Reviewerly/exHarmony huggingface dataset:
IMPACT OF STEREOTYPES ON DIGITAL TRANSFORMATION OF RUSSIAN EDUCATION

Digital transformation takes place almost in all spheres of life. But passage of some spheres to online format evokes users’ resistance. For example, online education of children studying in primary school in Russia gives rise to many questions among teachers and parents.Digital transformation accelerates many processes, to which human psychology can’t adjust fast enough (and humans need more time to reorganize). Even taking into account the obvious benefits of remote learning, there are those who are ruled by their previous experience, which can be already out-dated in the current situation and for demands of present time and restrictions caused by Covid-19. Modern systems of communication, chats, social nets help parents to interact, exchange opinions. But sometimes the communication has a «viral» character, when the unsubstantiated doubts of one parent give rise to anxiety in other parents. In the beginning of October the news from Mayor of Moscow appeared. It said that apart from other measures, the introduction of express polymerase chain reaction testing in schools for maintaining their normal work is possible. This news was actively discussed in forums and chats with huge worries and assumptions. Many parents of school children speak up against making polymerase chain reaction testing for Covid-19 in schools. This prompted the growth of anxiety and the number of citizens’ appeals to the Moscow Government in more than 10 times. Author has analysed citizen petitions in reports of the Public Opinion Monitoring Centre of Moscow government, arguments in forums, and online polls against remote learning and doing polymerase chain reaction testing in schools. Thus, citizens suppose that the implementation of distant learning threatens the physical and psychological health of school children. Among arguments there is also the one stating that distant learning forces children to spend more time in front of a computer or gadget, reduces physical activity of children and limits interpersonal communication with age-mates.The stereotypes of children’s parents about studying at school have been defined. Among others, there are stereotypes related to the effectiveness of real-life communication with the teacher, option of selecting a teacher, importance of school reputation and its high rating, effectiveness of Russian programs of education, according to which, the parents themselves were receiving their education without using gadgets and the Internet. Also, there’s a stereotype stating that the school — is not only the place for getting knowledge, but also for communication with age-mates.Author assumes that introduction of measures to ensure physical and psychological health of children studying online, and also increasing parents' awareness regarding these measures and role of remote learning in the current context of all-round digital transformation and Covid-19, will help parents feel less anxious about distant studies of their children and to successfully adjust to new conditions.
Appraisal of Flood Prone Area Management Using Artificial Intelligence Methods in Jakarta Basin, Indonesia

Jakarta often experiences floods every rainy season. Some major floods that crippled human activities have occurred in 2002, 2007, 2013, and 2020. The factors affecting the floods are the lowland basin and land subsidence of Jakarta. The analysis used in this study is geographic information systems (GIS) tools with artificial intelligence (AI) methods to produce flood distribution models. Also, hydrogeochemical analysis is conducted to determine seawater intrusion and its correlation with land subsidence that causes floods in Jakarta. The AI methods show that the Genetic Algorithm Rule-set Production, GARP (AUC-ROC = 0.90) has a greater value than the Quick Unbiased Statistical Tree, QUEST (AUC-ROC = 0,79). The results show that GARP is the best method to produce the model distribution of flood hazard points which has been dominating in Northern Jakarta. The correlation between the results of the flood distribution model and the seawater intrusion shows that the condition of land subsidence rate in Jakarta is very massive. The output of this research serves as the basis for determining a better spatial plan for Jakarta in the future.
DIGITAL EDUCATIONAL RESOURCES AS A PART OF INNOVATIVE PRACTICES IN MATHEMATICAL ENGINEERING EDUCATION

The field of resources, especially digital, has developed the most in the last decade - and so is naturally an area that provides space for innovative approaches to teaching and learning. The COVID-19 pandemic has contributed to an even greater use of digital resources and online training courses. The digital resources and technologies offer several options for solving current didactic problems generated by the decline or variability of mathematical knowledge and skills among students starting their studies at technical universities. The paper will focus on answering the questions that arise these days in didactic of university mathematics: What teaching resources (cognitive, material, digital, social) are used in current teaching practice and what resources appear to be suitable for innovative approaches to teaching and learning engineering mathematics? Innovative teaching and learning resources (especially digital resources) create a new, so-called a digital learning space to which access to teaching and learning must be adapted not only by the teacher but also by the student. This pandemic period is also demanding in terms of development of the student's personal competencies: the emphasis is on self-education, on self-discipline in the study. In order to prevent the failure of beginning students, the authors present possible ways in which teachers can help beginning students to develop their own learning trajectories, their personal study paths in engineering mathematics. They propose a teaching and learning procedure on an e-learning platform (Cisco Webex, Moodle) for a selected mathematical subject as well as a procedure for checking and verifying mastery of a given topic.
Cross-GAN: Unsupervised Image-to-Image Translation

In recent years, unsupervised image-to-image translation is a topic in computer graphics. This paper proposes a new architecture to translate the image through the idea of sharing and decoupling, termed as Cross-GAN, which can better extract the style in the process of translation, and does not change the original outline and background. We improve the cycle consistency loss and reconstruction loss for the unique properties of the model to make the image more realistic. Because GAN[1] only generates the target style image by inputting the content image, and the information of the target domain image is not used in the training process. We input style images and content images through cross-input. The style images play a guiding role in the generator, and the effect of image are better by fusing style features and content features. Due to the fact that the image of the target domain is input, it is easy to produce the phenomenon of over-fitting. In order to prevent over-fitting training, we improve the cycle consistency loss and reuse the input encoder to make the model more compact.
Smart Civic Issue Reporting System

Reporting road and citizen issues was no longer an easy process for citizens. Reporting problems such as road damage, street cleaning, dents, trash can overflows, street lightpost damage, or anything under the supervision of a municipality requires lengthy steps and procedures. There is still no guarantee that this is the case. Reported complaints will be processed by the competent authority. If daily 1000 complaints get registered then Because of this, most complaints are unheard, unanswered, and unresolved because the company is too large to handle small complaints from one person. To facilitate this grievance, we will implement an Smart Android application that citizens can use to report infrastructure issues in their city to relevant authorities. Therefore, whenever people encounter urban infrastructure, transportation, environmental cleanliness, or disruptions in their daily lives, they can share, discuss, and resolve issues with relevant authorities through this online Android portal. Complaints are registered via the mobile application. A hybrid CNN-RNN image processing algorithm &amp; using the SVM-NLP model detects the severity of registered problems, so based on that higher severe issues get addressed first. The GPS (Global Positioning System) sensor on smart mobile devices can pinpoint the exact location of the problem zone and use the camera to record the problem area as visual evidence. The system then generates a form containing all the data and location and visual evidence entered by the user and sends it to a central server. The central server notifies the relevant agencies.
MODERN TRENDS IN THE ORGANIZATION OF MUSIC LISTENING ACTIVITIES IN MUSIC LESSONS

R. Samigullina1, Z. Yavgildina 2, L. Borodovskaya 2, I. Kamalova 1 1Kazan (Volga Region) Federal University (RUSSIAN FEDERATION) 2Kazan State Institute of Culture (RUSSIAN FEDERATION)
Potensi Keberhasilan Bakal Calon Legislatif Menggunakan Algoritma K-Nearest Neighbors

A candidate for legislative member is someone who is sent to represent his political party in a legislative part which will be directly elected by the people through general elections. General election is a means of implementation in a democratic system to elect representatives of the people in the people's representative institutions directly, publicly, freely, confidentially, honestly and fairly. Legislative elections are a five-year democratic agenda that is held simultaneously, one of which is to elect members of the Provincial DPRD. Provincial DPRD is a regional people's representative institution that is domiciled as an element of provincial government administration. The purpose of this study is to understand the application of the K-Nearest Neighbor Algorithm, to design a system that can determine the success of legislative candidates in the general election in the Jambi provincial constituency using Data Mining with the K-Nearest Neighbor Algorithm, to test using Rapidminer supporting software, to test the system. Data Mining that has been built uses test data, and analyzes the output generated from the Data Mining system. The appropriate method for this research is Data Mining. Data mining is a data processing method to find patterns in data, the k-nearest neighbor algorithm is one of the classification algorithms on objects based on learning data that is closest to the object. The data used and processed in this research is the data of the elected DPRD members for the previous period, namely the data of the 2009-2014 DPRD members, the 2014-2019 DPRD members' data, and the 2019-2024 DPRD members' data. The results of this study are a system that can provide recommendations for prospective Provincial DPRD candidates for the 2024-2029 period in choosing electoral districts that match the profile and have a greater potential for victory during the General Election based on profile data of elected DPRD members in the previous period.
Research on Travel Reimbursement Behavior Management Based on Deep Learning in Financial Sharing Mode

The standardization, transformation and upgrading of financial management plays an important supporting role in promoting the standardized management and healthy operation of corporate expense reimbursement behaviors. This paper starts with the behavioral portrait of enterprise personnel travel expense reimbursement. Based on the problems of lengthy process and complex financial accounting in most reimbursement behaviors at this stage, an efficient and efficient expense reimbursement processing method is proposed, that is, reimbursement through collection Information images, using the convolutional neural network algorithm under deep learning, to detect target images and identify valid information, and rely on the financial shared service center's screening function of expense reimbursement behavior portraits under big data, the integrity of expense reimbursement data. Compliance is judged, thus forming a complete set of standardized and procedural personal self-service reimbursement model, which optimizes the management of enterprise expense reimbursement behavior, avoids subsequent losses and resource waste caused by human risks, and improves enterprise operation efficiency and expenses. Control has certain value and reference significance.
Implementation of YouTube in Teaching Writing: Perception of Malaysian Primary School English Teachers

Technology plays an essential part in people's daily lives and may be efficiently employed in education for a variety of purposes. YouTube is one internet resource that may be used in traditional English classes. It's also regarded as a valuable source of internet content for teaching and learning. This study examines how Malaysian primary school teachers feel about using YouTube videos to teach writing. English writing abilities are among the most important skills that students at all levels must develop. However, it was discovered that just a few researches have looked at the perceptions of primary school teachers, which is critical because teachers work with early experience learners. As a result, the purpose of this study is to find out how the primary school teachers perceive YouTube videos as a tool for teaching writing as well as the challenges they faced while utilizing it. As to garner the data, 32 Malaysian primary school teachers from National Schools (Sekolah Kebangsaan, SK) and National-Type Schools (Sekolah Jenis Kebangsaan, SJK) were given survey questionnaires that included a 5-point Likert scale and open-ended questions The acquired data is interpreted using a quantitative and qualitative research technique. To display the data acquired from the findings, descriptive statistics are employed. The outcomes of this study showed that teachers have favourable views of teaching writing via YouTube. The major issues were a bad Internet connection and unsuitable video material.
THE CHALLENGES OF UNDERSTANDING THE EMOTIVE CONCEPTS BY STUDENTS OF ENGLISH

E. Antonova , S. KhanbalaevaMoscow State Institute of International Relations (University) (RUSSIAN FEDERATION)
Homological assessment of data representations

In this paper* we discuss the concept of the Cross-Barcode (P,Q) introduced and studied in the recent work [1]. In particular, we describe the emergence of this concept from the combinatorics of matrices of the pairwise distances between the two data representations. We also illustrate the applications of the Cross-Barcode (P,Q) to the evaluation of disentanglement in data representations. Experiments are carried out with the dSprites dataset from computer vision.
Barcode Image Identification Based on Maximum a Posterior Probability

This paper presents a new method robust to noise for vision-based barcode recognition. We propose an effective and stable barcode recognition technique based on the maximum a posterior probability of observation signals. For robust recognition of barcode image mixed with noise signals, first, the center and angle of the barcode area are extracted without rotation, and second, the barcode recognition techniques using the likelihoods based on multiple barcode scan lines is proposed. This proposed method shows superior performance as a result of its application to various barcode images.
UNIVERSITY DIGITAL EDUCATION ENVIRONMENT FOR THE TRAINING OF FUTURE CHEMISTRY TEACHERS

S. Gilmanshina 1, Z. Zholdasbayeva2, G. Abyzbekova2 1Kazan (Volga region) Federal University (RUSSIAN FEDERATION) 2Korkyt Ata Kyzylorda University (KAZAKHSTAN)
Face recognition methods analysis

Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one userâ€™s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMM) which are explained and analyzed. Also algorithms like; Eigen face, Fisher face and Local Binary Pattern Histogram (LBPH) which are simplest and most accurate methods are implemented in this project for AT&amp;T dataset to recognize the most similar face to other faces in this data set. To this end these algorithms are explained and advantages and disadvantages of each one are analyzed as well. Consequently, the best method is selected with comparison between the results of face reconstruction by Engine face, Fisher face and Local binary pattern histogram methods. In this project Eigen face method has best result. It should be noted that for implementing face recognition algorithms color map methods are used to distinguish the facial features more precisely. In this work Rainbow color map in Eigen Face algorithm and HSV color map in Fisher Face algorithm are utilized and results shows that HSV color map is more accurate than rainbow color map.
RESEARCH AND EDUCATION OF PROPER NAMES TRANSLATION AT THE HUNGARIAN INSTITUTE IN NITRA

The study reports on the research and education of proper names translation at the Institute of Hungarian Linguistics and Literary Science of Constantine the Philosopher University in Nitra. It points out that not only lecturers but also university students are involved in translational onomastic research.The study of the translation of the proper names is interdisciplinary and relies on the outputs of onomastics and translation studies. Proper names are translated from the source language into the target language; the translator must be competent in both the source and target culture. When translating names into another language, the applicable procedures can range from a complete lack of translation to a modification that substantially transforms the name form.The study examines the translation techniques observed when translating personal names, toponyms (place and street names), and institution names (names of schools, offices, museums, libraries, cultural and health centres) from the source language into the target language in bilingual captions that occur in a bilingual environment. Using examples from a database collected in Hungarian-inhabited settlements in Slovakia, it illustrates the applied translation strategies to transfer proper names from the source language to the target language: 1. transference – lack of translation, leaving the original form of the name; 2. transcription – transcribing the spelling of the source name to the target language; 3. substitution – replacing the name to be translated with the conventional target language equivalent; 4. semantic literal or partial translation – translating the source name or its part with the semantically compatible target language element; 5. full or partial modification – replacing the name to be translated with another name in the target language; changing the proper name to a common name or its paraphrasing; partial modification of the information content of the source language name in the target language by adding (explicitation) or omitting (implicitation) the names.
A Review Paper on Computer Vision and Image Processing

Computer vision has been investigated from a variety of angles. It progresses from raw data capture to approaches and concepts that combine digital image processing, pattern recognition, machine learning, and artificial intelligence. Graphics on a computer Many academics have been drawn to the widespread use. To work with a wide range of disciplines and fields This document provides an overview of a review of modern technology and an explanation of theoretical concepts. The main focus of computer vision research has been on picture recognition. Processing through several aspects of their field application Computer Scholars can use vision to evaluate photos and video to get the information they need. Information, comprehend event or description information, and Pattern is lovely. It made advantage of the multi-range application mechanism. Domain with a lot of data to analyse This paper adds to a growing body of work. Growth of reviews in the fields of computer vision and image processing , as well as related research.
Can satellite images provide supervision for cloud systems characterization?

&amp;lt;p&amp;gt;With ever-increasing resolution, geostationary satellites are able to reveal the complex structure and organization of clouds. How cloud systems organize is important for the local climate and strongly connects to the Earth's response to warming through cloud system feedback.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Motivated by recent developments in computer vision for pattern analysis of uncurated images, our work aims to understand the organization of cloud systems based on high-resolution cloud optical depth images. We are exploiting the self-learning capability of a deep neural network to classify satellite images into different subgroups based on the distribution pattern of the cloud systems.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Unlike most studies, our neural network is trained over the central European domain, which is characterized by strong land surface type and topography variations. The satellite data is post-processed and retrieved at a higher spatio-temporal resolution (2 km, 5 min), enhanced by 66% compared to the current standard, equivalent to the future Meteosat third-generation satellite, which will be launched soon.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We show how recent advances in deep learning networks are used to understand clouds' physical properties in temporal and spatial scales. In a purely data-driven approach, we avoid the noise and bias obtained from human labeling, and with proper scalable techniques, it takes 0.86 ms and 2.13 ms to label an image at two different spatial configurations. We demonstrate explainable artificial intelligence (XAI), which helps gain trust for the neural network's performance.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To generalize the results, a thorough quantified evaluation is done on two spatial domains and two-pixel configurations (128x128, 64x64). We examine the uncertainty associated with distinct machine-detected cloud-pattern categories. For this, the learned features of the satellite images are extracted from the trained neural network and fed to an independent hierarchical - agglomerative algorithm. Therefore the work also explores the uncertainties associated with the automatic machine-detected patterns and how they vary with different cloud classification types.&amp;lt;/p&amp;gt;
Quantum Computing and Its Application

Quantum computing is different from classical computing. It is based on quantum mechanics principles, utilizing the attributes such as entanglement and superposition. The significant difference between a quantum computer and a classical computer is the ability to process information astonishingly faster. This is possible due to the quantum entanglement concept. Interests in the quantum entanglement concept have been exponentially growing over the years. In the last decade, significant progress in implementations of quantum computation in quantum information has been achieved. This study selected secondary resource indexed literature from several databases with specific keywords in almost a century. In this paper, quantum computing and its application is presented along with the essence of quantum entanglement role in quantum computing. This study will provide a general understanding of quantum computing and its application and quantum entanglement.