Reviewerly/exHarmony huggingface dataset:
Art Education Of Russia And The Competition Movement

The article provides an overview of the competition movement in the light of the Russian Federation state policy implementation to identify and support talented young people in the field of art. Three lists of competitions, contests and festivals for the period from 2016 - 2019 were analyzed. The composition of competitions in nominations, the composition of participants by age, the conditions for participation in competitions, the criteria for selection and evaluation of works and the determination of winners and laureates were analyzed. The results are correlated with the age psychology requirements of childrens creativity, the modern understanding of creative activity definition and its results. It is concluded that the proportion of art contests remains consistently low compared with other areas of knowledge. It is noted that the traditional system of "selection approach" in the selection of students is difficult to change. Especially great difficulties arise in organizing competitions to identify early talent of children from 7 to 12 years. The algorithms of the academic school prevail in the competitive criteria for the visual arts. It is noted that the most advanced in the field of orientation to the finished artistic product, showing more clearly the signs of the creative process novelty and freedom, are such areas as design and research activities. It is concluded that the competitive-festival movement as an alternative to traditional education, the form of gifted and talented children and adolescents selection encounters great difficulties in their development.
창의융합형 인재 양성을 위한 고등학교 영미문학읽기 수업: 로이스 로리의 기억 전달자 를 중심으로

This study introduces the 2015 revised curriculum with four core competencies— self-administered competency, knowledge information...
IN SEARCH OF KNOWLEDGE IN PHILOSOPHY (Reflections on the book A. M. Starostin “Research Philosophy”. Rostov-on-Don: Rostov State University of Economics, 2019)

In the monograph of Professor A. M. Starostin the notions “philosophical novations” and “research philosophy” was introduced and approved. The author divides the whole array of philosophic research into fundamental and applied – the sphere of philosophical novations. Fundamental and philosophic investigations are directed to the study of the problems of objective reality, thinking, cognition, the truth, freedom and other basic categories. The sphere of fundamental research is slowly changing and it’s development is marked by the outstanding names (Platon, Descartes, Kant, Schopenhauer, Heidegger etc.). As to the sphere of the philosophic innovations, its emergence and development are connected with application of methods of philosophical reflection to the realization of interdisciplinary problems of science, development of political, religious, artistic, ethical trends, which can't be researched only with the help of their own methods. The sphere of philosophic novations develops dynamically and according to its own scales and is twice larger than the sphere of fundamental philosophy.In his monography the author, from the viewpoint of his treating of fundamental and innovative projection of the philosophic knowledge and philosophic methods, analyses contemporary problems of politics, education, culture, science.
CNDS-Rules and Pattern Based Signal Recovery for Multi-level Signal Decision Making

This paper focuses on the pattern based decision making over a multilevel symbol coding, Duty-cycle Division Multiplexing (DCDM).The pattern based decision making is used to increase the bit generation reliability at the receiver.It is used in conjunction with the existing rules-based decision making, which leads to better Bit Error Rate (BER).In this study, a reference pattern is first established from a combination of signals of multiple users' data.Subsequently, a new signal is generated according to the established patterns.This signal is then transmitted over the optical medium.At the receiver, signal validation and bit generation are then taking place based on the DCDM regeneration rules.Due to the signal impairments, the received signal will be corrupted.The impairments may cause the signal patterns to differ from the transmitted patterns.These non-conforming patterns are classified as invalid patterns, which are then subjected to the error evaluation and pattern re-mapping.In this paper for the first time we have used a new pattern based decision making in order to increase the reliability of bit sequence generation at the receiver, which leads to better BER.
A Systematic Review of Cryptocurrency Scholarship

Abstract Purpose – The purpose of this paper was to conduct a systemic review of extant cryptocurrency research in order to identify important features of these studies and to provide directions for future cryptocurrency research. Methodology - The Systematic Quantitative Assessment Technique (SQAT) was used to identify and review relevant peer-reviewed journal articles that investigated various facets of cryptocurrency. Findings – 54 journal articles were identified from 12 high-quality databases. The findings of the review revealed that most of the studies took place in Europe, North America and Asia, while Africa has been largely ignored. The main focus of cryptocurrency articles has been on a call for regulation of cryptocurrency without much work done on how to mitigate its vulnerability to the financing of terrorism and as a tool for money laundering. Finally, most cryptocurrency articles adopted a single research method – survey. There is a need for future studies to combine a variety of methods so as to gain additional insight into the issues of cryptocurrency’s vulnerability, risk identification and mitigation, regulation and acceptability. Research limitations - The use of limited but high quality academic databases means that some articles were not considered for this review. Originality/value – This study is one of the few studies to conduct a systematic review on a phenomenon which has the potential to transform the global financial landscape. Keywords – Cryptocurrency, Systematic review, Regulation. JEL Classification – G23, G38.
Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation.The results produced by these techniques depend to a great extent on the data sets used to train the used network.Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks.This makes the idea of segmentation as a cloud-based service attractive.In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments.We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection.We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.
Mobile Agent Platform based Wallet for Preventing Double Spending in Offline e-Cash

Electronic cash (or e-cash) research has been going on for more than three decades since it was first proposed.Various schemes and methods are proposed to improve privacy and security in e-cash, but there is one security issue that less discussed mainly in offline e-cash, namely, double-spending.Generally, the mechanism to deal with double-spending in offline e-cash is performing double-spending identification when depositing the coin.Even though the mechanism is successful in identifying double-spender, but it cannot prevent double-spending.This paper proposes the Mobile Agent Platform based Wallet (MAPW) to overcome the double-spending issue in offline e-cash.MAPW uses the autonomy and cooperation of agents to give protection against malicious agent, counterfeit coin and duplicate coin.This model has been verified using Colored Petri Nets (CPN) and has proven to be successful in preventing double-spending, and overcoming malicious agent, and counterfeit coins.
Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women.For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors.Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis.State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time.In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and ( 2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign.The results obtained show that the proposed algorithm has an accuracy of 97.375%.These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.
Unequal Probability Sampling in Active Learning and Traffic Safety

This thesis addresses a problem arising in large and expensive experiments where incomplete data come in abundance but statistical analyses require collection of additional information, which is costly. Out of practical and economical considerations, it is necessary to restrict the analysis to a subset of the original database, which inevitably will cause a loss of valuable information; thus, choosing this subset in a manner that captures as much of the available information as possible is essential. Using finite population sampling methodology, we address the issue of appropriate subset selection. We show how sample selection may be optimised to maximise precision in estimating various parameters and quantities of interest, and extend the existing finite population sampling methodology to an adaptive, sequential sampling framework, where information required for sample scheme optimisation may be updated iteratively as more data is collected. The implications of model misspecification are discussed, and the robustness of the finite population sampling methodology against model misspecification is highlighted. The proposed methods are illustrated and evaluated on two problems: on subset selection for optimal prediction in active learning (Paper I), and on optimal control sampling for analysis of safety critical events in naturalistic driving studies (Paper II). It is demonstrated that the use of optimised sample selection may reduce the number of records for which complete information needs to be collected by as much as 50%, compared to conventional methods and uniform random sampling.
Modern Approaches To Continuous Architectural Education In Japan

The article focuses on a comparative analysis of modern approaches to continuous architectural education in Japan, which includes the additional education of children, students of architectural departments and architects and the public. The author studies the grounds of the worldwide interest in Japanese architecture and Japanese architects, examines the features of the organization of the system of professional architectural education in Japan. It is concluded that many of the characteristic features of Japanese culture associated with the philosophy of Confucianism and Shintoism. Respect for nature and the use of natural materials by architects in architecture is coupled with the popularity of childrens studios for teaching origami -the art of folding paper. The author, using her own experience of visiting leading architectural schools in Japan, interviews of students and teachers, data from official websites of the Universities of Kyoto and Tokyo, studies the features of additional education for students of higher architectural schools in Japan. The study of the next professional stage of continuing education of the architect is based on a comparative analysis of the activities targeted at obtaining architectural skills of two creative Unions: the Architectural Institute of Japan (AIJ) and the Japanese Institute of Architects (JIA). A new trend is identified in ensuring the quality of services in architectural design - transition from personal certification of architects - leaders of design bureaus - to certification of all employees. Conclusions are drawn about the potential and limitations in the use of the Japanese experience in Russia and other countries.
METODE FASTICA UNTUK REDUKSI DATA DIMENSI TINGGI PADA ANALISIS SENTIMEN PARIWISATA KOTA SEMARANG MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

Some communities have a voice attractions via Twitter. The opinion can be used as sentiment analysis to determine the ratings of a tourist attraction. Results of sentiment analysis is expected to assist in the improvement and evaluation of the attraction. In related research sentiment analysis previously used linear dimension reduction method, but has the disadvantage produce a linear combination of all the features that will have difficulty if dealing with data that is non-linear. Therefore, in this study used methods of non-linear dimension reduction, namely FastICA in order to improve the accuracy of Support Vector Machine classifier that can handle high-dimensional and non-linear data. This study uses the Indonesian language text contained on the social networking site Twitter. Validation is done by using a 10-Fold Cross Validation. While the measurement accuracy is measured by the Confusion Matrix and ROC curves. Results application of dimension reduction FastICA gain accuracy of 92.90% and the AUC 0.9157 which means the accuracy of 0.95% better than on Support Vector Machine itself, is proven to increase the accuracy of the SVM algorithm on the non-linier tweet data of attractions in the city of Semarang that can be classified by both in positive and negative class.
Special Considerations for the Implementation of Data Processing Technique for Gesture Recognition

The main stages of solving the problem of machine recognition of hand gestures received from a webcam are considered. The task was to obtain a simple, accurate, and resource-efficient visual recognition technique. The main stages of data preparation and processing were determined; experiments were carried out with different values of hyperparameters, the order, and the number of layers of a convolutional neural network. As a result, we obtained the recognition accuracy of 98.46% with a loss function value of 0.02. This technique has a low computational complexity at the recognition stage and makes it possible to classify hand gestures in real time. Figs.: 2. Refs.: 12 titles.Keywords: data; recognition; gesture; convolutional neural network; computational complexity.
Reconstructed Face Recognition

Computer-aided craniofacial reconstruction technology has very important application in the field of criminal investigation. But reconstruction a face from skull is not the end of work. The reconstructed face needs to be automatically identified in the missing population photo database. This paper proposed a reconstructed face recognition method based on deep learning. We trained a weighted fusion deep network for feature extraction, built two different neural network models for reconstructed face verification and use KNN for reconstructed face recognition. This paper uses 166 sets of data for experiments. In reconstructed face verification, the accuracy of using the Pseudo Siamese neural network is 98.33%. In reconstructed face recognition, the Top1 accuracy of the method using Pseudo Siamese neural network is 99.57%. Experiments show that the proposed method can effectively improve the accuracy of reconstructed face recognition.
Event Management Of Educational Interaction Networks Of Children And Adults

The article analyzes the implementation of the idea of event networks creating on the initiative of the educational organization that broadcast the educational interaction between an adult and a schoolchild, thus creating conditions for a student to form a further educational path. The features of modern schoolchildren, that determine the characteristics and difficulties of high school children in choosing their professional and life path, are described. This article presents common difficulties and mistakes of high school children when choosing a further path associated with the use of inadequate information sources, with inadequate self-assessment of their capabilities, the choice of the next step in education based on positive emotional perception of certain personal or non-professional qualities of a person, region, nationality, denomination, etc. The problem of the content of educational activities with young people in the educational organization, in the family, and other social and cultural institutions has been outlined. The idea of the network event management, created on the educational organization initiative, with an assessment of the impact on the outlook and schoolchildrens activities of event-related events, the formation of an active, subjective position of young people and their readiness for independent choice of a further path, is considered. Such an approach creates conditions for building an individual educational path for a person with the participation of not only traditional educational institutions, but also of a wide range of organizations with educational potential, but located in other sociocultural fields.
Conditions Of Effective Influence Of Pedagogical Research On Education Practices

The article considers methodological aspects of interaction between pedagogical science and education practice, identifies the conditions and mechanisms of the influence of pedagogical science research on education practices. The problems of education whose solution depends crucially on science contribution are determined. They are, first, problems of the philosophy of education that identify the place of education in the development of the global world; second, problems of the content of education, i.e. what is to be taught today now that man has been confronted with phenomena for which there are no analogs in the past. This renders irrelevant traditional assimilation of past experience. Science is also needed to estimate the effectiveness of modern online forms of education in the public domain. The author demonstrates that pedagogical knowledge possesses some specific features. It is marked by dependence on the goals pursued by the researcher, variant interpretations of the same ideas, impossibility of replicating facts and establishing laws recognized by all. For these reasons the findings of pedagogical studies may lead to different results when applied by different teachers. The article shows that the impact of pedagogical research on pedagogical practices is not always positive for two reasons. The first is the shortcomings of the actual research which the author identifies. The second is differences in the way scientists and practical teachers interpret the same concepts. The author concludes the investigation by suggesting ways of bringing closer together the science of education and education practice.
Triplifying equivalence set graphs

In order to conduct large-scale semantic analyses, it is necessary to calculate the deductive closure of very large hierarchical structures. Unfortunately, contemporary reasoners cannot be applied at this scale, unless they rely on expensive hardware such as a multi-node in-memory cluster. In order to handle large-scale semantic analyses on commodity hardware such as regular laptops we introduced [1] a novel data structure called Equivalence Set Graph (ESG). An ESG allows to specify compact views of large RDF graphs thus easing the accomplishment of statistical observations like the number of concepts defined in a graph, the shape of ontological hierarchies etc. ESGs are built by a procedure presented in [1] that delivers graphs as a set of maps storing nodes and edges. In this demo paper (i) we show how facts entailed by an ESG and the graph itself can be specified in RDF following a novel introduced ontology; and, (ii) we present two datasets resulting from the triplification of two ESG graphs (one for classes and one for properties).
A research on classification performance of fuzzy classifiers based on fuzzy set theory

Due to the complexities of objects and the vagueness of the human mind, it has attracted considerable attention from researchers studying fuzzy classification algorithms. In this paper, we propose a concept of fuzzy relative entropy to measure the divergence between two fuzzy sets. Applying fuzzy relative entropy, we prove the conclusion that patterns with high fuzziness are close to the classification boundary. Thus, it plays a great role in classification problems that patterns  with high fuzziness are classified correctly. Meanwhile, we draw a conclusion that the fuzziness of a pattern and the uncertainty of its class label are equivalent. As is well known, entropy not only measures the uncertainty of random variable, but also represents the amount of information carried by the variable. Hence, a fuzzy classifier with high fuzziness would  carry much information about training set. Therefore, in addition to some assessment criteria such as classification accuracy, we could study the classification performance from the perspective of the fuzziness of classifier. In order to try to ensure the objectivity in dealing with unseen patterns, we should make full use of information of the known pattern set and do not make too much subjective assumptions in the process of learning. Consequently,  for problems with rather complex decision boundaries especially, under the condition that a certain training accuracy threshold is maintained, we demonstrate that a fuzzy classifier with high fuzziness would have a well generalization performance.
Changes In Corporate Education Environment In The Context Of Digital Economy

The article presents comparative analysis of changes taking place in corporate education environment in enterprises in the context of digital economy when the main emphasis in production switches from capital to human resources. Main trends in education in the context of digitalization of Russian economy are studied on the example of aircraft-engine-building corporation. The corporation establishes common resources for some professions/disciplines with options of online learning. Modern practices and trends of corporate education environment improvement and ways of raising the efficiency of staff training with the use of digital technologies are discussed. The pedagogical competency deficits are defined experimentally, and the results of monitoring of education environment in corporate training centers are discussed. The results of comparative study made in 2013, 2017 and 2019 based on three basic criteria – changes in logistics base, teachers’ pedagogical competencies and teaching methods are presented. The system approach for the teacher training and competency model of their training are proposed. In such a learning process besides pedagogical competencies special professional environment is forming that allows sharing the experience and developing professional skills. The main trends in development of corporate education environment revealed are: evolution of teaching methods from classic approaches to establishing the classrooms allowing imitation of production processes, from lectures to webinars, electronic courses, distant learning. The methodology of developing an electronic interactive course oriented on individualized learning of engine construction and functioning has been created. These changes fit within world trends of education development.