Reviewerly/exHarmony huggingface dataset:
EVALUASI PROGRAM BROADBAND LEARNING CENTER DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA LUBUKLINGGAU

The purpose of the study was to determine the implementation of programs related to the evaluation of the Program Broadband Learning Center at the Lubuklinggau City Library and Archives Service. The research method is descriptive quantitative. The research sample totaled 93 (ninety three) respondents from participants who attended training in 2017, 2018, 2019, and the samples were taken randomly. The results of the component research input obtained a result of 4.09 (four point nine blanks) included in the Good criteria. The component Activities and Processes obtained a result of 3.84 (three point eighty four) included in the Good criteria, but there were 2 (two) statements in this component that obtained an average score with poor criteria.The component Output obtained a result of 4.10 (four point ten) which is included in the Good criteria. The results of the recapitulation of the components Input, Activities and processes, as well as the Output of 4.01 (four point blank one) are included in the Good criteria with the predicate successful.
PERSONAL ORIENTATION OF PRACTICAL ENGLISH LANGUAGE CLASSES IN CONDITIONS PEDUNIVERITY

The article reveals the necessity of personality-oriented teaching at the practical lessons of the English language at the Pedagogical University. Great attention is focused on the changing of the teacher’s role in the process of personality-oriented teaching.
COVID-19 PANDEMIC IN THE CONTEXT OF PRIMARY HEALTH CARE IN BRAZIL: A NARRATIVE REVIEW

COVID-19 established a pandemic framework and imposed the biggest health challenge of this generation¹.Community coping is one of the strongest strategies to fight the disease.In Brazil, Primary Health Care (PHC) remained the user's gateway to the Unified Health System, being the first, closest and most accessible place for patient care².For this, PHC dealt with existing demands and the new circulating disease, continuing its role of coordinating care and solving mild and moderate cases. 1,2.OBJECTIVE: To analyze the new coronavirus pandemic in the context of primary health care.METHODS: This is a narrative literature review with research in the SciELO and PubMed databases using the descriptors "COVID-19", "Primary Health Care" and "Coping Strategies".Articles published from 2020 to 2022 were searche. RESULTS:The COVID-19 pandemic required adjustments in PHC.Therefore, telemedicine began to be implemented and became a strong ally2,3.Through the integrality attribute, primary and secondary prevention were carried out, essential to avoid overloading the system 4 .Finally, it is up to the PHC to assume a decisive role in light cases, in addition to the early identification and fast and correct referral of severe cases, maintaining the coordination of care. 4.CONCLUSION: PHC ensured the resolution of the vast majority of mild to moderate cases of COVID-19 and promoted population awareness and vaccination and disseminated prevention strategies 4,5 .
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning

Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines. However, users tend to mistrust the optimization process and its results due to a lack of transparency, making manual tuning still widespread. We introduce DeepCAVE, an interactive framework to analyze and monitor state-of-the-art optimization procedures for AutoML easily and ad hoc. By aiming for full and accessible transparency, DeepCAVE builds a bridge between users and AutoML and contributes to establishing trust. Our framework's modular and easy-to-extend nature provides users with automatically generated text, tables, and graphic visualizations. We show the value of DeepCAVE in an exemplary use-case of outlier detection, in which our framework makes it easy to identify problems, compare multiple runs and interpret optimization processes. The package is freely available on GitHub https://github.com/automl/DeepCAVE.
DELONE AND MCLEAN ON THE SUCCESS OF LEARNING MANAGEMENT SYSTEMS IN DISTANCE LEARNING

This study aims to see how the level of success of a learning management system used by a private university that runs the learning process with distance learning, where the method used in this study is a quantitative method using the Delone and McLean approach. system quality has no effect on user intensity with an R square value of 2.6%, system quality affects user satisfaction with an R square value of 19.3%, information quality does not affect user intensity with an R square value of 0.1%, Information quality affects user satisfaction with an R square value of 28.6%, service quality does not affect user intensity with an R square value of 0.4%, service quality affects user satisfaction with an R square value of 49.8%. usage satisfaction has no effect on user intensity with an R square value of 0%, use satisfaction affects benefits with an R square value of 53.6%, use affects user satisfaction with an R square value of 16.9%, use affects benefits with an R square value of 20.6%. benefits affect use with an R square value of 20.6%, benefits affect use satisfaction with an R square value of 53.6%.
FPGA Based Facial Recognition System

Introduction: Face recognition research is motivated not just by fundamental security concerns, but also by the fact that it is required in many practical applications where human identity is required. Rapid improvements in technology such as digital cameras, the internet, mobile devices, and technological demands on security have facilitated and encouraged face recognition as one of the key biometric technologies. Face detection, feature extraction, and classification methods for face identification utilizing hardware description language HDL implemented in a Field Programmable Gate Array are investigated in this paper (FPGA).The research goals include.&#x0D; Methods: The Viola-Jones algorithm for face detection was developed, followed by developing an algorithm for feature extraction using Artificial Neural Networks (ANN),and finally feature matching using Hamming Distance. The whole system was implemented on FPGA, using VHDL.&#x0D; Results: The system successfully identifies the eye region of the face from the image, and extracts the features of each image then perform matching. The program output or display a result of image matched or image not matched. The execution time of the overall system speeds up due to the parallel processing of FPGA.&#x0D; Conclusion: The program was able to distinguish between two images of people based on their eye image, as well as detect minor expressional changes in the test image. Designing the full algorithm using FPGA help in speeding up the execution time of the processes, which gives the opportunity to build the system to work in real time with low cost due to its flexibility.
Features of training of vocational education teachers in Germany

Influenced by the processes of European integration, globalization and, in particular, in order to build a single European higher education area, an important task of Ukrainian educators is to study the practical experience of our foreign colleagues in training teachers for vocational schools at all levels. Among the European countries where close attention is paid to the quality of teacher training, in particular in the field of vocational education, Germany stands out. The purpose of the study is to analyze the features of modern training of teachers of vocational education in Germany with the prospect of using progressive German experience in the Ukrainian system of training/retraining of teachers for institutions of professional and higher technical education. A detailed analysis of the German teacher training system, carried out in the course of this study, allowed us to make some important generalizations. First, it is worth noting the high expediency and effectiveness of Germany's extensive system of institutions, which, thanks to a clear division of functions, contribute to the effective organization of three-stage training of qualified teachers for vocational education. Secondly, the analysis of the content of the curriculum for future teachers of vocational education in Germany shows an attempt to train competitive professionals not only in the vocational education system, but also in the labor market in general. Third, the developed Standards for teachers in the field of educational sciences effectively influence the implementation of training programs for teachers of vocational education, requiring the formation and further improvement of their competencies in the field of teaching, education, evaluation and implementation of innovations. Finally, it is worth noting the urgent need to use the achievements of the German experience of practical training of VET teachers in the Ukrainian system of training and retraining of teachers for institutions of professional and higher technical education
Comparing Supervised Machine Learning Algorithms on Classification Efficiency of multiclass classifications problem

Multi-class classification is a fascinating field to study. However, evaluating the classification performance of classifiers is difficult. Class indices such as accuracy, precision, recall, and F-measure, Kappa and area under the curve of receiver operating characteristics (AUC), can be used to evaluate classification performance. These indices describe the classification results achieved on each modelled class. Several measures have been introduced in the literature to deal with this assessment, the most commonly used being accuracy. In general these metrics were proposed to address binary classification tasks, whereas multiclass classification is the more difficult and currently active research area in machine learning (ML). In this paper, we intended to compare classification performance of nine supervised machine learning algorithms based on three learner types: statistical learner, rule-based learner and neural-base learner by considering accuracy, precision, recall and F-measure and ROC area achieved on four different datasets from UCI machine repository. Among these, Random forest has been the best performance in both 10 fold cross validation and percentage split with overall average accuracy of predictive power of 92.20% and 91.76% respectively, with less variability, whereas Naïve Bayes has the worst also in both 10 fold cross validation and percentage split by average correct classification performance of 79.18% and 76.92% respectively, and also with higher variability next to Decision Table.
HIGH LEVEL OF INFORMATION CULTURE THE FUTURE TEACHER AS HIS BASIS CREATIVE ACTIVITY

The ways of development of future teacher’s information culture as a basis of his creativity are being analyzed in the article. The operations with the received information are being considered and their main role in forming of teacher’s information competence is being underlined.
WEBSCAF

Abstract: This is a project that mainly concerns web developers' areas of interest. While creating a project developers face the problem of scaffolding and especially doing it the right way is another major task that needs to be accomplished. It is a NodeJSbased project through which a fresh project structure can be created with the help of the Command Line Interface. Setting up a new project may require setting up various folders which might get hectic hence this project is a perfect solution as it performs scaffolding with the help of a few CLI commands. The idea of creating such a project hit the minds of our team while building other web projects during our bachelor's in technology. This project is an operating system independent product that can be used on any machine with any operating system like Windows, Linux, Mac, etc. The pre-requisite of this project is that Node.JS should be installed in the device on which the product has to be used. NodeJS is an open-source server environment that helps to run JavaScript on the server.
Integrated methods of teaching listening and speaking

Due to the lack of time English teachers have per week for their subject, the best way to practice listening and speaking is integrating them. According to Bueno, Madrid and McLaren: "Listening is important for speaking because it establishes the good basis for successful communicative exchanges (2006:344).
Effect of Brainstorming Technique as a Pre-Writing Activity on the Students’ Achievement in Writing Descriptive Text

The objective of this research was to find out the effect of brainstorming technique on the students’ writing achievement. The approach of the research was quantitative. The design was one group pre-test and post-test design. The population of the research was students of the first grade of SMAN 1 Pringsewu and the sample of the research was class X consisting of 33 students. The activities were started from administering pre-test, doing treatment, and administering post-test. The instruments used were writing tests. The data were analyzed by using Repeated Measure t-test. The result showed that there was a statistically significant difference of the students’ writing ability between the pre-test and the post-test with the significant level of 0.05. This suggests that brainstorming technique facilitates the students to improve their ability in writing skills.
The Power Graph of a Dihedral Group

Graph theory is one of the topics in mathematics that is quite interesting to study because it is applicable and can be combined with other mathematical topics such as group theory. The combination of graph theory and group theory is that graphs can be used to represent a group. An example of a graph is a power graph. A power graph of the group is defined as a graph whose vertex set is all elements of and two distinct vertices and are connected if and only if or for a positive integer x and y. In this study, the author discusses the power graph of the dihedral group The results obtained from this study are the power graph of the dihedral group where with prime numbers and an natural number is a graph consisting of two non-disjoint subgraphs, namely complete subgraphs and star subgraphs. And we find that its radius and diameter are 1 and 2.
Hypersyn: A Peer-to-Peer System for Mutual Credit

The Hypersyn protocol is a new type of permissionless and peer-to-peer payment network that is based on the concept of mutual credit and mutual arbitrage. Unlike blockchain-based systems, Hypersyn does not rely on any consensus algorithm. It does not require a distributed ledger to store the history of events nor a set of validators. Hypersyn does not have a system-imposed hard-cap on the number of transactions per second that it can perform, and can therefore easily scale up or down depending on network usage. Unlike in other payment systems, money in Hypersyn does not get transferred from person $A$ to person $B$ in the conventional sense. Instead of transferring a token between each other, peers in Hypersyn change their exchange value of their credit (i.e. their purchasing power) within the network. Just as in centrally-issued fiat systems, money in Hypersyn is treated as freely tradable debt, which inherently requires trust. But unlike centrally-issued fiat systems, money issuance in Hypersyn is not controlled by an authority, but is instead created on the spot as mutual credit. In blockchain-based systems and even in centrally-issued fiat systems, money is treated as a scarce commodity. In the Hypersyn protocol on the other hand, money supply within the system is elastic in nature. Because of these fundamental differences in assumptions, the Hypersyn protocol does not aim to compete with, or substitute blockchain-based systems. Instead, Hypersyn should be viewed as a tool that aims to offer a qualitative change in the way we exchange. It has the potential to increase the autonomy and self-organization that people can have, by enabling people to become both the creditors and debtors of their own "money" through mutual credit.
Ultra-high bandwidth optical neuromorphic processing with Kerr soliton crystal microcombs: Scaling the network to the PetaOp regime

&lt;p&gt;Artificial neural networks (ANNs) are able to distill the hierarchical aspects of raw data. They are central to machine learning functions including speech and pattern recognition, medical diagnosis, playing board games, computer vision, and many other areas [1-7]. Optical neural networks (ONNs) in particular can significantly increase the computing speed of ANNs in order to overcome the intrinsic bandwidth bottleneck of electronics. Convolutional neural networks (CNNs), are inspired by biological systems such as the visual cortex, and are a powerful approach to greatly reduce the parametric network complexity in order to enhance the accuracy of the predictions of the system. In this paper, we demonstrate a universal optical convolutional accelerator that can be used in conjunction with both electronic and optical neural networks. It operates beyond 10 Tera-OPS (TOPS - operations per second) and produces convolutions of extremely large scale images of 250,000 pixels in size with a resolution of 8-bits. It generates 10 convolutions simultaneously in parallel, with 10 different kernels. This processing simultaneously — enough for facial image recognition. After demonstrating this, we then use the exact hardware to form a convolutional neural network consisting of a convolutional front-end followed by a deep optical neural network fully connected layer, together forming a CNN with ten neurons at the output. We successfully perform the recognition of all 10 hand written digits, each consisting of 900 pixel handwritten digit images. We achieve an accuracy of 88% which is very close to the theoretical accuracy of 90%. We use an approach that exploits the simultaneous multiplexing, or interleaving, within the time, space and wavelength dimensions, using an optical frequency comb supplied by an integrated Kerr micro-comb source. We compare the performance of different optical neural networks, explicitly showing that our approach is intrinsically scalable in both size and speed, up to the PetaOPs per second (POPs) regime in speed and to well over 24,000 synapses in size. We perform theoretical evaluation of the scaled system performance and show that it is trainable to much more complex networks for real-world demanding applications including real-time video recognition and autonomous unmanned vehicle control. &lt;/p&gt;
IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PEDERITA DIABETES MELLITUS

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel. Keywords: diabetes mellitus; HbA1c; classification; support vector machine; 10-fold cross validation.
At Implementasi Algoritma Haversine Pada Absensi Kepegawaian Berbasis Android

At this time the development of the era is much influenced by technology. This development has an impact on various aspects and fields in the expected life in the field of employment. Al-Azhar 32 Padang Islamic Elementary School is one of the private schools located in the city of Padang, West Sumatra. Al-Azhar schools have staff such as principals, teachers and administration so that the data entered is quite a lot.The staffing system at the Al-Azhar school is still manual by inputting it with excel. To take attendance at the employee using fingerprints and some use the attendance form. However, errors often occur during scanning because the scanner cannot detect someone's finger when the scanner is dirty. So that many employees update attendance by filling out the attendance update form. Given the problems above, it is possible to propose a mobile-based personnel application system. This staffing application has various features that can be used for taking attendance, leave forms, attendance history, school information, and employee profiles. This Android-based attendance can only be taken around the school or less than 20 meters from the school distance. To find out the distance between th0e user and the school, this attendance application uses the haversine algorithm to measure the distance between the user and the school
Design and Implementation of City-level Smart Mathematics Education Platform Based on Big Data

In the context of big data, with the rapid development of cloud computing and mobile networks, smart education has opened up the era of digitization, networking and smart cloud.Smart learning, smart education, interactive online classrooms and personalized learning have become new trends in future education development.The era of big data has had a great impact on my country's education field.A variety of network technologies and smart classrooms have been widely used in the teaching process, realizing large-scale reforms of traditional education models.The realization of the smart education platform can provide students with a learning platform with a personalized learning experience and a good smart learning atmosphere, which greatly improves the traditional teaching and learning mode.This article first analyzes the key points of smart education and provides the design principles of smart math education platform.In order to promote smart education and smart learning, secondly, it designs and implements an open and shared interactive smart math education platform.Provide specific reference materials for researchers in smart service, smart management, smart evaluation, and smart education.