Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN E-LEARNING SCHOOLOGY PADA MASA PANDEMI COVID-19 DI PGSD FKIP UNIVERSITAS SARJANAWIYATA TAMANSISWA

Schoology e-learning is a learning process by utilizing technology because online e-learning media uses computer networks to provide information and communication that if the lecturers use e-learning, there can be changes in learning activities between students and lecturers. This study aims to develop and describe the attractiveness, convenience and usefulness and effectiveness of e-learning which is used as an enrichment in terms of student activity and learning outcomes. It is a research &amp; development research referring to development models that are Analyze, Design, Develop, Implement, Evaluation (ADDIE). Schoology-based e-learning has similar features to Facebook and Schoology also combines social networking and LMS (Learning Management System). The development of schoology-based e-learning can easily obtain information on the learning materials provided by the lecturer even though the lecturer is unable to attend class because through schoology the lecturer can check student attendance, and between lecturers and students can interact socially and also learn together.
Techniques for Reducing Iron (Fe) Content in Groundwater: an Article Review

Iron is a chemical element that is found in almost every place on earth, including in well water or groundwater. Iron can be suspended in water with organic substances or inorganic solids in the form of ferrous cations (Fe2+) and ferries (Fe3+). The presence of iron that exceeds the threshold can cause detrimental effects such as corrosion of the piping, the color of the water turns brown, smells bad, and can cause health problems. The iron content in groundwater can be reduced so that it can be used as water that is fit for consumption or use in everyday life. This study aims to determine several types of techniques used to reduce iron in groundwater or well water through literature studies. Techniques covered include filtration, aeration, adsorption, coagulation, electrocoagulation, and cascade aerators. The results showed that the greatest reduction in efficiency was found in the combination type of adsorption technique and filtration technique by more than 99.1%, then followed by the electrocoagulation technique of 99.74%. From all the techniques studied in this study, it can be concluded that all techniques can reduce iron levels in which the combination of adsorption and filtration techniques is the most effective.Keywords: Technique, reduction, iron content, groundwater
Improving Student’s Reading Comprehension on Narrative Text Through Directed Reading Think Pair Share (TPS) at Madrasa Aliyah

&lt;p class="05IsiAbstrak"&gt;The purpose of the research was to find out the improvement of student’s reading comprehension on narrative text through directed reading and think pair share at madrasa Aliyah of Manahijul Huda Ngagel in academic years 2019/2020. This research was done by applying Classroom Action Research which carried out through four steps. They were planning, action, observation, and reflection, in cycle 1 and cycle 2. The subject of research was the grade X-MIA, they are consisted of 30 students. Based on the result of the data analysis showed that mean of the score has increased in the cycle 2, the mean in the pre-test was 63,33%, the mean of cycle 1 was 74,33%. Then the mean of cycle 2 was 84,17%. The students who got up 75 also grew up. In the pre-test, students who got up 75 were only 5 of 30 students (16,67%). In the post test of cycle 1, students who got up 75 were only 17 of 30 students (56,67%). It means that there was improvement about 40%. In the post test of cycle 2, students who got up 75 were 24 of 30 students (80%). The improvement was about 23,33%. The total improvement of the student’s score from the pre-test to post test of cycle 2 was 63,33%. So, the result of the research showed that Directed-reading and Think Pair Share (TPS) strategy significantly improved student’s reading comprehension on narrative text.&lt;/p&gt;
Active Participation in the Student-to-Teacher Interaction in Online Synchronous Sessions in Higher Education

Mexican institutions promoted e-learning to conclude the school year 2019-2020. In the higher education institution where this study was conducted, teachers were not familiar with online teaching. In Spring 2020, teachers prepared themselves or with students’ assistance to teach online. In Summer 2020, the institution offered workshops to update teachers on Blackboard LMS, Microsoft Teams, and Moodle to work in Autumn 2020. After that training, teachers started videoconferences in online synchronous sessions (OSSs). However, they observed that students did not participate actively in OSSs. Given this contextual background, this qualitative exploratory study addressed a threefold objective: 1. To identify students’ perceptions of active participation in OSSs. 2. To explore the degree of participation in the student-teacher interaction in OSSs at a higher institution, and 3. To identify factors affecting student active participation while teachers are lecturing. Sixteen participants with ages ranging from 19 to 25 years old were interviewed. The results showed that most participants consider active participation essential in online synchronous sessions; the level of participation was high, moderate, and low. In addition, affective, cognitive, moral and technical factors affected students’ active participation. The results of this study are useful in shaping our understanding of the whole panorama that is taking place behind synchronous sessions.
PENERAPAN DISCRETE COSINE TRANSFORM (DCT) TERHADAP KOMPRESI CITRA DIGITAL

&lt;p&gt;Image compression is a process that can reduce image size. In general, there are two types, lossless compression and loss compression. The method usually used in the JPEG standard is lossy compression, which removes some of the image information and takes advantage of the weakness in the insensitivity of the human eye in recognizing color gradations. With such large storage, data transfer or exchange is also increasingly difficult, especially data exchange using small storage areas. Tools that usually limit the size of storage for exchanging data. From the existing results using the DCT method is able to compress files up to 96%. Can be seen from the overall file average up to 74%&lt;/p&gt;
Encryption method of privacy information in student archives based on blockchain Technology

In order to overcome the problem that the accuracy of privacy information mining in the existing privacy data encryption methods of student archives is lower than that of encryption security, this paper proposes a privacy information encryption method in student archives based on blockchain technology. In order to improve the effectiveness and security of encryption, singular value decomposition (SVD) method is used to mine the privacy information in students' archives. Based on the results of privacy information mining, combined with chaotic encryption technology in blockchain, the encryption function of privacy information in student archives is constructed to complete the encryption of privacy information in student archives. Experimental results show that, compared with the traditional encryption method, this method can effectively improve the accuracy of privacy information mining, and has high encryption security.
Implementasi Rencana Kerja Tahunan Sekolah Dasar di Era New Normal

The purpose of this research is to find out the lecturers' strategies in e-learning management to improve learning outcomes in college. The lecturer strategy in managing e-learning includes how to plan, implement, supervise and evaluate online learning (e-learning). This research used a qualitative approach with descriptive methods. The subjects of the study were lecturers who did online learning and were able to improve the results of learning through e-learning. The research instrument used interview guidelines and documentation. Interview, survey, and observation were done to collect the data. Data analysis done by using triangulation techniques. The results of this study shows that the lecturer's strategy in managing e-learning, including 1) Planning, namely drawing up a learning plan on the e-learning website, with a sub-view of RPS (lesson plan), learning achievements, diagnostic tests (pre-test), module materials, learning videos, journal links related to materials, discussion forums, tasks, and absorption tests (posttest).2) Execution, focus on how to solve material problem (reward and punishment) by understanding modules and videos. 3) Supervision is by utilizing the agreed e-learning time, monitoring process through zoom media, video conference and whatsapp group to know the students’ participation in e-learning. 4) Evaluation, with diagnostic tests and absorption tests as an indicator of student atteanity in receiving e-learning materials and independent tasks to improve HOTS (higher order of thinking skills) students. Evaluation in maximizing e-learning content in the website is done with CIPP (context, input, process, and output) so that innovation from lecturers is always increasing.
BREAST TUMOR DETECTION USING CLASSIFICATION ALGORITHM

Breast cancer is the most common cancer in women.It is the leading cause of cancer death in developing countries and the second leading cause of cancer death in women in the United States, behind only lung cancer.Females are more likely to develop breast cancer.However, in a few instances, it is clear that males have also been affected.Breast cancer has been discovered.[1] Breast tumors may be either cancerous or non-cancerous.Benign tumors are easily treated with doctor-prescribed medications.Malignant tumors are a sign of a high risk of breast cancer and should be removed as soon as possible.Early identification and treatment of tumor lowers the risk of cancer-related death.The survival rate of breast cancer patients in America has been reported to be 90% in recent years, but it is as low as 60% in India.[2] The key cause of this shortcoming is late tumor diagnosis and classification, which causes treatment delays.Using machine learning to perform a task will reduce the workload of physicians and radiologists.According to research, doctors can only diagnose breast cancer with a 79 percent accuracy rate, while machines can diagnose it with a 91 percent accuracy rate.Early diagnosis is the best way to increase the chance of treatment and chance of survival.
Domain Adaptation via Bregman divergence minimization

In recent years, Fisher linear discriminant analysis (FLDA) based classiﬁ-cation is one of the most successful approaches and has been shown eﬀectiveperformance in diﬀerent classiﬁcation tasks. However, when the learning data(source domain) have a diﬀerent distribution against the testing data (tar-get domain), the FLDA-based models may not be optimal, and the perfor-mance will be degraded, dramatically. To face this problem, in this paper, wepropose an optimal domain adaptation via Bregman divergence minimization(DAB) approach, in which the discriminative features of source and target do-mains are simultaneously learned via domain invariant representation. DABis designed based on the constraints of FLDA, with the objective to adaptthe coupled marginal and conditional distribution mismatches with Breg-man divergence minimization. Thus, the resulting representation can havewell functionality like FLDA and simultaneously have better discriminationability. Moreover, our proposed approach can be easily kernelized to dealwith nonlinear tasks. Extensive experiments on various benchmark datasetsshow that our DAB can eﬀectively deal with the cross domain divergence andoutperforms several state-of-the-art domain adaptation approaches on cross-distribution domains.
Automated feature detection of black-box continuous search-landscapes using neural image recognition

Extracting search-landscape features of high-dimensional Black-Box Optimization (BBO) problems, by minimal number of queries, is a grand challenge. Knowledge of such features per a given BBO problem-instance offers valuable information in light of the Algorithm Selection and/or Configuration problems. In practice, by solving this challenge, the query complexity of BBO may be reduced when capitalizing on algorithm portfolios. In this study we target the automated recognition of BBO benchmark functions. We propose an identification framework for d-dimensional continuous BBO test-suites, provided as input with a set of N search-points, sampled at random, together with their query values. We address it as a supervised multi-class image recognition problem, by introducing the concept of Landscape Images, and applying the basic LeNet5 Neural Network to classify them. The solution's core lies within the encapsulation of the BBO functions' data as Landscape Images, and the application of neural image recognition to learn their features. The efficacy of our approach is numerically validated on the noiseless COCO/BBOB test-functions, which are demonstrated to be correctly classified at high precision rates (≈90%) when N is in the order of d. This successful learning is another step toward automated feature detection of BBO problems.
Multilevel Clustering Explainer: An Explainable Approach to Electronic Health Records

Machine learning (ML) algorithms have been used in many areas of activity, and their results can often be applied without further human intervention. The ML algorithms have also been widely used in medical contexts, but in this area, the result needs to be thoroughly confirmed by a specialist, who needs explanatory information on how the results were obtained. Aimed at such scenarios, we propose the Multilevel Clustering Explainer (MCE), a method capable of providing explanatory information to health professionals about the knowledge discovery process. The MCE was developed for the analysis of medical data, providing a synthesis of explanatory information for the specialist to quickly and clearly understand how the results were obtained.
Providing security using CAPTCHA: CAPTCHA as a graphical password

Various security primitives uses hard mathematical problems.Use of hard AI problems for security is emerging and exciting new pattern, but has not yet been explored.In our task, we present another security crude dependent on hard AI issues, this framework is named as Captcha as graphical passwords (CaRP).CaRP is Captcha just as graphical secret key plan.CaRP symbolize various security issues together, for example, web-based speculating assaults, transfer assaults, and shoulder-surfing assaults.By and large, a CaRP secret phrase can be discovered just probabilistically via programmed web based speculating assaults regardless of whether the secret key is in the hunt set CaRP likewise offers well way to deal with address the notable picture hotspot issue in mainstream graphical secret phrase frameworks, as PassPoints, that by and large prompts decisions of feeble secret word.
Diversification of ceramic craft for tourism souvenir: local culture as art creation and production idea

The tourism industry and craft products are two sectors that are long developed, complementing and strengthening each other. However, not many people consider ceramic craft product design with local culture basis. The research aims to show how local culture can be used as a source for ceramic craft product design ideas. Through design innovation and production techniques, we can increase varieties of ceramic craft product design. However, to make it come true, it needs a creative method as its solution. Creative method application includes: (1) Developing ceramic craft design for souvenir, based on local culture; (2) ceramic craft visual exploration for souvenir sourced from the local culture with Kerapan Sapi Madura (Cow Racing) as creation idea; (3) Ceramic craft production technique for souvenir; (4) Improvement and analysis of ceramic craft product innovation for a souvenir. (5) Analysis of creation result. Creation results can be seen from two design sets of ceramic craft products based on local culture with the “Kerapan Sapi Madura (Cow Racing)” theme as a product design idea.
Modeling Academic Research Collaborator Selection Using an Integrated Model

Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users' needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.
REINVEST THE RELATIONSHIP BETWEEN EXPORTS AND ECONOMIC GROWTH IN AFRICAN COUNTRIES: NEW INSIGHTS FROM INNOVATIVE ECONOMETRIC METHODS

This research examined the relationship between exports and economic growth in Africa. It employed many innovation econometric methods including Panel FMOLS and DOLS Estimates; Panel VECM; Panel ARDL Model; Pooled OLS, Random Effect Model, Fixed Effect Model and Hausman Test; Panel Pairwise Granger Causality Tests; Panel Toda-Yamamoto Causality Test; and Panel GMM Model. The findings suggested that the estimates of each model prove that there is a positive bidirectionnel relationship between exports and economic growth. Data includes 49 African countries for the period 1960-2018. These empirical results have some notable policy implications.
OPTIMIZATION OF FACILITIES AND INFRASTRUCTURE MANAGEMENT IN IMPROVING THE QUALITY OF LEARNING

&lt;p&gt;Infrastructure is a standard of education considered and provided by every educational institution, because of complete facilities and infrastructure are a support for the learning process in an educational institution. Therefore, every educational institution must have feasible and useful in order to support the learning process. Effective learning is supported by complete facilities and infrastructure. Every school community should maintain and maintain existing facilities and infrastructure and also optimize the management to create durable school facilities and well maintained. The well service of these facilities and infrastructure is benefit to the school community, especially students in their learning. The main purpose of managing school facilities and infrastructure is to provide good and professional services in creating the effective and conducive school environment.&lt;/p&gt;
Predicting of Banking Stability Using Machine Learning Technique of Random Forests

The purpose of this research is to create a predicting model of banking stability in Indonesia. Authors use a small set of explanatory indicators primarily related to the banking industry and some relevant economic variables. Among the indicators candidate to be used in this study are the indicator of banking industry, the money markets, capital markets and creditors, and the macro-economic indicator. The source of data in this research is obtained from CEIC and SEKI (Indonesian Economic and Financial Statistics) that published by Central Bank of Indonesia from 2004 and 2011. Principal Component Analysis is used to avoid the multi-collinearity problem when construct the model. Authors train the model using Random Forest Regression with data over the 2004-2007 period, and made predictions of global financial crisis that happened in 2008/9. Python 2.7.10 and scikit-learn version 0.20.0 module has been exploited for simulations and evaluation of the model. Numerical illustration is provided to demonstrate the efficiency of proposed model. As the result nine most components analysis obtained as input for the machine learning model with explained variance ratio around 97%, accuracy around 89%, and precision 91% and mean absolute error around 11%.
Sovereignty by Personalization of Information Search: A Collective Wisdom May Influence My Knowledge

With the experiment that we outline in this paper, we have had the ambition to pave the way for addressing the problem of supporting, enhancing and measuring collective AND informal learning, in particular serendipity. We want to support a new type of free navigation on Web resources (Documents, Topics, Events and Agents – human and artificial -) that is driven by the learner’s current needs and the preferences of the community of trust chosen by the learner, not by external actors. The experiment exploits the ViewpointS Web Application (VWA) prototype, that restructures a private version of a subset of the Web according to personalized choices in order to determine distances/proximities among resources. The process allows to enable, empower and measure the influence of members of the community of trust of the learner, on the learner’s choices when navigating in search of THE resources corresponding to THE immediate need, goal, strategy, wish. In the following, we will outline: 1. the rationale of our efforts and 2. the user’s reactions during the phase of -formal and informal- learning the functions and use of the prototypical software environment VWA, i.e.: a proof of concept for VWA.