Reviewerly/exHarmony huggingface dataset:
Modeling of Tuberculosis Case In Central Java 2018 With Three Knot Point

Tuberculosis is a contagious disease caused by infection with the bacteria Mycobacterium Tuberculosis or known as Acid-Resistant Bacteria (BTA). Central Java is one of the provinces that has a high number of tubuerculosis cases in Indonesia. In 2018, Central Java was in second place after West Java in the highest number of Tuberculosis cases in Indonesia with the number of Tuberculosis cases of all types of 67,941 cases. Many variables can affect the number of TB cases. Therefore, a study was conducted in the form of modeling to determine the variables that affect the number of tuberculosis cases in Central Java. Based on data obtained from the Central Java Provincial Health Office in 2018, it shows that the pattern between the number of tuberculosis cases and the variables that are thought to influence it is not linearly related, then a spline regression approach is carried out. The results of this study indicate that the best spline regression model is to use three point knots with significant variables, namely population density and malnutrition. The value of 𝑅2 obtained is 54.6%.
A Study on the Legislative Process of Revision of the LOCAL EDUCATION SUBSIDY ACT in 2000

본 연구의 목적은 2000년 개정『지방교육재정교부금법』입법과정을 정책 네트워크 이론을 통해 분석함으로써 교육재정정책의 결정과정이 어떠한 특징을 갖고 있는가를 밝혀내는 것에 있다. 정책 네트워크 분석 요소들의 분석결과는 다음과 같다. 첫째, 2000년 개정과정은 단계마다 주도적인 행위자가 달라졌으며 타 정책들과 다르게 정책의제설정은 시민단체가 주도하였고 기존의 조직 중심적 연대 활동 방식에서 벗어나 사안 및 이슈 중심의 연대방식을 취하여 교육재정 확보에 힘을 기울일 수 있었다. 특히, 교육부가‘교육재정의 확보’이슈에 대해서는 새교육공동체위원회라는 대리인을 사용하며 앞에 나서서 행동하지 않았다는 것이 특이점이라고 할 수 있다. 둘째, 정책행위자들 간의 상호작용으로는 협력이 주로 나타났다. 가장 두드러지는 협력적 상호작용은 여당 국회의원과 야당 국회의원의 입법공조로 나타났다. 셋째, 2000년 개정과정은 정책의제설정 단계에서는 다양한 참여자들이 이슈네트워크 모형이지만, 법률 개정단계에서는 하위정부 모형의 모습을 보였다. 따라서 시민사회가 발달하여도 행정 관료의 역할이 중요하다는 것을 알 수 있었다. 정책환경의 변화를 예측하고 실제 입법과정에서 정책행위자들 간에 네트워크 관계가 형성되는지 설명 가능할 때 국가와 시민사회가 상생할 수 있는 입법이 가능하다. 따라서 정책 네트워크에 대한 이해는 우리의 정책현실에 많은 시사점을 던져주고 있다.The purpose of this study is to analyze the legislation process of the Local Education Subsidy Act 2000 through the policy network theory to discover the characteristics of the decision process of education finance policy. The analysis results of the policy network analysis elements are as follows. First, the revision process in 2000 changed the leading actor from stage to stage. Agenda-setting was led by civic groups. In addition, we were able to focus on securing education finances by taking an issue-oriented solidarity approach away from the existing organizational-centered solidarity mode. In particular, the peculiarity is that the Ministry of Education did not act in front of the Presidential Commission for the New Education Community using its representative on the issue of securing education finances . Second, the interaction between policy makers has largely shown cooperation. The most prominent cooperative interaction was shown by legislative cooperation between ruling party lawmakers and opposition lawmakers. Third, the revision process in 2000 showed a sub-government model in the revision of the law, although the various participants were the issue network model in the formulation phase of the policy. Thus, we could see that the role of administrative officials was important even when civil society developed. An understanding of the policy network has many implications for our policy reality.
PENERAPAN APLIKASI ZOOM PADA MATERI KETERAMPILAN BERBICARA MAHASISWA BAHASA INDONESIA

ABSTRACT This study aims to determine how the learning process using the Zoom application is applied to the speaking skills material for students of the Indonesian Language and Literature Education Study Program. This application was implemented when the Indonesian Government decided to make rules for the entire community to keep their distance and not hold mass activities anywhere. The government enforces the rules #dirumahaja and #amandirumah. With the application of Zoom media, it is hoped that students can compete in the Digital era, especially for Asahan University Teaching and Education Faculty students. During the Covid-19 pandemic, students only studied online, for this reason, in this Speaking Skills course, researchers tried to apply Zoom media to Indonesian Language and Literature Study Program students. Speaking skills are a form of subject that teaches students to be skilled at honing their oral by using regular, polite, and easily understood languages. In this study, students are expected to be able to use the Zoom application properly, so that the material presented by the lecturers can be understood and applied in life. Keywords :Media Zoom; Speaking Skills
Parquet Cube to store and process gridded data

&amp;lt;p&amp;gt;The volume of data in the earth data observation domain grows considerably, especially with the emergence of new generations of satellites providing much more precise measures and thus voluminous data and files. The &amp;amp;#8216;big data&amp;amp;#8217; field provides solutions for storing and processing huge amount of data. However, there is no established consensus, neither in the industrial market nor the open source community, on big data solutions adapted to the earth data observation domain. The main difficulty is that these multi-dimensional data are not naturally scalable. CNES and CLS, driven by a CLS business needs, carried out a study to address this difficulty and try to answer it.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Two use cases have been identified, these two being complementary because at different points in the value chain: 1) the development of an altimetric processing chain, storing low level altimetric measurements from multiple satellite missions, and 2) the extraction of oceanographic environmental data along animal and ships tracks. The original data format of these environmental variables is netCDF. We will first show the state of the art of big data technologies that are adapted to this problematic and their limitations. Then, we will describe the prototypes behind both use cases and in particular how the data is split into independent chunks that then can be processed in parallel. The storage format chosen is the Apache parquet and in the first use case, the manipulation of the data is made with the xarray library while all the parallel processes are implemented with the Dask framework. An implementation using Zarr library instead of Parquet has also been developed and results will also be shown. In the second use case, the enrichment of the track with METOC (Meteo/Oceanographic) data is developed using the Spark framework. Finally, results of this second use case, that runs operationally today for the extraction of oceanographic data along tracks, will be shown. This second solution is an alternative to Pangeo solution in the world of industrial and Java development. It extends the traditional THREDDS subsetter, delivered by the Open source Unidata Community, to a bigdata implementation. This Parquet storage and associated service implements a smoothed transition of gridded data in Big Data infrastructures.&amp;lt;/p&amp;gt;
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation.

Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton poses. In contrast, we propose an approach that can exploit small annotated data sets by fine-tuning networks pre-trained via self-supervised learning on (large) unlabeled data sets. To drive such networks towards supporting 3D pose estimation during the pre-training step, we introduce a novel self-supervised feature learning task designed to focus on the 3D structure in an image. We exploit images extracted from videos captured with a multi-view camera system. The task is to classify whether two images depict two views of the same scene up to a rigid transformation. In a multi-view data set, where objects deform in a non-rigid manner, a rigid transformation occurs only between two views taken at the exact same time, i.e., when they are synchronized. We demonstrate the effectiveness of the synchronization task on the Human3.6M data set and achieve state-of-the-art results in 3D human pose estimation.
Aplikasi Pemetaan Kos Di Pekanbaru Berbasis Android

Rent-house is one of place to prepare inn service. Rent-house has become need for new comer or people that their house is so far from daily routine such as univercity student, employee and married people. Yet, many people get diffucult to find out rent-house based on their willing. Many things to be problem from facility, price, and location, they should be considered. So that to find appopriate location wastes many times. From the above reasons, it is built rent-house mapping application in Pekanbaru based on android system this aplication gives information about location price,addres, and facility of the rent-house especiality in Pekanbaru. This application is used by functional test and usability this  application is expected  to help new comers to get locarion of rent-house and the owners of rent-house in promoting theirs. The result of this study are application that can help bearders to search for bearding in the new week. There are two tests namely functional and usability testing which test qespondents. The result obtained that all test have been fulfilled and the application is in line with expectations. Keywords: Application, Functional, Usability, Mapping
PENINGKATAN VOCABULARY BUILDING MELALUI MEDIA VISUAL PEMBELAJARAN MATEMATIKA DI SEKOLAH DASAR SWASTA ISLAM AN-NAHLU KISARAN

ABSTRACT Students of An-Nahlu got difficulties in mastering English vocabulary. It can be seen from the observation that has been done by a researcher whereas there were many students who didn’t know a base vocabulary. It’s like a  vocabulary of fruit, vegetables, numbers, noun, etc. Based on the condition above, it must create a research to increase students’ mastery in building English vocabulary by using visual media in mathematics’ learning in SD An-Nahlu. Based on this activity, student’s vocabulary can be built through mathematics learning and it also increase students’ ability in accounting. The target that can be raised in this research was increasing a students’ English vocabulary and students’ enthusiastic in accounting and increasing teachers’ skill in developing media in learning activity. Key words: Vocabulary, Visual media, Mathematic
Out of Purpose Behavior AI

In this paper, we will consider how to configure AI which behaves differently from the original purpose of the game, namely, Out of purpose behavior AI (OAI). In the design of OAI, based on the idea that human setting goal of action on their own sense of values, we systematized “sense of values” to automatically adjust the goal setting according to the game’s play situation and the “attachment” that is the basis for the adjustment. Experimental results showed that OAI changed the play tendency to draw more attached shapes in the out of purpose behavior of drawing shapes on the map of maze games.
Photonic Quantum Error Correction of Qudits Using W-state Encoding

In this paper, we present a passive linear optics error correction scheme for qudits using W-state encoding, based on post selection, which reduces the effects of dephasing noise in photonic quantum communication.
Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply RLNN to quantum hypothesis testing and determine the optimal measurement strategy for distinguishing between multiple quantum states <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo fence="false" stretchy="false">{</mml:mo><mml:msub><mml:mi>&amp;#x03C1;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo fence="false" stretchy="false">}</mml:mo></mml:math> while minimizing the error probability. In the case where the candidate states correspond to a quantum system with many qubit subsystems, implementing the optimal measurement on the entire system is experimentally infeasible. We use RLNN to find locally-adaptive measurement strategies that are experimentally feasible, where only one quantum subsystem is measured in each round. We provide numerical results which demonstrate that RLNN successfully finds the optimal local approach, even for candidate states up to 20 subsystems. We additionally demonstrate that the RLNN strategy meets or exceeds the success probability for a modified locally greedy approach in each random trial.While the use of RLNN is highly successful for designing adaptive local measurement strategies, in general a significant gap can exist between the success probability of the optimal locally-adaptive measurement strategy and the optimal collective measurement. We build on previous work to provide a set of necessary and sufficient conditions for collective protocols to strictly outperform locally adaptive protocols. We also provide a new example which, to our knowledge, is the simplest known state set exhibiting a significant gap between local and collective protocols. This result raises interesting new questions about the gap between theoretically optimal measurement strategies and practically implementable measurement strategies.
Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification

The image classification machine learning model was trained with the intention to predict the category of the input image. While multiple state-of-the-art ensemble model methodologies are openly available, this paper evaluates the performance of a low-cost, simple algorithm that would integrate seamlessly into modern production-grade cloud-based applications. The homogeneous models, trained with the full instead of subsets of data, contains varying hyper-parameters and neural layers from one another. These models' inferences will be processed by the new algorithm, which is loosely based on conditional probability theories. The final output will be evaluated.
Statistical Indicators System for Assessing the Higher Education Quality

In the article the concept of «higher education», its goals and main objectives of legal regulation in federal legislation are considered. A system of statistical indicators is formed that measures the quality of higher education based on the review of the Federal Law «On Education in the Russian Federation», the methodical recommendations of the Ministry of education and science of Russia and local regulations of the NSUEM on conducting an independent assessment of the quality of educational activities and the quality of education. The concepts of «quality of education», «quality of educational services» and «quality of instruction» are discussed.
Mitigating Gender Bias in Machine Translation with Target Gender Annotations

When translating "The secretary asked for details." to a language with grammatical gender, it might be necessary to determine the gender of the subject "secretary". If the sentence does not contain the necessary information, it is not always possible to disambiguate. In such cases, machine translation systems select the most common translation option, which often corresponds to the stereotypical translations, thus potentially exacerbating prejudice and marginalisation of certain groups and people. We argue that the information necessary for an adequate translation can not always be deduced from the sentence being translated or even might depend on external knowledge. Therefore, in this work, we propose to decouple the task of acquiring the necessary information from the task of learning to translate correctly when such information is available. To that end, we present a method for training machine translation systems to use word-level annotations containing information about subject's gender. To prepare training data, we annotate regular source language words with grammatical gender information of the corresponding target language words. Using such data to train machine translation systems reduces their reliance on gender stereotypes when information about the subject's gender is available. Our experiments on five language pairs show that this allows improving accuracy on the WinoMT test set by up to 25.8 percentage points.
Measuring the electrical and optical properties of fog using balloon borne instrumentation in the UAE

&amp;lt;p&amp;gt;Countries in arid and desert climates that have small amounts of rainfall each year use cloud seeding techniques to enhance the little rainfall that is present. Typically, this is achieved by seeding the cloud with hygroscopic nuclei to increase the rainfall. A possible alternative method is to inject the cloud with electric charge, which has been shown in models to alter the droplet size and distribution and influence rainfall properties.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, in-situ observations of the electrical and optical properties of clouds are described from a desert site. These are used to inform droplet growth models. For this, a yearlong campaign, during which 10 weather balloons carrying electric charge and optical sensors were launched through fog layers from Abu Dhabi airport, United Arab Emirates. Here we present 2 case studies. The first is a clear air ascent comparison between the desert site at Abu Dhabi and a temperate site in northern Finland. The second is a fog comparison between Abu Dhabi and a temperate site in the United Kingdom&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results show that the fogs in Abu Dhabi are highly charged with a charge density of 0.1-1 nC m&amp;lt;sup&amp;gt;-3&amp;lt;/sup&amp;gt; as opposed to the charge densities of fogs in Northern Hemisphere temperate regions which have a typical charge density of 10 pC m&amp;lt;sup&amp;gt;-3&amp;lt;/sup&amp;gt;. The droplet concentration in the Abu Dhabi fog case study is significantly smaller, approximately 150 cm&amp;lt;sup&amp;gt;-3&amp;lt;/sup&amp;gt; as opposed to droplet concentrations of 300-400 cm&amp;lt;sup&amp;gt;-3&amp;lt;/sup&amp;gt; in fog over a temperate site.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results suggest that dust contributes strongly to the atmospheric electrical conditions in the UAE region, due to charging of the dust tribo-electrically. This dust charge may also affect the droplet distribution within the fog. These new measurements of the vertical profile of charge through fog layers in desert climates will be used to improve understanding in droplet growth models.&amp;lt;/p&amp;gt;
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression

Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could lead to misdiagnosis. Recently, the probabilistic Variational AutoEncoder (VAE) has become a popular model for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate from this learned distribution. The VAE models the output as a conditionally independent Gaussian characterized by means and variances for each output dimension. VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. We describe an alternative approach that avoids this variance shrinkage problem by using quantile regression. Using estimated quantiles to compute mean and variance under the Gaussian assumption, we compute reconstruction probability as a principled approach to outlier or anomaly detection. Results on simulated and Fashion MNIST data demonstrate the effectiveness of our approach. We also show how our approach can be used for principled heterogeneous thresholding for lesion detection in brain images.
Multi-sensations Mechanism of Users on the Learning Platform Design of Music Aural Skills

This paper discusses the nature of aural skills learning with technology implementation in multi-sensations mechanism. By providing the learning platform design of aural skills with different interface design guidelines, the use of technology with multi-sensations to assist teaching and learning could facilitate more novices to music world.
E-Learning: Analysis of rural youth’s problems impeding accessibility

The present study has analysed the process of e-learning in the rural areas done by the youths and investigated the problems that impede the accessibility. It was conducted in randomly selected Seven Senior Secondary Schools of the Udaipur district of Rajasthan. A sample of 140 rural youth was selected for the study. Findings of the study revealed that the majority of the respondents’ mothers were illiterate (70%) whereas respondents’ fathers were educated up to the primary level (34.29%) and secondary level (20 %). It was observed that 92.85 per cent of the respondents possessed smartphones. It was reported that while accessing e-learning respondents always faced the problem of difficult vocabulary and content not being available in the preferred language (29.28-33.57%). Respondents also mentioned that they always get stress on their eyes, increased fatigue levels due to e-learning and neck muscles were affected adversely by continuously studying via e-learning.
Context-Aware Drive-thru Recommendation Service at Fast Food Restaurants

Drive-thru is a popular sales channel in the fast food industry where consumers can make food purchases without leaving their cars. Drive-thru recommendation systems allow restaurants to display food recommendations on the digital menu board as guests are making their orders. Popular recommendation models in eCommerce scenarios rely on user attributes (such as user profiles or purchase history) to generate recommendations, while such information is hard to obtain in the drive-thru use case. Thus, in this paper, we propose a new recommendation model Transformer Cross Transformer (TxT), which exploits the guest order behavior and contextual features (such as location, time, and weather) using Transformer encoders for drive-thru recommendations. Empirical results show that our TxT model achieves superior results in Burger King's drive-thru production environment compared with existing recommendation solutions. In addition, we implement a unified system to run end-to-end big data analytics and deep learning workloads on the same cluster. We find that in practice, maintaining a single big data cluster for the entire pipeline is more efficient and cost-saving. Our recommendation system is not only beneficial for drive-thru scenarios, and it can also be generalized to other customer interaction channels.
Zero-shot Entity Linking with Efficient Long Range Sequence Modeling

This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we find a simple yet effective way is to expand the long-range sequence modeling. Unlike many previous methods, our method does not require expensive pre-training of BERT with long position embedding. Instead, we propose an efficient position embeddings initialization method called Embedding-repeat, which initializes larger position embeddings based on BERT-Base. On Wikia's zero-shot EL dataset, our method improves the SOTA from 76.06% to 79.08%, and for its long data, the corresponding improvement is from 74.57% to 82.14%. Our experiments suggest the effectiveness of long-range sequence modeling without retraining the BERT model.