Reviewerly/exHarmony huggingface dataset:
Editorial, Volume 10 (2019)

On behalf of the Editorial Board, it is with great pride and sincere privilege that I am writing this message to present the volume 10 (2019) of the INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION METHODOLOGY The issue comes from a long process, and we took all the necessary steps to make it a high-caliber scientific publication. We are relying on the collaboration of all our Editors, reviewers, and contributors to make it a contemporary, lively, and relevant publication.
INTRINSIC MOTIVATION CORRELATION AND SOCIAL SUPPORT WITH SELF-EFFICACY OF YOUTH ON LEMBAGA PEMASYARAKATAN KHUSUS ANAK (LPKA) TANGERANG, INDONESIA

Main purpose of this research is to see the correlation of intrinsic motivation and social support with self-efficacy of Youth on Lembaga Pemasyarakatan Khusus Anak (LPKA) Tangerang. (Ha1): there is correlation between intrinsic motivation with self-efficacy. (Ha2): there is correlation between social support with self-efficacy. (Ha3): there is correlation between intrinsic motivation and social support with self-efficacy. The research population is Youth on LPKA Tangerang with total of 82 peoples. Researcher’s sampling using saturation sampling techniques (census) based on Likert scale. Measurement of intrinsic motivation scale consist of 34 valid items, with coefficient correlations range between 0.512 until 0.693 with 0.885 reliability. Efficacy scale consist of 23 valid items with correlations range between 0.631 until 0.738 with 0.736 reliability. From the result of bivariate correlation analysis known that r = 0.241, means (Ho1) rejected and (Ha1) accepted. Result test of (Ha2) known that r = 0.244, means (Ho2) rejected and (Ha2) accepted. From the test result of multivariate correlation known that R = 0.298, means (Ho3) rejected and (Ha3) accepted. Based on the result of this research, there is positive correlation between intrinsic motivation and social support with self-efficacy of Youth on LPKA Tangerang. The better of intrinsic motivation and the higher social support received by Subject, the higher self-efficacy of the Subject. Article visualizations:
Peningkatan keterampilan membaca pemahaman dengan menggunakan strategi directed reading thinking activity (drta) kelas v pada mata pelajaran bahasa indonesia

&lt;p class="Abstract"&gt;&lt;strong&gt;&lt;em&gt;Abstract.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; This research purposed to increase the reading comprehension skill by using the DRTA strategy on students class V SDN Bibis Wetan student year 2018/2019. The form of this research was Classroom Action Research (CAT) held in two cycles. Everycycle was consist of 4 steps as following planning, action, observe, and reflection. The subject of this research was teacher and students was amounts 36 students. The technique of collecting datas were documentation, observation, interviewing, and test. The datas were analysed by using interactive analysis (Miles and Huberman). The data validity used source triangulation and technical triangulation. The result of this research, there was an improving the reading comprehension skill after the teacher used DRTA strategy on student.&lt;/em&gt;&lt;/p&gt;&lt;p class="Abstract"&gt;&lt;strong&gt;&lt;em&gt; Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: Directed Reading Thingking Activity (DRTA), Reading Comprehension Skill, Elementary School&lt;/em&gt;&lt;/p&gt;
USING NEWSPAPER ADVERTISMENTS ON TEFL (LINGUISTIC STUDY)

Language helps people to communicate with their society and gives a lot of contribution in dealing with other people, People are able to tell story, bargain, persuade, and even explain things using language. Language plays a vital role in human’s life. However, the way to use a language is different from one nation to another or from written text to oral communication. The study on presupposition lies on the border of semantics and pragmatics. It concerns information, assumed to be part of discourse context by a speaker. Presupposition can be used to analyze the text of advertisement. In processing presupposition, the intended meaning is assumed to be true based on the writer’s statement and it’s analyzed by the reader. In consequence, presupposition can be used by the readers to know the intended meaning of the advertisement. This research studies about the presupposition used in “Jakarta Post” and “Strait Times” Newspaper Advertisements. The aims of the study are to find of the types of presupposition in “Jakarta Post’ and “Straits Times” advertisement, to analyze the meaning behind the presupposition investigated. In this research, the writer used descriptive qualitative research that use based types of qualitative research that is documentation to analyze the written presupposition. The result of the study shows that: The Jakarta Post has existential presupposition (5), lexical presupposition (3), and non-factive presupposition (1). Strait Times has existential presupposition (4), lexical presupposition (1), factive presupposition (12), and non-factive presupposition (3).
Role of Second Formant Frequency( F2) in Forensic Speaker Identification

Identification of a speaker specific feature is an almost impossible task because of the enormous number of features which can affect a single voice utterance.F2, (marker of vowel position) plays an important role in vowel identification. The present study is an statistical analysis conducted on the 16 speakers’ F2, F2- F1 and F3-F2 values for the vowels; “/a:/, /i:/ and /u:/. Vowel /a/ showed considerable height difference in Known sample (where the researcher knew the Speaker) and Unknown sample (someone else collected the sample).
Designing for the Long Tail of Machine Learning

Recent technical advances has made machine learning (ML) a promising component to include in end user facing systems. However, user experience (UX) practitioners face challenges in relating ML to existing user-centered design processes and how to navigate the possibilities and constraints of this design space. Drawing on our own experience, we characterize designing within this space as navigating trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We suggest that the theoretical description of how machine learning performance scales with training data can guide designers in these trade-offs as well as having implications for prototyping. We exemplify the learning curve's usage by arguing that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.
An Update on Known Invariants of Vectorial Boolean Functions

Almost Perfect Nonlinear functions are of great practical importance for cryptography as they provide optimum resistance to differential cryptanalysis. Due to the large number of such functions, they are classified up to CCZ-equivalence, which leaves this resistance to differential cryptanalysis invariant. Deciding the equivalence of two given functions from the definition is computationally difficult. We summarize computational results on some properties that remain invariant under CCZ-equivalence for the known APN functions, including more than 8000 new CCZ-inequivalent functions that have previously never been considered with respect to these invariants. Knowing these invariants greatly facilitates the classification of APN functions, since different values of these invariants immediately imply that two functions are CCZ-inequivalent.
Peningkatan keterampilan membaca pemahaman melalui model pembelajaran connecting organizing reflecting extending (core) pada peserta didik kelas iv sekolah dasar

&lt;p class="Abstract"&gt;&lt;em&gt;The purpose of this study is to improve reading comprehension skills with the Connecting Organizing Reflecting Extending (CORE) learning model&lt;/em&gt;&lt;em&gt;. &lt;/em&gt;&lt;em&gt;This type of research was classroom action research. This research carried out for two-cycle with each cycle consisting of four activities that are planning, implementation, observation, and reflection. The techniques used to collect data in this study include observation, interviews, tests, and documentation. The data validity techniques used were instrument content validity, source triangulation, and technique triangulation. Data analysis in this study uses comparative descriptive analysis techniques and interactive data analysis.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;The results of research that have been carried out at the pre-action stage, the class average is 45.69 with classical completeness is 14.29%. The class average increased in the cycle I with an average grade is 68.25 and classical completeness 57.14%, then increased again in cycle II with an average class 79.30 and classical completeness 85.71%. Based on the research results, it can be seen that the application of the Connecting Organizing Reflecting Extending (CORE) learning model can improve reading comprehension skills on the fourth-grade students of the elementary school of Banyurip academic year 2018/2019.&lt;/em&gt;&lt;/p&gt;
Particle-Gibbs Sampling For Bayesian Feature Allocation Models

Bayesian feature allocation models are a popular tool for modelling data with a combinatorial latent structure. Exact inference in these models is generally intractable and so practitioners typically apply Markov Chain Monte Carlo (MCMC) methods for posterior inference. The most widely used MCMC strategies rely on an element wise Gibbs update of the feature allocation matrix. These element wise updates can be inefficient as features are typically strongly correlated. To overcome this problem we have developed a Gibbs sampler that can update an entire row of the feature allocation matrix in a single move. However, this sampler is impractical for models with a large number of features as the computational complexity scales exponentially in the number of features. We develop a Particle Gibbs sampler that targets the same distribution as the row wise Gibbs updates, but has computational complexity that only grows linearly in the number of features. We compare the performance of our proposed methods to the standard Gibbs sampler using synthetic data from a range of feature allocation models. Our results suggest that row wise updates using the PG methodology can significantly improve the performance of samplers for feature allocation models.
Study on the Improvement of Experiment Teaching of Gravity Center Location Based on Spreadsheet and Web Map

The use of the center of gravity method to select the address experiment is a key and difficult point in the teaching of “logistics system design”. The traditional teaching process is provided by teachers to provide experimental data and arrange experimental steps, which is not conducive to stimulating students' exploration enthusiasm and experimental interest. This article combines spreadsheet software and web map tools to redesign and arrange the experimental teaching process for the location of the center of gravity. Because most textbooks lack the use of spreadsheet software for multi-facilities centering site selection experiments, this paper explores the feasibility of using spreadsheet software to model multi-facility location problems. By establishing a mathematical model of multi-facility siting problems and using Excel spreadsheet software for example analysis, the traditional gravity center location experiment teaching process was improved and redesigned, and the student’s learning interest and exploration enthusiasm were stimulated, while satisfying the requirements of the general experimental environment.
Library: Digitisation Service: Digitisation

The Library offers several services to help academics to get the highest quality and copyright-compliant scanned resources for their students.
Enriching the User Experience of a Connected Car with Quantified Self

This paper presents the R’SENS project, the design for a system leveraging the Internet of Things to connect the dots between the user, her car and her environment through the integration of tools belonging to different domains, such as the connected car, the quantified self and web services. The aim of this study is to test a concept through a prototype to explore the possibility of enhancing the user experience of a connected car through the opportune and ubiquitous display of relevant information not only about the car status and the environment, but also the physical status of the driver. The integration of three different user interfaces (i.e., wearable, smartphone and infotainment system) to be used in different moments played a crucial role in the preliminary evaluation of the perceived usefulness and usability of this proof-of-concept.
Drug Recommendation System for Geriatric Patients Based on Bayesian Networks and Evolutionary Computation

Geriatric people face health problems, mainly with chronic diseases such as hypertension, diabetes, osteoarthritis, among others, which require continuous treatment. The prescription of multiple medications is a common practice in that population, which increase the risk of unwanted or dangerous drug interactions. The quantity of drugs is constantly growing, as are they interactions. It is therefore desirable to have support systems for medical that digest all available data and warn for possible drug interactions. In this paper we proposed a drug recommendation system that takes into account pre-existing diseases of the geriatric patient, current symptoms and verification of drug interactions. A Bayesian network model of the patient was built to allow reasoning in situations of limited evidence of the patient. The system uses also a genetic algorithm, which seeks the best drug combination based on the available patient information. The system showed consistency in simulated settings, which were validated by a specialist.
From Pick-Up Artists to Incels: A Data-Driven Sketch of the Manosphere.

In this paper, we present a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements roughly focused on men's issues, which has seen significant growth over the past years. We do so by gathering and analyzing 28.8M posts from 6 forums and 51 subreddits. Overall, we paint a comprehensive picture of the evolution of the Manosphere on the Web, showing the links between its different communities over the years. We find that milder and older communities, such as Pick Up Artists and Men's Rights Activists, are giving way to more extremist ones like Incels and Men Going Their Own Way, with a substantial migration of active users. Moreover, our analysis suggests that these newer communities are more toxic and misogynistic than the former.
Face Verification via learning the kernel matrix

The kernel function is introduced to solve the nonlinear pattern recognition problem. The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. Over the past few years, some methods which have been proposed to learn the kernel have some limitations: learning the parameters of some prespecified kernel function and so on. In this paper, the nonlinear face verification via learning the kernel matrix is proposed. A new criterion is used in the new algorithm to avoid inverting the possibly singular within-class which is a computational problem. The experimental results obtained on the facial database XM2VTS using the Lausanne protocol show that the verification performance of the new method is superior to that of the primary method Client Specific Kernel Discriminant Analysis (CSKDA). The method CSKDA needs to choose a proper kernel function through many experiments, while the new method could learn the kernel from data automatically which could save a lot of time and have the robust performance.
Automatic Classification of Galaxies Based on SVM

Viewing heavenly objects in the sky helps astronomers understand how the world is shaped. Regarding the large number of objects observed by modern telescopes, it is very difficult to manually analyze it manually. An important part of galactic research is classification based on Hubble's design. The purpose of this research is to classify images of the stars using machine learning and neural networks. Particularly in this study, the galaxy's image is employed. The galaxies are divided into regular two-dimensional Hubble designs and an irregular bunch. The regular bands that are presented in the shape of the Hubble design are divided into two distinct spiral and elliptical galaxies. Spiral galaxies can be considered as elliptical or circular galaxies depending on the shape of the spiral, so the identification or classification of the spiral galaxy is considered important from other galaxies. In the proposed algorithm, the Sloan Digital Sky is used for testing, including 570 images. In the first step, its preprocessing operation is performed to remove image noise. In the next step, extracting the attribute from the galactic images takes place in a total of 827 properties using the sub-windows, the moments of different color spaces and the properties of the local configuration patterns. Then the classification is performed after extracting the property using a Support vector machine. And then compared with other methods, which indicate that our approach has worked better. In this study, the experiments were carried out in two spiral and elliptic classes and three spiral, elliptic and zinc-edged classes with a precision of 96 and 94 respectively.
Machine Learning and value generation in Software Development: a survey

Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. This work is meant to serve as a starting point for practitioners willing to add ML to their software development toolbox. It categorises recent literature and identifies trends and limitations. The survey shows as some authors have agreed that industrial applications of ML for SD have not been as popular as the reported results would suggest. The conducted investigation shows that, despite having promising findings for a variety of SD tasks, most of the studies yield vague results, in part due to the lack of comprehensive datasets in this problem domain. The paper ends with concluding remarks and suggestions for future research.
Circllhist -- A Log-Linear Histogram Data Structure for IT Infrastructure Monitoring

The circllhist histogram is a fast and memory efficient data structure for summarizing large numbers of latency measurements. It is particularly suited for applications in IT infrastructure monitoring, and provides nano-second data insertion, full mergeability, accurate approximation of quantiles with a-priori bounds on the relative error. Open-source implementations are available for C/lua/python/Go/Java/JavaScript.
Exploration Of Horse Leather Material As Raw Materials In Making Footwear

Horse leather material has not been popular among consumers, of course, being a separate obstacle in the sale of products that use horse leather material. Horse leather is a material that is rarely used, if this horse leather material is used appropriately, this can be a selling point for that product. In addition, if you use an attractive selling strategy, this can attract the attention of consumers and increase sales figures. Treatment of horse skin material is not as difficult as imagined, it is enough to use a toothbrush and shampoo to clean it if it is dirty and brushed in the direction of the horse's hair so as not to damage its texture.The process of designing starts from seeing the trends that are taking place in the community, of course, trends related to footwear, by looking at the trends that are taking place in the community we can get a target market from which people will be the target in the design process footwear.�The design is done by going through several stages such as sketches, digital rendering by inserting material into the rendering to make it easier to imagine, and also making alternative colors.
Rancang Bangun Sistem Informasi Geografis Pemetaan Tanaman Pangan Kabupaten Pulau Morotai

Abstrak:Penilitian ini bertujuan merancang dan mengimplementasikan Sistem informasi Geografis Pemetaan Potensi Tanaman Pangan di Kabupaten Pulau Morotai, dilakukan di Kabupaten Pulau Morotai, Pengumpulan data diperoleh melalui observasi, wawancara dan dokumentasi, Metode Analisis Sistem yang digunakan adalah model driven secara terstruktur dengan pendekatan bottom-up membuat analisa sistem yang berjalan menggunakan alat bantu flowchart serta usulan sistem yang baru menggunakan pendekatan berorientasi objek dengan alat bantu UML, Metode pengembangan Sistem menggunakan Rapid Aplication Development (RAD). Bahasa Pemrograman yang digunakan HTML, PHP, MySQL serta Maps API untuk menampilkan media peta berbasis Web. Hasil dari penelitian ini diharapkan dapat memberikan kemudahan bagi pihak Dinas Pertanian dalam Menginformasikan data Potensi lokasi tanaman pangan agar pengunjung dapat mengkases informasi lokasi Potensi Tanaman Pangan Kabupaten Pulau Morotai secara cepatKata Kunci: Sistem Informasi Geografis, Potensi, Tanaman Pangan, WebsiteAbstract:This research aims to design and implement Geographic Information Systems Mapping Potential Food Plants in Morotai Island Regency, carried out in Morotai Island Regency. Data collection is obtained through observation, interviews and documentation. The System Analysis Method used is a structured driven model with a bottom-up approach making system analysis that runs using a flowchart tool and a new system proposal using an object-oriented approach with UML tools, System development method uses Rapid Application Development (RAD). The programming languages used are HTML, PHP, MySQL and Maps API to display Web-based map media. The results of this study are expected to provide convenience for the Department of Agriculture in Informing the potential data of the location of food plants so that visitors can access information on the location of Potential Food Plants in Morotai Island Regency quicklyKeywords: Geographic Information Systems, Potential, Food Plants, Websites
Optimal Representation of Large-Scale Graph Data Based on Grid Clustering and <i>K</i><sup>2</sup>-Tree

The application of appropriate graph data compression technology to store and manipulate graph data with tens of thousands of nodes and edges is a prerequisite for analyzing large-scale graph data. The traditional K 2 -tree representation scheme mechanically partitions the adjacency matrix, which causes the dense interval to be split, resulting in additional storage overhead. As the size of the graph data increases, the query time of K 2 -tree continues to increase. In view of the above problems, we propose a compact representation scheme for graph data based on grid clustering and K 2 -tree. Firstly, we divide the adjacency matrix into several grids of the same size. Then, we continuously filter and merge these grids until grid density satisfies the given density threshold. Finally, for each large grid that meets the density, K 2 -tree compact representation is performed. On this basis, we further give the relevant node neighbor query algorithm. The experimental results show that compared with the current best K 2 -BDC algorithm, our scheme can achieve better time/space tradeoff.