Reviewerly/exHarmony huggingface dataset:
School Communities on VKontakte as Part of the Media Learning Environment: Teachers’ Media Competences

The article addresses the issue of media competences of school teachers responsible for managing their schools' social media accounts, communities, etc.The new types of communication, instant messengers, social networking websites and online learning platforms place modern education in a very special and sensitive environment with a shortened distance between the communicators, danger of personal or classroom information exposure and risks of damaging the reputation of the educational institution.Consequently, school principals and teachers face a problem of acquiring necessary competences in order to advantageously use their school online communities.We surveyed school employees responsible for managing their schools' virtual accounts and revealed that school teachers do not professionalize their management of social media networks: they neither include it in the sphere of pedagogical competence nor correlate it with journalism.Besides, they feel lack of motivation due to little support from the school administration.We claim that the duty to manage schools' social media pages should be perceived in the context of the media competence theory and include the ability to collect and process information, produce multimedia content, manage creative teams and form social media communities.This activity should be monitored by school principals and duly rewarded.
A WebRTC Signaling Method Among Web Browsers on a Web-Based Virtual World

This paper proposes a signaling method of WebRTC among web browsers without a web server. The goal of our study is the constructing a web-based virtual world. The virtual world needs the sharing the information among the clients. Using WebRTC, our system constructs a web browsers network. In this network, each web browser connects to the other web browsers directly. Therefore, each web browser can share the information, which does not through the web server. However, network topology is decided by the web server because only the web server understands all of the web browser information. When a web browser leaves the network, the web server decides the new network topology and each web browser connects to some new web browser. In this situation, the web server needs the calculation of the network topology every time a web browser leave the network, which leads the increasing the work load of the web server. In this study, we proposes a signaling method without the web server. This method makes the web browsers trade the end-point information directly. As a result, our system can decrease the web server work load.
Question Classification for Albanian Language: An Annotated Corpus and Classification Models

Question classification has an important role in Question Answering systems by determining the expectation for the correct answer to the question. The question classification system aims to determine the question type and based on it can be given the correct answer for the question. This paper presents the first questions annotated corpus in Albanian language using a six-classes tag-set. The annotated corpus is used to train and test three machine learning algorithms for question classification task using a variety of metrics. Furthermore, we conclude our work by analyzing and comparing the performance of the implemented models and giving direction for future improvement of the models. In terms of accuracy, the best performant algorithm is SVM.
Side channel analysis based on feature fusion network

Various physical information can be leaked while the encryption algorithm is running in the device. Side-channel analysis exploits these leakages to recover keys. Due to the sensitivity of deep learning to the data features, the efficiency and accuracy of side channel analysis are effectively improved with the application of deep learning algorithms. However, a considerable part of existing reserches are based on traditional neural networks. The effectiveness of key recovery is improved by increasing the size of the network. However, the computational complexity of the algorithm increases accordingly. Problems such as overfitting, low training efficiency, and low feature extraction ability also occur. In this paper, we construct an improved lightweight convolutional neural network based on the feature fusion network. The new network and the traditional neural networks are respectively applied to the side-channel analysis for comparative experiments. The results show that the new network has faster convergence, better robustness and higher accuracy. No overfitting has occurred. A heatmap visualization method was introduced for analysis. The new network has higher heat value and more concentration in the key interval. Side-channel analysis based on feature fusion network has better performance, compared with the ones based on traditional neural networks.
Versatile Education Domains of Schools, Colleges and Universities

Day to day life has become changed after the wide-spreading adaptability of AI Researchers and AI scientists are making their unstinting efforts to make human life more comfortable and technologically advanced by using AI tools and techniques. Sustainable education is an approach of aiming to establish the sustainability act in students, and communicate the values among schools, colleges and communities the values and motivations future-in one’s own life, in their communities, and on a worldwide platform.Along with I.C.T. students, their fresher professionals also need an intelligent system that can understand each professional’s learning needs and help them excel in their skills.
Multi-key FHE without CRS from RLWE

&lt;p&gt;Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys. However, most MFHE schemes require a Common Random\slash Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext. Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext. Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently.&lt;/p&gt; &lt;p&gt;In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without CRS. To this end, we remove the CRS by designing a new relinearization algorithm. Like previous MFHE schemes, our RLWE-based MFHE without CRS has a simple 1-round threshold decryption, which implies a $3$-round secure MPC protocol in the plain model from the RLWE assumption.&lt;/p&gt;
Sim-Radon-based shape descriptor for deformable pattern recognition

It is a challenging but important task to design an abstract and robust shape descriptor for deformable shapes in a highly discriminative way. However, most of the existing low-level descriptors are designed using hand-crafted features and are sensitive to local variations and large deformations. To solve this problem, in this Letter, we propose a shape descriptor based on the Radon transform and SimNet for shape recognition. It effectively overcomes structural barriers, such as rigid or non-rigid transformations, irregular topologies between shape features, and similarity learning. Specifically, we use the Radon features of the objects as the input to the network and calculate the similarity using SimNet. Deformation of objects may affect Radon feature maps, and SimNet can overcome such effects to reduce the loss of information. Compared with SimNet, which consumes the original images as the input, our method shows higher performance.
Comparison of On-site Assessment of Testing and Inspection Laboratories

The quality control of testing and inspection laboratories operated in Korea, the United States, and Canada was compared. The basic framework of on-site evaluation operated by each country was similar in terms of evaluation procedure, document evaluation, issuance of verification certificate, and follow-up management. For conducting on-site evaluation and granting the certificate, the preparation materials and evaluation processes are subdivided in Korea, while the dates of granting the certificate are restricted to four times a year in the United States. After receiving accreditation as a testing/inspection laboratories for the first time in Canada, follow-up management is divided into re-evaluation, verification evaluation, and monitoring evaluation.
AT-CRF: A Chinese Reading Comprehension Algorithm Based on Attention Mechanism and Conditional Random Fields

Machine reading comprehension (MRC) is an important research topic in the field of Natural Language Processing (NLP). However, traditional MRC models often face challenges of information loss, lack of capability to retain long-distance dependence, and inability to deal with unanswerable questions where answers are not available in given texts. In this paper, a Chinese reading comprehension algorithm, called the Attention and Conditional Random Filed (AT-CRF) Reader, is proposed to address the above challenges. Firstly, RoBERTa, a pre-trained language model, is introduced to obtain the embedding representations of input. Then, a depthwise separable convolution neural network and attention mechanisms are used to replace the recurrent neural network for encoding. Next, the attention flow and self-attention mechanisms are used to obtain the context–query internal relation. Finally, the conditional random field is used to handle unanswerable questions and predict the correct answer. Experiments were conducted on the two Chinese machine reading comprehension datasets, CMRC2018 and DuReader-checklist, and the results showed that, compared with the baseline model, the F1 scores achieved by our AT-CRF Reader model has improved by 2.65% and 2.68%, and the EM values increased by 4.45% and 3.88%.
A Study on Official Development Assistance based on Big Data

Korea(R.O.K) is actively participating in the so-called concessional Overseas Official Development Assistance (ODA) project for developing countries. In this regard, this study examines the experience of constructing and utilizing Korea's commercial district information system based on various big data, and checks the experience under the 'Korean ODA model in the ICT sector' related to the 'ODA based on big data' of the OECD.&#x0D; Through this, this study emphasizes that the development of new ICT businesses and the expansion of added value are an opportunity to support the low-income class in recipient countries and to grow into emerging and developing countries, and also suggests that it can be a useful opportunity to enhance competitiveness. As an example for this, it examines the possibility of establishing a system for providing various information that can increase the added value of the tourism business in Kyrgyzstan.
Super-Resolution Transmission Electron Microscope Image of Nanomaterials Using Deep Learning

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis.1169 paired images with 256  256 pixels (high resolution: HR) from TEM measurements and 32  32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models.The TEM images were related to DyVO 4 nanomaterials synthesized by hydrothermal methods.Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models.First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision.In the SR image at low magnification, the shape of the nanomaterial improved.However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials.Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map.In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased.In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications.The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.
MOBILE ASSISTED LANGUAGE LEARNING IN THE CONTEXT OF COMMUNICATIVE CLASSROOM PRACTICES

This paper presents a mixed-methods approach to researching the viability of supplementing mobile-assisted language learning (MALL) apps focusing on receptive skills with a course curriculum that emphasizes meaningful communication, mitigation of error-aversion tendencies, cultural awareness, and presentation skills. Adult language learners working for a company were assigned fifty units of the online mobile assisted language learning application, ReallyEnglish, while concurrently taking weekly hour-long lessons that focused on meaningful communication, mitigation of error-aversion tendencies, cultural awareness, and presentation skills. Many researchers have lamented the fact that most MALL applications do not take full advantage of technological affordances to create opportunities for learners to use their devices as tools of interaction. Instead, apps that focus on receptive skills including vocabulary memorization and grammatical rules are more popular. This paper acknowledges that creating more apps focusing on productive skills is necessary but focuses on how to create a robust curriculum surrounding such receptive-skills apps. Learners improved as shown by standardized test scores, learner reflection, and surveys.
Pengaruh Parenting Self-Efficacy, Keterlibatan Orang Tua dalam Pembelajaran Online dan Tiingkat Pendidikan Orang Tua terhadap Hasil Belajar Siswa

During the COVID-19 pandemic students had to learn at home. In this mode of learning students experienced difficulties in understanding the learning material which impacted on student assignments completion. This condition requires parental involvement in solving the problem. Parental background has effects on the quality of student mentoring. This study aims to determine the impact of parenting self-efficacy, parental involvement in online learning, and parental education level on student learning outcomes. The population of this study was all parents (mothers) of active students enrolled in the school. This research uses ex post facto research. The Measurement of variabels using a Likert Scale with multiple regression analysis. The results show that the variabel that has the most influence on student learning outcomes is the level of parental education which contributes 68.3%, followed by the variabel of parental involvement of 22.5% and the variabel of parenting self-efficacy of 9.2%.
Heterogeneous Graph-Neural-Network with TimeSequence Information Integration

Abstract Recommendation systems, as a kind of information filtering system, can understand users' interests based on their personal data or historical behavior records, and are widely used in Web applications such as e-commerce, search, and streaming websites. Based on the fact that users with similar preferences may be interested in similar items, most existing recommendation methods mine this synergistic information through user-item interactions, called collaborative filtering. Recent interaction information can better reflect users' dynamic interests over time. Another group of work is called temporal-based recommendation, which takes into account temporal information to model the user's dynamic interests. A temporal recommendation system is based on the user's temporal interaction data as a context to predict which item the user is most likely to interact with next time. Markov chain-based recommendation models are a typical example of temporal sequential recommendation, which assume that the next interaction is only related to the previous one. Recently, much work has used recurrent neural networks to model previous behaviors and use hidden states to predict the next behavior. However, almost all temporal recommendation methods model user embeddings based only on their own temporal interaction history, ignoring the heterogeneous information widely available in recommender systems, such as product attributes, and encounter cold-start problems when data are sparse and users have fewer interactions. In this paper we proposed a model, the heterogeneous dynamic graph neural network. This network uses a static graph encoder to process the node representation of each heterogeneous graph at different time steps, which includes the node attribute information and edge information, to capture the changing node information in all time steps of the dynamic graph and obtain a valid node representation at each time step. The long and short-term memory model is then used to aggregate the temporal information between different time steps and to mine the connections and interdependencies of different types of nodes between different time steps. The goal of the heterogeneous dynamic graph neural network is to capture the changing node representations of the same nodes as well as different nodes at different time steps in the graph network, expecting to better perform the task of node classification in dynamic graphs. In this paper, we validate the model of heterogeneous dynamic graph neural networks from data, experimenting with node classification tasks on real graph structured data including social networks, e-commerce networks and business review networks, and show that heterogeneous dynamic graph neural networks outperform the latest representation learning methods for static and dynamic graphs.
htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size

When observations are collected in/organized into observational units, within which observations may be dependent, those observational units are often referred to as \"clustered\" and the data as \"clustered data\". Examples of clustered data include repeated measures or hierarchical shared association (e.g., individuals within families). This paper provides an overview of the R package [htestClust](https://CRAN.R-project.org/package=htestClust), a tool for the marginal analysis of such clustered data with potentially informative cluster and/or group sizes. Contained in htestClust are clustered data analogues to the following classical hypothesis tests: rank-sum, signed rank, $t$-, one-way ANOVA, F, Levene, Pearson/Spearman/Kendall correlation, proportion, goodness-of-fit, independence, and McNemar. Additional functions allow users to visualize and test for informative cluster size. This package has an easy-to-use interface mimicking that of classical hypothesis-testing functions in the R environment. Various features of this package are illustrated through simple examples.
A new smoothing spline that acts like a low-pass filter

Abstract Additional weighting factors are introduced into the smoothing spline, proportional to the sampling spacing, in agreement with a mathematicalphysical model for a thin elastic beam at small deflection. This improves the robustness of the spline with respect to variations in spacing of the data. The spline is then shown to correspond to a low pass (long pass) filter. This allows to set the level of smoothing in a more intuitive way, on the basis of the cut-off wavelength.
Distance Learning Activation in Higher Education

This article discusses the pedagogical features, problems and directions of development distance learning organization.Comparative characteristics of preparation for traditional and distance learning are covered, and strategies for activating distance learning are described.The authors conducted research to improve the quality of distance learning for university lecturers.The aim of the study was to create an optimal environment for improving the quality of education by stimulating the activity of students in the context of distance learning.It is implemented through the active introduction of information, software, technical and methodological environment for distance learning in the relationship between learner and lecturer.Active methods, software, platforms, which played a special role in the study, were identified and recommendations for use in distance learning practice were described.The pedagogical conditions for the activation of distance learning and "mixed learning" are formulated.Although there is research on the problems of distance learning, there is a lack of methodological training of lecturers and learners, and even readiness of the educational organizations.This was revealed during the transition to mass distance learning under the influence of the COVID-19 pandemic.In this regard, the study complements the work on the formation of digital competencies of lecturers and learners of higher education.Research methods are focused on the adaptive use of teaching and learning strategies, teaching materials in distance learning in higher education.
Identification of the Research Potential of Students in the Process of Revealing Integrative Connections of the Subject Content of Mathematical Courses

The existing array of scientific publications on modern methods, innovative forms and means of forming research abilities and skills solves this problem at the primary and secondary levels of education.The issues of identifying the research potential of students considered in the field of theory and methodology of vocational education (bachelor's degree), remain poorly studied.The purpose of the study is to develop and reveal the research potential of students in the process of teaching classical sections of mathematical science by defining a holistic integrative construct (using the example of a generalized function).The assessment of the research potential of students in the process of revealing integrative connections of the subject content of mathematical courses was carried out on a sample of students of the 1-3 years of study in Applied Mathematics and Computer Science.The diagnostics was performed using the methodology of studying the research potential -the IP51 questionnaire (by N.V. Bordovskaya et al.).The empirical results of the study were analyzed using a set of mathematical and statistical methods (methods of descriptive statistics, Student's t-test).The mechanisms of identifying the research potential in the process of revealing integrative connections of the subject content of mathematical courses are considered.The effectiveness of the inclusion of research tasks was proved by establishing significant differences in all components of the research potential between the control and experimental groups: in the cognitive component (t = 16.11,p < 0.05 and p < 0.01); in the motivational component (t = 6.16, p < 0.05 and p < 0.01 by behavioral component (t = 11.64,p < 0.05 and p < 0.01).The results of the study have practical value, as they act as an effective mechanism for unlocking the research potential of students by setting special tasks to give integrity to the subject content of mathematical courses.The proposed research tasks for establishing integrative