Reviewerly/exHarmony huggingface dataset:
Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure

&lt;p&gt;The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.&lt;/p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; bank customer, random forest, logistic regression, multilayer perceptron
Pemanfaatan Teknologi Informasi Dalam Pembelajaran Pada Masa Pandemi COVID-19 Di Smp Negeri 02 Baras

This study was conducted to determine the use of information technology in the learning process during the Covid 19 pandemic at SMP Negeri 02 Baras. By using the library research method. This Pandemic period has had a huge impact on education in Indonesia, especially in areas that have been contaminated by the presence of the Covid 19 virus which is very threatening to the safety of the public because there is no patent anti-covid vaccine. So this has a very significant impact on the learning process that was initially carried out in schools are now online learning that is done from home. The use of information technology is very helpful in the learning process during the COVID-19 Pandemic. The advancement of information technology that is very advanced today, with the internet network can connect students with teachers via WhatsApp groups, google class rooms, messengers and so on. The Ministry of Education and Culture also provides an online learning system called “Rumah Belajar”. Meanwhile, for areas where the internet connection is not very good, the government collaborates with TVRI so that it can deliver learning materials in the Learning from Home program. the use of information technology in the learning process during the Covid 19 pandemic at SMP Negeri 02 Baras, an effective application used in the learning process is to use the WhatsApp application and the Messenger application, while for students who are not reached by the internet network learn to use HT (Handy Talky). In the learning process what students go through is under the supervision of parents and teachers.
METHODS AND MODELS FOR TEXTURE ANALYSIS OF LUNG PATHOLOGICAL CHANGES BASED ON COMPUTED TOMOGRAPHY FOR COVID-19 DIAGNOSIS

Abstract. In recent years computed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks such as highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the images.
Retos al avanzar en la Aritmética

espanolAl avanzar en el estudio de la aritmetica los estudiantes se enfren-tan a cambios significativos en las matematicas que ya han aprendido. Estos cambios afectan no solo al conocimiento matematico en si mismo sino tambien al conocimiento matematico como objeto de ensenanza.En este capitulo, se describen estos cambios, destacando aquellos fenomenos en que los estudiantes pueden tener problemas de aprendizaje o aquellos aspectos que hacen que un contenido curricular sea mas dificil de ensenar. EnglishAs we move forward in the study of arithmetic, students face significant changes in the mathematics they have already learned. These changes affect not only the mathematical knowledge itself but the mathematical knowledge as an object of teaching,In this chapter, these changes are described, pointing out those phenomena in which students may have learning problems or those as-pects which make a curricular content more difficult to teach.
The Simplicial Generalized Beta distribution - R-package SGB and applications

A generalization of the Dirichlet and the scaled Dirichlet distributions is given by the simplicial generalized Beta, SGB (Graf, 2017). In the Dirichlet and the scaled Dirichlet distributions, the shape parameters are modeled with auxiliary variables (Maier, 2015, R-package DirichletReg) and Monti et al. (2011), respectively. On the other hand, in the ordinary logistic normal regression, it is the scale composition that is made dependent on auxiliary variables. The modeling of scales seems easier to interpret than the modeling of shapes. Thus in the SGB regression: • The scale compositions are modeled in the same way as for the logistic normal regression, i.e. each auxiliary variable generates D − 1 parameters, where D is the number of parts. • The D Dirichlet shape parameters, one for each part in the compositions, are estimated as well. • An additional overall shape parameter is introduced in the SGB that proves to have important properties in relation with non essential zeros. • Use of survey weights is an option. • Imputation of missing parts is possible. An application to the United Kingdom Time Use Survey (Gershuny and Sullivan, 2017) shows the power of the method. The R-package SGB (Graf, 2019) makes the method accessible to users. See the package vignette for more information and examples
Study on Teaching Korean in the Context of “Ideological and Political Education in Courses”

As the economic exchange and trading grow between China and South Korea in the 21 st century, educational exchange and collaborations between the two countries have flourished.Students who have pursued further study in South Korea and have been deeply affected by the Korean culture featured by parades, religions, adornment of sissies and Cinderella, often fail to develop correct legal and critical thinking, and their values are distorted.To solve this problem, Korean language teachers at universities should build on the characteristics of the major, carry out ideological and political education in their classes, elaborate on the history of Korean culture, improve the students' critical thinking and help develop students into talents who can undertake the mission of national rejuvenation of China in the new era.
Assessment of dissolved oxygen in Shatt Al-Arab River by other quality parameters of water using Artificial Neural Networks

The Major sources of water are surface and subsurface.Surface water includes Rivers, Reservoirs, Creek, Streams, etc.This paper deals with using a neural network model to recognize dissolved oxygen in Shatt Al-Arab.Within the present study, Shatt Al-Arab River (Basrah-Iraq) is considered as the study area with monthly observed data from 2009-2014.Artificial Neural Network (ANN) has been applied to pattern the relations among eight (8) water quality parameters which are devoted for predicting one parameter (1) so that to decrease the load of long experimental procedure.Physical and chemical parameters that are inserted in the model are: pH, total dissolved solids, electrical conductivity, sulphate, phosphate, calcium, magnesium and nitrate.Dissolved oxygen (DO) is included in the output models.The three layered feed-forward model with back-propagation multi-layer perception (MLP) models architecture of 8-8-1 for DO.The artificial neural network has got training successfully and has been tested with 70% and 30% of the data groups.Statistical criteria of correlation coefficient (R 2 ) and mean square error (MSE) are used to evaluate performance of the models.The correlation coefficients of the artificial neural network model for predicting DO have been 0.99354 and 0.98237, and mean square error for the model are 0.007698 and 0.00122 respectively.It can be concluding that these techniques provide similar accuracy in estimating DO concentration and predicting the dissolved oxygen (DO) in Shatt Al-Arab
Kalman filtering with empirical noise models

Most Kalman filter extensions assume Gaussian noise and when the noise is non-Gaussian, usually other types of filters are used. These filters, such as particle filter variants, are computationally more demanding than Kalman type filters. In this paper, we present an algorithm for building models and using them with a Kalman type filter when there is empirically measured data of the measurement errors. The paper evaluates the proposed algorithm in three examples. The first example uses simulated Student-t distributed measurement errors and the proposed algorithm is compared with algorithms designed specifically for Student-t distribution. Last two examples use real measured errors, one with real data from an Ultra Wideband (UWB) ranging system, and the other using low-Earth orbiting satellite magnetometer measurements. The results show that the proposed algorithm is more accurate than algorithms that use Gaussian assumptions and has similar accuracy to algorithms that are specifically designed for a certain probability distribution.
EEG Processing Using VHBCSE Based FIR Filter

There is a major role for filter in noise removal. because of simple structure, stationary response and adaptively with embedded microprocessors. The Finite Impulse Response (FIR) filter has many advantages on signal processing . to process the Electroencephalography (EEG) signal the FIR filter is the best suited option. The noise in the EEG signal can reduced by using FIR filter. The hardware complexity of FIR filter is high because of multipliers present in the FIR filter. By reducing the multipliers fast and less area using FIR filter is obtained. This paper present a vertical and horizontal Binary common subexpression elimination (VHBCSE) algorithm based FIR filter to process the EEG signal. The common subexpression elimination (CSE) algorithm eliminate the bit patterns which are often occurring. The proposed FIR filter reduce noise of EEG signal rapidly ,by consuming less power and design only required less area. Xilinx ISE design suite 14.7 is used to simulate the FIR filter in FPGA and the MATLAB environment is to generate the EEG signal.
The Effectiveness of Using Comic Strips to Teach Students’ Reading Comprehension on Narrative Text at SMK Negeri 2 Sragen Academic Year 2018/2019

This research is aimed at finding the effectiveness of using comic strips as an alternative media to teach reading comprehension of narrative text at tenth grade students of SMK Negeri 2 Sragen academic year 2018/2019.The research design of this study was Quasi-experimental. This study was using accidental sampling which used the sample provided by the school. The sample of this study was Class X TKR 4 as control group and X TP 1 as experimental group. The instrument used in this study was test, 20 multiple choice questions of reading comprehension test. Before the test was given, the test has been trial out in X TP 4 to see validation and reliability.Based on the results of data analysis since pre-test up to post-test between control group and experimental group. The post-test average score of experimental group which was taught using comic strip is higher than the post-test average score of control group which was taught using without comic strip or module media. The post-test average score of experimental group was 67,00 and the post-test average score of control group was 52,13. The significant improvement of experimental group could also be shown in the gained score from the pre-test (50,75) to post-test (67,00) with the t-test value was 13,265 (>2,039), the significance 0,000 ( 2,001), the significance 0,000 (<0,05). It can be conclude that application of using comic strip is effective in teaching reading comprehension of narrative text at the tenth grade students of SMK Negeri 2 Sragen in the academic year 2018/2019.
Feature Extraction for Sentiment Analysis in Indonesian Twitter

Twitters sentiment analysis is one of the most interesting fields of research lately. It intertwines the natural language processing techniques with data mining. Up to this point, many algorithms have been proposed to better understand sentiment from text. The proposed method can be focused on the preprocessing step, dataset splitting method (training and testing), dataset balancing method (when the data is unbalanced), to the improvement of the existing algorithm. But, the main focus of this paper is on feature extraction from tweets using TF-IDF. The features obtained from this process are expected to improve the accuracy of the classification process. The dataset used in this research is in Indonesian, which has a very different form when compared to English. This dataset consists of 1068 manually labeled tweets related to the "school from home" policy caused by the COVID-19 outbreak, taken from March to July. All steps required to process this data will be implemented using python. To validate its utility, the performance of the proposed method is compared with each other. Finally, the results are summarized by reflecting on the impact of the inclusion of the proposed features for each classification algorithm for sentiment detection
Installing and Using the Apache Web Server

The Apache web server has as its core function the ability to serve web pages. It is a highly specialized and powerful software application that is being used to serve billions of web pages daily. The Apache web server is the single most oft-used web server on the Internet today.
Development and Application of Dance Literacy Program: Exploring the Effects of Culture Class in University

The purpose of this study is to create academic framework to understand the reality and value of dance literacy education as a convergence education and to seek strategies to practice it. The research procedure was conducted from July 2016 to June 2019. The key findings are as follows: First, dance literacy education emphasizes practical knowledge and has a convergent characteristic that can be called academic integration. Second, the development of interdisciplinary education programs was categorized into subjects by deriving related elements, common elements and contents between art and other studies. Third, the educational effect of each of the five components of Dance Literacy could be specified. This paper will be the basic data that can promote the quality improvement of art and dance literacy as a convergence education, and can provide the typical curriculum framework of dance education.
Comparison of Data Augmentation Methods in Pointer-Generator Model Using Various Sentence Ranking Methods

In the existing research, we proposed a data augmentation method using topic model for Pointer-Generator model.In this study, we add to the sentence ranking method in the data augmentation method.Specifically, we add two ranking methods using LexRank and Luhn.LexRank is based on Google's search method and Luhn defines sentence features and ranks sentences.We compare three data augmentation methods.We considered which method is suitable for data augmentation.We confirm that most accurate model is the model using data augmentation method by topic model.
Sarcasm Detection using Twitter Analysis

Sаrсаsm is а sорhistiсаted fоrm оf irоny widely used in sосiаl netwоrks аnd miсrоblоgging websites. It is usuаlly соnveys imрliсit infоrmаtiоn within the message а рersоn trаnsmits. Sаrсаsm might be used fоr various рurроses, for example сritiсism оr mосkery. Hоwever, it’s hаrd even fоr humаns tо reсоgnize. Therefоre, reсоgnizing sаrсаstiс stаtements саn be very useful tо imрrоve аutоmаtiс sentiment аnаlysis оf dаtа which is соlleсted frоm miсrоblоgging websites оr sосiаl netwоrks. Sentiment Analysis refers tо the identifiсаtiоn аnd аggregаtiоn оf аttitudes аnd орiniоns exрressed by internet users towards a particular tорiс. In this рарer, we have рrороsed а pattern-bаsed аррrоасh tо deteсt sаrсаsm оn Twitter. We рrороse fоur sets оf feаtures thаt соvers all the different types of sаrсаsm we defined. We use thоse tо сlаssify tweets аs sаrсаstiс аnd nоn-sаrсаstiс. Оur рrороsed аррrоасh reасhes аn ассurасy оf 82%. We аlsо study the imроrtаnсe оf eасh оf the рrороsed sets оf feаtures аnd evаluаte its аdded vаlue tо the сlаssifiсаtiоn. In раrtiсulаr, we emрhаsize the imроrtаnсe оf раttern-bаsed feаtures fоr the deteсtiоn оf sаrсаstiс stаtements.
Three-dimensional Visualization and Interpretation of Pre- and Post-earthquake Data of 2017 Pohang Earthquake

We analyze pre- and post-mainshock earthquake data of the 2017 Mw 5.5 Pohang earthquake, South Korea to visualize the geometry of subsurface seismogenic faults and understandearthquake mechanisms. We use a total of 53 high-quality earthquake focal mechanism data from pre-mainshock earthquakes, which were triggered by hydraulic fracturing at the Pohang EGS (Enhanced Geothermal System) site, and 202 post-earthquake data, which are aftershocks of the Pohang earthquake. In order to perform three-dimensional visualization, we determine fault planes from the two nodal planes in individual focal mechanism solutions using the fault instability criterion under a given stress states. The sizes of individual fault planes are also scaled based on earthquake magnitudes. We carefully construct plausible fault planes by grouping individual fault planes based on their geometry and location to visualize them three-dimensionally as hexahedral elements. We then interpret how water injection through geothermal wells stimulated the faults.
Graphical Solutions of Fuzzy Models

In the knowledge of dynamical system Fuzzy Initial Value Problems (FIVPs) have been vigorouslyinvestigatedover the modeling of numerousexhilaratingactual world problemwith all branches of engineering andscience.In this paper, numerical solution isassociated(1)gh and (2)gh differentiability.We propose a fuzzy versions of Picard's Method, solutionsinlinear fuzzy differential equations with first-order.The proposed technique is exemplified by a real-life sample and found to be in good agreement.The efficiency and accuracy of the suggestedtechnique is demonstrated in a series of graphical representations.
UPAYA MENINGKATKAN HASIL BELAJAR MELALUI PENERAPAN MEDIA Power Point PADA PROSES PEMBELAJARAN IPA DI KELAS VII.C SMPN 2 KOTA BENGKULU PADA SEMESTER GANJIL TAHUN PELAJARAN 2018/2019

The aims of the study is to see the effect of using power point media on student learning outcomes. Student interest and attention to the subject matter will be further increased during the course of the learning process, that is the way of presentation and delivery of subject matter is made more attractive by using various media, such as using power point as a media through computer devices and Liquid Crystal Display (LCD). The user of this media, will create a lively and interesting learning process than the usage of white board.Â So that by using power points, it can increase interest in learning process, which will have an impact on increasing learning outcomes.
TINJAUAN SINGKAT OPTIMALISASI PEMANFAATAN ENERGI SURYA PADA SEKTOR RUMAH TANGGA

Energy use always increases, especially fossil energy. Through the National Energy Policy, the government continues to strive to increase the role of new and renewable energy sources so as to reduce dependence on fossil energy. Solar power generation is a type of renewable energy generator that capable to convert solar energy to electric energy. The main components of solar power generatios are batteries, solar panels, charger controllers, and inverters. Solar power generations technology itself is always being developed, such as automatic monitoring and sun tracking systems designed to improve system performance. One of the applications of solar power generations is in the household sector. In this sector consumes 49% of the national electricity energy in 2018. This type of generator is categorized as a roof solar power generations. Based on existing data, there were 1400 roof solar power generations users in September 2019. The development of solar energy utilization for the household sector is very appropriate because it can help achieve renewable energy about 23% in 2025 and 31% in 2050 in the national energy mix.
The structure of online social networks modulates the rate of lexical change

New words are regularly introduced to communities, yet not all of these words persist in a community's lexicon.Among the many factors contributing to lexical change, we focus on the understudied effect of social networks.We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade.Using Poisson regression and survival analysis, our study demonstrates that the community's network structure plays a significant role in lexical change.Apart from overall size, properties including dense connections, the lack of local clusters and more external contacts promote lexical innovation and retention.Unlike offline communities, these topic-based communities do not experience strong lexical levelling despite increased contact but accommodate more niche words.Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.
Predicting Mental Health of Scholars Using Contextual Word Embedding

Frustration, anger and mental fatigue are some of the most prevalent issues faced by scholars these days which are often due to the cut-throat competition and peer pressure among adolescents increasing day by day. The objective of the work was to predict the mental health of scholars by tracking strongly negative words or offensive slangs used very deliberately by them in their tweets. The data to analyze the sentiments exhibited by them has been used from their Twitter timelines. The most common topics they talked about and the common keywords for each of these topics were identified and thereafter using BERT, the word embedding and cosine similarities had been found between these keywords and a bag of words that contained a number of strongly negative emotion words collected manually. In this paper, contextual word embedding has been done on the twitter data of frustrated individuals to analyze their temperament and behavior exhibited by them. Also, we have found if there was any correlation between frequently used negative words and frustrated individuals or not. We then found that there was a correlation and individuals using such words had negative emotions prevalent among them.