. Depth-interviews were carried out with six respondents (Two Clinicians, Three Managers, and One Director), interviews were recorded and transcribed for analysis, a list of codes has produced representing themes identified in the textual data. Results: Data in the HIS were not optimal due to the insufficiency of the system in generating the necessary information for decision-making. Raw data, not real time data, unstable connection, and incomplete data between units were the most obstacles that decision makers faced. Conclusion: One of the crucial keys of a good performance of healthcare providers is hospital information system that ensures the production, analysis, and providing a reliable and timely information, making it easy for managers and doctors to make managerial and clinical decisions more effectively. Upgrading HIS to enable data analysis is suggested.&#x0D; &#x0D; Keywords: Hospital Information System, Obstacles, Decision Making.
Centroids Matching: an efficient Continual Learning approach operating in the embedding space

Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new task. Existing approaches have achieved remarkable results in mitigating CF, especially in a scenario called task incremental learning. However, this scenario is not realistic, and limited work has been done to achieve good results on more realistic scenarios. In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint. Specifically, the approach classifies the samples directly using the feature vectors produced by the neural network, by matching those vectors with the centroids representing the classes from the current task, or all the tasks up to that point. Centroids Matching is faster than competing baselines, and it can be exploited to efficiently mitigate CF, by preserving the distances between the embedding space produced by the model when past tasks were over, and the one currently produced, leading to a method that achieves high accuracy on all the tasks, without using an external memory when operating on easy scenarios, or using a small one for more realistic ones. Extensive experiments demonstrate that Centroids Matching achieves accuracy gains on multiple datasets and scenarios.
Challenges in Explaining Source Code Quality Assessment

Creating and maintaining high-quality source code is especially important for critical systems such as those made for aerospace and military domains as well as for software product lines where long-lived, reusable modules are intended to be shared by multiple systems. CBR-Insight is an open-source automated assessment tool that relies on data science and interface design to provide an objective and understandable measure of source code quality. CBR-Insight supports the ability of technical and non-technical decision makers to verify that a project&#x0027;s software implementation follows through on promises around developing and sustaining reliable and maintainable software while managing technical debt. The primary contribution of this work is an in-depth discussion on how source code quality assessment is communicated to the entire development team in CBR-Insight, spanning from non-technical decision makers overseeing the project to the software developers responsible for source code. Specific issues discussed include understandability of the explanations provided by the user interface along with transparency and trust in the underlying, technically complex, calculations and scoring algorithms.