Reviewerly/exHarmony huggingface dataset:
The status of art in the modern digital space

The article examines the status of art in the digital space that is based on the study of technologically determined changes in the three main vectors of the system: production, consumption and circulation of art. In this context, the research task related to the definition of aspects of transformation and diversification of the art system in the formation of digital culture of modern society is relevant and scientifically determined, which ultimately gives us an understanding of the status of art in modern digital space. The multifaceted nature of the proposed research topic foresees the use of approaches of a number of humanities disciplines, first of all art history, culturology, social communications and philosophy. The authors found out how the process of artistic creation, the nature of artistic communication, the means of presentation and promotion of an artistic product, and the mechanisms of its consumption are changing in the conditions of society digitalization.
Data Mining Algorithm Testing For SAND Metaverse Forecasting

Metaverse is a technology that allows us to buy virtual land. In the future life in the real world can be duplicated into the Metaverse to increase efficiency, effectiveness, and a world without being limited by space and time. To buy land in the Metaverse, one can be done by using SAND. SAND is a crypto asset from a game called The Sandbox which functions as a transaction tool where in that game we can buy land and build it for various purposes just like we can store our Non-Fungible Tokens there. Metaverse is a digital business that will promise in the future because it offers easy and fast transactions. This study aims to compare the exact algorithm for making predictions about the SAND cryptocurrency used to buy Metaverse land. 7 algorithms are being compared, namely Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, Generalized Linear Models, Gaussian Process, and K-Nearest Neighbors. The research method used is Knowledge Discovery in Databases. The research results show that the Support Vector Machines Algorithm has the most optimal Root Means Square Error value, root_mean_squared_error: 0.022 +/- 0.062 (micro average: 0.062 +/- 0.000). Based on this comparison, the Support Vector Machines Algorithm is suitable for predicting SAND Metaverse prices.
Application of Absorption Spectra to Study Order of Sequence in Intersecting Printed and Pen Strokes

This work is performed to establish chronological order in crossing strokes between printed stroke and pen strokes. Nine different types of pens including cello maxriter pen (black and blue), pilot pen (red and black), cello pointec pen (black and blue) and cello techno tip pen (red, black and blue) are used to produce pen strokes and for printed stroke Canon LBP 3300 printer is used. In the case of printed stroke, only black color stroke is applied. As a result, samples of both homogeneous and heterogeneous intersecting strokes are prepared. This work is based on the assumption that nature and peak characteristics of absorption spectra from crossing stroke should be similar to that of second stroke. Here, absorption spectra is generated by Video Spectral Comparator-6000 by using light of wavelength ranging from 400 nm to 1000 nm. From this experiment, it is found to be possible to find chronological order for heterogeneous crossing strokes (crossing stroke of different colors) only if printed stroke is over pen stroke. It means, only if print is done over writing strokes from red and blue pen, then order of sequence of writing can be determined whereas in other cases it is not possible.
The Role of Writing Process Components and Cognitive Components in Improving the Quality of Narrative

This study aims to study the relationship between the writing process through an online learning system and students' writing products. This study also studied cognitive factors in the process component and the final product of writing. The study used a factorial analysis design because it aims to determine what factors contribute significantly to narrative writing skills. The students who became the sample were 125 students comprising 60 males and 65 females. The research findings show that the writing process component has a significant contribution to the quality of students' narrative writing. The process components that affect the quality of narrative writing are spelling ability and the number of revisions made by students. Cognitive components that affect students' writing skills are oral language skills, reading skills, selective attention, capitalization, and spelling. Thus, this component of the process of writing narrative text is only correlated with the cognitive abilities of spelling and reading, in contrast to the quality of the final product which is correlated with spoken language skills, attention, and working memory. The implication of this research is to strengthen the cognitive role in narrative writing; teachers must encourage students to be active in spoken language and optimize reading teaching in the learning process.
MOTIVATION OF PRE-SERVICE ENGLISH TEACHERS TO LEARN ENGLISH AS A FOREIGN LANGUAGE IN CHALLENGING CIRCUMSTANCES

The problem of motivation in foreign language learning has always been of great importance. Researchers agree on the non-static, dynamic nature of motivation, investigate the role of the motivational context, and recognize the fact that some aspects of motivation are not internal but are externally determined by the learner’s sociocultural environment. The study aims to investigate the prevalent type of motivation among pre-service English teachers in challenging circumstances. The analysis of motivation sources and ways to protect and maintain motivation to learn English as a foreign language in challenging circumstances constitutes the novelty of the study. 60 fourth-year students (57 females and 3 males) participated in the investigation. The questionnaire adapted from Gardner's Attitude/Motivation Test Battery (AMTB) has been used as a tool to collect quantitative data. The results reveal that the level of motivation is high with a prevalence of instrumental motivation. Qualitative data have been obtained from interviewing students. During the interviews, the students identified the sources of their motivation, critically evaluated their learning situation, and described their learning circumstances as challenging. The study suggests pedagogical implications in line with positive psychology recommendations and the latest research on the protection and maintenance of а motivation to learn English as a foreign language in challenging circumstances. The implications for teachers include developing motivational strategies, maintaining engagement, providing role models, using technology.
Acceptance of the GeoGebra Application in Learning Circle Theorems

The learning area of circle theorems is one of the most difficult topics in geometry, resulting in low student performance. GeoGebra has been shown in studies to enhance learners' proficiency in circle theorems. However, pre-service teachers' use of GeoGebra is not at the expected level in Eswathini. The adoption of an information system is reliant on its acceptance by individuals. However, little is known regarding pre-service teachers' use of GeoGebra to understand circle theorems. The goal of this study was to investigate pre-service teachers' perceptions of GeoGebra's suitability for learning circle theorems. A cross-sectional survey design was used in this investigation, with a total of 187 pre-service instructors as participants. The model explained 74.9% of the variance in the acceptability of GeoGebra for learning circle theorems by Eswatini pre-service teachers. According to the findings, task-technology fit, system quality, system compatibility, perceived ease of use, perceived usefulness, perceived attitude toward, and user satisfaction account for 74.9% of the variance in actual use. The study's findings revealed that rural Eswatini pre-service teachers' reported attitude toward using the mathematics software application GeoGebra for learning circle theorems was the strongest direct predictor of actual use. This research shows that pre-service teachers' views toward technology integration in education should be positive for educational learning applications to be successfully adopted in Eswatini teacher training institutes.
Optimality of T-gate for generating magic resource

Abstract In the stabilizer formalism of fault-tolerant quantum computation, stabilizer states serve as classical objects, while magic states (non-stabilizer states) are a kind of quantum resource (called magic resource) for promoting stabilizer circuits to universal quantum computation. In this framework, the T -gate is widely used as a non-Clifford gate which generates magic resource from stabilizer states. A natural question arises as whether the T -gate is in some sense optimal for generating magic resource. We address this issue by employing an intuitive and computable quantifier of magic based on characteristic functions (Weyl transforms) of quantum states. We demonstrate that the qubit T -gate, as well as its qutrit extension, the qutrit T -gate, are indeed optimal for generating magic resource among the class of diagonal unitary operators. Moreover, up to Clifford equivalence, the T -gate is essentially the only gate having such an optimal property. This reveals some intrinsic optimal features of the T -gate. We further compare the T -gate with general unitary gates for generating magic resource.
Implementation of the Greedy Algorithm for Coloring Graph Based on Four-Color Theorem

Graph theory is an advanced subject of mathematics that can be utilized to resolve issues in science. Graph coloring is one of the most well-known problems for determining the color of the map. The map that will be colored here is one of the 21 sub-districts that cover the Medan regency. In order to color the map, a graph model of the map must first be created. The use of a greedy algorithm is one technique to find a graph's minimal color. The dual graph with 21 vertices and 45 edges will be what we extract from the map. Based on the greedy method that has been used, just four colors—blue, green, red, and yellow—are found as the smallest number of colors, with each city that borders another having a distinct color. The Python computer language is used to get the map coloring results using the greedy technique.
The Dimensions And Student Satisfaction In Clinical Skill Laboratory (CSL) Medical Surgical Nursing With Online Method

Introduction: The pandemic due to the Covid-19 Virus has various impacts on people in the world, including Indonesia. All learning activities are carried out online, including the Clinical Skill Lab (CSL) in Medical-Surgery Nursing at ITEKES Bali. Objective: To prepare students for clinical skills, communication, physical examination, or medical procedures and invasive procedures. In teaching and learning activities, ITEKES Bali Nursing Team Lecturers use video conference media, WhatsApp group, zoom, practical learning videos and others in delivering practical subjects. This policy carried out by ITEKES would certainly affect student satisfaction in learning habits. The level of student satisfaction will be reflected in their dimensions when participating in the academic process. These dimensions include engaged learning, agency and assessment. Methods: this research employed a cross-sectional design. There were 189 students who took CSL at ITEKES Bali participated in this study. The data were analyzed by using chi-square, to analyze the dimensions of students with the level of student satisfaction. Results: The finding showed that there was a significant correlation between the student dimension and the level of student satisfaction (p 0.001) where the dimension of students who disagreed had a 3.6 times chance of being dissatisfied with online learning. Conclusion: The level of student satisfaction can be seen from the dimensions of students when participating in online learning which consists of engaged learning, agency and assessment.
Tackling a Sense of Insecurity: Enhancing Digital Literacy as a Call to Action for Educators During the Pandemic

The chapter describes the perspectives of a mother and educator regarding the effects of COVID-19 within the Russian educational system. I give a brief overview of the prerequisites of the digital transformation of the educational system, which should have guaranteed a fast and seamless transition to online learning at the beginning of the pandemic. However, being a mother of school children, I witnessed the numerous problems which Russian schools faced on the way to urgent digitalisation. In addition, I argue that only a few Russian higher educational institutions have successfully segued into the online mode while a great number of regional universities have encountered difficulties. The chapter shares first-hand experience of an educator working at a small regional university who had to deal with various problems such as the university administration’s delay in making vital decisions, a lack of clear guidance on how to organise online learning, insufficient digital literacy skills, and unstable internet connections. I argue for the improvement of digital literacy as a call to action for educators and students in order to cope with the challenges of the abrupt digitalisation of Russian education. In conclusion, the chapter provides some tips for developing digital literacy skills.
Design and Testing of a Bungee Cord Based Launcher for LSU-02 UAV

The LSU-02 is one of the unmanned aerial vehicles (UAVs) developed by LAPAN (now BRIN). It has a good endurance and flight range, i.e., it can fly for four hours and up to 200 km. However, the UAV needs a good and long runway to do the takeoff and landing operations. In real missions, sometimes it is hard to find the proper runway. Therefore, a method for taking-off without a runway, namely using a launcher, is required. The two most frequently used launcher systems are pneumatic launcher and bungee cord launcher. However, based on our experience using the launcher for LSU-03 UAV, a pneumatic launcher is considered less practical due to its complex and heavy construction. For the LSU-02 to be able to carry out missions in remote areas, a simpler and lightweight launcher is needed. Therefore the bungee cord-based launcher was chosen. The initial requirement for the launcher is that the launcher should able to push the LSU-02 with a maximum takeoff mass of 15 kg put on a 7 kg cradle (total mass 22 kg) and reach the launch speed of 15.2 m/s at the end of launching track. The simulation result shows that the launcher needs a time of 0.28 s to achieve a velocity of 15.2 m/s. Meanwhile, in 0.28 s, the UAV travel distance is 2.55 m. This is the minimum effective length required by the launcher. The real launcher was built with an effective length of 2.7 m. The launcher was tested for launching the LSU-02 with the UAV takeoff mass of 14.4 kg and the cradle mass of 7.5 kg (total of 21.9 kg). It was able to successfully launch the LSU-02 in 0.27 s with a travel distance on the launching rail of 2.5 m.
Estimation and application of matrix eigenvalues based on deep neural network

Abstract In today’s era of rapid development in science and technology, the development of digital technology has increasingly higher requirements for data processing functions. The matrix signal commonly used in engineering applications also puts forward higher requirements for processing speed. The eigenvalues of the matrix represent many characteristics of the matrix. Its mathematical meaning represents the expansion of the inherent vector, and its physical meaning represents the spectrum of vibration. The eigenvalue of a matrix is the focus of matrix theory. The problem of matrix eigenvalues is widely used in many research fields such as physics, chemistry, and biology. A neural network is a neuron model constructed by imitating biological neural networks. Since it was proposed, the application research of its typical models, such as recurrent neural networks and cellular neural networks, has become a new hot spot. With the emergence of deep neural network theory, scholars continue to combine deep neural networks to calculate matrix eigenvalues. This article aims to study the estimation and application of matrix eigenvalues based on deep neural networks. This article introduces the related methods of matrix eigenvalue estimation based on deep neural networks, and also designs experiments to compare the time of matrix eigenvalue estimation methods based on deep neural networks and traditional algorithms. It was found that under the serial algorithm, the algorithm based on the deep neural network reduced the calculation time by about 7% compared with the traditional algorithm, and under the parallel algorithm, the calculation time was reduced by about 17%. Experiments are also designed to calculate matrix eigenvalues with Obj and recurrent neural networks (RNNS) models, which proves that the Oja algorithm is only suitable for calculating the maximum eigenvalues of non-negative matrices, while RNNS is commonly used in general models.
The phenomenon of science in the challenges of transdisciplinarity: the search for paradigmatic responses to social turbulence

The article discusses the features of the transdisciplinarity of science in actively developing areas of science. The author emphasizes the dīerence between transdisciplinarity and interdisciplinarity and polydisciplinarity. In conditions of social turbulence, when the development of science and technology is ahead of changes in society, it becomes necessary to compensate for the lag with paradigm decisions. Since the Enlightenment and the positive science of Auguste Comte, science as a special kind of human intellectual activity aimed at achieving new knowledge and its dissemination has undergone signiŤcant changes. The subject of research has become so reduced that instead of clear boundaries between sciences, points of intersection appear. Future research, cognitive science, artiŤcial intelligence research, and others are considered as actively developing modern transdisciplinary studies. Jean Piaget’s contribution to the development of semantics and pragmatics of the term “transdisciplinarity” is demonstrated. In 1998, UNESCO oыcially documented the importance of addressing the issue of transdisciplinarity. In 2013, in the United States, the American Academy of Science and Culture, ARISE-2 published a report, which testiŤed to the necessity to make a transition in American science from interdisciplinarity to transdisciplinarity.
A Study on Developing QBL-MODEL in the Debate Classroom for Liberal Education at University

The purpose of this study is to develop a teaching-learning model using argumentative questions as an effective method for a discussion class. The researcher guided the students to learn the concept of argument as an essential part while learning the ＜Reading Classics of Great Literature＞ course at Pusan National University. This learning process can improve the student’s ability to analyze classical works logically, and to identify correct arguments and errors in discussion activities. As a result learners can realize that the argumentation of a specific topic can be opened in another way through analysis of classical works and discussion activities based on the concept of argumentation. In other words, the learner discover new question to argumentation. This researcher thinks that the learner’s ability to ask such questions eventually approaches the pedagogical significance of promoting true creativity.
A Study on Foreign Students’ Perception of Metaverse-Based Korean Language Course as General Education

As basic research for establishing the metaverse-based Korean language course as general education, this study aimed to seek concrete direction for the development of curriculum by conducting a survey on learners’ perception of the teaching/learning of Korean language using the metaverse, and then to apply the results to actual classes on a trial basis. As of late, the metaverse is being actively applied to language education as a new educational medium that could improve the immersion and understanding of learning among students through interaction with a high level of presence in the virtual world. Thus, this thesis aimed to understand the familiarity, preference, and expected effect of the metaverse targeting Korean language learners. From the results of conducting a perception survey targeting a total of 534 foreign students of A university located in Seoul, we learned that the students showed a low degree of experience with and understanding of the metaverse, but that they professed a high level of interest in the establishment of a Korean language course using this new educational medium. In particular, they expressed the highest expectation for the establishment of a course connected with speaking among their communication skills. Thus, this study provided a pilot class using the metaverse to the divided speaking class of A university, and then conducted a survey on learners’ responses. Concerning the operation of the class, the learners preferred offline or an on/offline combined method instead of fully using the metaverse online. Also, they wanted to learn in a kind of metaverse that realistically reflected a familiar school space or some kind of Korean symbolic space.
Cyberattacks Detection in IoMT using Machine Learning Techniques

Information and Communication Technology (ICT) has changed the computing paradigm. Various new channels for communication are created through these developments, and the Internet of Things (IoT) is one of them. Internet of Medical Things (IoMT) is a part of IoT in which medical devices are connected through a network. IoMT has resolved many traditional health-related problems and has some security concerns. This article uses three Machine Learning algorithms, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), to detect cyberattacks. Machine Learning models are best for performing cyberattack detection. Proposed Machine Learning models are evaluated on the WUSTL EHMS 2020 dataset, which consists of main in-themiddle, data injection, and spoofing attacks. The evaluation of the result analysis shows that the proposed Machine Learning models outperformed existing techniques.
An Enhanced Online Boosting Ensemble Classification Technique to Deal with Data Drift

Over the last two decades, big data analytics has become a requirement in the research industry. Stream data mining is essential in many areas because data is generated in the form of streams in a wide variety of online applications. Along with the size and speed of the data stream, concept drift is a difficult issue to handle. This paper proposes an Enhanced Boosting-like Online Learning Ensemble Method based on a heuristic modification to the Boosting-like Online Learning Ensemble (BOLE). This algorithm has been improved by implementing a data instance that retains the previous state policy. During the boosting phase of this modified algorithm, the selection and voting strategy for an instance is advanced. Extensive experimental results on a variety of real-world and synthetic datasets show that the proposed method adequately addresses the drift detection problem. It has outperformed several state-of-the-art boosting-based ensembles dedicated to data stream mining (statistically). The proposed method improved overall accuracy by 1.30 percent to 14.45 percent when compared to other boosting-based ensembles on concept drifted datasets.