Reviewerly/exHarmony huggingface dataset:
Social Networks To Arouse Cognitive Interest Of Adolescents In Learning Foreign Languages

The article substantiates the relevance of developing the cognitive interest of students in the educational process, in particular, the cognitive interest of adolescents in learning foreign languages using the capabilities of social networks by teachers.This is proved by the modern trends in social development identified by the theoretical and methodological analysis of scientific literature (globalization and digitalization of society and education, etc.) and is confirmed by the data of the conducted empirical study, which showed the forced need to organize distance learning during the pandemic and the demand for an attractive environment of social networks for teenagers and underestimation of its pedagogical capabilities by teachers.Structural and comparative approaches to the theoretical analysis of studies enabled to consider the socio-psychological functions of social networks in conjunction with the structure of cognitive interest, in which, in addition to the intellectual and volitional components, the emotional one is particularly significant.This complex includes general pedagogical opportunities that increase the effectiveness of various aspects of the educational process, and specific opportunities for developing the cognitive interest of adolescents in learning foreign languages.The generalization of the existing experience of using social networks for various educational purposes enabled to meaningfully reveal this complex by differentiating 14 opportunities of applying them in the educational process into general pedagogical and specific ones (aimed to develop cognitive interest of adolescents in learning foreign languages).
GitHub service as a tool for developing competencies in the field of programming

Introduction. The theoretical and practical significance of the research is justified by the need to develop a programming training model aimed at the formation of competencies in the field of programming using relevant didactic and software tools and the possibil-ity of introducing the developed model into the process of training specialists in the field of programming. The purpose of the work: justification of the expediency of using the GitHub service in the study of programming and analysis of the effectiveness of the service in the framework of developing competencies in the field of programming. Ma-terials and methods. The materials for the study included the Federal State Educational Standard of the Specialty 09.02.07 “Information Systems and Programming”, a concep-tual Basic educational program for training middle-level specialists, an analysis of peda-gogical experience in order to identify effective practices for the formation of students' competencies in the field of programming. Results. Considering the identified ad-vantages and disadvantages of the GitHub service, a programming training model was designed using the GitHub Internet service. The effectiveness of the developed pro-gramming learning model using the GitHub service was investigated experimentally. As a result of the experiment, the correlation coefficient of ranks was calculated according to Spearman's method. The resulting correlation coefficient exceeded the critical value for the significance level of 1%. This made it possible to reject the null hypothesis of similarity and confirm that the studied rank indicators are associated with a positive cor-relation. Consequently, the higher the student's result of practical and control work, the higher his/her levels of competence formation in the field of programming. Conclusion. The model of programming training using the GitHub Internet service really contributes to the development of the level of programming competencies of future programmers, therefore, allows them to prepare more fruitfully for their subsequent professional activities.
SYNTHESIS OF THE SYMBOLOGIES OF MULTICOLOR INTERFERENCE-RESISTANT BAR CODES ON THE BASE OF MULTI-VALUED BCH CODES

Context. The problem of constructing a set of barcode patterns for multicolor barcodes that are resistant to distortions of one or two elements within each pattern is considered.&#x0D; Objective. The goal of the work is ensuring the reliability of the reading of multi-color barcode images.&#x0D; Method. A multicolor barcode pattern has the property of interference immunity if its digital equivalent (vector) is a codeword of a multi-valued (non-binary) correcting code capable to correct errors (distortions of the pattern elements). It is shown that the construction of barcode patterns should be performed on the basis of a multi-valued correcting BCH code capable to correct two errors. A method is proposed for constructing a set of interference-resistant barcode patterns of a given capacity, which ensure reliable reproduction of data when they are read from a carrier. A procedure for encoding data with a multi-valued BCH code based on the generator matrix of the code using operations by the modulo of a prime number has been developed. A new method of constructing the check matrix of the multivalued BCH code based on the vector representation of the elements of the finite field is proposed. A generalized algorithm for generating symbologies of a multi-color barcode with the possibility of correcting double errors in barcode patterns has been developed. The method also makes it possible to build symbology of a given capacity based on shortened BCH codes. A method of reducing the generator and check matrices of a multi-valued full BCH code to obtain a shortened code of a given length is proposed. It is shown that, in addition to correction double errors, multi-valued BCH codes also make it possible to detect errors of higher multiplicity – this property is enhanced when using shortened BCH codes. The method provides for the construction of a family of multicolor noise-immune barcodes.&#x0D; Results. On the basis of the developed software tools, statistical data were obtained that characterize the ability of multi-valued BCH codes to detect and correct errors, and on their basis to design multi-color interference-resistant bar codes.&#x0D; Conclusions. The conducted experiments have confirmed the operability of the proposed algorithmic tools and allow to recommend it for use in practice for developing interference-resistant multi-color barcodes in automatic identification systems.
Improving Explanations of Image Classifiers: Ensembles and Multitask Learning

In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. We address two important limitations of heatmaps. First, they do not correspond to type of explanations typically produced by human experts. Second, recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose using multitask learning to identify diagnostic features in images and averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts and the multitask learning supports the type of explanations produced by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.
Generalized Net Model of Coyote Optimization Algorithm

In the presented paper, the functioning of the coyote optimization algorithm (COA) is described using the apparatus of generalized nets (GNs). The COA is a population-based metaheuristic for optimization inspired by the Canis latrans species. Based on a Universal GN-model of population-based metaheuristics, а GN-model of COA is constructed by setting different characteristic functions of the GN-tokens. The presented GN-model successfully describes the considered metaheuristic algorithm, conducting basic steps and performing an optimal search.
Converting Real Human Avatar to Cartoon Avatar Utilizing CycleGAN

Cartoons are an important art style, which not only has a unique drawing effect but also reflects the character itself, which is gradually loved by people. With the development of image processing technology, people's research on image research is no longer limited to image recognition, target detection, and tracking, but also images In this paper, we use deep learning based image processing to generate cartoon caricatures of human faces. Therefore, this paper investigates the use of deep learning-based methods to learn face features and convert image styles while preserving the original content features, to automatically generate natural cartoon avatars. In this paper, we study a face cartoon generation method based on content invariance. In the task of image style conversion, the content is fused with different style features based on the invariance of content information, to achieve the style conversion.
APPLICATION OF NEXT WORD PREDICTION

Next Word Prediction is additionally called Language Modeling. It is the undertaking of predicting what word comes straightaway. Attempting to make model utilizing nietzsche default text record which will foresee clients sentence after the clients composed 40 letters, the model will comprehend 40 letters and anticipate impending top 10 words utilizing RNN neural organization which will be executed utilizing Tensorflow. Our Aim of creating this model to predict 10 or more then 10 words as fast as possible utilizing minimum time. As RNN is Long Short Time memory it will understand past text and predict the words which may be helpful for the user to frame sentences and this technique uses letter to letter prediction means it predicts letter after letter to create a word.
A Survey on Identification of Deepfake Videos using Artificial Intelligence

In the world of ever-expanding social media platforms, deepfakes are seen as the biggest threat posed by AI. There are many scenarios where realistic deepfakes with face swaps are used to create political pranks, and fake terrorist incidents and people intimidation are easy to imagine. Examples include Brad Pitt and Morgan Freeman fake videos. Advances in computing power have made deep-learning algorithm so powerful that it has become so easy to create human-synthesized indistinguishable videos, commonly known as deepfakes. It's easy to imagine scenarios where this realistic face could be traded for a fake video to do political pain, fake terrorist attacks and intimidation. This work describes a survey on novel deep learning- based techniques that are effective in telling apart phoney and authentic videos produced by AI.
PENINGKATAN MOTIVASI DAN HASIL BELAJAR DENGAN MENGGUNAKAN MEDIA FLANELGRAF PADA SISWA SEKOLAH DASAR

Abstract: This study aims to describe the increase in motivation and student learning outcomes in social studies learning using flannelgraph media. The method used is Classroom Action Research (CAR) which adapts the Kemmis and Mc. Taggart uses a spiral system through stages starting from planning, action, observation, and reflection. The instruments used are observation sheets and tests. From the results of data analysis that using flannelgraph media in social studies learning can increase motivation and learning outcomes. This can be seen from the percentage increase in student motivation in the first cycle 67.84%, the second cycle increased to 78.95%, and in the third cycle increased back to 93.57%. While the increase in learning outcomes can be seen from the completeness of each cycle. The first cycle was completed by 6 people or 31.57%, the second cycle increased to 16 people or 84.21% and in the third cycle it increased to 19 people or 95%. Therefore, the authors suggest that in planning social studies learning, it is better to use creative, innovative, and varied learning media. One of them is by using flannelgraph media.Keywords: Motivation; Learning outcomes; Flannelgraph Media
A Study on Identifying Underwater Species - Challenges and its Limitations

Recently, human being’s curiosity has been expanded from the land to sea to visualize the important aspect of image processing in which different anatomical structure are of underwater images. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Fish species identification is traditionally based on external morphological features, including body shape, pattern of colors, scale size and count, number and relative position of fins, number and type of fin rays, or various relative measurements of body parts. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. A Convolutional Neural Network (CNN) with three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. This survey reviewed various methods and techniques from recent works to enhance the preprocessing methods, features extraction techniques, and classifiers to conduct future research directions and compensate for current research gaps.
GENERAL LEDGER AND REPORTING SYSTEM CYCLE : TRADITIONAL VS DIGITAL ACCOUNTING INFORMATION SYSTEM ERA (IMPLEMENTING IN PHARMACEUTICAL SECTOR AND LOCAL BANK)

The development of the current digital era has changed life, as well as the world of accounting, the manual accounting era has shifted to the computerized era, and now we see the accounting process has been digitized. Banks are one of the most adaptive financial institutions to technological changes, most of which have adopted digitization of accounting in their operations. The computerized process of recording up to ledgers to create financial reports. This situation continues to develop, until now Bank SUMUT has implemented an Online integrated banking system (OLIB's) in its accounting technology so that the ledger/ledger can be accessed in real time. besides that also in other industries such as pharmaceuticals apply the same thing.
PENDAMPINGAN PENERAPAN SISTIM INFORMASI AKUNTANSI MELALUI PARAREL CONNECTED USER PADA UMKM

ABSTRACT&#x0D; Trading business competition in the City of Surabaya is very competitive, therefore, the best service for customers by providing accurate and fast information needed by customers and suppliers. Partners need the application of a sales accounting information system that uses a parallel-connected user in the reporting of financial statements. The partner in this community service is UD Rino who is engaged in the trade sector. The purpose of this community service is to provide training and assistance in the use of sales information systems, so that the resulting financial reports can be used as information for accurate and fast business decision makers. The results of community service information that partners have successfully operated the sales information system installed on computers in their trading businesses. The results also inform that partners can manage financial information and use it as information for business decision-making. The training and mentoring have been successfully carried out by involving the community service team and partners. The basic contribution to the target audience is the existence of tools that can facilitate partner in managing their finance statement more systematically with the Parallel Connect User approach. In addition, this information system is a medium for the formation of new entrepreneurs who can manage business finances properly and correctly.
Implementasi Metode Single Moving Average pada Aplikasi Order Indent Berbasis Web

PT. Hadji Kalla is Toyota's official distributor for South Sulawesi, Central Sulawesi, West Sulawesi and Southeast Sulawesi. In running its business, the sales department often has difficulty meeting the public's need for Toyota brand car products, especially in handling the demand for cars that are indented. Indent orders occur due to increased orders but less car stock. To handle indent orders, it can be done by forecasting future consumer needs using previous data, it is necessary to design a sales forecast application using the single moving average method. The purpose of this study is to implement the single moving average method to reduce the demand or order for indent cars, and to test the system using the white-box method. The prediction results of indent car sales in August 2022 are 654 units with a MAPE value of 17.19%, and normal car predictions in August 2022 are 2.842 units with a MAPE value of 22.9%. The results of testing the system using the black-box method on the 18 modules, it was found that all the forms that were designed as a whole had worked as expected.
Stream as a metapoetic performance

Олег Сергеевич ГореловИвановский государственный университет, г.Иваново, Россия, og-
Structuring ontologies from natural language for collaborative scenario modeling in agri-food systems

Prospective studies require discussing and collaborating with the stakeholders to create scenarios of the possible evolution of the studied value-chain. However, stakeholders do not always use the same words when referring to one idea. Constructing an ontology and homogenizing vocabularies is thus crucial to identify key variables, which serve in the construction of the needed scenarios. Nevertheless, it is a very complex and time-consuming task. In this paper we present the method we used to manually build ontologies adapted to the needs of two complementary system-analysis models (namely the "Godet" and the "MyChoice" models), starting from interviews of the agri-food system's stakeholders. The objective of the paper is to explore whether and how prospective studies may have to gain from complementing the methodologies used (here Godet) with formal approaches from other disciplines, such as knowledge engineering (here MyChoice), which is usually not the case currently.
Problems of integrating digital and humanitarian technologies into the educational and training process of the University of the Ministry of Internal Affairs of Russia

The modern educational process carried out in educational organizations on the territory of the Russian Federation is a continuous purposeful activity of authorized entities for the education and training of persons receiving education. Like any process that obviously involves the development and improvement of all subjects involved in it, continuous education is impossible without the constant integration of innovative technologies and techniques into it that determine its relevance and effectiveness. These include digital and humanitarian technologies that are being actively introduced into the educational and training activities of modern educational institutions of higher education of the Ministry of Internal Affairs of the Russian Federation.&#x0D; The research is devoted to the analysis of the results of the introduction of digital and humanitarian technologies into the educational process of educational institutions of higher education, the identified problems and ways to solve them.
Analisa Predictive Berbasis Supervised Machine Learning Terhadap Kerusakan Peralatan Pembangkit

Predictive maintenance is a treatment for the actual operation of the equipment to optimize the company's operations. The output of predictive program maintenance is data, this treatment includes the type of "condition based maintenance" where changes in the condition of the machine or equipment are detected so that proactive actions are taken before the occurrence of machine damage. The K-nearest Neighbor (K-NN) algorithm is a simple supervised machine learning algorithm that is used to solve problems based on classification and regression. K-NN works by finding the query distance and all database examples, selecting a certain number of examples (K) adjacent to the query, then selecting the frequent label (in classification) or the average label (in regression). The purpose of this algorithm is to classify new object conditions based on attributes and samples from the training database. So that a predictive analysis is carried out on the damage to generating equipment using the machine learning application method of the Nearest Neighbor type or the classification of conditions used to predict the age or condition of an equipment by modeling according to the standard Operation &amp; Maintenance of equipment. By doing predictive analysis, maintenance will lead to condition based maintenance so that the KPI (Key Performance Indicator) of operating performance in the form of increasing values, such as Capacity Factor (CF), Equivalent Availbility Factor (EAF) becomes optimal and prevents the generator from tripping suddenly. which is called sudden outage frequency (SdOF), as well as more efficient maintenance costs.
DEVELOPMENT STRATEGY OF A REGIONAL ECONOMIC UNIVERSITY OF RUSSIA IN THE CONDITIONS OF A POST-INDUSTRIAL SOCIETY (BY THE EXAMPLE OF THE URAL STATE ECONOMIC UNIVERSITY)

Analysis of the current state of higher education system in Russia is carried out. Basing on the analysis results recommendations are proposed for working out an effective strategy for regional economic university development in the realities of a changing society (case of the Ural State University of Economics is considered). It is shown that a prerequisite for the success of a regional university is thoughtful and active interaction with regional authorities, business, and social institutions to provide the development of the region.
Central Kurdish Automatic Speech Recognition using Deep Learning

Automatic Speech Recognition (ASR) as an interesting field of speech processing, is nowadays utilized in real applications which are implemented using various techniques.Amongst them, the artificial neural network is the most popular one.Increasing the performance and making these systems robust to noise are among the current challenges.This paper addresses the development of an ASR system for the Central Kurdish language (CKB) using a transfer learning of Deep Neural Networks (DNN).The combination of Mel-Frequency Cepstral Coefficients (MFCCs) for extracting features of speech signals, Long Short-Term Memory (LSTM) with Connectionist Temporal Classification (CTC) output layer is used to create an Acoustic Model (AM) on the AsoSoft CKB speech dataset.Also, we have used the N-gram language model on the collected large text dataset which includes about 300 million tokens.The text corpus is also used to extract a dynamic lexicon model that contains over 2.5 million CKB words.The obtained results show that the use of a DNN improves the results compared to classical statistics modules.The proposed method achieves a 0.22%-word error rate by combining transfer learning and language model adaptation.This result is superior to the best-reported result for the CKB.