Reviewerly/exHarmony huggingface dataset:
Application of Discovery Learning Model for Training German Vocabulary Mastery of Students of Class XI IBB SMA Negeri 1 Gondanglegi

This article explains the results of research that aimed to describe the application of the Discovery Learning model to practice students’ mastery of German vocabulary, especially in the “Wohnung” theme and to find out students' responses to the application of this model in learning German.This research used descriptive qualitative method, by which the researcher was the key instrument and the data were gathered using supporting instruments, namely observation sheets and questionnaire sheets. Furthermore, the data sources were 22 students of class XI IBB SMA Negeri 1 Gondanglegi. The results showes that the application of Discovery Learning model to train students' German vocabulary mastery worked successfully as planned. In addition, this learning model is able to make students think critically, creatively, collaboratively, and more actively during learning process. According to the students, this model is pleasurable because it makes them more motivated to learn German. Finally, they think that this model allows them to remember vocabulary related to the “Wohnung” theme easier.
Analisis Dampak Game Online Free Fire terhadap Minat Belajar Siswa Kelas IV SD Negeri

Abstract: Online games are one of several entertainments that are currently popular among elementary school students. The impact of playing online games for students is twofold, namely the positive impact and the negative impact. The purpose of this study is to analyze the impact of free fire online games on students' interest in learning. This research is a descriptive research using mixed methods (Mix Methods) with an explanatory sequential design (sequential explanatory). Data collection techniques are questionnaires, interviews and documentation. Test the validity of the data using triangulation techniques. The results showed the intensity of playing online games in the medium category with a score of 51.19 percent and the level of student interest in learning in the high category with a score of 72.77 percent.&#x0D; Abstrak: Game online merupakan salah satu dari beberapa hiburan yang saat ini populer dikalangan siswa sekolah dasar. Dampak dari bermain game online bagi siswa ada dua, yakni dampak positif dan dampak negatif. Tujuan dari adanya penelitian ini yaitu untuk menganalisis dampak dari permainan game online free fire terhadap minat belajar siswa. Penelitian ini berjenis penelitian deskriptif menggunakan metode campuran (Mix Methods) dengan desain sekuensial eksplanatori (sequential explanatory). Teknik pengumpulan data yaitu dengan kuesioner, wawancara dan dokumentasi. Uji keabsahan data menggunakan triangulasi teknik. Hasil penelitian menunjukkan intensitas bermain game online dengan kategori sedang dengan skor 51.19 persen dan tingkat minat belajar siswa dengan kategori tinggi dengan skor 72.77 persen.
FLOW, ITS STRANDS AND MICROFLOW IN THE GAME DESIGN OF HORROR GAMES

First of all, in the mission of looking for answers about how engage the player during the game we found some theories, how flow, Dynamic Difficult Adjustment and some tips how make a great game, but it is normal understand that some gaps appears when the developers must be some decisions, this difficult increase during the tests and blind the developers in time-life of development, making a hard task understood in all of perspectives which will be the behavior of the player.The micro-flow comes up to help understand this and make good decisions about game design.
Analysis of Indonesian Society's Perceptions of the COVID-19 Vaccine in Youtube Comments Using Machine Learning Algorithms

The coronavirus disease (COVID-19) was first identified in Wuhan, China, in December 2019. Based on the Indonesian COVID-19 Gugus Tugas report on July 14, 2021. This virus has infected 2.6 million people and has claimed the lives of 69,210 Indonesians. The spread of COVID-19 and the dangers it causes have prompted the government to take a policy to initiate the vaccination movement immediately. Currently, in Indonesia, the vaccination program is still running, but the response from the community is still pros and cons. This study aims to determine the results of Indonesian public opinion after the vaccination program runs by conducting sentiment analysis. The dataset used is Youtube commentary data, taken from the YouTube channel of the Indonesian Ministry of Health, the Presidential Secretariat, President Joko Widodo, and Najwa Shihab, using the keyword ‘Covid-19 Vaccine’. Comment data is retrieved from November 2020 to July 2021. The crawling of the data is done manually using the access token received from the YouTube API and the python programming language to extract the requested information data. Comment data is divided into three categories: positive, negative, and neutral. To find the best classification model, applying several machine learning algorithms, namely: K-nearest neighbor (KNN), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Adaboost, and Stochastic gradient descent (SGD). The results of sentiment analysis research related to COVID-19 tend to get negative responses with sentiment measurements obtained more than 4.6 thousand positive comments (44.9%), more than 3.4 thousand positive comments (33.3%), and the rest more than 2.2 thousand. neutral comments (21.8%). The best accuracy value was obtained with the Linear Regression (LR) algorithm of 68%.
Employing the PERT Method in Planning Academic Leadership Training Programs in Saudi Universities in Light of Digital Transformation

The study aimed to reveal the role of employing the PERT method in planning academic leadership training programs in Saudi universities. The PERT method was also applied in the aspect of time and cost on EXCEL software, one of PERT's computer software, in planning a training program for academic leaders in Saudi universities with the aim of ensuring its implementation in the least time and lowest cost and achieving the highest possible efficiency and quality. Whereas, the PERT method was employed in the technical aspect of time and cost in planning a training program for academic leaders in Saudi universities to ensure its implementation in the least time and lowest cost and to achieve the highest possible efficiency and quality. To achieve this, the descriptive approach was used, and the most prominent findings of the study were that the total time estimated to plan the training program according to Bert is (18) days and at a cost of 2670, unlike what was traditionally planned at (20) days and the cost of (3000), as well as Bert's applied steps showed that the possible paths on the network for the program application steps amounted to 3 paths. Path (C) represents the critical path for taking longer in implementation. There is many technical software that enable decision makers based on planning training pro-grams to reduce the duration of preparing a program plan with the least percentage. There are mistakes, especially in large training programmers, and the study recommended the need to work on employing scientific methods in planning the completion of training programs approved by universities, because the use of these methods achieves effective results in reducing time and cost.
Analisis Code Switching dalam Proses Belajar Mengajar di SMP Negeri 7 Sukapura Probolinggo 2020

This research investigates a sociolinguistic phenomenon called code switching in teaching learning process in the seventh grade of SMP negeri 7 Sukapura Probolinggo. It aims to describe the used and types of code switching in teaching learning process. Moreover, the researchers find the probable reasons for the seventh grade SMP Negeri 7 Sukapura Probolinggo to switch English into Indonesian and Tenggerese in teaching learning process. This research used descriptive qualitative method. This research was conducted in the teaching learning process to retrieve data with data sources from the material delivered by the teacher. The three types of code switching were presented by the percentage acquisition. The researcher analyzed that 66% data were included intra-sentential code switching, 27% data were included inter-sentential code switching, and 7% data were inclided tag-switching. In this research, the teacher used three languages there are English, Indonesian and Tenggerese in the teaching and learning process. The researcher found three factors regarding to the reasons for code switching in this research, namely speaker factor, addressee factor, and topic factor.
Augmented reality marker technology as a means of geometric literacy development: efficiency and cognitive load

Introduction. The introduction of augmented reality technology into the educational process as an optimal means for visualizing three-dimensional objects opens up prospects for solving the problem of low geometric literacy, which has been detected over a number of years according to the results of the exam, GIA and PISA. One of the insufficiently studied pedagogical aspects of the use of educational AR tools is the volume of cognitive load on students in the learning process. The purpose of this study is to find out whether the use of three-dimensional AR-visualization can increase the effectiveness of training in solving geometric problems and reduce extraneous cognitive load and increase the relevant. Materials and methods. The study was organized according to the scheme of a training experiment using the control and experimental groups with measurements of the skills of solving geometric problems "before and after". The subjects were 40 first-year students of the Far Eastern Federal University, studying in the program of the pedagogical bachelor's degree in the humanities. For the diagnosis of perceived cognitive load, we used the translated into Russian and modified questionnaire of cognitive load CSL by Lepping et al., which includes three subscales – internal, extraneous and relevant cognitive loads. Primary results were analyzed using the apparatus of descriptive statistics, the Kolmogorov-Smirnov Z-test and T-tests for two independent samples and for paired samples Research results. The results showed that training using a three-dimensional AR-visualization leads to a higher growth in the results of solving geometric problems than training using three-dimensional visualization of the model on the plane (Т = -3,00**,р=0,008). It was also found that AR-mixing positively affects the volume of cognitive load in students, allowing you to reduce the extraneous load (t = - 2,04*, p=0,04) and increase the relevant релевантную (t = 2,75**, p=0,009). Conclusions. The data identified in the study contribute to scientific justifications of the methods of teaching mathematical disciplines, the development of educational AR applications, as well as to deepening the understanding of the cognitive-psychological mechanisms of the learning process
Research on traffic scheduling based on private cloud platform

With the development of cloud computing applications in recent years, the problem of IDC traffic scheduling in small and medium-sized data centers has become increasingly prominent.This paper starts from the consideration of data traffic scheduling of small and medium-sized private cloud centers, and uses the small and medium-sized private cloud built by 40 HUAWEI servers as the experimental environment.It tries to consider the classification of different service applications, the requirements and requirements of different types of traffic, and build a hybrid traffic scheduling strategy to provide a reference for traffic scheduling research of small and medium-sized private cloud.
Inferring Region Types via an Abstract Notion of Environment Transformation

Region-based type systems are a powerful tool for various kinds of program analysis. We introduce a new inference algorithm for region types based on an abstract notion of environment transformation. It analyzes the code of a method only once, even when there are multiple invocations of the method of different region types in the program. Elements of such an abstract transformation are essentially constraints for equality and subtyping that capture flow information of the program. In particular, we work with access graphs in the definition of abstract transformations to guarantee the termination of the inference algorithm, because they provide a finite representation of field access paths.
LocRDF: An Ontology-Aware Key-Value Store for Massive RDF Data

With the rapid development of the Semantic Web, the scale of RDF graphs surges. To describe ontology information, RDFs and OWL are endorsed by W3C, which further enhances the expressiveness of RDF graphs. A great challenge of managing RDF graphs is how to store massive data and efficiently reason ontology information at query time. There are two main issues with the existing RDF graph storage systems: 1) the relational data model is mainly used as the underlying storage architecture, which not only leads to exceeding the storage capacity, but also may incur high overhead while performing complex queries or multi-join queries; 2) the ontology reasoning module is either relatively independent of storage layer or used as an upper-layer application of storage and query system, causing redundancy and inefficiency in query. To address these issues, we present LocRDF, a novel storage system for RDF graphs via key-value store supporting ontology reasoning. LocRDF integrates ontology information into the underlying storage scheme with the application of a fixed-length interval encoding, promoting the efficiency of ontology reasoning at runtime. Experimental results on LUBM datasets show that extended ontology reasoning on large-scale RDF graphs scarcely affects query performance which is even significantly better than the existing state-of-the-art RDF query engines.
Unsupervised Structure-Consistent Image-to-Image Translation

The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation. We improve this work by introducing a simple yet effective auxiliary module based on gradient reversal layers. The auxiliary module’s loss forces the generator to learn to reconstruct an image with an all-zero texture code, encouraging better disentanglement between the structure and texture information. The proposed attribute-based transfer method enables refined control in style transfer while preserving structural information without using a semantic mask. To manipulate an image, we encode both the geometry of the objects and the general style of the input images into two latent codes with an additional constraint that enforces structure consistency. Moreover, due to the auxiliary loss, training time is significantly reduced. The superiority of the proposed model is demonstrated in complex domains such as satellite images where state-of-the-art are known to fail. Lastly, we show that our model improves the quality metrics for a wide range of datasets while achieving comparable results with multi-modal image generation techniques.
MODEL OF FLEXIBLE SKILLS FORMATION OF STUDENTS IN PROFESSIONAL EDUCATIONAL ORGANIZATIONS

This article discusses a problem that describes a model for the formation of flexible skills of students of professional educational organizations. Based on the analysis of psychological and pedagogical literature, the interpretation of the working fundamental concepts of this topic is given. A model of flexible skills formation including organizational-target, motivational, technological and evaluative-effective blocks is offered. The content of the blocks is described. It is concluded that the realization of the described model will promote the formation of marketable highly qualified specialist demanded on the labor market in digitalization time.
Educational health technology for patients with arterial hypertension: An integrative review

Systemic Arterial Hypertension (SAH) is a chronic multifactorial disease that aggravates public health, characterized by high and sustained levels of blood pressure (BP), systematically, equal to or greater than 140 x 90 mmHg, being responsible for the emergence of several other diseases of greater or lesser potential conditioning the morbidity of the target population. The use of educational technologies by health professionals is a growing reality. This study aims to synthesize what already exists in the literature on intervention strategies using educational technology as a contribution to the pharmacist's action in promoting quality of life and adherence to treatment of health workers with hypertension. It is a bibliographic, descriptive study, of the integrative literature review type, which enables the synthesis of the results of researches already carried out and shows, above all, the conclusions about something specific, having as a time frame the period between the years 2018 and 2022. The search strategy included the descriptors Pharmaceutical Assistance; Educational technology; Educational technology in health; Institutionalization; Interventions (Medical Subject Headings / Health Sciences Descriptors), using the Boolean operators AND and OR. Macedo considers that technology is a product of science and engineering that involves a set of instruments, methods and techniques aimed at solving problems. It is a practical application of scientific knowledge in several areas of research. In the field of health, we can define technology as an instrument for approaching users, allowing the sharing of knowledge about prevention, health promotion and rehabilitation of diseases.
High-Performance Reconfigurable Architecture for Lightweight Block Ciphers

Aiming at the problem that the current hardware implementation architecture using ASIC is difficult to meet the flexibility requirements of different applications, a high-performance reconfigurable architecture for lightweight block cipher (HRALBC) is proposed. By analyzing 42 mainstream lightweight block cipher algorithms, the pattern features and combination features of the algorithm are extracted; The reconfigurable processing unit is designed based on the pattern feature results and combined feature results; The reconfigurable processing unit array is designed according to the algorithm mapping law, and then the overall architecture is designed. Map different types of algorithms to HRALBC, verify their functional correctness, and analyze the mapping results. The experimental results show that under TSMC 55 nm CMOS process, the working frequency of HRALBC can reach 429 MHz, and the total area is 1.23M equivalent gate (GE). For different cryptographic algorithms, the area efficiency can be up to 22.33 Gbit·s<sup>-1</sup>·MGE<sup>-1</sup>. Compared with the Anole, for PRESENT64/80, SPECK64/128 and SIMON64/128 algorithms, the utilization of reconfigurable units is increased by 16.67%, 16.67% and 13.89% respectively, and the area efficiency is increased by 66.64%, 66.64% and 11.04% respectively.
Identification and Diagnosis of Breast Cancer using a Composite Machine Learning Techniques

Throughout the world, cancer is the top cause of mortality in women.Whatever improvement in cancer ailment identification and prognosis is vital to living a healthy life.As a consequence, excellent cancer prediction accuracy is crucial for maintaining treatment and survival criteria current for patients.Machine learning techniques, which have been found to have a major impact on the identification and early treatment of breast cancer, have emerged as a hotspot for study and have been demonstrated to be a potent methodology.We used five machine learning algorithms in this research.There have been various empirical investigations employing machine learning and soft computing techniques to treat breast cancer.This article provides an overview of the available technologies for advanced treatment of breast cancer, as well as an introduction to the concepts and current achievements in personalised medicine that depend on technology.The many kinds of breast cancer are addressed, as well as their worldwide occurrence.It is discussed the importance of recognising irregularities and distinguishing between benign and malignant breast cancer.Levenberg-Marquardt (LM) and Sparse Representation (SR) have been combined to model a new approach for diagnosing breast cancer.With LM's training accuracy and SR's robustness in identifying small deformations, an impressive performance was achieved in terms of accuracy and mean square error.Data has been acquired from the Wisconsin Dataset (WD).This model proves the combination of several classification algorithms could enhance the model in a significant manner and can be used for pre-clinical diagnosis.
Knowledge Management System Design for Health Stores in PT. ABC

This study aims to identify, analyse needs, and design a Knowledge Management information system for marketing personnel who sell health products and by using interviews and observation methods as data collection techniques. The research was conducted when the process was running at PT ABC, it was found that the marketing staff had difficulty explaining hundreds of health products in terms of size, function, price, and promotion to customers. In addition to the difficulty of explaining the product, there are also new products that are always present every time and products that are no longer produced. with dozens of shops and hundreds of marketing personnel spread throughout Indonesia, it is not easy for PT. ABC to explain product knowledge and understanding to all marketing personnel, therefore the results of this research are then poured into the design of Knowledge Management information systems in the form of mobile applications and web-based applications. The design of the Knowledge Management system is a knowledge management tool that functions as an online learning tool and provides the latest changes in the provision of applicable knowledge, a media facility to improve the soft skills and hard skills of marketing personnel, as well as an effective and efficient means to store knowledge information. a product and promotion that is beneficial for employees and companies.
Uji Daya Hambat Lendir Bekicot (Achatina Fulica) terhadap Pertumbuhan Bakteri Staphylococcus Aureus dan Salmonella Typhi

Background: Snail mucus (Achatina fulica) contains achasin protein which can inhibit the growth of Staphylococcus aureus and Salmonella typhi bacteria. This study aims to determine the effect of snail mucus (Achatina fulica) on the growth of Staphylococcus aureus and Salmonella typhi bacteria.&#x0D; Methods:This research is an experimental research using Posttest Only Control Group Design as the research design. This research was conducted at the Microbiology Laboratory, Riau University, from December 16, 2021 to January 11, 2022. The population of this study was pure culture of Staphylococcus aureus and Salmonella typhi. The sample consisted of 30 snails which had previously been adapted for 1 month. The negative control group was only given distilled water, the positive control group was given amoxicillin, treatment group 1 was given snail slime with a concentration of 30%, treatment group 2 was given snail slime with a concentration of 60%, and treatment group 3 was given snail mucus with a concentration of 90%. The result of this research will be analyzed using statistical test, ANOVA.&#x0D; Results:There is an inhibition of snail mucus (Achatina fulica) against the growth of Staphylococcus aureus and Salmonella typhi bacteria. The most effective results in inhibiting the growth of Staphylococcus aureus and Salmonella typhi bacteria is at a concentration of 90%.&#x0D; Conclusion:Based on the results of this study, it can be concluded that snail mucus (Achatina fulica) can significantly inhibit the growth of Staphylococcus aureus and Salmonella typhi bacteria.