Reviewerly/exHarmony huggingface dataset:
Helen Parkhurst: the first female reformer in the field of organization of education

Introduction. The problem of improving the level of teaching in the field of general and vocational education is one of the most significant in modern pedagogy. In this regard, modern didactics carefully study the legacy of their outstanding predecessors, – teachers of the past. Among such scientists who have left a unique mark in pedagogical science and practice is the American teacher Helen Parkhurst (1886-1973). Materials and methods. Research methods, – analysis of historical and pedagogical, methodological and fiction literature on the research topic, biographical, historical and comparative methods, axiological (value) approach to the study of methodological innovations by H. Parkhurst. Results. American teacher-innovator Helen Parkhurst, alongside with Maria Montessori, violated the monopoly of men on the right to be considered a major scientist in the field of pedagogy. Her pedagogical activity included the desire for constant professional self-improvement, the search for new ways to solve the pedagogical problems that confronted her. Her main methodological achievement was the development of the Dalton plan, according to which she successfully rebuilt the work in one of the American schools. The methodological innovation of Parkhurst was approved first by teachers in the USA, and then everywhere in the world. In the USSR in the 1920s and 1930s, this method was also actively used, although without much success. The application of the Dalton plan in the Soviet school was reflected in a number of works of fiction (M. G. Rozanov, N. I. Kochin). Conclusion. Helen Parkhurst's methodological ideas are in great demand today and are actively used in modern educational practice in many countries of the world, including Russia. At the same time, their positive potential has not yet been fully explored and mastered, so the legacy of H. Parkhurst deserves further careful study by didactics and historians of pedagogy.
Image feature matching method based on improved ORB

The feature points extracted by the FAST algorithm in the ORB algorithm are densely distributed, and the repeated appearance of the feature points leads to a long time-consuming process in the matching process. Aiming at this problem, this paper proposes an improved method. First, the image is partitioned, then the feature points obtained by the FAST detection algorithm in the original algorithm are sorted, and the feature points that are ranked first and meet the conditions are selected for binary description, and finally the homography matrix is added after hamming matching to further improve the matching. Accuracy. The experimental results show that the algorithm improves the time-consuming of the original algorithm from 1.4s to 0.7s.
PROBLEM DIALOGUE AS A WAY ORGANIZATION OF PRODUCTIVE ACTIVITIES PRIMARY SCHOOL STUDENTS IN THE LESSONS

No abstract.
IDEAS OF FOLK PEDAGOGY AS A BASIS 1114 EDUCATIONAL SYSTEM ACTIVATION POLYCULTURAL SCHOOL

No abstract.
THE ROLE OF THE PSYCHOLOGICAL SERVICE IN TRAINING OF THE PEDAGOGICAL TEAM TO USE INTERACTIVE TECHNOLOGIES

No abstract.
Design of Foreign Language Intelligent Translation Recognition System Based on Improved GLR Algorithm

With the continuous progress of information technology, foreign language intelligent translation recognition is also constantly updated. From the comparison of mobile translation tools at home and abroad, foreign mobile translation tools started earlier, with mature technology and rich types. This paper summarizes the concept of GLR algorithm, puts forward the improved scheme of GLR algorithm, discusses statistical machine translation, analyzes intelligent matching, briefly introduces the speech recognition system, and finally tests the online translation software system. The results show that the query module can not work, mainly because after opening the voice setting, some models of operating systems do not support Pico voice engine.
Improving Multi-Task Generalization via Regularizing Spurious Correlation

Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are less correlated. One possible reason that hurts generalization is spurious correlation, i.e., some knowledge is spurious and not causally related to task labels, but the model could mistakenly utilize them and thus fail when such correlation changes. In MTL setup, there exist several unique challenges of spurious correlation. First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other. Second, the confounder between task labels brings in a different type of spurious correlation to MTL. We theoretically prove that MTL is more prone to taking non-causal knowledge from other tasks than single-task learning, and thus generalize worse. To solve this problem, we propose Multi-Task Causal Representation Learning framework, aiming to represent multi-task knowledge via disentangled neural modules, and learn which module is causally related to each task via MTL-specific invariant regularization. Experiments show that it could enhance MTL model's performance by 5.5% on average over Multi-MNIST, MovieLens, Taskonomy, CityScape, and NYUv2, via alleviating spurious correlation problem.
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning

Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances. In this paper, we propose a novel mask-guided vision transformer (MG-ViT) to achieve an effective and efficient FSL on ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT to focus on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pre-trained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning based sample selection method to further improve the generalizability of MG-ViT based FSL. We evaluate the proposed MG-ViT on both Agri-ImageNet classification task and ACFR apple detection task with gradient-weighted class activation mapping (Grad-CAM) as the mask. The experimental results show that the MG-ViT model significantly improves the performance when compared with general fine-tuning based ViT models, providing novel insights and a concrete approach towards generalizing data-intensive and large-scale deep learning models for FSL.
Online Examination System Using EJS View Engine and ExpressJS

Abstract: The online Examination System project is a web application that is developed using NodeJS. According to this project, it provides the facility to the user to conduct the online exams easily. In today’s world we can see that almost all the major exams are being conducted in online mode like Jee mains, Neet exams, Gate exam etc. This project also helps students to practice for their main exams, it provides facility of taking mock tests online. Online Examination System (OES) portal have two modules, Student module and the admin module. In Admin module, an Admin can add different exams inn the examination section and student can refer that section to access the exams. After registering into the portal, students can choose the exams and appear in the exams as per their interest. Keyword: online exam, web server, database, web application, examination system (Exam), EJS, Embedded JavaScript
Data compression for quantum machine learning

The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of achieving quantum speedups in machine learning tasks. These devices, however, are composed of a small number of qubits, and can faithfully run only short circuits. This puts many proposed approaches for quantum machine learning beyond currently available devices. We address the problem of efficiently compressing and loading classical data for use on a quantum computer. Our proposed methods allow both the required number of qubits and depth of the quantum circuit to be tuned. We achieve this by using a correspondence between matrix-product states and quantum circuits, and further propose a hardware-efficient quantum circuit approach, which we benchmark on the Fashion-MNIST dataset. Finally, we demonstrate that a quantum circuit based classifier can achieve competitive accuracy with current tensor learning methods using only 11 qubits.
EDUCATIONAL AND METHODOLOGICAL COMPLEXES IN THE SYSTEM OF EDUCATIONAL PROCESS

No abstract.
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varying expertise or criteria and thus contain different amounts of noises. Local training on noisy labels can easily result in overfitting to noisy labels, which is devastating to the global model through aggregation. Although recent robust FL methods take malicious clients into account, they have not addressed local noisy labels on each device and the impact to the global model. In this paper, we develop a simple two-level sampling method "FedNoiL" that (1) selects clients for more robust global aggregation on the server; and (2) selects clean labels and correct pseudo-labels at the client end for more robust local training. The sampling probabilities are built upon clean label detection by the global model. Moreover, we investigate different schedules changing the local epochs between aggregations over the course of FL, which notably improves the communication and computation efficiency in noisy label setting. In experiments with homogeneous/heterogeneous data distributions and noise ratios, we observed that direct combinations of SOTA FL methods with SOTA noisy-label learning methods can easily fail but our method consistently achieves better and robust performance.
MORAL EDUCATION STUDENTS IN THE PROCESS OF STUDYING DISCIPLINES PHYSICAL TRAINING

No abstract.
FORMATION OF EXISTENTIAL VALUES OF FUTURE TEACHERS ON THE BASIS INTERACTIVE TECHNOLOGIES

No abstract.
Real Time Multi-Object Detection for Helmet Safety

The National Football League and Amazon Web Services teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign specific players to each helmet, which would help accurately identify each player's "exposures" throughout a football play. We are trying to implement a computer vision based ML algorithms capable of assigning detected helmet impacts to correct players via tracking information. Our paper will explain the approach to automatically track player helmets and their collisions. This will also allow them to review previous plays and explore the trends in exposure over time.
Voice to Sign Language Converter

Abstract: The aim of this paper is to design a convenient system that is helpful for the people who have hearing difficulties and in general who use very simple and effective method; sign language. This system can be used for converting sign language to voice and also voice to sign language. A motion capture system is used for sign language conversion and a voice recognition system for voice conversion. It captures the signs and dictates on the screen as writing. It also captures the voice and displays the sign language meaning on the screen as motioned image or video Keywords: Motioned Image, Sign Language Converter, Voice Recognition.
ExMo: Explainable AI Model using Inverse Frequency Decision Rules

In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statements with a decision rule in the condition. This way, ExMo naturally provides an explanation for a prediction using the decision rule that was triggered. ExMo uses a new approach to extract decision rules from the training data using term frequency-inverse document frequency (TF-IDF) features. With TF-IDF, decision rules with feature values that are more relevant to each class are extracted. Hence, the decision rules obtained by ExMo can distinguish the positive and negative classes better than the decision rules used in the existing Bayesian Rule List (BRL) algorithm, obtained using the frequent pattern mining approach. The paper also shows that ExMo learns a qualitatively better model than BRL. Furthermore, ExMo demonstrates that the textual explanation can be provided in a human-friendly way so that the explanation can be easily understood by non-expert users. We validate ExMo on several datasets with different sizes to evaluate its efficacy. Experimental validation on a real-world fraud detection application shows that ExMo is 20% more accurate than BRL and that it achieves accuracy similar to those of deep learning models.
FORMATION OF COGNITIVE INDEPENDENCE FUTURE TEACHER IN THE STUDY PROCESS FOREIGN LANGUAGE AS A CURRENT PROBLEM MODERN PEDAGOGICAL SCIENCE

The article deals with some actual problems concerning the formation o f future teacher cognitive self-directed activities in the process of foreign language studies.
MOTIVATION FOR OBTAINING HIGHER EDUCATION BY FIRST-YEAR STUDENTS

Relevance. The process of forming a student’s readiness for future professional activity requires a developed motivational sphere of personality. Professional motivation is the leading driving factor of successful learning. The actual needs of the individual here should have a close connection with their satisfaction through the solution of educational tasks related to the chosen field of activity. However, the authors note that the motives of admission to university for first-year students do not sufficiently coincide with the motives of learning a particular profession.&#x0D; The aim of the work was to pose the problem of insufficient use of the axiological approach in teaching as a condition for professional development.&#x0D; The research methodology is related to the substantiation of a value-based approach to choosing and obtaining a profession.&#x0D; Results. Analysis of the data obtained during the survey of 310 students showed that for the vast majority of them, higher education is an important factor for getting a high-paying job, but many also believe that even without higher education, a person is able to get a good job in life and get a prestigious profession. It is characteristic that respondents mostly consider their choice to be independent, positively affecting a person’s personal growth, however, almost a third of them prefer not to think about the fact that they will need to work according to the chosen profile. Such a choice cannot be called fully conscious. As a result, the motivation to learn and the quality of the educational result decreases. The authors come to the conclusion that the axiological approach in the learning process already in the first years allows them to direct their value orientations towards showing more interest in the chosen profession.
INFORMATION CULTURE AS A COMPONENT OF PROFESSIONAL COMPETENCES OF THE TEACHER

No abstract.
INTENSIFICATION MUSICAL AND PEDAGOGICAL PROCESS IN HIGHER SCHOOL

Two ways of the intensification of a musical and pedagogical process at higher educational institutions are examined in the article: a) improvement o f the educational system; b) change of methodological approaches. The resources of the intensification in the context o f the holistic and synergetic approach are the following: state support, the results o f latest scientific researches, the personal potential o f teachers and students, the regularities of a musical and pedagogical process.
TO THE PROBLEM OF DEVELOPMENT OF PERFORMANCE SKILLS FUTURE MUSIC TEACHERS

In article problems, of development of performing skills of the future musicians - teachers are considered. Classification is submitted and concrete performing skills, which are necessary for forming in the higher school, are determined.
FORMATION OF SELF-ORGANIZATION SKILLS FOR STUDENTS INNOVATIVE TECHNOLOGIES

No abstract.
Question Paper Generator System

Abstract: In this modern world e-book has become a basic requirement for the candidates to appear and prepare for their competitive exams within college premises. In this paper we are proposing a model system for smart question paper generation of universities. The mechanism behind this system is that many random question papers are generated along with the difficulty level of the questions in terms of percentage. After generation that particular quest ion is then mailed to the respective university. In this system administration of the database inputs set of question paper with an option of check box to tick th e correct answer. More ever weightage of the particular question in terms of marks and hours and the complexity of the question is determined. After this process whole question paper along with the weightage is stored in the database. In order to make question paper for 100 marks, admin sets all the weightage and difficulties to solve the problem. As soon as the difficulty and weightage is specified a pre doc file as per selected format will be downloaded to the admin and an electronic mail will be triggered. Range of difficulty may vary from easy, medium and hard.
PREPARATION OF TEACHERS FOR MODERN VOCATIONAL SCHOOL: POLICULTURAL ASPECT, NEW TASKS AND DIRECTIONS OF RESEARCH

The article deals with general problems the mankind is facing in the 21st, century and investigates the role of CISteachers, teachers from the Central and Western Europe in their decision. The author conducts a retrospective analysis of papers of inter national organizations (the UNO, UNESKO, MOP) in terms of a teacher rs role in the process of modernizing education.
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory

A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive empirical study of the statistics of model updates in FL, as well as the role and benefits of various compression techniques. Motivated by these observations, we propose a novel method to reduce the average communication cost, which is near-optimal in many use cases, and outperforms Top-K, DRIVE, 3LC and QSGD on Stack Overflow next-word prediction, a realistic and challenging FL benchmark. This is achieved by examining the problem using rate-distortion theory, and proposing distortion as a reliable proxy for model accuracy. Distortion can be more effectively used for optimizing the trade-off between model performance and communication cost across clients. We demonstrate empirically that in spite of the non-i.i.d. nature of federated learning, the rate-distortion frontier is consistent across datasets, optimizers, clients and training rounds.
PROBLEM-SEARCH TRAINING AS EFFECTIVE MEANS OF THE MIND STUDENT DEVELOPMENT

The right organization of the problematic-searching education and its effectiveness in the development of the mental forces and abilities of the students is considered in this article.
ISSUES OF MODELING THE FORMATION OF TRAINING AND PROFESSIONAL SUCCESS OF FUTURE OFFICERS OF THE NATIONAL GUARD TROOPS OF THE RUSSIAN FEDERATION

Relevance. Modeling and application of models as a special method of cognition has gained recognition in many fields of science. This method is also important in pedagogical science, which indicates its constructiveness. At present, a large number of studies have been conducted on the problems of modeling, revealing the use of models and methods for modeling educational activities in military educational organizations of higher education of the troops of the National Guard of the Russian Federation.&#x0D; Materials and methods. The model does not exist by itself, but due to the fact that it is created and used by the researcher as a tool of knowledge in scientific activity. The peculiarity of the modeling method is that the object of study is not studied directly, but by studying the object. Between the researcher and the object of cognition there is a model that reflects only the specific aspects of the process being studied that are of interest to researchers, without completely covering the object under study. Modeling, as a rule, is used when the analogy of the model with the object of pedagogical research reveals new features of the process being studied, thereby giving new knowledge to the researcher.&#x0D; Results of the study: to test the effectiveness of the constructed model and the formulated pedagogical conditions, we single out emotional-activity, value-semantic, communicative-behavioral, regulatory-motivational criteria and their indicators of the formation of a future officer of the National Guard of the Russian Federation to achieve educational and professional success.
GENERAL EDUCATIONAL MATHEMATICAL PREPARATION IN UNIVERSITY AS A MEANS PERSONAL DEVELOPMENT PROFESSIONAL TRAINING

No abstract.
Quality Service, Customer Retention, and the mediating role of customer satisfaction on: an exploratory study in healthcare institutions in Mosul City

The purpose of this paper is to examine customer satisfaction as a mediator variable between quality service and customer retention in the healthcare institutions in Mosul.The sample group for the study included individuals from the healthcare organizations in Mosul city.The sample included 120 individual, and was designed based on a stratified and proportional distribution.The scale of the quality service dimensions which introduced in this research was found to be appropriate and well matched.The connection of the quality service dimensions found on the real perception of healthcare services through the individual with the individuals' satisfaction and retention to their institutions was investigated using SEM.The results confirmed that all three variables are distinct constructs.As a result, there is a significant relationship of customer satisfaction as a mediator variable between quality service and customer retention.However, quality service significantly impacts to customer retention through customer satisfaction as a mediator variable and consequently leads to an increase the demand of patients to our health institutions.The study will enable healthcare institutions to have a better understanding of the effects of quality service, which will lead to patient satisfaction and behavioral intention in order to build long-term relationships with their patients.This study proposes a model to understand the effect of quality service on customer retention and customer satisfaction in developing countries.This opens the door for the possibility that other forms of the construct may moderate the relationship between quality service and customer retention.
CRITERIA FOR FORMATION OF EXPERIENCE SELF-REGULATION OF THE FUTURE MUSICIANS-TEACHERS

No abstract.