Reviewerly/exHarmony huggingface dataset:
Specifics of Organizing Distance Learning of Organic Chemistry at a Pedagogical University in Conditions of a Pandemic Coronavirus Infection

Th e purpose o f the study is to identify the features of distance learning of organic chemistry in Omsk State Pedagogical University (OmSPU) during the coronavirus pandemic in conditions of self-isolation. Materials and methods. To solve this problem the authors have analyzed pedagogical literature and practical experience to organize distance learning of students, and self-analysis experience of distance learning of organic chemistry for the bachelors of the 3rd course at Omsk State Pedagogical University (direction “Pedagogical Education”, profile “Biology and Chemistry”) using the electronic information and educational environment of the University on the Moodle platform, followed by generalization and systematization of the identified features. Based on a survey of students, their attitude to distance learning of organic chemistry is revealed, and the problems and difficulties faced by students and lecturers are shown. Results. The possibilities of conducting lectures and practical classes in organic chemistry in the BigBlueButton video conference format are described. Methodological techniques of using static and quasiinteractive multimedia presentations and demo videos, developed by the authors are shown. The possibilities of the course elements “chat” and “forum” on the OmSPU portal and WhatsApp messenger for solving organizational issues, consulting and correcting students’ activities, building their individual educational trajectory are revealed. The article shows the features of practical training, methodological techniques of using video demonstrations. Based on the analysis of the results of the survey of students and their exam answers, the most important disadvantages of distance learning in organic chemistry were identified. Learning in an electronic environment does not allow you to fully develop the skills necessary for future lecturers to communicate with students. The damage is caused to the development of oral speech, the ability to correctly use chemical terminology, explain the material using various teaching tools, and the formation of skills for group and pair interaction. Replacing a real chemical experiment with a virtual one does not allow you to form experimental skills specific to organic chemistry. Therefore, according to the authors, when studying organic chemistry, distance learning must necessarily be combined with traditional, with classes in a chemical laboratory. Conclusion. The most effective tools for organizing distance learning of organic chemistry are identified, taking into account the specifics of the discipline. The ways of solving the difficulties faced by students and lecturers in the conditions of distance learning are shown. The developed and described methods of working with students in the organization of distance learning of organic chemistry can be used to improve the effectiveness of the development of the content of the discipline and the formation of professional competencies of future lecturers.
Decentralizing Supply Chain Anti-Counterfeiting Systems Using Blockchain Technology

An interesting research problem in supply chain industry is evaluating and determining provenance of physical goods - demonstrating authenticity of luxury goods. Yet, there have been a few innovative software solutions addressing product anti-counterfeiting and record provenance of today's goods that are produced and transported in complex and internationally-spanning supply chain networks. However, these supply chain systems have been implemented with centralized system architecture, relying on centralized authorities or any form of intermediaries, and leading to issues such as single-point processing, storage and failure, which could be susceptible to malicious modifications of product records or various potential attacks to system components by dishonest participant nodes traversing along the supply chain. Blockchain technology has evolved from being merely a decentralized, distributed and immutable ledger of cryptocurrency transactions to a programmable interactive environment for building decentralized and reliable applications addressing different use cases and existing problems in the world. In this research, the Decentralized NFC-Enabled Anti-Counterfeiting System (dNAS) is proposed and developed, decentralizing a legacy anti-counterfeiting system of supply chain industry using Blockchain technology, to facilitate trustworthy data provenance retrieval, verification and management, as well as strengthening capability of product anti-counterfeiting in supply chain industry. The proposed dNAS utilizes decentralized blockchain network on a consensus protocol compatible with the concept of enterprise consortium, programmable smart contracts and a distributed file storage system to develop a secure and immutable scientific data provenance tracking and management platform on which provenance records, providing compelling properties on data integrity, are validated automatically.
Mathematical model of transformation, recognitions and memorising sound information by human

This article is devoted to the development of a mathematical model of a human auditory analyzer based on already proven electrophysiological experimental data and existing mathematical methods for describing the transformation and recognition of sound information. New algorithms for converting, transmitting memorization and recognition of sound information are proposed. The calculations of the error in converting audio information are given, the mathematical principles of adding and combining the source encoded information are described.
Perceptions of Information Systems Security Compliance: An Empirical Study in Higher Education Setting

Ensuring information systems security policy compliance is an integral part of the security program of any organization. This paper investigated the perceptions of different stakeholder groups towards information security policy compliance constructs of the Unified Model of Information Security Compliance (UMISPC)[1] in a higher education environment.The research findings showed that faculty/staff generally has higher tendency towards security policy compliance comparing to students in a higher education institution.In addition, students with security knowledge are more incline to have security policy compliance activities.Our finding not only added to the knowledge base of information systems security compliance research, but also offers practical implications.
The prospects of a CMS development

This article contains the description of the prospects of a CMS development and the short description of the stages of the Internet-formation before an appearance of the CMS. And the assumed way of a CMS development along the MODx-scenario is described in this article.
THE DESIGN OF THE PROTOTYPE MOBILE MUSIC APPLICATION

The article analyses the UI and UX concepts of popular music platforms and streaming services in terms of usability and functionality. Popular analogues of music apps are analysed – Apple music, YouTube music, Yandex music, Boom. The results of a design study devoted to the problem of promoting young performers to the music market are presented. Topicality of the problem is confirmed by the growing popularity of music&#x0D; services and applications among users. The stages of developing a prototype of a music application using the Figma programme are presented. The process of team work on a prototype of a mobile application with planning of project stages and distribution of tasks between team members, using scrum technology is shown.
Comparison of RISC-V and transport triggered architectures for a postquantum cryptography application

Cryptography is one of the basic phenomena of security systems. However, some of the widely used publickey cryptography algorithms can be broken by using quantum computers. Therefore, many postquantum cryptography algorithms are proposed in recent years to handle this issue. NTRU (Nth degree truncated polynomial ring units) is one of the most important of these quantum-safe algorithms. Besides the importance of cryptography algorithms, the architecture where they are implemented is also essential. In this study, we developed an NTRU public key cryptosystem application and designed several processors to compare them in many aspects. We address two different architectures in this work. The RISC-V (reduced instruction set computer-V) is chosen as it is the most lately version of classical RISC architecture. As competitor to this, we preferred transport triggered architecture (TTA) which offers high level customization and scalability. Details of all different implementations and the test results obtained with them are shared and discussed.
Klasifikasi Text Judul Buku Perpustakaan Untuk Menentukan Kategori Buku Menggunakan K-Nearest Neighbor

The need for information in the form of books or scientific articles at the Library of UIN Raden Fatah Palembang continues to increase. To make it easier to find book information, one of which is by classifying books based on the type of category. In classifying library book data, the Nearest Neighbor Classifier method in data mining can be combined with text data extraction techniques to classify library book title text data. The purpose of this study was to classify the text title of library books using the Nearest Neighbor Classifier to determine the type of book category. This research method uses the Nearest Neighbor Classifier data mining classification technique. The results of this study are that the highest accuracy value is found at K = 12, which is 72.50%, and the model formed can be used to classify books with labels 2x0, 150, 2x2, 400, 020, 2x1, 657, 500, 375, 302.2, 800. and cannot be used for classifying books with class labels 070, 370, 330, 300, 600, 340, 700.
Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks

This paper presents a new framework for human body part segmentation based on Deep Convolutional Neural Networks trained using only synthetic data. The proposed approach achieves cutting-edge results without the need of training the models with real annotated data of human body parts. Our contributions include a data generation pipeline, that exploits a game engine for the creation of the synthetic data used for training the network, and a novel pre-processing module, that combines edge response maps and adaptive histogram equalization to guide the network to learn the shape of the human body parts ensuring robustness to changes in the illumination conditions. For selecting the best candidate architecture, we perform exhaustive tests on manually annotated images of real human body limbs. We further compare our method against several high-end commercial segmentation tools on the body parts segmentation task. The results show that our method outperforms the other models by a significant margin. Finally, we present an ablation study to validate our pre-processing module. With this paper, we release an implementation of the proposed approach along with the acquired datasets.
ATNN and SVM for Autonomous Mobile Robot

If a robot does not know where it is, it can be difficult to determine what to do next. In order to localizeitself, a robot has access to relative and absolute measurements giving the robot feedback about its driving actions andthe situation of the environment around the robot. Given this information, the robot has to determine its location asaccurately as possible. What makes this difficult is the existence of uncertainty in both the driving and the sensing ofthe robot. The uncertain information needs to be combined in an optimal way. The Kalman Filter is a technique fromestimation theory that combines the information of different uncertain sources to obtain the values of variables ofinterest together with the uncertainty in these. In this work we provide a thorough discussion of the robot localizationproblem resolved by Kalman Filter, Adaptive Time Delay Neural Network and Support Vector machines.
3D modeling in the studies of the portable art geometrical images of the Upper Paleolithic and Mesolithic of Eastern Europe

One of the plots of the portable art forms of Eastern Europe is abstract, geometric, symbolic images that cannot be deciphered directly. Most of the currently known such geometric images from the archaeological sites of the Upper Paleolithic and Mesolithic of Eastern Europe were applied to finished bone products — tools, weapons, household and non-utilitarian items, as well as bones of various animals without traces of processing using various cutting techniques. The bone is well preserved in the cultural layers of the sites of the late Pleistocene — early Holocene of the Russian Plain, which makes objects from this material an important source for the study of geometric images. However, despite the richness of the source base, the problem of classification and systematization of geometric images in the art of small forms remains poorly developed. The purpose of the article is to consider the issues of practical application of three-dimensional 3D modeling in the study of art objects of small forms of the Upper Paleolithic and Mesolithic. The authors describe the practice of photographing such objects and the peculiarities of the lighting schemes and camera positioning, developed taking into account the specifics of the geometry and material of the documented objects. The minimal technical requirements for the resolution of the models have been determined, which provide the possibility of using visualization algorithms to study fine engravings on bone and stone. The practical application of mathematical visualization algorithms both directly on polygonal models and on height maps built on their basis is described. A method for visualizing and systematizing research results and providing remote access to them using modern web technologies is proposed.
Correlation minor norms, entanglement detection and discord

In this paper we develop an approach for detecting entanglement, which is based on measuring quantum correlations and constructing a correlation matrix. The correlation matrix is then used for defining a family of parameters, named Correlation Minor Norms, which allow one to detect entanglement. This approach generalizes the computable cross-norm or realignment (CCNR) criterion, and moreover requires measuring a state-independent set of operators. Furthermore, we illustrate a scheme which yields for each Correlation Minor Norm a separable state that maximizes it. The proposed entanglement detection scheme is believed to be advantageous in comparison to other methods because correlations have a simple, intuitive meaning and in addition they can be directly measured in experiment. Moreover, it is demonstrated to be stronger than the CCNR criterion. We also illustrate the relation between the Correlation Minor Norm and entanglement entropy for pure states. Finally, we discuss the relation between the Correlation Minor Norm and quantum discord. We demonstrate that the CMN may be used to define a new measure for quantum discord.
Scalable Bayesian Inverse Reinforcement Learning

Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves scale often require extensive interaction with the environment to perform well, being inappropriate for high stakes or costly applications such as healthcare. In this paper we introduce our method, Approximate Variational Reward Imitation Learning (AVRIL), that addresses both of these issues by jointly learning an approximate posterior distribution over the reward that scales to arbitrarily complicated state spaces alongside an appropriate policy in a completely offline manner through a variational approach to said latent reward. Applying our method to real medical data alongside classic control simulations, we demonstrate Bayesian reward inference in environments beyond the scope of current methods, as well as task performance competitive with focused offline imitation learning algorithms.
On-site Earthquake Early Warning: Predictive Models for Acceleration Response Spectra Considering Site-Effects

&amp;lt;p&amp;gt;Including site specific amplification factors in ground motion prediction models represented an advance for PSHA (Atkinson 2006; Rodr&amp;amp;#237;guez-Marek et al. 2013; Kotha et al. 2017) that has become nowadays a standard. However, this issue has only recently received attention by the seismological community of earthquake early warning (EEW) (Spallarossa et al., 2019; Zhao and Zhao, 2019), which applications require a real-time prediction of ground motion and the delivery of alert messages to users for mitigating their exposure to seismic risk. Indeed, all EEW systems are high-technological infrastructures devoted to the real-time and automatic detection of earthquakes, rapid assessment of the associated seismic hazard for targets and the prompt delivery of alerts trough fast telecommunication networks. Among them, the on-site approaches are based on seismic networks placed near to the target, indifferently by the location of seismic threats and they issue the alert predicting the ground motion at the target from P-wave parameter. This configuration cause that On-Site EEWS are generally highly affected by site conditions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work, we calibrated ground motion prediction models for on-site EEW considering acceleration response spectra (RSA) and the P-waves EEW parameters Iv2 and Pd, and we investigated the role of site-effects. We considered a dataset of nearly 60 earthquakes belonging to the Central Italy 2016-17 sequence. The high density of stations near to the sequence has allowed us to use a non-ergodic random-effect regression approach to explore and to reduce the contribution of site-effects to the uncertainty of the On-site laws predictions. We grouped the records in two ways: by stations and by EC8 classification. Then, we validated the estimated models by the Leave One Out (L1Out) technique and applied a K-means analysis to assess the performance of the EC8 classification.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The residuals analysis proved that grouping by station provides a set of relations that improves the predictions at many stations. On the contrary, L1Out cross-validation proved that the regressions retrieved grouping by EC8 classification produce higher uncertainties on the predictions than the others. Furthermore, the cross-validation proved that Iv2 is more correlated to RSA than Pd. Finally, the analysis of the random effect vs period curves confirmed that EC8 classification is unrelated to the site effect on RSA even looking only at the trend of these curves.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In conclusion, non-ergodic random-effect regression can be used also in the EEW applications to predict site-specific ground motion. EEWS that use this approach are less dependent by site-effect and able to provide more precise and reliable alerts.&amp;lt;/p&amp;gt;
Cybersecurity Curricula Recommendations Development for Technical Background and Engineering Skills in International Dimension

The risks of information security threats in conditions of rapidly increasing use of information and communication technologies in society, economy and public administration have led to the need for a significant increase of information reliability. The efficient education for international career building of young engineers in the technical aspects of Cyber security is discussed in this article. The mission of such engineers is the development of technical assets, mechanisms, electronic devices and systems, as well as integration legacy solutions and reengineering of systems, taking into account protection of information assets. It has become one of the fastest growing information technology in industries today, mainly due to two trends: (1) exponential growth of data from business systems and security sensors to protect this business; (2) lack of qualified cyber security staff to analyze and respond to incidents related to these data. The market analysis on the international and national levels, chows, that the number of vacancies in area is growing, for example the number of specialists required by CERT.LV only is around 200, and they estimate that the number of employees required by private companies is several hundreds on national level. Developed countries laws require companies to designate a qualified person responsible for IT security issues and the development and maintenance of technological solutions. The task of a cyber security specialist in companies and different organizations is to manage security solutions, consult users and provide expertise not only engineering background, but also background in IT security issues. Implementation of these tasks requires a wide range of multidisciplinary knowledge, including computer networking, software, integrated systems, critical infrastructure security and security management.
An Efficient Method for Face Quality Assessment on the Edge

Face recognition applications in practice are composed of two main steps; face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detections for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detections of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach’s efficiency through comparison with state-of-the-art face quality regression models on different data sets and real-life scenarios.
Search for a saddle point of a convex-concave stochastic game by the adaptive method of mirror descent.

A stochastic game problem of 2 persons with a zero sum is considered, leading to the search for a saddle point of the game function based on the gradient approach. We study mirror descent algorithms, both adaptive and non-adaptive. The main results are proved. An illustrative example is discussed.