Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Open Broadcast Software Studio dan Youtube Live Streaming sebagai Media Pembelajaran IPS Kelas 9 SMP Negeri 2 Panggang

The Covid-19 virus is the beginning of a new face change in the world, one of the sectors affected is the education sector. The new policy is provided through the Ministry of Education and Culture in the form of distance learning. Students should study at home, and under the supervision of the teacher. Online communication is one of the keys for keeping educational programs running well. Therefore, this becomes a new challenge by a the teachers to provide interesting learning and according to the characteristics of students. Through this research, it is hoped that it can provide insight to readers, especially educators, in designing innovative learning during a pandemic based on Open Broadcast Software and Youtube Live Streaming applications. In addition, this application also makes it easier for students to participate in learning, both in real time and recording. The research method used a literature study, using a qualitative descriptive approach, by collecting several previous research results from articles and journals related to the topic of this article and through direct observation of student learning in class. The results show that the interactive video learning model based on the OBS application and Youtube Live Streaming can make learning more interesting and effective, especially for most students with audio-visual learning styles. Teachers can use OBS and Youtube Live Streaming to design distance learning more effectively.
ASPECTS OF FORMATION OF PEDAGOGICAL CONSCIOUSNESS OF HIGHER EDUCATION TEACHERS

The relevance of the study is to determine the role of the teacherʼs personality in providing quality training for students of higher education. The problem of determining the role of the teacherʼs personality in ensuring the quality of professional training of students of higher education is quite relevant: first, the teacher has an important mission − the younger generation of the nation, and secondly, the new socio-economic conditions of our country; thirdly, there is a direct relationship between psychological readiness for teaching and the level of development of pedagogical consciousness of higher education teachers. The aim of the research is the theoretical substantiation of the technological model of the organizational and methodological system of formation of pedagogical consciousness of higher school teachers. To achieve this goal, research methods were used: theoretical and empirical. In the process of research the technological model of organizational-methodical system of formation of pedagogical consciousness of teachers of higher school is substantiated. This system is presented as a holistic education, which includes the purpose, objectives, principles, functional and activity components of the system and the holistic relationship of components: content-activity, motivational-value, evaluation-reflective, which determine the personality of each teacher and provide efficiency and optimality of formation and development of his pedagogical consciousness. The study found that the formation of pedagogical consciousness in higher education teachers, their self-development, self-improvement and self-realization will be manifested in the creation of a plan of self-development of pedagogical consciousness, which can be implemented only on the basis of self-diagnosis of personal consciousness as well as their own choice of forms and methods of work, which will indicate the formation of higher education teachersʼ ability to reflect and self-identify.
THE INFLUENCE OF INDEPENDENT LEARNING ON THE ABILITY TO WRITE EXPOSITION TEXTS IN CLASS X

This research was motivated by the writer's curiosity about the effect of independent learning on writing skills.Â The hypothesis to be tested is the influence of independent learning on the ability to write expository texts.Â This study uses a quantitative descriptive method because the research data is in the form of numbers, analyzed using statistics that connect between variables.Â The number of samples was 32 students of class X SMK PP Negeri Sumedang.Â The instruments used are test and non-test.Â The test instrument is in the form of an Exposition text writing skill test using Quizziz.Â The non-test instrument was in the form of a questionnaire containing 15 indicators of student learning independence including dependence, self-confidence, discipline, responsibility, and self-control.Â The result of correlation analysis isÂ 0.414Â proves that there is a close relationship between learning independenceÂ and theÂ ability to write exposition texts.Â The regression value of r square is 0.797.Â This shows a significant effect between the independent variables on the dependent variable.Â As for the significance test of the regression coefficient obtained sig.Â 0.000 &lt; 0.05 and t count =Â 11,566.Â This means that there is a significant influence between learning independence on the ability to write expository textsÂ Â
Programming of pedagogical technology for formation of professional competence when studying natural and general technical disciplines

Introduction of new state educational standards into the educational process, orientation towards a competent learning paradigm, and increase in the amount of independent work of the students necessitated the improvement of the existing approaches to modelling the integration processes in the system of higher agrotechnical education.Unfortunately, under modern conditions, the educational potential of physics and general technical disciplines in the integrative version of their content and methodological aspects is not realised at the required level.The purpose of this work is to study the problem of the formation of professional competence of the future engineers in the process of integrating natural (physics) and general technical disciplines.Efficiency of the models for the formation of professional competence of the future engineers by integrating physics and general technical disciplines, which are based on personal activity, competence-based, problem-integrative, contextual and system approaches, is substantiated and experimentally tested.There are determined the psychological and pedagogical conditions for implementation of these models in the process of studying physics, technical mechanics, hydromechanics, technical thermodynamics and electrical engineering.The positive dynamics of the results of the formation of professional competencies of models and their impact upon the components of professional competence are recorded.This was carried out by applying a frame approach to the assimilation of scientific knowledge, using integrative learning technologies, as well as increasing their cognitive activity, motivating students to study physics and general technical disciplines, and developing educational reflection.
CULTURE AND EDUCATION

Aspects of culture and education should be complementary in terms of complementing and reinforcing each other. Both are real and research results in various countries show the presence and potential of efforts to bridge the two possible. The influence of capable technology in the learning process requires transformation and it is directly related to educators who understand the student process. From among the institutions as the organizers apply to be adaptive to change. The connotation of being able to adapt to the rapid and complex flow of change. This article tries to answer this real problem.
Analisis Sentimen Pembelajaran Campuran Menggunakan Twitter Data

Mixed learning methodologies have been disputed in terms of educational quality when it comes to self-study. Educators face specific challenges when making hybrid learning work since they must adjust to teaching while also strengthening their technological abilities. The blended learning paradigm, which is commonly adopted in many educational institutions, can produce a slew of issues for students. The goal of this essay is to gather thoughts about distance learning based on social media comments. This study creates a classification model using Twitter tweet data by assessing public perceptions and acceptance of the mixed learning model. The findings of examining thoughts regarding this model include categorizing tweets as favorable or unfavorable using the Twitter sentiment analysis approach. The results revealed an almost equal polarization of positive and negative sentiments, with 44.51 percent positive and 45.80 percent negative. More research can be done to analyze attitudes not just on Twitter but also on other social media platforms to improve public opinion accuracy about mixed learning in a pandemic crisis.
Learning in Observable POMDPs, without Computationally Intractable Oracles

Much of reinforcement learning theory is built on top of oracles that are computationally hard to implement. Specifically for learning near-optimal policies in Partially Observable Markov Decision Processes (POMDPs), existing algorithms either need to make strong assumptions about the model dynamics (e.g. deterministic transitions) or assume access to an oracle for solving a hard optimistic planning or estimation problem as a subroutine. In this work we develop the first oracle-free learning algorithm for POMDPs under reasonable assumptions. Specifically, we give a quasipolynomial-time end-to-end algorithm for learning in "observable" POMDPs, where observability is the assumption that well-separated distributions over states induce well-separated distributions over observations. Our techniques circumvent the more traditional approach of using the principle of optimism under uncertainty to promote exploration, and instead give a novel application of barycentric spanners to constructing policy covers.
Pedagogy and new humanism at the time of Covid

Schools, society as a whole, have been deeply affected by the emergence of Covid19. The shift to distance teaching and learning has exacerbated many pre- existing vulnerabilities and inequalities in education systems. This paper argues for the urgency for school systems to accept new challenges in training, organizing inclusive environments towards the promotion of collective social and critical capacities, in synergy with the digital within a school that broadens its horizons, its classrooms, capable of bringing in human riches, assuming the richness of the student as a unique and unrepeatable subject who develops thanks to the relationship with the other, capable of transformative resilience, which looks at the human becoming by virtue of the encounters of places, including virtual ones, that are experienced in the course of life. A school pervaded by a new humanism, the only true wealth.
Experimental Evaluation and Approach of Enhancement in Generation of Automatic Unsupervised Extractive Text Summarization of Marathi Text By Using Machine Learning Algorithm

The Text summarization has immense importance in the arena of Natural Language Processing. The summarization can be done in two ways: abstractive and extractive text summarization. Here, the language is a vast query, because we have large number of national and international languages. By studying the literature, the Marathi language is chosen for this work and we are trying to make a framework where all the students giving competent examinations can have summarized Marathi e-news articles by using extractive text summarization. For this objective we have used TextRank algorithm which has proven very effective for different languages. This paper demonstrates the summarization techniques on Marathi e-news articles using Gensim Library of TextRank algorithm and comparative analysis of summaries generated by using both TextRank and ROUGH method.
Selçuk weather station and web-mobile applications

In this paper, a system for reading meteorological data like temperature, humidity, air pressure, wind speed, wind direction, and rainfall at regular intervals, deployed in Selçuk University Alaeddin Keykubat Campus. The system also provides real-time images and video time-lapses of the campus sky. These data are made available to university people via a website and mobile applications for both iOS and Android. The website and mobile applications provide a clear experience for the users, also explaining the icons and terms used on the website. Users can access the system archive in graphical ways.
An effective methodology for software defects prediction using Principal Component-based Multi-layer Perceptron (PC-MLP): A Hybrid Technique

Abstract Nowadays, the software is used almost in every field. The software industry is evolving rapidly, and developing quality software is challenging for developers. Defects in the software are the primary reason for degrading the quality. Software defects prediction is a difficult task. Though researchers have developed several methods to overcome this issue that support the problem, there is room for improvement in software defects prediction. The curse of dimensionality occurs when research is undertaken without feature reduction. Therefore, after consulting several other works of a similar caliber, a hybrid machine learning approach that combines Principal Component Analysis (PCA) for feature optimization with Multi-layer perceptrons for classification has been designed to overcome the hurdle. The proposed model reduces the time complexity of the traditional methods by reducing the number of attributes used for classification. We have used the PROMISE dataset KC1 &amp; CM1 repository from the NASA directory (KC1 2109 observations &amp; CM1 344 observations) to perform our research work. We have divided the datasets into training (1476 from KC1 &amp; 240 from CM1 observations) and testing (633 from KC1 &amp; 104 from CM1 observations). We have employed precision, recall, F-measure, and accuracy in evaluating the model. Using the proposed model, we reduced the time complexity and increased the accuracy by 6% using the KC1 dataset and the previously proposed models. In addition, the proposed model receives a greater score on additional criteria. The primary limitation with Multi-layer perceptrons is that it requires a linearly separable dataset, which can make classifications relatively inflexible. Soon, several new strategies may be introduced to overcome this limitation and maintain a soft-based categorization margin.
CHALLENGER: Training with Attribution Maps

We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. Regularization is key in deep learning, especially when training complex models on relatively small datasets. In order to understand inner workings of neural networks, attribution methods such as Layer-wise Relevance Propagation (LRP) have been extensively studied, particularly for interpreting the relevance of input features. We introduce Challenger, a module that leverages the explainable power of attribution maps in order to manipulate particularly relevant input patterns. Therefore, exposing and subsequently resolving regions of ambiguity towards separating classes on the ground-truth data manifold, an issue that arises particularly when training models on rather small datasets. Our Challenger module increases model performance through building more diverse filters within the network and can be applied to any input data domain. We demonstrate that our approach results in substantially better classification as well as calibration performance on datasets with only a few samples up to datasets with thousands of samples. In particular, we show that our generic domain-independent approach yields state-of-the-art results in vision, natural language processing and on time series tasks.
A reconfigurable processor for mix-precision CNNs on FPGA

To solve the problem of low computing efficiency of existing accelerators for convolutional neural network (CNNs), which caused by the inability to adapt to the characteristics of computing mode and caching for the mixed-precision quantized CNNs model, we propose a reconfigurable CNN processor in this paper, which consists of the reconfigurable adaptable computing unit, flexible on-chip cache unit and macro-instruction set. The multi-core CNN processor can be reconstructed according to the structure of CNN models and constraints of reconfigurable resources, to improve the utilization of computing resources. The elastic on-chip buffer and the data access approach by dynamically configuring an address to better utilization of on-chip memory. Then, the macroinstruction set architecture (mISA) can fully express the characteristics of the mixed-precision CNN models and reconfigurable processors, to reduce the complexity of mapping CNNs with different network structures and computing modes to reconfigurable the CNNs processors. For the well-known CNNs-VGG16 and ResNet-50, the proposed CNN processor has been implemented using Ultra96-V2 and ZCU102 FPGA, showing the throughput of 216.6 GOPS, and 214 GOPS, the computing efficiency of 0.63 GOPS/DSP and 0.64 GOPS/DSP on Ultra96-V2, respectively, achieving a better efficiency than the CNN accelerator based on fixed bit-width. Meanwhile, for ResNet-50, the throughput and the computing efficiency are up to 931.8 GOPS, 0.40 GOPS/DSP on ZCU102, respectively. In addition, these achieve up to 55.4% higher throughput than state-of-the-art CNN accelerators.
PPT Mobile based with Animated Agent Application for Gauging Students Motivation to Further Study in University

Program Pra Pengajian Tinggi (PPT), UiTM, is a corporate social responsibility that aims to provide an opportunity to the B40 children to pursue their studies in the higher institution through pre-diploma programmed.It is a unit that is actively involved in nationwide outreach by visiting schools and community centers.The main objective of this study is to develop animated pedagogical agents (APAs) and analyze motivational factors among youth perceptions through this application.This application is an animated on-screen character that assists learners in multimedia learning environments, and it is used in this to make the application more interactive by assisting the user throughout the questionnaires.Aside from APA, sound effects are also used as a form of feedback such as when users click on the choices of answers given.Some visual images related to the questions are also added to give further understanding of the questions to the respondent.In addition, the visual images are used to grab users' attention for more engagement.A software called Unity is used to develop this application from scratch, converting paper-based surveys into an application with interactive animations.This application is compatible with Android devices version 4 and up to the latest version but not compatible with the iOS version yet.Therefore, this study proposes an animated agent-based virtual instructor to be embedded in the survey apps.This will create a responsive environment in every survey session.The statistical analysis began with descriptive analysis and later continued with inferential analysis.
Quantum Control of Nanoparticles at Low Temperature

In this work, quantum control of nanoparticles at low temperature s considered. It would be quite interesting for scientists and researchers to take the well known nanoparticles at the scale of 10􀀀9m(=1nm) as they are at proper low temperature. It is desired to create Bose-Einstein-Condensates (BEC) with the nanoparticles. In our former study (cf. [Wang, 2016]) of controlling nanoparticle, the density function theory (DFT) described by time-dependent Schr¨odinger equation had been utilized to apply control theory to nanoparticles at matter surface. In the framework of Thomas-Fermi (TF) model, nanoparticles governed quantum system had been considered in the complex Hilbert spaces. In this investigation, the factor of temperature is taken account into time dependent Schr¨odinger equation. First, physically, a lot of questions will be arising in here for the control purpose of BEC phenomena of low temperature. Such as, whether BEC can be created using nanoparticles? what can the nanoparticles do at low temperature? which differences have for the nanoparticles BEC than other size particles? how control would be proceeded for nanoparticles at magnetic-electric field? Second, theoretically, how to apply of quantum control theory to nanoparticles BEC in the framework of variational theory? Then, how to get the first hand theoretic results to do nanoparticles control at low temperature. Review current contributed work and literatures, the survey of control Bose-Einstein-Condenstates had been occurred at amount of areas and had obtained significant milestone results as atomic particles (e.g. 87Rb, 7Li, 23Na,52Cr, 39K) cooling to temperature below of BEC thresholds. Indeed, the behavior of nanoparticles at the room temperature had also been considered, and had already been created to Carbon (C) nanotube, nanowire, nanomotor, nanorod as advanced materials. Particularly, the focus point is nanoparticles at low temperature, what is happened? the exciting and attractive conclusion will be hoped in this paper. As a kind of prediction, this theoretical research for control of nanoparticles would be fairly interesting, the control theory could be applied to these sort of nanoparticles perfectly. Without lost of generality, the nanoparticles composed BEC will be much more useful and can be quickly utilized to real society in the world. It is good to connect these researches to chemical and physical laboratory, and to do further interdisciplinary work concerning the control of quantum system.
Fully Persistent Spatial Data Structures for Efficient Queries in Path-Dependent Motion Planning Applications

Motion planning is a ubiquitous problem that is often a bottleneck in robotic applications. We demonstrate that motion planning problems such as minimum constraint removal, belief-space planning, and visibility-aware motion planning (VAMP) benefit from a path-dependent formulation, in which the state at a search node is represented implicitly by the path to that node. A naive approach to computing the feasibility of a successor node in such a path-dependent formulation takes time linear in the path length to the node, in contrast to a (possibly very large) constant time for a more typical search formulation. For long-horizon plans, performing this linear-time computation, which we call the lookback, for each node becomes prohibitive. To improve upon this, we introduce the use of a fully persistent spatial data structure (FPSDS), which bounds the size of the lookback. We then focus on the application of the FPSDS in VAMP, which involves incremental geometric computations that can be accelerated by filtering configurations with bounding volumes using nearest-neighbor data structures. We demonstrate an asymptotic and practical improvement in the runtime of finding VAMP solutions in several illustrative domains. To the best of our knowledge, this is the first use of a fully persistent data structure for accelerating motion planning.
The Support Vector Regression Model: A new Improvement for some Data Reduction Methods with Application

Support Vector Regression (SVR) formulates is an optimization problem to learn a regression function that maps from input predictor variables to output observed response values. The SVR is useful because it balances model complexity and prediction error, and it has good performance for handling high-dimensional data. In this paper, we use the SVR model to improve the principal component analysis and the factor analysis methods. Simulation experiments are performed to assessment the new method. Some useful applications to real data sets are presented for comparing the competitive SVR models. It is noted that with increasing sample size, the -SVR type under the principal component analysis is the best model. However, under the small sample sizes the SVR type under the factor analysis provided adequate results.