Reviewerly/exHarmony huggingface dataset:
Zoom-Storytelling : an Activity to Reduce Students’ Speaking Anxiety

AbstrakDari empat keterampilan bahasa Inggris, keterampilan berbicara merupakan keterampilan yang paling sulit bagi siswa. Banyak alasan yang membuat siswa enggan berbicara bahasa Inggris, antara lain takut ditertawakan oleh temannya dan kurangnya rasa percaya diri. Faktor-faktor tersebut membuat siswa merasa cemas untuk berbicara dalam bahasa Inggris. Sejak diterapkannya pembelajaran dari rumah oleh pemerintah pusat akibat merebaknya Covid-19, siswa semakin tidak pernah berlatih berbicara dalam bahasa Inggris. Hal tersebut memperburuk tingkat kecemasan siswa untuk berbicara bahasa Inggris. Oleh karena itu, penelitian studi kasus kualitatif ini dilakukan untuk membantu mengurangi kecemasan siswa dalam berbicara bahasa Inggris dengan menggunakan zoom-storytelling, terutama ketika mereka diharuskan belajar dari rumah. FLCAS digunakan untuk mengukur tingkat kecemasan siswa. Hasil penelitian menunjukkan bahwa zoom-storytelling dapat mengurangi tingkat kecemasan siswa dalam berbicara bahasa Inggris. Melalui kegiatan zoom-storytelling ini, siswa terbantu untuk mendapatkan pembelajaran tatap muka secara virtual meskipun tidak berada di sekolah. Kata kunci: Aplikasi Zoom, Storytelling, Speaking Anxiety.AbstractAmong the four English skills, speaking is the most difficult skill for students. Many reasons make students reluctant to speak English, including being afraid of being laughed at by their friends and lack of confidence. These factors make students anxious to speak English. Since the implementation of learning from home by the central government due to the outbreak of the Covid-19, students have increasingly never practiced speaking English. It worsens the level of students' anxiety to speak English. Therefore, this qualitative case study research was conducted to help to reduce students' anxiety in speaking English by using zoom-storytelling, especially when they were required to learn from home. FLCAS was used to measure the student's anxiety level. The results showed that zoom-storytelling can reduce students' level of anxiety in speaking English. Through this zoom-storytelling activity, students were helped to get learning face-to-face virtually even though they were not in school.Keywords: Zoom Application, Storytelling, Speaking Anxiety.
Distance, Online and Blended Learning as Main Educational Trends During Pandemic 2020 Conditions

Distance learning and self-education have been an actual trend for many years but during the pandemic, it became critically relevant and important. The closure of schools and higher education institutions around the world had a negative impact on the educational sector in many countries in general and has affected almost every student and educator. This paper examines Ukrainian educators’ level of knowledge and skills in using modern distance learning tools and trends and the way of improving the teaching-learning process during the conditions of the 2020 pandemic. The authors present their review of educational trends under the conditions of distance, online and blended learning. They also examine the Ukrainian educators’ level of knowledge and skills in using modern distance learning tools and trends. For this purpose, the authors have elaborated and implemented a local survey for a Ukrainian university teaching staff (target group) who need to use distance learning tools in their research and professional activity during the quarantine. The authors also present the ways of improving the learning process via distance learning tools.
The symptom-syndrome analysis of multivariate categorical data based on Zhegalkin polynomials

In this article, we study the distribution, entropy and other informational properties of finite projective subspaces (syndromes) parameterized by impulse sequences with basic elements in the form of symptoms - polynomials over the field F2 which are known as Zhegalkin polynomials. It has been proven that the super syndrome, which is a linear syndrome with basic elements in the form of a multiplicative syndrome, is closed. If in the multiplication of two symptoms one is neutral, then we are talking about its majorization. The ordered by majorization symptoms form a majorized syndrome. Is proved that the majorized syndrome is closed and coincides with the super syndrome. The statements formulated in the first part of the paper are used to justify the convergence of the iterative procedure (PI), in which the most informative symptoms selected from partial super syndromes are again used in the next step. The stationary state of PI is obtained if all elements of the input set belong to either the same partial super syndrome or to the majorized syndrome. Thanks IP it is possible to quickly find the optimal syndrome from a large set of variables. An example from phthisiology shows how the specificity of classification can be improved using symptom analysis.
Object Detection and Recognition for Visually Impaired People

Good vision is an expensive gift but now a day’s loss of vision is becoming common issue. Blind or visually impaired people does not have any conscious about the danger they are facing in their daily life. To help the blind people the visual world has to be transformed into the audio world with the potential to inform them about objects. Various challenges are faced by visually impaired patients even in the familiar environment. Visually impaired individuals are at drawback due to lack of sufficient information about their familiar environment. This project employs a Convolution Neural Network for recognition of pre-trained objects. This project employs in deep learning a deep Neural Network (DNN) for recognizing the object which is captured from the real world. The captured image is compared with some pre trained objects that is stored in dataset .The comparative of the object is based on the shape and size of an objects. In deep neural network, TensorFlow package using a model called Mobile Net SSD that is comparing the real time capture image with pre trained object based on shape, size of the object. If the image is matched with that trained object, it will display the name of the object. Then the name of the object is converted into audio output with the help of gTTS. This will helps to identify and detect what object is present in front of blind people and give output as audio.
Evaluation of TF-IDF Algorithm Weighting Scheme in The Qur'an Translation Clustering with K-Means Algorithm

The Al-Quran translation index issued by the Ministry of Religion can be used in text mining to search for similar patterns of Al-Quran translation. This study performs sentence grouping using the K-Means Clustering algorithm and three weighting scheme models of the TF-IDF algorithm to get the best performance of the Tf-IDF algorithm. From the three models of the TF-IDF algorithm weighting scheme, the highest percentage results were obtained in the traditional TF-IDF weighting scheme, namely 62.16% with an average percentage of 36.12% and a standard deviation of 12.77%. The smallest results are shown in the TF-IDF 1 normalization weighting scheme, namely 48.65% with an average percentage of 25.65% and a standard deviation of 10.16%. The smallest standard deviation results in a normalized 2 TF-IDF weighting of 8.27% with an average percentage of 28.15% and the largest percentage weighting of 48.65% which is the same as the normalized TF-IDF 1 weighting.
Geochemistry and Determination of Mineral Properties of Dolomite Deposit in Ikpeshi Southern, Nigeria

This work focused on the geochemistry and determination of the mineral properties’ distribution of dolomite deposit from Ikpeshi, using variogram analysis, Samples collected from the study area were subjected to laboratory analysis to determine the porosity, water content and geochemical properties. Variograms maps were constructed and fitted to the model. The results of the variogram analysis were used in plotting of predictive maps which show the property distribution of the dolomite. The porosity varies between 0.68%–3.24% and the Water Content varies between 0.1–1.65%. The geochemical analyses reveal that the average concentration of the elemental oxides is 1.790, 0.461, 0.299, 0.045, 20.380 and 46.130 for SiO2, Al2O3, Fe2O3, MnO, MgO and CaO, respectively. CaO and MgO are more abundant when compared with other major elements; the predictive maps of the distribution and directions of the mineral properties and the distribution are not uniform in all directions which indicates that the mineral properties of the dolomite deposit are anisotropic.
A Comparison between a Relational and a Graph Database in the Context of a Recommendation System

This paper presents a comparison between relational and graph-based database systems' performance in a modern web application recommendation system.The comparison is conducted on five different queries starting with simple ones, leading up to more complex queries, that are performed in a typical web social application.The implementation is done in C# using .NET framework and the database systems used are SQL Server and Neo4j.For the comparative study we used a database designed in the context of a recommendation system for a culinary application.In order to effectively test the performance of both graph and relational database systems, tests were performed on four data sets that contain 350.000, 700.000, 1.400.000and 2.100.
Verifying the Conversion into CNF in Dafny

We present two computer-verified implementations of the CNF conversion for propositional logic. The two implementations are fully verified: functional correctness and termination is machine-checked using the Dafny language for both. The first approach is based on repeatedly applying a set of equivalences and is often presented in logic textbooks. The second approach is based on Tseitin’s transformation and is more efficient. We present the main ideas behind our formalization and we discuss the main difficulties in verifying the two algorithms.
Hand Gesture Recognition using Deep Learning

Hearing impaired individuals use sign languages to communicate with others within the community. Because of the wide spread use of this language, hard-of-hearing individuals can easily understand it but it is not known by a lot of normal people. In this paper a hand gesture recognition system has been developed to overcome this problem, for those who don't recognize sign language to communicate simply with hard-of-hearing individuals. In this paper a computer vision-based system is designed to detect sign Language. Datasets used in this paper are binary images. These images are given to the convolution neural network (CNN). This model extracts the features of the image and classifies the images, and it recognises the gestures. The gestures used in this paper are of American Sign Language. In real time system the images are converted to binary images using Hue, Saturation, and Value (HSV) colour model. In this model 87.5% of data is used for training and 12.5% of data is used for testing and the accuracy obtained with this model is 97%.
Imbalanced Data Mining Using Oversampling and Cellular GEP Ensemble

The paper proposes a novel classifier for mining imbalanced datasets. The approach integrates three techniques – oversampling, cellular Gene Expression Programming, and dynamic selection of classifiers. The oversampling algorithm is based on the Local Distribution-Based Adaptive Minority Oversampling technique. Cellular GEP is an extension of cellular genetic algorithms. In the first step of the approach minority and majority datasets are balanced by replicating or synthesizing minority class examples. Next, cellular GEP is used to produce a set of base classifiers in the form of expression trees. Finally, a dynamic selection of classifiers is used during testing. To validate the approach an extensive computational experiment has been carried out. Its results show that the proposed classifier outperforms classic learners used for mining imbalanced datasets and is comparable with the state-of-the-art ensemble classifiers.
FACTORS AFFECTING CUSTOMERS’ ACCEPTANCE OF THE ADOPTION OF BLOCKCHAIN TECHNOLOGY AT DONG A COMMERCIAL JOINT STOCK BANK, HUE BRANCH

Abstract: This study aims to develop and test the integrative model of the factors affecting customers’ acceptance intention of the adoption of blockchain technology. Data were collected from a sample of 195 customers who have been conducting transactions at Dong A Bank – Hue branch. Samples are selected by using the systematic random sampling method. Structural equation modelling (SEM) is used to test the hypothesized relationships. The findings indicate that six out of eight tested relationships are supported. Perceived usefulness (PU) and Perceived ease of use (PEU) are the most critical factors affecting customers’ Attitude (AT). Attitude also has a direct and positive correlation to customers’ acceptance Intention (IN). Notably, Personal characteristics (PC) and Risk perception (RP) are the two most influential factors affecting Perceived usefulness. And, the Perceived ease of use factor is only affected by customers’ Self-command (SC). In general, this study contributes to enriching the existing knowledge of blockchain adoption in banks and helps banks figure out an efficient way to adopt blockchain technology.Keywords: customers’ acceptance, blockchain adoption, Dong A Bank, Hue branch
Android-Based Horspool Algorithm for Proverb Search

A proverb is a sentence or group of words that expresses a purpose or will, a state of a person, or a thing that says the behavior, actions, and things about a person that, although indirect, expresses something that the reader or listener can understand. Horspool algorithm simplifies the boyer-Moore algorithm, which in this text search is searched in large text to find the first pattern. Since the text sought can be so large (allowing hundreds of thousands of characters), it is essential to use more efficient techniques. Based on this, an android-based proverb search application was created to make it easier for someone to find proverbs because not everyone understands them, making it more practical and efficient in terms of its use without having to carry sayings in the form of print media such as books.
Predicting The Number of Passengers on CV. MAKMUR Using The Backpropagation Method

Often, the number of passengers up and down at the CV company. Backpropagation is one solution to the problem. The number of passengers can be predicted so that the company CV. Prosperous have a large income. Based on the research, it was stated that MSE at the time of training predicted the number of passengers on CV. Makmur Medan was 0.000999. Based on the coefficient value in the training process, it can be concluded that ANN can predict the number of passengers well.
AN ANALYSIS OF LOGICAL FALLACY ON JOKO WIDODO’S ARGUMENTS DURING 2019 INDONESIA PRESIDENTIAL DEBATE

This research aimed at finding the types of logical fallacies on Indonesia presidential debates made particularly by one of the presidential candidates, Joko Widodo, during the 2019 Indonesia presidential debates. The theory of fallacy classification by Damer (2009) which introduces five categories and sixty types of fallacies was employed in this research. Of all the sixty types, only twelve of which were found. The results show that, first, fallacies that violate the rebuttal criterion became the most dominantly occurred category with the occurrence of more than a half number of all occurrences and with five types out of twelve types. Second, abusive ad hominem was the most dominant type made by Jokowi with the frequency of 21.73% and followed by the fallacy of red herring and false alternatives with the same frequency of 17.39.
PENERAPAN MODEL PEMBELAJARAN TREFFINGER UNTUK MENINGKATKAN KREATIVITAS DAN KEMAMPUAN MENULIS TEKS DESKRIPSI (Studi pada Mata Pelajaran Bahasa SMP Negeri 6 Lahat)

This study aimed to: (1) describe the application of treffinger model to improve students’ creativity skills. (2) describe the application of treffinger model to improve description writing skills students. This study was carry out using Classroom Action Research (CAR) and quasi-experiment. The subject was VII.3 of SMPN 6 Unggul Lahat as a Classroom Action Research class. The VII.1 as experiment and VII.4 as control class.To collect data, researcherused observation sheets to measure the ability of teachers applying treffinger model and students’ creativity skills, as well as tests to measure improvement in description writing. To analyze data, researcher used an average score analyze and t-test. Based on the results of this research concluded that the application of terffinger model can improve student’ creativity and description writing skills students
Financial Literature Education to Increase Saving Motivation in Elementary School Students

Financial literacy includes the human understanding of personal financial management. Financial literacy needs to be done from an early age. The purpose of this study was to determine how big the level of understanding of elementary school students towards financial literacy and how much motivation to save elementary school students. This research was conducted by a quantitative method by producing primary data. Primary data was obtained from the activities of filling out pre-test and post-test questions to elementary school students. The results of this study indicate that many students have made a habit of saving but their level of understanding of financial literacy is still low. The results of the research show that the average post-test score of students is 88.7%, which is greater than the average pre-test score of 81.3%, so there is an increase of 7.4%. Students' understanding of financial literacy increased by 7.4% after being given a learning video. Elementary school students should build the habit of saving from now on. Saving habits make elementary school students trained in managing their finances.
Using Blockchain to Achieve Decentralized Privacy in IoT Healthcare

With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud in healthcare programs, making it difficult for users to have enough control over their data. The recent increment in announced cases of security and surveillance breaches compromising patients' privacy call into question the conventional model, in which third-parties gather and control immense amounts of patients' Healthcare data. In this work, we try to resolve the issues mentioned above by using blockchain technology. We propose a blockchain-based protocol suitable for e-health applications that does not require trust in a third party and provides an efficient privacy-preserving access control mechanism. Transactions in our proposed system, unlike Bitcoin, are not entirely financial, and we do not use conventional methods for consensus operations in blockchain like Proof of Work (PoW). It is not suitable for IoT applications because IoT devices have resources-constraints. Usage of appropriate consensus method helps us to increase network security and efficiency, as well as reducing network cost, i.e., bandwidth and processor usage. Finally, we provide security and privacy analysis of our proposed protocol.
Combining Fuzzy Logic and k-Nearest Neighbor Algorithm for Recommendation Systems

Recommendation systems are a type of systems that are able to help users finding relevant and personalized content in a wide variety of possibilities. To help computers perform recommendations, there are several approaches used nowadays such as the Content-based approach, the Collaborative filtering approach and the Hybrid recommendation approach. However, these approaches are sometimes inappropriate for use cases where there is no prior large datasets of users’ feedbacks or ratings needed for training Machine Learning models. Thus, in this work, we proposed a novel approach based on the combination of Fuzzy Logic and the k-Nearest neighbor algorithm (KNN). The proposed approach can be applied without any prior collected feedbacks of users and performs good recommendations. Moreover, our proposal uses Fuzzy Logic to infer values based on inputs and a set of rules. Furthermore, the KNN uses the output values of the Fuzzy Logic system to do some retrieval tasks based on existing distance measures. In order to evaluate our approach, we considered an expert system of food recommendation for people suffering from the two deadliest diseases in Cameroon: HIV/AIDS and Malaria. The obtained results are closed to the recommendation made by nutritionists. These results demonstrate how effective our approach can be used to solve a real nutrition problem for people suffering from Malaria or HIV/AIDS. Furthermore, this approach can be extended to other fields and even be used to perform any recommendation task where there is no prior collected user’s feedback or ratings by using the proposed approach as a framework.
Extending RC4 to Construct Secure Random Number Generators

We consider a general framework for constructing non-linear generators by adding a (32-bit or larger) pseudo-random number generator (PRNG) as a baseline generator to the basic RC4 design, in which an index-selection scheme similar to RC4 is used. We refer to the proposed design as the eRC (enhanced/extended RC4) design. We discuss several advantages of adding a good baseline generator to the RC4 design, including new updating schemes for the auxiliary table. We consider some popular PRNGs with the nice properties of high-dimensional equi-distribution, efficiency, long period, and portability as the baseline generator. We demonstrate that eRC generators are very efficient via extensive empirical testing on some eRC generators. We also show that eRC is flexible enough to choose minimal design parameters for eRC generators and yet the resulting eRC generators still pass stringent empirical tests, which makes them suitable for both software and hardware implementations.
A simple, effective distance and density based outlier detection algorithm

Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.
An integrated corpus-based text mining approach used to process military technical information for facilitating EFL troopers’ linguistic comprehension: US anti-tank missile systems field manual as an example

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.