Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF AUDIO-VISUAL MEDIA IN LISTENING LEARNING FOR GRADE IV ELEMENTARY SCHOOL STUDENTS

This study aims to produce an audio-visual learning media product that is useful in listening learning for fourth grade elementary school students. And to determine the feasibility of the audio-visual media that the researchers have developed. This study uses the Research and Development (R&amp;D) method and using the Four-D model research stage (Define, Design, Develop, Disseminate). Validation is carried out by expert validators and practitioner validators. Product trials were carried out in stages, namely stage 1 and stage 2. The results showed that the validation of design experts obtained an assessment with a very valid category. In validation linguists obtained assessment with a very valid category. In the validation of the material experts, they obtained an assessment with a very valid category. And the validation of the material by the practitioner validator obtained an assessment with a very valid category. The students written test in the form of objective questions received an assessment with a very good category. The results of the questionnaire responses from students and teachers obtained an assessment with a very practical category. So, the results of this study indicate that development of audio-visual media in listening learning is very suitable for use for fourth grade elementary school students.
Using mobile 3D models for demonstration unaccessible laboratory experiments and objects during study chemistry and biology in basic school

Problem statement. The technological development of augmented reality is becoming more impressive every year. But, despite all the advantages and possibilities of using augmented reality in the modern world, it is still in its infancy in the field of education. One of the biggest benefits of using augmented reality technology in natural sciences is its ability to visualize abstract concepts, reproduce expensive, dangerous or time-consuming experiments, and showcase rare objects. These augmented reality abilities are great for teaching chemistry and biology in basic school, as they can visualize inaccessible objects, processes and phenomena in the classroom without using large time, material and technical resources. At the moment, sufficient experience has not been accumulated in the application of this technology in the study of natural sciences, in particular chemistry and biology. Thus, there is a need to conduct an appropriate scientific and pedagogical research to determine the effect of the introduction of augmented reality technology in the study of natural sciences. Methodology. The methods of designing a learning model, pedagogical experiment, observation, conversation, and generalization of the experience gained were used. Results. The results of a pedagogical experiment on the use of mobile 3D models to demonstrate inaccessible laboratory experiments and objects in the study of chemistry and biology in basic school, aimed at analyzing and highlighting the most significant examples of the application of this technology in practice. The essence and features of the use of mobile 3D models in the study of chemistry and biology are described. Significant aspects of the use of such technology in the modern school are revealed. Conclusion. The experience of organizing lessons with a demonstration of inaccessible laboratory experiments and objects in the study of chemistry and biology in a basic school allows us to conclude that there are significant differences in the educational process implemented using mobile 3D models and augmented reality technology.
Research on private data security based on multiparty computation

In recent years, privacy data has been stolen frequently, and the security of privacy data has been concerned by society. In the big data environment, privacy is facing unprecedented challenges, and some traditional privacy protection technologies are facing failure, so how to choose a reasonable privacy protection technology is a challenging task. To solve the problem of a large amount of data theft in the application of traditional methods in the privacy data security protection, this paper proposes research on privacy data security based on multi-party computation. The hash function is used to encrypt the private data, and the data is stored in the block chain in the form of a private data encryption file. Based on the theory of multi-party computation, a secure multi-party technology protocol is designed, and the protocol is used to verify the identity of the participants in the private data transmission to realize the security protection of private data. The experimental results show that the amount of privacy data stolen by the application design method is less than that of the traditional method, which has important application value for the security protection of privacy data.
PELATIHAN MICROSOFT OFFICE DALAM MEDUKUNG HARD SKILL REMAJA MASJID AL-HIKMAH PERUM TAMAN HANG TUAH LEGENDA MALAKA BATAM CENTER

The implementation of community service activities carried out in the form of training on the use of Microsoft Word is very useful for young people of the Al-Hikmah Mosque Housing Taman Hangtuah Legenda Malaka Batam Center, because with this ability it will make it easier for Youth of the Al-Hikmah Mosque Housing Taman Hangtuah Legenda Malaka Batam Center in management and operational of the Youth Secretariat of Al-Hikmah Mosque Housing Taman Hangtuah Legenda Malaka Batam Center in its creative activities which are not only spiritual in nature, but also other social activities that support hard skill abilities which are not only obtained in the school environment but can also be obtained in the residential environment in the form of Youth organization Al-Hikmah Mosque Housing Taman Hangtuah Legenda Malaka Batam Center. community service activities by applying the method of making activity reports, correspondence and correspondence as well as making information technology-based outreach brochures by utilizing existing applications in Microsoft Word with the hope that this activity will be able to provide creativity, optimize existing IT facilities and increase capabilities and human resources especially for the young person of the Al-Hikmah Mosque, Taman Hangtuah Residential, the Legenda Malaka, Batam Center. For this activity wil make a good shoft skil to the youth of the Al-hikmah mosque
PENERAPAN MNEMONIK SEBAGAI METODE ALTERNATIF UNTUK MENGHAFAL HURUF IKHFA’

Memorizing is the way of the learning process to receive the material that has been learned. There is an important subject at elementary school, it is tajwid. The materials in Tajwid must be memorized by the learner. There are 15 alphabets of ikhfa’ that must be memorized quickly and easily and also have strong memorization if they will be needed soon. Thus, the researcher researched that has titled implementation of mnemonic as an alternative method to memorize ikhfa’ alphabets. The researcher used a qualitative approach. This research has 14 participants then who are called learners. In the mnemonic method, the teacher arranged the words that represent the alphabet of ikhfa’ and then combined them with the picture that has found or searched by the teacher. The learner follows the voice of the teacher three times about arranging the word ikhfa’. The word is Pak Dhodzo Tsadzin teko Shoqo Kutho pakai Jaz, Syafatu dan Dasi. Then, after memorizing and the learner has memorized the sentences, the teacher tested them to write or speak the 15 alphabets of ikhfa’. Based on the researcher’s survey, all learners can mention 15 alphabets of ikhfa’. This research explains that the implemented mnemonic can be used as an alternative method to memorize 15 alphabets of ihkfa’.
On the benefits of automated tuning of hyper-parameters: an experiment related to temperature prediction on UAV computers

Finding the best configuration of a neural network to solve a problem has been challenging given the numerous possibilities of values of the hyper-parameters. Thus, tuning of hyper-parameters is one important approach and researchers suggest doing this automatically. However, it is important to verify when it is suitable to perform automated tuning which is usually very costly financially and also in terms of hardware infrastructure. In this study, we analyze the advantages of using a hyper-parameter optimization framework as a way of optimizing the automated search for hyper-parameters of a neural network. To achieve this goal, we used data from an experiment related to temperature prediction of computers embedded in unmanned aerial vehicles (UAVs), and the models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to perform these predictions. In addition, we compare the hyper-parameter optimization framework to the hyper-parameter exhaustive search technique varying the size of the training dataset. Results of our experiment shows that designing a model using a hyper-parameter optimizer can be up to 36.02% better than using exhaustive search, in addition to achieving satisfactory results with a reduced dataset.
Generalizing over data sets: a preliminary study with BERT for Natural Language Inference

Natural language inference is the task of automatically identifying whether a given text (premise) implies another (hypothesis). Among multiple possible applications, it is especially relevant in the legal field to understand textual entailment between legal sentences, being the focus of recent research efforts. In this work, we evaluated the usage of BERT for natural language inference by conducting experiments and comparing results obtained by testing on a larger corpus with texts from multiple domains and a smaller corpus of legal sentences. Furthermore, we conducted a cross-experiment by training on the larger corpus and testing on the legal corpus. As a result, we obtained a mean accuracy of 88.91% in the corpus with multiple domains, a value comparable to related work. However, the same technique presented lower scores in the legal corpus and the cross-experiment.
On the performance of uncertainty estimation methods for deep-learning based image classification models

Previous works have shown that modern neural networks tend to be overconfident; thus, for deep learning models to be trusted and adopted in critical applications, reliable uncertainty estimation (UE) is essential. However, many questions are still open regarding how to fairly compare UE methods. This work focuses on the task of selective classification and proposes a methodology where the predictions of the underlying model are kept fixed and only the UE method is allowed to vary. Experiments are performed for convolutional neural networks using Deep Ensembles and Monte Carlo Dropout. Surprisingly, our results show that the conventional softmax response can outperform most other UE methods for a large part of the risk-coverage curve.
Application of Learned OWA Operators in Pooling and Channel Aggregation Layers in Convolutional Neural Networks

Promising results have been obtained in recent years when using OWA operators to aggregate data within CNNs pool layers, training their weights, instead of using the more usual operators (max and mean). OWA operators were also used to learn channel wise information from a certain layer, and the newly generated information is used to complement the input data for the following layer. The purpose of this article is to analyze and combine the two mentioned ideas. In addition to using the channel wise information generated by trainable OWA operators to complement the input data, replacement will also be analyzed. Several tests have been done to evaluate the performance change when applying OWA operators to classify images using VGG13 model.
Establishing the Parameters of a Decentralized Neural Machine Learning Model

The decentralized machine learning models face a bottleneck of high-cost communication. Trade-offs between communication and accuracy in decentralized learning have been addressed by theoretical approaches. Here we propose a new practical model that performs several local training operations before a communication round, choosing among several options. We show how to determine a configuration that dramatically reduces the communication burden between participant hosts, with a reduction in communication practice showing robust and accurate results both to IID and NON-IID data distributions.
K-Nearest Neighbors based on the Nk Interaction Graph

The K-Nearest Neighbors (KNN) is a simple and intuitive nonparametric classification algorithm. In KNN, the K nearest neighbors are determined according to the distance to the example to be classified. Generally, the Euclidean distance is used, which facilitates the formation of hyper-ellipsoid clusters. In this work, we propose using the Nk interaction graph to return the K-nearest neighbors in KNN. The Nk interaction graph, originally used in clustering, is built based on the distance between examples and spatial density in small groups formed by k examples of the training dataset. By using the distance combined with the spatial density, it is possible to form clusters with arbitrary shapes. We propose two variations of the KNN based on the Nk interaction graph. They differ in the way in which the vertices associated with the N examples of the training dataset are visited. The two proposed algorithms are compared to the original KNN in experiments with datasets with different properties.
Comparing Computational Architectures for Automated Journalism

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for datato-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available.
On the evaluation of example-dependent cost-sensitive models for tax debts classification

Example-dependent cost-sensitive classification methods are suitable to many real-world classification problems, where the costs, due to misclassification, vary among every example of a dataset. Tax administration applications are included in this segment of problems, since they deal with different values involved in the tax payments. To help matters, this work presents an experimental evaluation which aims to verify whether cost-sensitive learning algorithms are more cost-effective on average than traditional ones. This task is accomplished in a tax administration application domain, what implies the need of a cost-matrix regarding debt values. The obtained results show that cost-sensitive methods avoid situations like erroneously granting a request with a debt involving millions of reals. Considering the savings score, the cost-sensitive classification methods achieved higher results than their traditional method versions.
How aspects of similar datasets can impact distributional models

Distributional models have become popular due to the abstractions that allowed their immediate use, with good results and little implementation effort when compared to precursor models. Given their presumed high level of generalization it would be expected that good and similar results would be found in data sets sharing the same nature and purpose. However, this is not always the case. In this work, we present the results of the application of BERTimbau in two related data sets, built for the task of Semantic Similarity identification, with the goal of detecting redundancy in text. Results showed that there are considerable differences in accuracy between the data sets. We explore aspects of the data sets that could explain why accuracy results are different across them.
Impact of Remittance, Money Supply and Inflation on Economic Growth of Nepal: Using Autoregressive Distributed Lag Model (ARDL)

Remittances have become a major source of funding in developing countries. A time series analysis was conducted by taking the 25 years data of economic growth as a dependent variable while remittance, money supply and Inflation as independent variables form the annual report of Economic survey of Nepal (2020/21). The autoregressive distributive lag (ARDL) was used to study the relationship among the variables. The trend of economic growth and total percentage of economic growth is in increasing order. The relation between then was found as LN(Economic Growth) =8.0740 +0.220*LNREM + 0.3123 *LNM2-0.0802*LNINF. The stability test of long-run coefficient including short run was studied using ARDL model and are confirmed using Cumulative Sum chart (CUSUM) and cumulative sum of squares test (CUSUMSQ) test. Finding shows that there is long run positive relationship between remittance, money supply, inflation on economic growth of Nepal. From the analysis it is clear that coefficients of error correction are highly significant this indicate that adjustment to longterm equilibrium in the dynamic model and ARDL model showed all the independent variables had positive impact on economic growth of Nepal.
Thermal Face Image Reidentification Based on Variational Autoencoder, Cascade Object Detector Using Lightweight Architectures

This chapter presents a model for generating thermal face identification by synthesizing visible pictures with the variational autoencoder model. This model successfully conserved the original face structure while simultaneously generating high-quality images from thermal data. In addition, in order to identify the images that have been generated, we make use of three different models that are based on lightweight architecture. This particular architecture has a fundamentally high accuracy in face detection because it reduces the number of parameters and executes at a high speed. After being trained and tested with a variety of datasets, the findings show that the proposed model is able to recognize thermal facial pictures. Because of this capability, the method may be used to new thermal face photos without the requirement for an additional training phase in order to identify the faces. The performance comparison reveals that the recently developed model is significantly more effective than the state-of-the-art approaches that are already in use, particularly with regard to the accuracy of scoring. In the future, generative adversarial networks (GAN) models may be included to the system. These models have the potential to narrow the gap between the synthetic and original pictures, which will result in an improvement in the image quality.
Systematic Review of Common Web-Application Vulnerabilities

Organizations are increasingly worried about web application vulnerabilities because they can be used by cybercriminals to access private data without authorization. In this review, we examine the current state of the vulnerabilities in the web application layer. We begin by discussing the different types of vulnerabilities that can affect web applications, including cross-site scripting (XSS), brute force, SQL injection, and cross-site request forgery (CSRF) attacks. And then for each vulnerability, we discuss the various approaches that have been developed to detect and prevent these vulnerabilities. Finally, we discuss the challenges and limitations of current approaches and suggest directions for future research. Key Words: web application, vulnerabilities, cyber-security, SQL injection, XSS, CSRF.
Design of Student Modeling System to Support Adaptive E-Learning

Based on the results of the study, it was found that the practice of using E-Learning at Politeknik Negeri Jember was only used to distribute material and collect assignments, even though there was an exam function which was widely used during the pandemic.However, the implementation of E-Learning like this is far from the learning concept being implemented by the institution, namely student-centered learning (problem and project-based learning).This study used learning motivation variables indicated by the students' interaction with E-Learning for clustering (K-Means) and classification (Naive Bayes).The results of the research showed that the accuracy value was 91.66%.As a result, the researchers are optimistic that the results of this study can be used to optimize the learning process at Politeknik Negeri Jember.
Esne-gazuratik eratorritako film biodegradagarriak

Euskadiko gaztandegietan esne-gazur ugari sortzen da eta haren isurketak kalteak eragin ditzake ingurumenean. Horregatik, ikerketa honen helburua film biodegradagarriak ekoiztea da, esne-gazuraren proteina erabiliz. Elikagaien industriako bio-hondakinetatik datorren proteinan oinarritutako elikadura-ontzien bidezko irtenbide honek, hondakinen kopurua murrizteaz gain, elikagaien bizitza erabilgarria handitzea du helburu. Esne-gazuraren proteinan oinarritutako filmak sortzeko, hainbat prozesatze-metodo erabil daitezke eta, lan honetan, konpresioa aukeratu da, ekoizpen-denborak murrizteko eta prozesu iraunkorragoak sustatzeko. Filmen eraketa-prozesua optimizatu ondoren, lortutako filmen propietate fisiko-kimikoak, optikoak, mekanikoak eta hesi-propietateak aztertu dira. Horretaz gain, filmen ingurumen-inpaktuaren azterketa egin da.
ORGANIZATIONAL AND ACADEMIC CONDITIONS FOR DIGITAL SKILLS DEVELOPMENT IN UNDERGRADUATE STUDENTS OF UDMURT STATE UNIVERSITY SPECIALIZING IN «SOCIAL STUDIES AND SOCIAL WORK»

The article considers the mechanisms for integrating the digital component into the academic courses developed for the students of the Institute of Social Communications of Udmurt State University getting the degree in «Social Studies and Social work». Modern educational paradigm requires both the integration of digital content into the educational process and the introduction of subjects targeting at the development of media culture of future specialists. The article covers basic methodology as well as methods and techniques of teaching and learning process. The importance and potential advantages of the academic courses aimed at acquisition and enhancement of managerial, creative and technological skills in digital communication as well as in overall media landscape are shown. Changes in the Russian educational system connected with the accelerated digitalization are also analyzed. The authors understand the notion of students’ digital skills as follows: digital environment expertise provided that a digital culture has been established, acquisition of informational and communicative skills necessary for studying and plotting an individual academic path.