Reviewerly/exHarmony huggingface dataset:
Correlation Achievement Motivation With Learning Achievement of Street Children in Elementary School Subdistrict Kemranjen District of Banyumas

Street children who can excel in formal schools can be an example for other street children who prefer to drop out of school. Learning achievements are not only influenced by high intelligence and only capable learning facilities, but learning achievements can also be influenced by other factors such as achievement motivation. The purpose of this research is intended to measure and identify or prove achievement motivation relationship with the learning achievement of street children in District Kemranjen Banyumas District. Penelitian ini dilakukan di kecamatan Kemranjen kabupaten Banyumas dengan 30 anak jalanan sebagai subjek penelitian. Metode yang digunakan dalam penelitian ini adalah metode kuantitatif korelasi dengan menggunakan uji korelasi pearson product moment. The results showed that there was a positive and significant relationship between achievement motivation and learning achievement with a correlation coefficient value of 0.622. The level of relationship between achievement motivated variables with learning achievements has a strong level of relationship with a correlation coefficient value of 0622 * * Located at intervals of 0.60 – 0799. It is hoped that the study of teachers can have a study that can foster the motivation to achieve their students so that the child can be more in the spirit of learning and achieving their achievements at school.
The Use Of Encantado Game To Develop Students Speaking Skills At Seventh Grade Students Of Mts Swasta Insan Cita

The objective of the research was expected to describe the use of Encantado Game indeveloping students’ speaking skills. The subject of the research were the seventh gradestudents of MTs Swasta Insan Cita in 2020/2021 academic year. The seventh gradestudents of MTS Swasta Insan Cita consisted of 38 students. This research applied byClassroom Action Research. The data of the research were qualitative data andquantitative data. The quantitative data were taken from the test (in the form of PreTest, Post test 1 and Post test 2) which given in the end of of every cycle. Whilequalitative data were taken from Interview, Observation sheet, and dairy notes. Theobjective of the research was to describe the use of encantado game in developingstudents speaking skills of the seventh grade students of MTs Swasta Insan Cita Medanin academic year 2020/2021. The showed that encantado games can developedstudents’ speaking skills of the seventh grade. It can be seen from the results of Pretest, Post Test I and Post Test II. In Pre-test the result showed that the mean score ofstudents speaking skills was 61,07 (the precentage was 29 %, and 11 students who gotsuccessful), and Post test I can be seen that mean score of students speaking skills was70,26 (the precentage was 50%, and 19 students who got successful), And the result ofPost test II can be seen that mean score of students speaking skills was 70,84 (therewas the precentage was 74 % and 28 students who got successful). And then the totalnumber of seventh grade students of MTs Swasta Insan Cita was 38 students. Inconclusion, students speaking skills was developed from Pre-test until Post Test II.Than it can indicates that using encantado game can develop students speaking skills.
Cambios en el rendimiento del alumno universitario de la docencia tradicional a la docencia remota de emergencia - [Changes in the performance of university students from traditional teaching to emergency remote teaching]

The objective of this work is to highlight the adaptation process, as a consequence forced by COVID-19, from face-to-face teaching to online teaching in a subject of the Degree in Business Administration and Management at the University of Cádiz (in hereinafter UCA).Thus, the performance of the students will be compared with this new teaching model, with the one that had been achieved in the traditional teaching model.For this, the tools used both for the development of the classes online through the Virtual Campus, and for the evaluation of the subject will be analyzed.And the returns obtained by the students enrolled in the 2018/2019 academic year, taught through traditional teaching, and those enrolled during the 2019/2020 academic year will be compared.
Distributed Principal Component Analysis with Limited Communication

We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case.
The Innovative Model of Family Education Guidance of Guiding First, then Supplementing and Further Solidifying

In the new era a new more effective family education model can ease unbalanced and inadequate education development. In the new era, parents look forward to a more personalized, active and interactive form of family education guidance. This new guidance model will start with improving behavior by focusing on emotional behavior and cognition. The Roast can ease parents' anxiety, lectures and reading groups can supplement parents' knowledge, and mutual discussion, vicarious practice and teaching others can solidify a scientific and effective family education behavior. The family education guidance model of guiding first, then supplementing and further solidifying focuses on the participation of parents in the whole process, which can improve the effectiveness of family education guidance.
Multi-disciplinarity as a way to strengthen the positions of Russian technical universities in international rankings

Subject. The article is devoted to the problem of improving the ranking of the leading Russian technical universities by transforming them into multidisciplinary research universities with the limitation of technical faculties (programs) to 40–50% of the admission plan of entrants. Objectives. The study focuses on improving the position of Russian technical universities in international rankings, providing an opportunity for students to change their specialty or shape it, according to the requirements of the employer. Methods. I analyzed the structure of world-class technical universities in terms of differentiation of specialties and methods of improving the rankings. Results. The analysis shows that the leading positions in international rankings belong to multidisciplinary research universities that provide training and conduct integrated research at the intersection of different sciences. Conclusions. There is a need for gradual reforms of Russian research technical universities, in terms of compliance with global trends in multidisciplinarity, differentiation of funding, and research activities. Such reforms can be carried out in the form of mergers and creation of network structures on the basis of agreements on cooperation between technical universities and research institutes of the Russian Academy of Sciences.
Cover story: Pork barrelling past its use-by date?

It is a practice more than a century old. But as Australia grapples with the standards of behaviour inside Parliament House, has the public's patience regarding pork barrelling - government cash sp...
Binarization of Ancient Malayalam Documents - A Novel Weight-based Denoising Approach

Even though several studies exist on denoising degraded documents, now a days it is a tedious task in the field of document image processing because ancient document may contain several degradations which will be a barrier for reader. Here we use old Malayalam Grantha scripts that contain useful information like the poem titled ‘Njana Stuthi’ and ancient literature. These historical documents are losing content due to heavy degradations such as, ink bleed, fungi-found to be brittleness &amp; show through. In order to remove these kind of degradations, the study is proposing a novel binarization algorithm which remove noises from Grantha scripts as well as notebook images and make the document readable. Here we use 500 datasets of Grantha scripts for experimentation. In our proposed method, binarization is done through a channel based method in which we are converting image in to RGB, further adding weights to make the image darker or brighter followed by morphological operation open and finally passing it RGB and HSV channel for more clarity and clear separation of black text and white background, remaining noise will be removed using adaptive thresholding technique. The proposed method is outperformed with good accuracy.
Pengembangan Sistem Informasi Pencatatan Riwayat Dan Notifikasi Penjadwalan Donor Darah Berbasis Website Menggunakan Framework Codeigniter

The Indonesian Red Cross is a national association organization in Indonesia that is engaged in the social and humanitarian field. One of PMI's main activities is blood donation. Currently, in handling the need for blood donors, UDD PMI Banyumas Regency has a system that helps in the donor process, namely SIMDONAR. SIMDONAR is an information system from the central IT provided to UDD PMI Banyumas Regency in the operation of blood donors, but in some aspects of blood donation, such as donor recording and notification of donor scheduling to donors, there is still no available so that it can run efficiently. Data collection in the blood donor history recording system still cannot be recorded properly, causing donors to also not be able to know the history of the last donor, when and how many times the donor has donated, there is also no notification of blood donor scheduling for donors, this makes the information conveyed to donors is incomplete so that donors are confused in taking action when to donate again. Therefore, to support the smooth running of the blood donor system, it is necessary to rebuild an appropriate information system that can provide a solution for the process of recording blood donation history at UDD PMI Banyumas Regency. The purpose of this system is to provide accurate and efficient information related to blood donation history records, and scheduling notifications to officers and donors. The benefit of this system is that it helps provide information related to donor history records for UDD PMI Banyumas Regency so that they can find out donor history records from donors, also helps donors to find out donor history or how many times the donor has donated blood. In this case, the system development method uses the prototype model method. Based on the results of the benefit test, the average value of the Efficiency value is 98.33%, the Usability value is 94.33%, the Correctness value is 95.56%, the Integrity value is 96.67%, and the Testability value is 96.67%. From the results of the benefit test, it can be concluded that the development of this system can be applied and used to facilitate the process of recording blood donor history and notification of blood donor scheduling at UDD PMI Banyumas Regency.
Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption

Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints. To adapt the classical LearnSPN scheme to learn the structure of these models, we propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Sum nodes are thus learned by recursively clustering batches in the initial data base, while the partitioning of the variables obeys a given input vtree. Preliminary experiments show that the proposed approach might properly fit training data, and generalize well to test data, provided that these remain consistent with the underlying logical base, that is a relaxation of the training data base.
Pesquisa em Design com crianças: caminhos possíveis

The article presents a reflection around aspects inherent to research in Design with children, based on two research experiences developed by the authors, and based on 6 conditions: the profile of children; research time; the research space; the methodology; resources and devices; ethics and commitment.The objective is to point out possible ways to carry out studies in the area involving children.As a result, it establishes a dialogue with researchers and presents viability of resources and research methods in Design for children, also touching on issues of inclusion.
A Flexible and Extensible Framework for Multiple Answer Modes Question Answering.

This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%.
Classification of Infection Types Based on Leukocyte Examination Results UsingK-Nearest Neighbor (KNN)

Leukocytes are blood cells that contain nuclei, also called white blood cells. Leukocytes have a role in the cellular and humoral defense of organisms against foreign substances. Laboratory tests of blood samples greatly influence the diagnosis of a disease. Manual blood tests do have a low price but still have some weaknesses such as the length of time needed will be longer, because health practitioners must examine them carefully to avoid misinformation. To help overcome these weaknesses, a classification of types of infections was carried out based on the results of leukocyte examination. Classification is a grouping of data where the data used has a label or target class. So that the algorithms for solving classification problems are categorized into supervised learning. The purpose of supervised learning is that label data or targets play a role as a 'supervisor' or 'teacher' who oversees the learning process in achieving a certain level of accuracy or precision. The algorithm used in this study is K-Nearest Neighbor (KNN). The data used in this study as many as 2,098 results of complete blood tests taken from one hospital in Medan. This study is in a classification accuracy of 92%.
О РЕЗУЛЬТАТАХ ВНЕДРЕНИЯ ЭЛЕКТРОННЫХ УЧЕБНИКОВ ПО БИОЛОГИЧЕСКИМ ДИСЦИПЛИНАМ НА ПРИМЕРЕ ШКОЛЫ – ЛИЦЕЯ «ДАРЫН» И КАФЕДРЫ «БИОЛОГИЯ» СКГУ им. М. КОЗЫБАЕВА

The article discusses the results of research conducted on the development and implementation of the electronic tutorials for high school pupils of the Daryn Lyceum and the 1st year students of the specialty 5B011300 «Biology» of the Department «Biology» of the NKSU named after M. Kozybayev. The influence of the use of an electronic tutorial in biology lessons on the level of knowledge, creative abilities and school motivation of 9th grade students is considered, as well as the effect of using developed multilingual electronic textbooks when conducting studies for 1st year students of multilingual groups in English on the degree of learning in a foreign language interest in learning disciplines in a foreign language, increasing vocabulary of students and interest in the use of informative communicative technologies in the future professional activity. The methods of mathematical statistics confirmed the positive dynamics of the considered indicators of the quality of education in the case of using the developed electronic textbook in biology class. The method of questioning confirmed the impact of the use of an electronic textbook on the students' indicators under consideration.
LibGuides: Reference Training: Tiered Reference: 5. Basic Topical Search with OneSearch

This guide will help train Library Assistants to respond to basic reference questions, record/track responses and introduce the libchat tool.
Smooth Exploration for Robotic Reinforcement Learning

Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploration, or even damage to the robot. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms. To enable this adaptation, we propose two extensions to the original SDE, using more general features and re-sampling the noise periodically, which leads to a new exploration method generalized state-dependent exploration (gSDE). We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car. The noise sampling interval of gSDE permits to have a compromise between performance and smoothness, which allows training directly on the real robots without loss of performance. The code is available at this https URL.
Meta-Learning Sparse Implicit Neural Representations

Implicit neural representations are a promising new avenue of representing general signals by learning a continuous function that, parameterized as a neural network, maps the domain of a signal to its codomain; the mapping from spatial coordinates of an image to its pixel values, for example. Being capable of conveying fine details in a high dimensional signal, unboundedly of its domain, implicit neural representations ensure many advantages over conventional discrete representations. However, the current approach is difficult to scale for a large number of signals or a data set, since learning a neural representation -- which is parameter heavy by itself -- for each signal individually requires a lot of memory and computations. To address this issue, we propose to leverage a meta-learning approach in combination with network compression under a sparsity constraint, such that it renders a well-initialized sparse parameterization that evolves quickly to represent a set of unseen signals in the subsequent training. We empirically demonstrate that meta-learned sparse neural representations achieve a much smaller loss than dense meta-learned models with the same number of parameters, when trained to fit each signal using the same number of optimization steps.
Translating Clients’ Translation Projects of Information Technology (IT), Automotive, and Marketing Products from English into Indonesian in PT STAR Software Indonesia

ABSTRACTThis final report was written based on the internship activity conducted at PT STAR Software Indonesia, which started from January 20 until March 13, 2020. The main objective of this internship report is to explain the activities done during the internship activity and the problems encountered along with the solutions. The main activity is to always deal with various translation projects of Information Technology (IT), Automotive, and Marketing products. This final report also covers the translation process that is implemented in every translation project.The problems encountered when translating the translation projects were unfamiliar words, foreign terms, poor Wi-Fi connection, and difficult CAT Tools features. The strategies to overcome those problems were finding more references related to the difficult words/terms, using personal hotspot, and asking the supervisor or project managers for clearer guidance.
World Geography With Augmented Realıty

Mobile technologies enable individuals to organize and manage their daily li ves through the mobile applications that are use d during the day. A ugmented reality is one of those applications that can be used on mobile devices. Expectations for the use of new technologies in educational environments increase in parallel with the technolog ical advancements. Thanks to advances in computer, internet and mobile technologies, numerous new applications have emerged that facilitate communication and interaction in educational environments. In particular, t he applications of the augmented reality technologies emerging with the advancing technology in the field of education are becoming widespread and easy -to-use. Augmented reality is a technology that enriches the learning process, created by real-time integration of the virtual elements and the real world elements. In geography teaching, different knowledge and skills are expected to be acquired by the learner , therefore augmented reality application s appeal ing to the different senses of learners can be utilized. AR technology can be used in various fields , especially in education. In addition to increasing productivity, mobile augmented reality applications would  be used for multiple purposes in the field of education with the interactive facilities they offer to user such as 2D and 3D visual support and video playback.
Research on Gesture Recognition and Denoising Technology of Electronic Information Teaching Demonstration Based on Kinect V2

Motion sensor is a kind of sensor which is commonly used in the field of human-computer interaction. This article uses Microsoft’s Kinect sensor to get in-depth information about gesture recognition in electronic instructional demonstrations. Here, we present the Kinect V2 sensor for use in demonstrating gesture capture and recognition in electronic information instruction. In this study, depth information is used to dynamically capture electronic information teaching demonstrative gestures. In addition, a combined denoising method is also proposed, which can effectively remove the interference and noise compared with the single denoising method. Then, the researchers programmed and moved the dynamic recognition of demonstrative gestures in electronic information instruction. Compared with some traditional denoising methods, the combined denoising method can effectively remove the blur and boundary burr. This study can be applied to the field of human-computer interaction in electronic information teaching to further improve the accuracy of information.
Frank-Wolfe algorithm for learning SVM-type multi-category classifiers.

Multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are lots of variants of MC-SVM, although different optimization algorithms were developed for different learning machines. In this study, we developed a new optimization algorithm that can be applied to many of MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both for the direction finding and for the line search exist even for the Moreau envelopes of the loss functions. We use several large datasets to demonstrate that the proposed optimization algorithm converges rapidly and thereby improves the pattern recognition performance.