Reviewerly/exHarmony huggingface dataset:
TO THE QUESTION OF TEACHING HIGHER MATHEMATICS IN DISTANCE LEARNING CONDITIONS

The article is devoted to current issues of using digital technologies in the educational sphere of higher education in the study of higher mathematics. Improvement and transformation of information space occurs at the expense of active mastering of information communication technologies. The experience in the use of new information technologies and interactive training means on the example of training students of Samara State University of Means of Communication in the discipline «Mathematics» is presented. Separate aspects of distance learning at the university are analyzed and compared to full-time learning in the modern educational space. The area of application of the results is the organization of the educational process in a distance learning format, effective interaction of the teacher with students in the new conditions, the maximum use of interactive environment, electronic information educational system of the university to illustrate and visualize mathematics as one of the possibilities of modern digital technologies. The authors conclude that it is necessary to combine face-to-face and distance learning to successfully develop an educational strategy.
A Super-Resolution Imaging Method for Real-Aperture Scanning Radar Based on MRF Prior Model

Deconvolution technology can be utilized to improve the angular resolution of real-aperture scanning radar (RASR) with high efficiency and low cost. However, it is an ill-posed problem and the solution is sensitive to noise. Regularization methods are considered to be efficient ways to ease the noise sensitivity by absorbing the prior information into the objective function. In this paper, we propose a new super-resolution imaging method for RASA based on the Markov random field (MRF). Compared with the published angular super-resolution methods for RASA, the proposed method takes advantage of the two-dimensional spatial prior information and can recover the shape of scene much better. Simulations are carried out to demonstrate the effectiveness of the proposed method.
Review of Web Service Technologies: REST over SOAP

Recently, most of the web applications depend on the web services to solve the application integration problem. Web service is an intermediate software that allows the interaction between the applications written by different languages. Web Service is a client-server, modular, self-contained, and dynamic application that ensures the communication between the applications via the World Wide Web (WWW). The aim of this paper is to choice right kind of Web services at design stage of the project. Thus, we provide an overview of web services. Next, we clarify the architecture of web services. After that, we focus on the most popular kinds of web services, the Representational State Transfer (REST) and the Simple Object Access Protocol(SOAP) then explain APIs details. Finally, we address the performance evaluation of the previous works and explained the results as comparison between SOAP and REST. A recommendation to use RESTful web services has been concluded from experimental result.
Developing Design Guidelines for Virtual Reality based Decision-Making Training for Team Sports

In team sports, decision-making is defined as the tactical choices that players make based on their understanding of the current situation. Previous studies have developed virtual reality prototypes to train decision-making. However, the training of decision-making skills for professional Australian Football League (AFL) players is still unexplored. We are collaborating with AFL specialists to validate components of decision-making training prototype that would benefit their athletes. The study proposes design guidelines for a virtual reality experience for decision-making training for AFL players based on the result of the decision-making training experiment using the prototype developed. This paper presents the work in progress, covering preliminary results from a prototyping workshop investigating the design and future deployment of a virtual reality prototype. Among the current findings, we identify the importance of spatial awareness and propose methods to elevate the spatial awareness of AFL players inside the virtual reality space.
Immersive and cultural transformation of the system of additional education as a tool for the development of environmental and social responsibility of the society

The article scientifically substantiates the cultural and creative concept of additional environmental education, as a methodological basis for the formation and development of environmental and social responsibility, which involves the development of four levels of value orientations of students: reproductive, heuristic, creative. The concept is aimed at the formation of the individual activity of students in solving environmental problems on the basis of social, scientific and aesthetic axiological systems that determine the content of ecological-biotechnical, ecological-research, ecological-aesthetic activities. The main result of the implementation of this concept is the ability of students to plan their activities for the study, preservation and creative transformation of the environment independently, using traditional and immersive technologies. Scientists have designed a cultural and creative system of additional environmental education for students using immersive learning technologies, which is considered as a tool for the development of environmental and social responsibility of society. The article presents the results of an experimental test of the effectiveness of the cultural and creative system of additional environmental education using traditional and immersive learning technologies, which has shown the high effectiveness of the influence of innovative forms, means and methods of additional environmental education in the formation of environmental value orientations of students.
Model-Based Knowledge Searching

As knowledge increases tremendously each and every day, there is a need for means to manage and organize it, so as to utilize it when needed. For example, for finding solutions to technical/engineering problems. An alternative for achieving this goal is through knowledge mapping that aims at indexing the knowledge. Nevertheless, searching for knowledge in such maps is still a challenge. In this paper, we propose an algorithm for knowledge searching over maps created by ME-MAP, a mapping approach we developed. The algorithm is a greedy one that aims at maximizing the similarity between a query and existing knowledge encapsulated in ME-maps. We evaluate the efficiency of the algorithm in comparison to an expert judgment. The evaluation indicates that the algorithm achieved high performance within a bounded time. Though additional examination is required, the sought algorithm can be easily adapted to other modeling languages for searching models.
Developing an Algorithm Used to Automatically Arrange Filling Diagrams of Outdoor Switchgears with Bridge Connection

Drawing up filling diagrams is an important stage in designing substations. This paper presents an algorithm used to automatically arrange filling diagrams of outdoor switchgears with bridge and block connections. Particular attention is paid to studying all possible types of bridge and block connections at 35 to 220 kV, the design of the algorithm used to draw up filling diagrams, as well as the debugging of designed algorithms. The novelty of these algorithms lies in the fact that each of them takes into consideration all possible types of outdoor switchgear arrangements. These include possible substation extension in the future, the addition of current and voltage transformers, and the arrangement of power line cells. The designed algorithms have been executed in the Outdoor Switchgear CAD software.
Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

We present work on summarising deliberative processes for non-English languages.Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text.We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model.Texts are translated into English, summarised, and translated back to the original language.We obtain promising results regarding the fluency, consistency and relevance of the summaries produced.Our approach is easy to implement for many languages for production purposes by simply changing the translation model.
AR Guide for Food Allergic Consumers

Augmented reality is a technology where real life environment is enhanced by incorporating digital or virtual elements like images, graphics, 3D objects into it. Augmented reality is a growing and trending technology which can make interactions easier when incorporated into normal applications. The aim of this project is to create an augmented reality food guide for food allergic consumers to aid them in choosing allergen free foods. A person who wants to order food online or in restaurants doesn’t know whether it would be suitable for him to eat or not. Especially if they are allergic to certain foods, they have to be preventive and cautious in choosing the type of food they eat. This AR guide will aid them in choosing the suitable food for them in an augmented environment where a consumer can view food in three dimension view along with the necessary information about the food such as the key ingredients and the presence of any allergens that will be helpful in choosing the allergen free food.
Information Management Database System Managerial Supervision of School Supervisors

This article aimed at managing information technology based school supervisory managerial supervision database. The approach in this study uses a system approach with a waterfall model. The steps of developing the model are requirements analysis, design, implementation and unit testing, integration and system test. The results show that School supervisors in the North Musi Rawas District Office still use the manual system, and design of managerial supervisory information systems for principals are designed based on planned flowcharts. The results of expert validation, the supervisory managerial supervisory database includes; curriculum and learning administration, class administration, school administration and management, organization and institutions, facilities and infrastructure, staffing, funding, students, community participation, school cultural environment are appropriate to be used. The appropriate Implementation of the School Supervisory Managerial with Supervision Database SIM.&#x0D; Keywords: Management Information System, Management Supervision, System Managerial Supervision
Between the assessment centre and reality: how and why biographical psychology makes human resource management more effective

Purpose The article makes a conceptual contribution to the substantiation of the foundations in personnel management for the development and decision-making in personnel assessment based on the biographical method. Design/methodology/approach When substantiating the scientific direction, a comparative analysis, a comparison of the theoretical provisions of leadership and the methodology of personnel assessment, a critical appraisal of the assessment centre and biographical method were used. Findings Modern science contains information about effective leadership and few grounds for objective selection for leadership positions. Why do some individuals become leaders in the churches, while others become leaders of business corporations? The accepted assessment tools allow obtaining information about the candidate, but do not have a scientific basis for making a decision about leadership in recruiting. Biographical psychology brings together leadership theories and assessment tools; it allows to establish a connection between developmental psychology, social, applied and organizational psychology and provides the integration of various directions in science. It allows a deeper understanding of the relationship between a person's biographical facts and his/her suitability in the profession to justify the reasons for leadership, as well as the grounds for selection for managerial positions. Originality/value Substantiation of biographical psychology will allow in the future to legitimize the conduct of such studies, to come closer to the truth in the selection for leadership and not only positions, to understand the connection between the biography of leader and organizational culture. Biographical psychology will contribute to the creation of scientific basis for recruiting. To consider biographical factors more significantly (gender, age, sexual orientation, differences in personal development, and not just executive experience in the resume), this will reduce the economic costs of introducing and supporting costly procedures in organizations, as well as increasing the objectivity of the assessment and selection of personnel in organizations.
Efficient Extensional Binary Tries

Lookup tables (finite maps) are a ubiquitous data structure. In pure functional languages they are best represented using trees instead of hash tables. In pure functional languages within constructive logic, without a primitive integer type, they are well represented using binary tries instead of search trees. In this work, we introduce canonical binary tries, an improved binary-trie data structure that enjoys a natural extensionality property, quite useful in proofs, and supports sparseness more efficiently. We provide full proofs of correctness in Coq. We provide microbenchmark measurements of canonical binary tries versus several other data structures for finite maps, in a variety of application contexts; as well as measurement of canonical versus original tries in two big, real systems. The application context of data structures contained in theorem statements imposes unusual requirements for which canonical tries are particularly well suited.
Face Super-Resolution via Triple-Attention Feature Fusion Network

In recent years, compared with the traditional face super-resolution (SR) algorithm, the face SR based on deep neural network has shown strong performance. Among these methods, attention mechanism has been widely used in face SR because of its strong feature expression ability. However, the existing attention-based face SR methods can not fully mine the missing pixel information of low-resolution (LR) face images (structural prior). And they only consider a single attention mechanism to take advantage of the structure of the face. The use of multi-attention could help to enhance feature representation. In order to solve this problem, we first propose a new pixel attention mechanism, which can recover the structural details of lost pixels. Then, we design an attention fusion module to better integrate the different characteristics of triple attention. Experimental results on FFHQ data sets show that this method is superior to the existing face SR methods based on deep neural network.
Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears

Abstract Manual microscopic examination of bone marrow aspirate (BMA) smears and counting of cell populations remains the standard of practice for accurate assessment of benign and neoplastic bone marrow disorders. While automated cell classification software using machine learning models has been developed and applied to BMAs, current systems nonetheless require manual identification of optimal regions within the slide that are rich in marrow hematopoietic cells. To address this issue, we have developed a machine learning-based platform for automated identification of optimal regions in whole-slide images of BMA smears. A training dataset was developed by manual annotation of 53 BMA slides across biopsy diagnoses including unremarkable trilineal hematopoiesis, acute leukemia, and plasma cell neoplasms, as well as across differences in total cellularity represented by a spectrum of marrow nucleated cell content and white blood cell counts. 10,537 regions among these 53 slides were manually annotated as either “optimal” (regions near aspirate particles with high proportions of marrow nucleated cells), “particle” (aspirate particles), or “hemodilute” (blood-rich regions with high proportions of red blood cells). Training of a neural network-based classifier on 10x magnification slides with region cropping and image augmentation resulted in a classifier with substantial accuracy on new testing-set BMA slides (one-vs-rest AUROC &amp;gt; 0.999 across 10 training/testing splits for all 3 region classes), with very few particle and hemodilute regions being classified as optimal (particle: 0.83%, hemodilute: 0.39%). Additionally, this classifier accurately classifies BMA regions on slides from hematological disorders not represented in the training data, including Burkitt lymphoma (AUROC &amp;gt; 0.999 across region classes), chronic myeloid leukemia (AUROC &amp;gt; 0.999 across region classes), and diffuse large B-cell lymphoma (AUROC = 1 across region classes), demonstrating the broad applicability of our approach. To assess the performance of our classifier on whole-slide images, tiles from 10x magnification slides were manually annotated by three participants with notable concordance (Krippendorff’s alpha = 0.424); substantial agreement was found between manual annotations and model predictions within whole-slide images (optimal AUROC = 0.958, particle AUROC = 1.0, hemodilute AUROC = 0.947). Based on these promising results, this machine learning-based region classification model is being connected to a previously-developed bone marrow cell classifier to fully automate differential cell counting in whole-slide images. The development of this novel automated pipeline has potential to streamline the diagnostic process for hematological disorders while enhancing accuracy and replicability, as well as decreasing diagnostic turnaround time for improving patient care.
A Distillation Learning Model of Adaptive Structural Deep Belief Network for AffectNet: Facial Expression Image Database

Deep Learning has a hierarchical network architecture to represent the complicated feature of input patterns. We have developed the adaptive structure learning method of Deep Belief Network (DBN) that can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and can obtain the appropriate number of hidden layers in DBN. In this paper, our model is applied to a facial expression image data set, AffectNet. The system has higher classification capability than the traditional CNN. However, our model was not able to classify some test cases correctly because human emotions contain many ambiguous features or patterns leading wrong answer by two or more annotators who have different subjective judgment for a facial image. In order to represent such cases, this paper investigated a distillation learning model of Adaptive DBN. The original trained model can be seen as a parent model and some child models are trained for some mis-classified cases. For the difference between the parent model and the child one, KL divergence is monitored and then some appropriate new neurons at the parent model are generated according to KL divergence to improve classification accuracy. In this paper, the classification accuracy was improved from 78.4% to 91.3% by the proposed method.
How Ip Struggles to Define AI-Generated Products and the Ownership Dilemma

Intellectual Property (IP) rights have always had difficulties to cope with disruptive technologies. Development of Artificial Intelligence (AI) and common use of it pushes the boundaries of copyright and patent protection. The interpretations of the patentability and copyrightability requirements for being protected by IP rights becomes ambiguous when assessing AI generated works, due to the ability of AI to learn and generate output without human intervention. This paper although there is no doubt that AI generates creative and inventive works which could obtain copyright and patent, existing framework of both IP rights in United States (US), United Kingdom (UK) and European Union (EU) is clearly insufficient to deal with AI development. This paper addresses and analyses that the question of who will be the author/inventor or owner of the autonomous work and reaches the conclusion that it does not seem to be answered adequately on the basis of current regulations. The author of this dissertation argues that rather than accepting AI as an author or not, some legal regulations should be done to overcome the uncertainty of AI generated works’ protection scope. The Dabus decision has shown that there is no unity in terms of assessing inventions across these jurisdictions. Whilst AI develops over time, if the ambiguity problem of AI generated works is not solved, this may become troublesome to deal with in the future.
Contemporary art thesauri in the context of the teaching profession development

Introduction. The problem of identifying the role of contemporary art thesauri in the teaching profession development has become aggravated in the context of global socio-economic changes, apparently irreversible after the COVID-19 pandemic. Contemporary art, actively exploring the reality of the digital age, makes it possible to comprehend the essence of the new in education, clearly see the ideal of a person who is in demand by the society of the 2020s, and choose a way to overcome the crisis of the teaching profession, which is no longer limited by the framework of person-to-person duality but is fundamentally open due to technology. The research purpose is to generalize scientific ideas for the study of contemporary art thesauri as a tool for developing the teaching profession in the cultural-educational space of the 21st century. The research methodology includes the thesaurus, interdisciplinary and integrated approaches; identification and systematization of units of knowledge about contemporary art through thesauri influencing the development of the profession of a teacher. The use of a thesaurus approach made it possible to identify and systematize units of knowledge about contemporary art and its educational opportunities that coincide in one way or another. An interdisciplinary approach showed the mechanism of integrating disciplines studied by future teachers with the space of modern artistic culture. An integrated approach was used to view contemporary art as a hierarchical system of a high degree of complexity in specific historical and psychological aspects. The research results contain a generalized description of the process of integrating contemporary art into education: the transition from a teacher’s professionalism to transprofessionalism through the introduction into the widespread practice of approaches that have resulted from the activities of representatives of creative professions; increasing the significance of a teacher as an organizer of cultural activities in the context of distance learning, with the emergence of new professional roles; the formation of concepts reflecting the commonality of thinking of a teacher and an author of artworks. Findings. Contemporary art thesauri help pedagogical education get to a higher level due to a new understanding of the profession of a teacher: as developing from professionalism to transprofessionalism through the assimilation of knowledge about the formation of a professional in the art sphere (“alien” becomes “own”), focused on organizing students’ cultural activities using distance technologies and new professional roles, explaining itself in terms of pedagogy and art. The field of application of research findings is the improvement of educational programs at all education levels (from the preschool establishment to university) in accordance with the requirements of state standards.