Reviewerly/exHarmony huggingface dataset:
Model for training of masters in pedagogical education for activity in virtual social and educational environment based on development of digital competences

Problem and objective. The article provides theoretical basis for model for forming of readiness of future teachers to activity in virtual social and educational environment based on development of their digital competences. Analysis of types of activities of a future teaches listed in the Federal State Educational Standards (3++) for Higher Education and areas for application of their digital competences reveals their interconnection and provides opportunities for development of digital competences of a future teacher in the framework of suggested masters program build up based on the analysis of approximate profile of teachers digital competences. Methodology. The research is based on the analysis of digital competences of a teacher and the logic for build-up of subject training of future teaches at masters level for their successful and effective professional activity in the conditions of virtual social and educational environment based on development of their digital competences. Results. The research analyzes the content of basic digital competences of a teacher; suggests a model for development of professional readiness of a future teacher to activity in a virtual social and educational environment based on development of digital competences; formulates basic content for masters program Digital Technologies and Network Interaction in Education, Pedagogical Education, Professional Education, Additional Education based on development of digital competences of a future teacher in the framework of his professional training in an institution of higher education; formulates basic content of training in the framework of suggested disciplines of masters program. Conclusion. It is shown that for the improvement of effectiveness of teachers activity in the contemporary conditions of society digitalization and development of virtual social and educational environment it is necessary to include subject disciplines aimed at development of their digital competences on a special level into the process of their professional training.
Feature Selection for Character Recognition of Handwritten Devanagari and Odia Scripts

Handwritten character recognition has been a challenging task and it finds uses in many real time scenarios such as language translation, automatic Braille transliteration, automatic cheque book reading, automatic scanning of handwritten forms etc.This paper takes two Indian scripts namely Devanagari and Odia for handwritten character recognition and compares different gradient based features such as LBP, LDP, HOG and LOOP which can be used for learning the character pattern by a support vector classifier on various parameters.The paper uses two existing databases of handwritten Devanagari and Odia characters to train the support vector classifiers and compares the results of various features selection.It proposes the best possible feature for Devanagari and Odia character recognition based on graphical comparisons of parameters such as accuracy, training time and recognition rate.The maximum accuracy achieved on the Devanagari dataset of 92000 characters is 95.65% and the maximum accuracy achieved on the relatively small Odia dataset of 15400 characters is approximately 99%.The paper further investigates for the Devanagari characters getting misclassified more frequently.
Measuring the effects of Bitcoin forks on selected cryptocurrencies using event study methodology

The objective of the study is to determine whether the Bitcoin forks have produced significant effects on the cryptocurrency market. The event study methodology is used in this paper in order to determine the statistical significance of the abnormal return of leading cryptocurrencies after three Bitcoin forks. The forks were viewed as three isolated events, with the estimations windows and the event windows constructed separately for each of them. There were statistically significant negative effects related to the creation of Bitcoin Gold and Bitcoin SV. Contrary to expectations, there was no statistically important effect throught out the most famous Bitcoin forking and emergence of Bitcoin Cash. Although cryptocurrencies are a current topic, the literature lacks quantitative research dealing with price changes. Without quantitative analysis, it is difficult to conclude whether the return change is a consequence of a statistically significant event The analysis would therefore provide the tool to determine the statistical significance of their impact on the market. A small number of observed cryptocurrencies is the main limitation of this research. Future researches could cover a wider scope of the market and include other famous cases of forking, for example, the Ethereum forks.
Evaluating and Comparing Skill Chains and Rating Systems for Dynamic Difficulty Adjustment

Skill chains define how in-game skills build on each other and the order in which players ideally acquire them during gameplay. This can enable dynamic difficulty adjustment (DDA) by serving levels based on the skills that players currently have and those required to solve a given level. Similarly, DDA can also be achieved by using rating systems to match players with suitable levels by assigning ratings to players and levels based on ability and difficulty respectively. However, the relative effects of these two methods remain unclear, particularly in the context of human computation games (HCGs). In this paper, we present a general model for using skill chains and rating systems in a combined DDA system along with an evaluation comparing the two for difficulty balancing within HCGs, focusing on the relative merits of both methods when used separately as well as together. We evaluate our methods using the HCGs Iowa James and Paradox. Our findings suggest that incorporating skill chains can improve upon previously shown benefits of using only rating systems for DDA in HCGs.
Implications of Implementation of Artificial Intelligence in the Banking Business in Relation to the Human Factor

The banks are known as monetary management institutions because they deal with money. There is a number of customers that a bank daily interacts with. In this technology era, when everything is moving to automation from the beginning step to final product manufacture, medical checkups, medical reports, and evaluation, the banking system is still working on the legacy system. Instead, with the participation and implementation of new Virtual Assistant-powered with AI and Machine Learning technology in the banking sector, the institutions are again using the legacy system or may be bound to use the legacy system. This research will help to elaborate and emphasize the impact of the implementation of automation, using artificial intelligence in the banking business process. This research will be based on the quantitative as well model base prof of system performance using different analytical tools like SPSS. This automation process will help the institutions to enhance profitability, performance, and reduce human dependency. In a nutshell, Virtual Assistants powered with Artificial Intelligence will improve the business process performance in every sector of business, especially the banking sector.
Improvements on Learning Kernel Extended Dictionary for Face Recognition

Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.
Mobile Robot Localization Based on Gradient Propagation Particle Filter Network

In order to solve the problem that the gradient information can’t propagate backward due to the non-differentiability of resampling process in the end-to-end training of Differentiable Particle Filters (DPFs) network model, a particle filter network with gradient propagation is proposed in this article. It uses a differentiable resampling process in the network and is called Gradient Propagation Particle Filter Network(GPPFN). Firstly, an auxiliary noise vector is added to approximate the particle distribution in the resampling process, so that it can be represented by a certain differentiable function. Then, the method is applied to GPPFN model to make the gradient information generated in the training process can be propagated. It can provide the information of previous prediction and update steps for the next step prediction, so as to improve the accuracy of robot localization. In this article, experiments are carried out in the simulation environment of Deepmind lab. GPPFN is used to train the motion model and measurement model respectively, and the training effect is expressed by likelihood degree. The lower the likelihood degree is, the more consistent the training results are. The experiments show that the likelihood loss of motion model and measurement model are reduced by using GPPFN. Finally, The GPPFN model is compared with other methods. The training results showed that compared with the others, the training results of GPPFN has lower error rate and better robustness.
Defragmenting Research Areas with Knowledge Visualization and Visual Text Analytics

The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods.
A Declarative PCG Tool for Casual Users

Declarative programming allows designers to create procedural content generators by providing descriptions of desired artifacts rather than bespoke algorithms to generate them. Unfortunately, these systems are notoriously inaccessible, requiring considerable sophistication with formal systems, and detailed understanding of the impact of equivalent formalizations on the system performance. Imaginarium is a declarative PCG system for tabletop role playing games (TTRPGs). Following Compton’s work on casual creators, it trades expressiveness for accessibility. As with Nelson’s Inform 7, its source language is a highly structured subset of English. A single sentence, such as children are parented by at least one adult can be used to simultaneously introduce the predicates child, adult, and parent, along with a cardinality constraint over the parent relation. We describe the system, its knowledge representation language, and the issues in their design. Together, they allow users with minimal STEM background to engage in playful experimentation.
ICT competence formation model of a future primary school teacher

Problem and purpose. The article is devoted to the problem of the formation of ICT competence in future primary school teachers. In the context of global mass communication, the most valuable resource today is the ability to analyze, systematize, and interpret information. Global informatization naturally affects the education process and the main task of the education system is to identify current trends in the development of society and introduce pedagogical technologies into the educational process that form positive experience and reflect modern social changes, leading the educational process into an ordered, controlled system. The aim of the study is to find effective ways of manifesting information activities, building a model that promotes the development of ICT competence in future primary school teachers by means of mathematical cycle disciplines. Methodology. During the study, an analysis of approaches to the creation of educational models was carried out, ways of developing ICT competence of future primary school teachers by means of mathematical cycle disciplines were revealed. The main conceptual idea of the model, contributing to the solution of the didactic problems (epistemological, axiological, praxeological, professional-personal, communicative), is the reflection of mathematics in the information field as an instrument of humanization of the modern information society and personality. The basic aspects of fragmented subject modeling of pedagogical reality is the application of an integrated approach to the conceptual, criteria and quantification of the model. Results. A model for the development of ICT competence of future primary school teachers has been developed. The technological component of the model is disclosed, the criterion component of which is represented by the matrix of conjugation of levels (elementary, sufficient, advanced) and components of ICT competency (epistemological, axiological, praxiological, professional-personal, communicative). Each component is revealed through the prism of knowledge, skills and experience. Conclusion. The developed model for the development of ICT competence of future primary school teachers by means of the mathematical cycle disciplines justifies itself in practice and needs further research and quantitative analysis.
Visualization, Interaction and Analysis of Heterogeneous Textbook Resources

Historically grown research projects, run by researchers with limited understanding of data sustainability, data reusability and standards, often lead to data silos. While the data are very valuable it can not be used by any service except the tool it was prepared for. Over the years, the number of such data graveyards will increase because new projects will always be designed from scratch. In this work we propose a Component Metadata Infrastructure (CMDI)-based approach for data rescue and data reuse, where data are retroactively joined into one repository minimizing the implementation effort of future research projects.
Connectivity Characteristics and Level of Acceptance Linked to Online Learning by Higher Education Students During the Confinement Generated by the Covid-19 Pandemic

Online education was the alternative used worldwide to provide continuity with training processes in higher education institutions during the confinement generated by the Covid-19 pandemic.The objective of this work is to analyze and identify the characteristics of connectivity and the level of acceptance linked to online learning by students of technological programs at the Universidad Distrital Francisco José de Caldas.For this purpose, 287 students from 3 different academic programs were surveyed, where it was possible obtain information on Internet connectivity, positive and negative impressions associated with the digital tools they were using in their classes and finally the perception of the accompaniment of their teachers in the training processes.
STUDI EKSPLORATIF PERSEPSI GURU TERHADAP KEBIJAKAN BELAJAR DARI RUMAH PADA MASA PANDEMI COVID-19

This study aims to obtain teacher perceptions about the impact of the implementation of the Learning from Home policy according to a circular from the Minister of Education and Culture dated March 24, 2020, which regulates the implementation of education during the coronavirus spread. The research method uses exploratory case studies, the research approach uses qualitative case study methods which are used to obtain information about the effectiveness of the learning process from home during the Covid-19 pandemic. This research was conducted in June 2020. Respondents of this study were 20 elementary school teachers spread across five districts in North Lombok Regency, West Nusa Tenggara. Respondents were drawn from each elementary school teacher spread over five districts. The data of this study were sourced from the results of questionnaire and interview answers developed based on a review of relevant literature. All respondents filled out a research questionnaire online through the Google forms application, then the questionnaire was confirmed via telephone interview. The results of this study conclude that learning policies from home during the Covid-19 pandemic period according to teacher perceptions have not been implemented well and according to teacher observations, only 25% of students actually carry out learning from home. As many as 60% of teachers feel that their burden becomes heavier when compared to the learning process that has been carried out in classi ﬁ cation or face-to-face and 65% of teachers state that they need additional skills to be able to carry out the learning process from home.
Teacher's Professional Competence: New Challenges, Realities And Prospects

Changes in the global labour market apply new requirements to participants in labour relations. Currently, experimental work on the formation of competencies of the 21st century is actively carried out in different countries, including Australia, Canada, Russia, the USA, Finland, South Korea and others. Researchers note that a significant part of professional tasks demands from specialists to have soft skills presented in the "4C" model. These challenges define new requirements for teachers at different levels of education. The Russian system of teacher education reflects global trends in determining priority development vectors: today it is necessary to “equip” a teacher with such competencies, technologies, and skills that will provide learning outcomes that are adequate to the requirements of the 21st century. The purpose of this article is to select and justify modern learning outcomes and the corresponding pedagogical competencies as tools for solving the problems facing modern Russian educational practice based on the analysis of foreign and domestic studies, the analysis of the request for educational practice. In accordance with the purpose, the article presents an analysis of foreign and domestic studies on the problem of highlighting and structuring "new learning outcomes" in the training of teachers; an expert analysis of the results of designing educational models and a comparative analysis of organizational decisions in the system of Russian and foreign education.
Single-Shot Readout of a Solid-State Spin in a Decoherence-Free Subspace

The efficient single photon emission capabilities of quantum dot molecules position them as promising platforms for quantum information processing. Furthermore, quantum dot molecules feature a "decoherence-free" subspace that enables spin qubits with long coherence time. To efficiently read out the spin state within this subspace requires optically cycling isolated transitions that originate from a triplet manifold within the quantum dot molecule. We propose and theoretically study a two-stage spin readout protocol within this decoherence-free subspace that allows single-shot readout performance. The process incorporates a microwave $\pi$-pulse and optically cycling the isolated transitions, which induces fluorescence that allows us to identify the initial spin state. This protocol offers enhanced readout fidelity compared to previous schemes that rely on the excitation of transitions that strongly decay to multiple ground states or require long initialization via slow, optically forbidden transitions. By simulating the performance of the protocol, we show that an optimal spin readout fidelity of over 97% and single-shot readout performance are achievable for a photon collection efficiency of just 0.12%. This high readout performance for such realistic photon collection conditions within the decoherence-free subspace expands the potential of quantum dot molecules as building blocks for quantum networks.
Development of the basic principles of distant teaching of mathematics in middle and high schools

Introduction and goal. The article dwells upon a topical problem of organizing distant learning, in particular, learning mathematics online. On the basis of literature analysis and analysis of teaching experience the principles of distant teaching of mathematics in middle and high schools were developed. The recommendations on creating effective cooperation of a teacher and learners while teaching online are given. Materials and methods. The identification of approaches to creating principles of teaching mathematics online in middle and high schools was carried out on the basis of analysis of academic literature, experimental teaching and surveying educationalists. Results. The article has practical value for educational institutions: the most popular electronic educational resources and software for teachers of mathematics were analyzed. It is shown how to organize the process of distant learning. The result of the experiment carried out to implement the developed principles of distant learning of mathematics at School No. 1234 (Moscow) are given. Conclusion. The implementation of the developed principles will allow teachers to ensure an effective change from full-time tuition to distant teaching of mathematics in middle and high schools. The rational use of electronic educational resources and software taking into account created recommendations (for example, a principle of basic PC knowledge, principles of completeness, system and interactivity) will allow teachers to optimize their working time without lowering the quality of teaching and interaction with learners.