Reviewerly/exHarmony huggingface dataset:
Ideological Origins of the Unified Labor School

The purpose of the article is based on the analysis of materials published in the pre-October period and in the first post-revolutionary years to identify theoretical ideas and practical developments in the field of pedagogy, which formed the basis for the design of the Soviet labor school. Research material. The main sources of the research are scientific and pedagogical publications of representatives of Western pedagogical thought and Russian pedagogy, published in the first decades of the XX century. Research results and conclusions. The analysis of the research source base allowed us to identify the ideological foundations formed in the late XIX – early XX centuries, on which the theory and practice of the labor school were built. The goal of the school was to, based on the labor principle of education The goal of the school was to ensure, based on the labor principle of education, the overall development of the intellectual abilities of the child, to teach him to observe the world around him and explore it independently, to gain knowledge by experience, to be able to continue and expand them, organically merging into one whole worldview. The developed ideological foundations became the basis for the theoretical development in 1918 of the project of a new Soviet school. However, by the end of the 1920s, in the conditions of the aggravation of the inconsistency of the proclaimed ideas with the requirements of the emerging Soviet state-political system, the increase of errors and shortcomings in their implementation, the model of the labor school ceased to be in demand.
Real-time face detection on a Raspberry PI

The article describes the implementation of different face detection algorithms to capture human faces from real-time video frames using a Raspberry PI microprocessor. This article examines this issue, proposes the implementation of two distinct real-time face detection algorithms, and presents a comprehensive architectural design. Used methods include Haar Cascades which is known as Viola-Jones algorithm, and Histogram of Oriented Gradients + Linear Support Vector Machines algorithm. The algorithms are implemented with the help of the OpenCV and Dlib libraries, and the Python programming language was used to build the face detection system. The OpenCV and Dlib libraries include a large number of built-in packages that assist with face detection and conduct face operations separately, resulting in reduced processing time and increased efficiency overall. The results confirm that both methods can detect faces in real time with acceptable accuracy and computation time but there are several differences. The Histogram of Oriented Gradients + Linear Support Vector Machines algorithm.method is much more preferable in terms of accuracy, but the image pyramid construction will be computationally demanding.
Library Sites that Provide Information to Users (Based on Domestic and Foreign Library Experience)

The main purpose of the research is to examine the interaction of electronic library sites as an elements of the information resource management system based on local and foreign experience. It consists of defining the location of electronic sites of libraries and information centers as an element of information resource management, interpreting information resources as a system, analyzing their logistics capabilities. &#x0D; In the digital environment, place of library sites is in the management of information resources. The article examines the characteristics of electronic sites, which form the main interface structure of library systems. Predicated on the experience of specific libraries, the requirements for the use of the electronic resource database are highlighted. Information resources published on domestic and foreign electronic library sites used by academic users and general reader groups were analyzed and their relevant search capabilities were explicated. The article also examines the interaction of the Azerbaijan Diplomatic Academy-ADA Library and The US Library of Congress website with the electronic library and electronic catalog interface as an example of domestic and foreign electronic library experience.
Planning with RL and episodic-memory behavioral priors

The practical application of learning agents requires sample efficient and interpretable algorithms. Learning from behavioral priors is a promising way to bootstrap agents with a better-than-random exploration policy or a safe-guard against the pitfalls of early learning. Existing solutions for imitation learning require a large number of expert demonstrations and rely on hard-to-interpret learning methods like Deep Q-learning. In this work we present a planning-based approach that can use these behavioral priors for effective exploration and learning in a reinforcement learning environment, and we demonstrate that curated exploration policies in the form of behavioral priors can help an agent learn faster.
Betti numbers of attention graphs is all you really need

We apply methods of topological analysis to the attention graphs, calculated on the attention heads of the BERT model ( arXiv:1810.04805v2 ). Our research shows that the classifier built upon basic persistent topological features (namely, Betti numbers) of the trained neural network can achieve classification results on par with the conventional classification method. We show the relevance of such topological text representation on three text classification benchmarks. For the best of our knowledge, it is the first attempt to analyze the topology of an attention-based neural network, widely used for Natural Language Processing.
ANALISIS PENGUKURAN DAN STANDAR WAKTU KERJA PADA PELAYANAN PEMBUATAN SURAT DI KANTOR CAMAT KECAMATAN MAKARTI JAYA (STUDI KASUS KABUPATEN BANYUASIN)

This study aims to determine the standard time for making letters and to analyze the procedures for measuring the standard time of making letters at the Makati Jaya Subdistrict Head Office so as to build the effectiveness of the work of employees in the office in the end to improve services, especially in administrative activities. The implementation of the research uses liberary research and field research methods, the data used are primary and secondary data with data analysis techniques using standard time measurements. The results obtained The normal time per work element for planning the concept of outgoing letters is 0.567 minutes and the next standard time is 0.646 minutes. The normal time for letter approval is 0.561 minutes and the standard time is 0.631 minutes. The normal time of letter numbering takes 0.638 minutes and produces a standard time of 0.717 minutes. The normal time for typing letter drafts is 0.541 minutes and the standard time is 0.617 minutes. Normal time Signing a letter takes 0.597 minutes and creates a standard time of 0.671 minutes. Normal time Stamping takes 0.595 minutes and produces a standard time of 0.678 minutes. Normal time Scheduled a letter for 0.541 minutes and resulted in a standard time of 0.617 minutes. From the results of observations and various data that have been obtained, the process of making outgoing letters obtains a standard time of 4,577 minutes. Keywords: Normal Time; Standard Time; Measurement
Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

&lt;p&gt;Assisted and automated driving functions are increasingly deployed to support improved safety, efficiency, and enhance driver experience. However, there are still key technical challenges that need to be overcome, such as the degradation of perception sensor data due to noise factors. The quality of data being generated by sensors can directly impact the planning and control of the vehicle, which can affect the vehicle safety. This work builds on a recently proposed framework, analysing noise factors on automotive LiDAR sensors, and deploys it to camera sensors, focusing on the specific disturbed sensor outputs via a detailed analysis and classification of automotive camera specific noise sources (30 noise factors are identified and classified in this work). Moreover, the noise factor analysis has identified two omnipresent and independent noise factors (i.e. obstruction and windshield distortion). These noise factors have been modelled to generate noisy camera data; their impact on the perception step, based on deep neural networks, has been evaluated when the noise factors are applied independently and simultaneously. It is demonstrated that the performance degradation from the combination of noise factors is not simply the accumulated performance degradation from each single factor, which raises the importance of including the simultaneous analysis of multiple noise factors. Thus, the framework can support and enhance the use of simulation for development and testing of automated vehicles through careful consideration of the noise factors affecting camera data. &lt;/p&gt;
Cloud-Based Bug Tracking Software DefectsAnalysis using Deep Learning

Abstract Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. Early detection of defect-prone components in software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Notwithstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.
Preface

The 17th International Conference on Electronic Devices and Control Systems 2021 (EDCS 2021) 17-19 November 2021 Tomsk, Russia The 17th International Conference on Electronic Devices and Control Systems was held on November 17-19, 2021. All sections were held online via the video conferencing service Greenlight. Bringing together the best high-tech universities, institutions, and companies, EDCS-2021 is aimed at providing platform for discussing issues of modern radio electronics, theory and practice of control systems, information security, presenting new devices, engineering solutions, software and systems. EDCS-2021 covers a wide range of topics related to Radio engineering; Microwave nanoelectronics; Nanotechnology in electronics; Antennas and microwave devices; Control devices and methods; Optoelectronics and photonics; Plasma electronics; Nonlinear optics; Intelligent control systems; Information security; Information technologies in management and decision-making; Video information technologies and digital television; Robotics and mechatronics; Mathematical modeling in physical and engineering sciences. The conference was organized with the support of Ministry of Science and Higher Education of the Russian Federation, Tomsk State University of Control Systems and Radioelectronics, Administration of Tomsk region, Administration of Tomsk, Tomsk group and student branch of the IEEE, TUSUR Research Institute of Automatics and Electromechanics, Information Satellite Systems Reshetnev, Milandr, Keysight Technologies, National Instruments, LLC Nauchnyye Pribory I Sistemy, 50ohm TECH. Chair of EDCS-2021 Dr. Victor M. Rulevskiy, Rector of Tomsk State University of Control Systems and Radioelectronics Editors : Dr. Mikhail Semenov, Associate Professor at the Department of Experimental Physics, Tomsk Polytechnic University Dr. Alexander V. Medovnik, Head of Research Office, Tomsk State University of Control Systems and Radioelectronics Anna M. Bogdan, School of Computer Science and Robotics, Tomsk Polytechnic University
Cloud Based Irrigation System with IoT and Machine Learning

There is a growing demand for electrical energy requirements in India. Nowadays, people tend to maintain home gardens, Terrace farms, and Mini farms. This Maintenance growth has been possible because of a timely and adequate supply of water. The irrigation depends on the groundwater availability, but as per the census of 2017, the groundwater levels have dropped down by 61% from 2007. Therefore, water usage with effective power consumption is of at most importance. Despite their desire to maintain, people take a step back, owing to time constraints, their busy lives, and the unavailability of a person to look after farms. Hence, the Internet of Things (IoT) and Machine Learning (ML) based smart irrigation system with Cloud integration is employed to address the farms. The system forecasts using SVM and reports graphically, the irrigation requirement of the field using several environmental parameters along with weather forecasting that assists the growth of the crops with the effective use of water and power conserving initiatives is presented in this paper.
Adapting to Online Label Shift with Provable Guarantees

The standard supervised learning paradigm works effectively when training data shares the same distribution as the upcoming testing samples. However, this stationary assumption is often violated in real-world applications, especially when testing data appear in an online fashion. In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not. The non-stationarity nature and the lack of supervision make the problem challenging to be tackled. To address the difficulty, we construct a new unbiased risk estimator that utilizes the unlabeled data, which exhibits many benign properties albeit with potential non-convexity. Building upon that, we propose novel online ensemble algorithms to deal with the non-stationarity of the environments. Our approach enjoys optimal \emph{dynamic regret}, indicating that the performance is competitive with a clairvoyant who knows the online environments in hindsight and then chooses the best decision for each round. The obtained dynamic regret bound scales with the intensity and pattern of label distribution shift, hence exhibiting the adaptivity in the OLaS problem. Extensive experiments are conducted to validate the effectiveness and support our theoretical findings.
Application of information technologies as a significant factor in the formation of conceptual thinking of schoolchildren

Problem and goal . The article is aimed at identifying factors in the development of conceptual thinking of schoolchildren in the conditions of informatization of education. The rapid informatization of society and education makes modern teachers look for new ways to form the conceptual thinking of students, while involving the latest information and communication technologies. There is a problem of determining the role and place of conceptual thinking in the evolution of a digital society, identifying effective domestic and foreign electronic educational resources that contribute to the formation of conceptual thinking in children of different ages. Methodology. A model for the formation of conceptual thinking of secondary school students based on an electronic course in Spanish has been created. Experimental training of schoolchildren was carried out using educational electronic resources, the development of which was completed on the basis of taking into account the provisions of this model. Results. It is experimentally substantiated that the positive dynamics of the formation of the readiness of primary school students to use terminological structures is due to the identified pedagogical conditions, the introduction of the proposed teaching methods and informatization tools. As a result of the experiment, schoolchildren revealed the improvement of conceptual thinking and an increase in motivation for communication. Conclusion. The use of the created and tested electronic textbook helps to increase the efficiency of the formation of conceptual thinking in primary school students in the framework of teaching a foreign language. The development of conceptual thinking using a foreign language can be carried out by taking into account the described pedagogical conditions, which provide for the widespread use of modern information technologies.
About the Results and Conclusions of Checking the Level of Formation of Textual Competencies of Younger Schoolchildren in Their Native (Chuvash) Language

The purpose of the article is to substantiate approaches to the development and description of tools for monitoring the level of formation of textual competencies of younger schoolchildren in the Chuvash language, to present the results of checking the level of formation of textual competencies of younger schoolchildren in their native Chuvash language. The main research methods: analysis of the methodology of formation and monitoring of general communication skills of younger schoolchildren, the results of All-Russian testing works of students in the 4th grade of the Chuvash Republic in Russian in order to identify difficulties, diagnostics of the level of formation of textual competencies in the native language of students in grades 1-4 of Chuvash schools. The importance of primary education and the importance of language learning, including the formation of textual competencies in younger schoolchildren, is pointed out. The main problems of providing the Chuvash primary school with didactic materials for the development of native speech, as well as tools for monitoring the level of formation of textual competencies are highlighted. An assumption about the expediency of creating and tools for monitoring the level of formation of textual competencies within the framework of the development of a set of didactic materials on the Chuvash language for grades 1-4 is put forward. Research results and conclusions. The necessity of developing verification works designed to ensure the unity of the educational space of the Chuvash Republic for teaching the Chuvash language, including by providing educational institutions with uniform verification materials and uniform criteria for evaluating educational achievements, is substantiated. The purpose and objectives, the methodology of developing diagnostic materials for determining the level of formation of textual competencies are clarified. The materials and results of diagnostics of textual competencies of younger schoolchildren in the Chuvash language are described. The ideas and provisions implemented in the materials for determining the level of formation of textual competencies in the Chuvash language were successfully tested in grades 1-4 in a number of Chuvash schools, and also approved by teachers.
A Cascade Model for Argument Mining in Japanese Political Discussions: the QA Lab-PoliInfo-3 Case Study

The rVRAIN team tackled the Budget Argument Mining (BAM) task, consisting of a combination of classification and information retrieval sub-tasks. For the argument classification (AC), the team achieved its best performing results with a five-class BERT-based cascade model complemented with some handcrafted rules. The rules were used to determine if the expression was monetary or not. Then, each monetary expression was classified as a premise or as a conclusion in the first level of the cascade model. Finally, each premise was classified into the three premise classes, and each conclusion into the two conclusion classes. For the information retrieval (i.e., relation ID detection or RID), our best results were achieved by a combination of a BERT-based binary classifier, and the cosine similarity of pairs consisting of the monetary expression and budget dense embeddings.
Facial Expression Recognition

Facial Expression RecognitionThe study of human facial expressions has been a longstanding focus of research in the fields of psychology, communication science and evolutionary biology.Facial expressions are commonly associated with emotions; we interpret them as observable manifestations of hidden/latent emotional states.In Co-Corporeality, we focus on facial expression as a mode of non-verbal communication that can enable interaction with machines.In the realisation of the E-Feed/ er performance, facial expression is the primary form of recognised communication.The E-Feed/er directly translates the emotions suggested by facial expressions into machine actions in order to interact directly with bacteria.This translation of facial expression to emotions is an ambiguous one, as the assumption that there is a causal and distinct relationship between human emotions and facial expressions is heavily contested.
Use Case Framework of Computerized Production Monitoring Processes in Textile Industry

Use cases are a description of system functions resulting from needs analysis and obtained from interviews and observations. In standard practices, this stage is also known as the most time-consuming stage. Although every use case produced in software development is unique, there is always a similarity in its function to systems made previously in other organizations. These similarities are studied to reduce time in the process during the requirements analysis stage. Many studies have built and used a Use Case Framework (UCF) to be used together by software developers. So far, UCF has been owned by the banking industry in mapping use case standards in ATMs, health in standardizing use cases in electronic medical records, libraries in standardizing information retrieval, and mapping processes in crowdfunding. This research adds to the list of the latest UCFs produced, namely in the related textile industry, in standardizing the functions that exist in computer-based production monitoring systems. It is based on the fact that there are many textile companies globally, with more than 1.000 of them are established in Indonesia. This study investigated eight Indonesian textile companies to obtain information data to determine what functions are required, t. The data collection techniques used were interviews and observation. More stages were carried out in this study afterward, namely defining Actor Analysis and Functional Methods, Combining Analysis, Classification of Use Cases, Describing Use Case Scenarios, and Visualizing Frameworks. The data analysis results obtained from each company, we managed to define 10 main use cases, 4 supporting use cases, and four specific use cases. This study’s products can help provide a reference in using case design to create a computer-based textile company monitoring system.