Reviewerly/exHarmony huggingface dataset:
Argentina y Perú: respuesta educativa a la revolución 4.0.”

Revolution 4.0 is causing a substantial transformation on the forms of work. In order to face the new demands of the professional world, avoid massive unemployment and reduce social inequalities, it is necessary for the States to design and implement strategic responses. Digital education is an essential tool to prepare society for the jobs of future. 228 The current working paper presents the digital literacy public policies adopted by the governments of Argentina and Peru during the period between 2016 and 2019 to develop the competences and skills required nowadays in the compulsory education levels. A qualitative methodology based on a bibliographic research and interviews with public officials responsible of these initiatives in both countries as well as experts on digital inclusion was applied. This endeavor is part of a larger research project which aims to present a comparative map of digital education in Latin America. The comparative analysis of the plan “Learning Connected” (Argentina) and “The Education we want for Peru” (Peru), regarding the Latin American region, concluded that there had been fundamental advances in their educational systems, according to demands of the 21st century. However, in comparison with other more developed regions, this one seems to be lagging behind. This report favors the approach between the academic and political spheres; it critically describes the current scenario and points out the way to travel.
Information in the Natural Cyber Systems (To Definition of the Term Information)

A model of the atmosphere as a natural cyber system with control according to Boltzmann principle is considered and it is shown that the information in natural systems is the growth of entropy when the energy balance is disturbed. The analysis of Shannon definition of information is carried out and an objective definition of information is formulated, which in all respects coincides with Shannon definition.
Roadside vertical solar-wind energy tower

Fossil fuels and Nuclear power is responsible for about 82% of the current Energy Production in the US. While a small part is still being met by renewable energy sources, engineers are working hard on finding new sources of Energy. Another approach to the global clean energy crisis is that instead of looking for a new energy source, we should become Energy Efficient. Road power generation is a new technology where the wasted energy of a moving vehicle can be extracted and converted to useful work done. This paper presents such a technology which when employed at the corner of a road can send power directly to the grid or run streetlights depending on the mode of operation. This is done by constructing a Savonius Wind Turbine and then converting the wasted wind energy produced by a moving vehicle. In addition, a solar panel can also be placed on the top of the turbine and produce more energy. A microcontroller decides how Energy is to be sent to the grid or stored to be used for street lighting load. While the power production may vary depending on the traffic and conditions of the city, the S-rotor and solar panel have a maximum voltage of 19.1 V and 19.65 V respectively.
PENERAPAN DECISION TREE DALAM AGEN CERDAS "UNDA ANAK PINTAR" PERMAINAN EDUKASI MUATAN LOKAL BAHASA BANJAR

The educational game "Unda Anak Pintar" is research and development of a puzzle game, it was designed to sharpen memories. In this game, players must read random letters and make words in the Banjar language. Artificial intelligence is also applied in this research. Using the decision tree, game agents can accompany children to play like teachers. The game agent as a virtual teacher can provide sad and happiness emoticon according to the game environment. The results of this study, AI make educational games more interesting and interactive for children.
Performance of Minkowski-type Distances in Similarity Search - A Geometrical Approach

This work is an attempt at exploring distances, in the context of Similarity Search (SS), where an approximate match for a given query q is sought from a given dataset X. One view is that the query q itself is a noise η corrupted version of an x ∈ X. Recently, François et al., [1] had studied the efficacy of Minkowski-type distances in retrieving the x given q in the presence of both white and highly coloured noise η. Noting that not all conclusions in [1] hold true, in high dimensions, in this work, we have undertaken a similar study but that which differs in the following way: Taking into account various other factors not considered in [1]. Our geometric approach to these investigations have revealed hitherto unknown impact of both the domain geometry and denseness of the data set and has led us to propose an index which aids in explaining the simulation results obtained and in understanding the impact of the 3D's of Dimensionality, Domain geometry and Denseness of the data on the appropriateness of a Distance function in the setting of SS algorithms.
Peningkatan Keaktifan Siswa Dalam Pembelajaran Pembuatan Busana Industri Melalui Metode Demonstrasi Dan Resitasi Pada Kelas Xi Tata Busana Smk Negeri 1 Sawahlunto TP. 2018/2019

ABSTRACT Nisa Kanita, 17075252/2017: Increasing Student Activity in Learning to Manufacture Industrial Clothing Through Demonstration and Recitation Methods in Class XI of Fashion Sawahlunto 1 N Vocational School TP. 2018/2019This research is motivated by the low activity of students in learning how to make industrial clothing especially in CAD Pattern Making. This study aims to describe the process of increasing student activeness in learning to make industrial clothing through demonstration and recitation methods in class XI of Sawahlunto 1 State Vocational High School.This type of research is classroom action research (CAR). The research subjects amounted to 35 people who were students of class XI at the Sawahlunto 1 nd Vocational High School. Data collection techniques are questionnaires, interviews, observations, field notes and documentation. The next data is reviewed and analyzed with steps in processing questionnaires, presenting data, and drawing conclusions. The validity of the data is done by triangulation.Based on classroom action research using demonstration and recitation methods, the results obtained in cycle 1 of the 6 activeness indicators were asking as many as 65% of active students, discussing 78.3% of active students, answering questions 89% of active students, summarizing the material 94% of students were active, Completing training 90.3% of active students, making digital patterns 86.3% of active students. While in the second cycle of the 6 indicators of activity, namely asking as many as 75% of active students, discussing 85.14% of active students, answering questions 81% active students, summarizing the material 98% active students, completing 93.14% of active students, making digital patterns 87% of students are active. Therefore, it can be concluded that the demonstration and recitation methods are very useful and can increase the activity of students in making CAD Pattern Making digital patterns.
Auxiliary Task Reweighting for Minimum-data Learning

Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to utilize auxiliary tasks to provide additional supervision for the main task. Assigning and optimizing the importance weights for different auxiliary tasks remains an crucial and largely understudied research question. In this work, we propose a method to automatically reweight auxiliary tasks in order to reduce the data requirement on the main task. Specifically, we formulate the weighted likelihood function of auxiliary tasks as a surrogate prior for the main task. By adjusting the auxiliary task weights to minimize the divergence between the surrogate prior and the true prior of the main task, we obtain a more accurate prior estimation, achieving the goal of minimizing the required amount of training data for the main task and avoiding a costly grid search. In multiple experimental settings (e.g. semi-supervised learning, multi-label classification), we demonstrate that our algorithm can effectively utilize limited labeled data of the main task with the benefit of auxiliary tasks compared with previous task reweighting methods. We also show that under extreme cases with only a few extra examples (e.g. few-shot domain adaptation), our algorithm results in significant improvement over the baseline.
Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints and formulate a prior that enables us to effectively incorporate them into Bayesian neural networks (BNNs), including a variant that can be amortized over tasks. The resulting Output-Constrained BNN (OC-BNN) is fully consistent with the Bayesian framework for uncertainty quantification and is amenable to black-box inference. Unlike typical BNN inference in uninterpretable parameter space, OC-BNNs widen the range of functional knowledge that can be incorporated, especially for model users without expertise in machine learning. We demonstrate the efficacy of OC-BNNs on real-world datasets, spanning multiple domains such as healthcare, criminal justice, and credit scoring.
Distance Learning Strategy in Covid-19 Pandemic for Primary Schools

Abstract: The COVID 19 pandemic has had a tremendous impact on the economy and education around the world. The unexpected shift from face to face classroom methods with distance learning at home also showed the need to develop the dimensions of teachers. Current conditions also have limitations on students who live in rural areas as well as less able parents. Therefore, this study aimed to describe how distance learning strategies for COVID 19 pandemics in primary school. This study uses a literature study or research library. Therefore investigators the right strategy illustrate various literary analysis which can be a way to solve problems, including online learning or e-learning, online teaching as the needs, problems connected with online teaching and learning, possible solutions to this problem, the policy approvals for the Indonesian government, and strategies for distance learning primary school. Keywords: distance learning, strategies, covid 19, primary school
Detecting explosion-induced dynamic phenomena using time-lapse seismic surveying

&amp;lt;p&amp;gt;Characterization of seismic events as underground nuclear explosions is a challenging task. Geophysical methods such as seismic monitoring systems are used by CTBTO to link post-explosion phenomena to potential sources. The main challenges in seismic monitoring involve accurately locating of sources and separating underground variations in seismic properties due to the explosion from naturally occurring variations. Underground detonations result in an immense change in pressure and temperature concentrated around the source origin. This results in the formation of characteristic static and dynamic phenomena. This study highlights the potential of using time-lapse seismic to identify ground zero by monitoring post-explosion dynamic phenomena. Time-lapse seismic, also known as 4D seismic, is successfully employed in the oil and gas industry for petroleum production monitoring and management. It involves taking more than one 2D/3D seismic at different calendar times over the same reservoir and studying the difference in seismic attributes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Following an underground explosion, dynamic changes in rock and fluid properties are observable for a prolonged period, even up to several decades. This is prominent near to source origin, and it is a result of the redistribution of residual energy, such as pressure, temperature, and saturation. Frequent seismic monitoring surveys (time-lapse seismic) enables one to monitor the changes in rock and fluid properties. This study presents the characteristics of the time-lapse seismic signature observed in a heterogeneous medium (or heterogeneous cavity). We will look into the impact of factors affecting land 4D repeatability on the 4D signature. The significance of identifying the 4D signature related to the explosion in a seismic section, and the feasibility of detecting it during the OSI with resource and time constraints in place will be discussed. We present a fast detection method using a convolutional neural network for the detection of explosion related time-lapse signature, which could be an identifier of the source location or ground zero.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Acknowledgments: Authors would like to thank EPSRC and AWE for funding this project.&amp;lt;/p&amp;gt;
On the diminishing return of labeling clinical reports

Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains. Whether the same holds true for medical NLP has by far not been thoroughly investigated. This work shows that this is indeed not always the case. We reveal the somehow counter-intuitive observation that performant medical NLP models may be obtained with small amount of labeled data, quite the opposite to the common belief, most likely due to the domain specificity of the problem. We show quantitatively the effect of training data size on a fixed test set composed of two of the largest public chest x-ray radiology report datasets on the task of abnormality classification. The trained models not only make use of the training data efficiently, but also outperform the current state-of-the-art rule-based systems by a significant margin.
Dirichlet process mixtures under affine transformations of the data

Abstract Location-scale Dirichlet process mixtures of Gaussians (DPM-G) have proved extremely useful in dealing with density estimation and clustering problems in a wide range of domains. Motivated by an astronomical application, in this work we address the robustness of DPM-G models to affine transformations of the data, a natural requirement for any sensible statistical method for density estimation and clustering. First, we devise a coherent prior specification of the model which makes posterior inference invariant with respect to affine transformations of the data. Second, we formalise the notion of asymptotic robustness under data transformation and show that mild assumptions on the true data generating process are sufficient to ensure that DPM-G models feature such a property. Our investigation is supported by an extensive simulation study and illustrated by the analysis of an astronomical dataset consisting of physical measurements of stars in the field of the globular cluster NGC 2419.
DIALOGUE MANAGEMENT USING EXTENDED DISCOURSE TREES

In this paper we learn how to manage a dialogue relying on discourse of its utterances. We consider two complementary approaches of dialogue management based on the discourse text analysis to extend the abilities of the interactive information retrieval-based chat bot.
Internet of Things based Predictive Mechanical Fault Detection System

In this study, it is aimed to analyze the data, continuously received from electric motors through temperature, vibration, current, and speed sensors, and to perform the mechanical predictive fault detection with the Internet of Things (IoT) platform. A system design has been implemented that enables the transfer of sensor data to the IoT platform and enables users to access these data regularly via a web interface over the cloud system. With the proposed system model less cost, time savings, reduction in mechanical breakdowns, an increase in motor life, and efficiency can be achieved.
A Study on the Observation and Analysis of Korean Language Teachers' Classes

이 연구는 한국어 교사의 성찰적 수업 관찰 보고서를 바탕으로 한국어 수업의 관찰 내용을 살펴보는 데 목적이 있다. 이를 위해 한국어 교사가 자신의 수업 행위를 이해하기 위해 스스로 수업 해석의 주체가 되어 자신의 수업을 총체적으로 바라볼 수 있도록 하였으며, 50편의 수업 관찰 보고서를 수집하여 Nvivo12 프로그램을 사용하여 분석하였다. 그 결과 362개의 수업 성찰 내용을 확인하였으며, 이를 교사 요인, 학습자 요인, 수업 요인, 환경 요인으로 범주화 하였다. 각 요인 중에서 수업 요인에 대한 성찰적 내용이 가장 많이 나타났다. 수업을 준비하고 구성하는 과정에서 발생하는 문제점과 수업을 진행하면서 교실 현장에서 부딪히게 되는 어려움에 대한 보고 내용이 주를 이루었다. 한국어 수업의 질적 개선을 위해서는 교사 스스로 자신의 수업을 객관적으로 관찰하고 분석하는 경험을 적극적으로 제공하는 과정이 필요하다. 이는 교사 스스로 수업성찰의 기회를 갖도록 하며 결과적으로 교사의 전문성 함양에 도움을 줄 수 있을 것이다.The purpose of this study was to examine the observations of Korean language classes based on the reflective class observation report of Korean teachers. Therefore, in this study, the Korean language teacher became the owner of self-observation in order to understand his or her instructional behavior, so that he could see his instruction as a whole. For this, 50 class observation reports were collected and analyzed using the Nvivo12 program. As a result, 362 class reflection contents were identified, and these were categorized into teacher factor, learner factor, instruction factor, and environmental factor. Among each factor, reflective content on the class factor was the most, and the report on the problems arising in the process of preparing and organizing the class and the difficulties encountered in the classroom during the course were mainly reported. In order to improve the quality of Korean language classes, it is necessary to actively provide the experience of objectively observing and analyzing their own classes. This will allow teachers to have an opportunity to reflect on their own classes, and as a result, it will be able to help teachers develop their professionalism
Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.
Educational Inequality and Professional Motivation of a Future Teacher

The professional motivation and interest of the teacher in the students' educational results are the key resources that allow educational institutions to minimize the existing inequality of educational opportunities for members of different social groups.On this point, one of the important issues of professional training of future teachers is the formation of motivation for their professional activity regarded as a factor that ensures the equality of educational opportunities for students from different social groups.Based on the results of empirical studies of different years, the article analyzes the changes that occurred in the professional motivation of students after the Ussuriysk State Pedagogical Institute joined the Far Eastern Federal University (FEFU) in 2011 as the School of Education.A questionnaire-based survey was conducted to collect primary empirical information.In the 2010 research, a stratified sample model was used; its size was 200 of first-year and graduate students enrolled in education studies.In 2019, the continuous survey method was used.The use of the same measuring instruments made it possible to conduct a comparative analysis of the professional motivation of students in different periods.The revealed positive changes in the dynamics of the indicators "motivation for professional activity" and "satisfaction with the profession of a teacher" were interpreted in the context of the influence of the family socioeconomic status on educational inequality.The data obtained during the comparative analysis of the two studies are the basis for the implementation of the model of research education for students enrolled in the field of "Education and Pedagogical Sciences" at the FEFU School of Education.A student who has mastered the required research processes is ready to use them in the educational process when working as a teacher.The inclusion of students in research will contribute to the intellectual development of students and increase the chances of a successful career in the knowledge economy for students from less socially privileged families.
Deep Learning with a Single Neuron: Folding a Deep Neural Network in Time using Feedback-Modulated Delay Loops.

Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron's dynamics. By adjusting the feedback-modulation within the loops, we adapt the network's connection weights. These connection weights are determined via a modified back-propagation algorithm that we designed for such types of networks. Our approach fully recovers standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.
Investigating speaker authentication system vulnerability to the limited duration of speech excerpts and voice cloning

The impact of the length of the reference sample and the authentication sample to the accuracy of the speaker authentication employing deep learning architecture is tested in bank branches and discussed in the paper. The presented work focuses on testing different approaches to parameterizing voice credentials employing: MFCC, LPC, and GFCC as extracted features. Also, a mixed approach with the use of supervector containing the most important coefficients for each parameterization method was examined. For the purpose of this work, standard corpora for the authentication of speakers like VoxCeleb2 and Librispeech were used along with our own recordings. Another subject of the work was to investigate the immunity of the speaker verification system based on machine learning to attack attempts using the method of voice cloning. The impact of the duration of speech excerpts on the vulnerability to this type of attack was examined. The influence of quality and length of the generated recordings used for the attack was studied. It turned out that the results obtained depend on the acoustic conditions in bank branches, where there is quite a lot of noise coming from the work of banknote counters, the clatter of stamping documents, and conversations. [Project No. POIR.01.01.01-0092/19 entitled: “BIOPUAP—a biometric cloud authentication system” is currently financed by the Polish National Centre for Research and Development (NCBR) from the European Regional Development Fund.]