Reviewerly/exHarmony huggingface dataset:
Formation of digital educational ecosystem for teacher education

This paper presents an example of the formation of a digital educational ecosystem, developed at the Teacher Training Institute of North-Eastern Federal University (NEFU), as an integral socially significant multi-level system, whose participants are students and employees of the educational system of the republic. In general, the digital educational ecosystem is a socio-technical ecosystem, which includes a set of interacting independent educational and social actors, representing students, educational institutions, and a variety of potential customers of educational services. The important thing for the functioning of the digital educational ecosystem is the availability of resources: material and technical, human resources represented by teachers with formed digital competencies, software (digital educational resource), and used modern technologies (blended learning, distance learning technologies, and so on). It is expected that practical activities within the project should create collective digital competencies, will accumulate new intellectual capital, and will develop new mechanisms of collaboration in the interaction of new participants from the perspective of territorial, geographical, national, and other aspects.
Infinite Gaussian Mixture Modeling with an Improved Estimation of the Number of Clusters

Infinite Gaussian mixture modeling (IGMM) is a modeling method that determines all the parameters of a Gaussian mixture model (GMM), including its order. It has been well documented that it is a consistent estimator for probability density functions in the sense that, given enough training data from sufficiently regular probability density functions, it will converge to the shape of the original density curve. It is also known, however, that IGMM provides an inconsistent estimation of the number of clusters. The current paper shows that the nature of this inconsistency is an overestimation, and we pinpoint that this problem is an inherent part of the training algorithm. It stems mostly from a "self-reinforcing feedback'' which is a certain relation between the likelihood function of one of the model hyperparameters (alpha) and the probability of sampling the number of components, that sustain their mutual growth during the Gibbs iterations. We show that this problem can be resolved by using informative priors for alpha and propose a modified training procedure that uses the inverse chi-square for this purpose. The modified algorithm successfully recovers the ``known" order in all the experiments with synthetic data sets. It also demonstrates good results when compared to other methods used to evaluate model order, using real-world databases. Furthermore, the improved performance is attained without undermining the fidelity of estimating the original PDFs and with a significant reduction in computational cost.
Perbandingan Kinerja Ipcop dengan Honeypot dalam Mengamankan Server Linux dari Serangan Hacker

Research to install IPCop and honeypot on server computers and implement IPCop by way of configuration to protect the server from unauthorized parties. The software used in this research is IPCop, Manjaro Linux, Shootpress, Windows 10, and Virtual Box. The results of this study indicate that the implementation of IPCop as a security system on the server can be done and can run well. IPCop is able to protect servers from hacker attacks very well, even using IPCop hackers are not able to ping the server, so hackers cannot find out information about the target server. Honeypot implementation as a security system on a linux server can be done and can run well. Honeypot is able to protect the server from hacker attacks well through its virtual server. So that hackers do not know where the original server is and the hacker will only attack the virtual server from the honeypot and think as if the server is the real server. In this case IPCop is better than the honeypot in the server security system
Group Link Prediction Using Conditional Variational Autoencoder

Though link prediction is a well-studied problem with a large body of solutions, existing methods do not handle the case where the predicted link is between an individual and a group. This limitation prevents link prediction models from being directly applicable to many real-life prediction problems where more than two individuals are involved. Examples of such problems include: finding missing members of a group, soliciting new members to join a group, or recommending a group for a person to join. The central aspect of all of the above problems is predicting linkage between a group and an individual, a task we refer to as group link prediction. In this work we propose an innovative method for solving group link prediction consisting of two complementary models: group-to-individual-link (member-recommendation) and individual-to-group-link (group-recommendation). Both of the proposed models use a conditional variational auto-encoder (CVAE) for solving the respective task. For a given individual (or group), the model learns a conditional probability distribution of a link for each candidate group (or individual) link. We compare our proposed model and a collection of competing models on various real-world datasets and show the superior performance of the proposed model in group link prediction.
The Education and Social Integration of the Human Individual

Abstract This paper presents the importance of the action of the educational phenomenon in the formation / development / transformation and social insertion of the human individual. In this sense, we consider the relationship between the development of the human individual (particularly the child) and its formation throughout life. At the same time, within this research approach we will present in detail the role of the schooling stage for becoming a personality able to integrate properly and harmoniously in various environments / social contexts.
АСПЕКТИ КІБЕРБЕЗПЕКИ ПЛАТФОРМ ДИСТАНЦІЙНОГО НАВЧАННЯ

The subject of study in the article is the platforms used to organize the educational process in the context of distance learning. The following platforms are selected: learning content management systems (open source systems and individual development), Google Classroom service, e-mail, and cloud data storage. The objects of the educational process for which the security state must be provided are shown. Such assets are files (lectures, tasks for laboratory work), a bank of questions (a total set of questions from which tests are created to control knowledge), and grades (for laboratory work and modular control of knowledge). The goal of the work is a comparative analysis of distance learning platforms in the aspect of cybersecurity. The main threats are a violation of the availability and confidentiality of data in the educational process. It is also possible to modify marks due to the exploitation vulnerabilities of the system or gaining access to the functions of the learning content management system administrator. The probability of data being compromised is higher than the probability of modification, as evidenced by information from vulnerability databases about numerous vulnerabilities in learning content management systems. An accessibility violation is a result of a denial of service, that is, the resource on which the necessary files are located becomes inaccessible to users. The method of expert evaluation with variables of fuzzy logic is used. As a result of the analysis, it was revealed that the most flexible and convenient platform is the learning content management system of individual development, at the same time it is the most unsecure among the platforms considered. An open-source learning content management system is a more secure platform due to the presence of a global community that can identify security problems faster than attackers. Using Google Classroom and using email with cloud storage is safer, but these approaches are inferior in usability and functionality. Conclusions. Choosing a distance-learning platform is about finding a compromise between security and convenience in the form of a wide functionality of the system. When deploying a centralized learning content management system, it is important to remember that this system is an object of critical information infrastructure, and the requirements for critical systems must be met for it.
Research on Verification Code Recognition Based on Improved CRNN Network Model

In order to solve the issue of verification code recognition methods ineffective and the addition or absence of characters, this paper proposes a Pro-CRNN based verification code recognition method by improving CRNN model. Firstly, multi-scale asymmetric convolution kernel is used to improve the network during image feature extraction. Secondly, NIN (Network in Network) structure is used to optimize the model to improve the efficiency of the model. Finally, the input size of the network is fixed according to the distribution of data samples and Softamx layer is used to improve the output of the network. In this paper, a large number of comparative experiments have been carried out. Compared with the original CRNN model, the accuracy rate, recall rate and F1 value of the Pro-CRNN model have increased by 0.05, 0.07 and 0.06 respectively. These experimental results can effectively show that the method proposed in this paper has remarkable performance.
Developing prospective teachers’ self-presentation culture in a purposefully orga-nized learning environment at university

Introduction. The authors investigate the problem of developing the culture of self-presentation among future teachers at the stage of university education within the framework of classroom instruction and initiatives of the center of psychological support for students. The purpose of the article is to determine the effectiveness of a set of psychological and educational interventions aimed at organizing and managing the process of developing a self-presentation culture in future teachers based on the author's theoretical ideas about the essence of this phenomenon. Materials and Methods. The study was carried out using a formative psychological and educational experiment. The empirical data obtained were analyzed and generalized. The sample consisted of 216 students majoring in different fields of teacher education: "Primary education", "German language and English language", "English language and German language", and "Physical education". The data obtained were interpreted and subjected to statistical analysis using the Student's t-test. Results. The research findings suggest that the developed and implemented author's set of psychological and educational interventions based on the approach to describing the essence of self-presentation culture ensures its successful formation in future teachers at the stage of university education. Conclusions. The article concludes that the implementation of the set of psychological and educational interventions within classroom instruction and extracurricular activities has contributed to a significant increase in the indicators of students’ self-presentation culture. The authors note that the data obtained can be used in the practice of universities to improve the culture of self-presentation within professional and interpersonal interaction among representatives of related professions in the educational process.
DEVELOPMENT OF INTERNATIONAL EDUCATIONAL PROGRAMS BASED ON THE UNIVERSITY WEBSITE MARKETING UNDER CONDITION OF PANDEMIC

The article is devoted to the study of features and problems of functioning of joint educational programs on the example of the program «double diploma», and also features of advancement of institutions of higher education on the basis of corporate web sites development. Goal. The aim of the article is to try to explore the features of the program «double diploma» under modern conditions, as well as to determine the importance of web sites of higher education institutions as a marketing tool that promotes them, as well as a communication tool under conditions of pandemic. Method. To solve this scientific problem, the method of comparative analysis of sites based on the use of Google Analytics, Google Data Studio, Google Analytics Academy, the method of abstract-logical generalization and the method of analogies were used. Results. The three main models of academic training within the "double diploma" program are identified, as well as the problems associated with its implementation in modern conditions, the importance of search systems and corporate sites, the use of which provides all necessary information for both students and for researchers and teaching staff.
Comparative Study Between Integrator Backstepping and Fuzzy Logic Control Applied to an Electric Powered Wheelchair

The aim of this paper is the control of electric powered wheelchairs (EPW) which was made for people suffering of temporary or permanent disabilities due to illnesses or accidents. The EPW is powered by two Permanent Magnet Synchronous Motors (PMSM) that are characterized by high efficiency, high torque, low noise and robustness; hence the dynamic model of the both EPW-motors is presented in the first. After that, a comparative study is made between two nonlinear command theory; Integrator Backstepping based on the second method of Lyapunov which combine the choice of the energy function with the laws control, and, fuzzy logic introduced to approach human reasoning with the help of an adequate representation of knowledge. To evaluate the performance of the two controls, numerical simulations are presented to show the evolution of electrical and mechanical quantities, the energy consumed and the squared error of the displacement and velocity. However, the reference trajectory used is that generated by the fifth-degree polynomial interpolation, which ensures a regular trajectory that is continuous in positions, velocities and accelerations.
ON THE ISSUE OF TRAINING FUTURE COMPUTER SCIENCE TEACHERS IN COMPUTER

The article discusses of training future computer science teachers in computer networks based on network simulation. Analysis of scientific and pedagogical and educational literature has shown that in the training of computer science teachers, computer networks are studied in the aspect of information simulation of their structures, while simulation the processes of their functioning is not given due attention, despite the possibility of its use in the organization of training. Most educational institutions face organizational, technical and material difficulties when organizing training in computer networks on real equipment. The above-mentioned difficulties in training future computer science teachers in computer networks can be avoided by using network simulation. By network simulation, we mean reproducing a dynamic image of the main network components (network cables, switches, hubs, routers, etc.) and visualization the processes of their configuration and operation on the computer screen.
Enhancing Pretrained Models with Domain Knowledge

Unsupervised pretraining has recently achieved significant success on a wide variety of natural language processing tasks.An important problem that remains understudied is how to effectively adapt such models to specific domains, which is critical for many real-life applications.In this paper, we explore to enhance pretraining by leveraging two typical sources of domain knowledge: unstructured domain-specific text and structured (often human-curated) domain knowledge.We propose models to jointly utilize these different sources of knowledge, which achieve the state-of-the-art results on two tasks of different domains: stock price movement prediction and software bug duplication detection, by adapting publicly available pretrained models obtained on generic domain-free corpora like book corpora and news articles.
Paths to Relation Extraction through Semantic Structure

Syntactic and semantic structure directly reflect relations expressed by the text at hand and are thus very useful for the relation extraction (RE) task.Their symbolic nature allows increased interpretability for end-users and developers, which is particularly appealing in RE.Although they have been somewhat overshadowed recently by the use of end-to-end neural network models and contextualized word embeddings, we show that they may be leveraged as input for neural networks to positive effect.We present two methods for integrating broad-coverage semantic structure (specifically, UCCA) into supervised neural RE models, demonstrating benefits over the use of exclusively syntactic integrations.The first method involves reduction of UCCA into a bilexical structure, while the second leverages a novel technique for encoding semantic DAG structures.Our approach is general and can be used for integrating a wide range of graphbased semantic structures. 1
Improving Model Generalization: A Chinese Named Entity Recognition Case Study

Generalization is an important ability that helps to ensure that a machine learning model can perform well on unseen data.In this paper, we study the effect of data bias on model generalization, using Chinese Named Entity Recognition (NER) as a case study.Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that may compromise model generalization ability.Firstly, the test sets of all the five datasets contain a significant proportion of entities that have been seen in the training sets.These test data are therefore not suitable for evaluating how well a model can handle unseen data.Secondly, all datasets are dominated by a few fat-head entities, i.e., entities appearing with particularly high frequency.As a result, a model might be able to produce high prediction accuracy simply by keyword memorization.To address these data biases, we first refine each test set by excluding seen entities from it, so as to better evaluate a model's generalization ability.Then, we propose a simple yet effective entity rebalancing method to make entities within the same category distributed equally, encouraging a model to leverage both name and context knowledge in the training process.Experimental results demonstrate that the proposed entity resampling method significantly improves a model's ability in detecting unseen entities, especially for company, organization and position categories.
DEVELOPING ENGLISH WRITING WORKSHEET OF NARRATIVE TEXT BASED ON SCIENTIFIC APPROACH FOR THE TENTH GRADE STUDENTS OF SMKN 7 MEDAN

DEVELOPING ENGLISH WRITING WORKSHEET OF NARRATIVE TEXT BASED ON SCIENTIFIC APPROACH FOR THE TENTH GRADE STUDENTS OF SMKN 7 MEDAN *Arif Sandi Budiman **Nora Ronita Dewi **Farida Hanim Saragih ABSTRACT Sandi, Arif. Registration Number: 2142121001Developing English Writing Worksheet of Narrative Text Based on Scientific Approach for The Tenth Grade Students of SMKN 7 Medan. English Educational Program, Medan State University, 2020. By using scientific approach as a learning material referring to curriculum 2013, the method used in this research is research and development. This research consists of five steps: 1) Gathering data and information, 2) Need analysis, 3) Developing the worksheet, 4) Validating the product, 5) Revising product and presented as a final product. The average score for student worksheet from the mean value from all aspects is 4.7. It could be concluded that the worksheet is categorized as “Very Good” since the position of the mean value was in the interval it is concluded that student worksheet by using scientific approach can be used and distributed as a learning material in English narrative text writing worksheet learning for SMKN 7 Medan grade X. Keywords: Scientific Approach, Teaching, Writing.
An Efficient K-Means Clustering Initialization Using Optimization Algorithm

In data mining has a lot of technique for knowledge discovery. In this Clustering method is very well technique for unsupervised learning. It's important objective is to find a high-quality cluster where the distance between clusters are maximal and the distance in the cluster is minimal. K-means algorithm is applied in this paper for its simplicity. It has been widely discussed and applied in pattern recognition and machine learning. However, the K-means algorithm could not guarantee unique clustering results for the same dataset because its initial cluster centers are select randomly. To avoid such issues a new initialization method is proposed in the Improved K-means algorithm with Cuckoo Search algorithm. The proposed method uses different numerical datasets like iris, wine and solar datasets (Ames, Chariton stations). The K-means clustering solutions are comparable with cuckoo search initialization methods using different measures such as Accuracy, Precision and Recall, F1-score, Silhouette value and MSE (Mean Square Error). The experimental solution represents the effectiveness of the proposed method.
KEMAMPUAN MEMPRODUKSI TEKS EKSPOSISI SISWA SEKOLAH MENENGAH ATAS DI KOTA MATARAM DITINJAU DARI ASPEK STRUKTUR TEKS EKSPOSISI

This study aims to describe the ability to produce Non-Literature Genre Text of High School in Mataram City,West Nusa Tenggara .The problem in this research are : How is the ability to produce exposition text by students senior high school based on the structure of the exposition text. The approach used in this study is a qualitative and quantitative approach. Population Data of this research are exposition text.The methode of data collection is done by listening to the method of proficiency involved.Data were analyzed qualitative and quantitative then presented through informal methods. The result of this study indicate that the ability of students Senior High School of Mataram city in producing exposition text is in the category of underprivileged, that is equal to 57,50 under category of sufficiently capable score (67-78). The inability appear also qualitatively in lack of proper use of understanding of based exposition text.
OPTIMIZATION OF ARTIFICIAL NEURAL NETWORKS USING ANT COLONY OPTIMIZATION TO IDENTIFY SIGNATURE IMAGES

Biometrics can be used in identification or recognition systems because it is a method for recognizing humans based on one or more physical features or unique behaviors, one of which is the Signature. In its application, signatures need to be examined (identification) because signatures are often imitated or falsified for various purposes. The signature that will be identified will be taken first by its characteristics by performing the feature extraction process using the Gabor Wavelet Transform (TGW) method. After extracting, the signature identification process is carried out using the Neural Network. In the process of applying the Neural Network method, optimization will be performed using Ant Colony Optimization. The results showed that the identification of signatures made using the Neural Network alone produced an accuracy of 77%, but after optimization using Ant Colony Optimization increased to 83%.
MULTIVARIATE ANALYSIS IN PEDAGOGY ON THE EXAMPLE OF RESEARCH OF A CHILDREN’S PUBLIC ORGANIZATION

The article provides examples of statistical data processing of pedagogical research of children’s public organization using methods of multivariate analysis and identification of a causal relationship.