Reviewerly/exHarmony huggingface dataset:
National Research Computer Network of Russia: Development Plans, Role and Facilities in Ensuring the Functioning of the National Supercomputer Infrastructure

The paper is devoted to the discussion of issues of ensuring the current functioning and development plans of the new generation National Research Computer Network (NIKS). The goals, main functions and tasks of NIKS as a national research and education network of Russia are denoted. The current state of NIKS is considered – telecommunications infrastructure within the country and abroad, service portfolio and user base. The key areas of work and activities to ensure the accelerated development of the NIKS infrastructure and service platform for the period 2021–2024 within the framework of the National Project “Science and Universities” are presented. The proposed criteria and principles for selecting of research organizations and higher education institutions of Russia for connecting to NIKS are discussed. Special attention is paid to the role of NIKS as a high-speed telecommunications infrastructure to connect supercomputer centers of the National Supercomputer Infrastructure.
Towards a Knowledge Discovery Framework for Data Science Job Market in the United States

The growth of the data science field requires better tools to understand such a fast-paced growing domain. Moreover, individuals from different backgrounds became interested in following a career as data scientists. Therefore, providing a quantitative guide for individuals and organizations to understand the skills required in the job market would be crucial. This paper introduces a framework to analyze the job market for data science-related jobs within the US while providing an interface to access insights in this market. The proposed framework includes three sub-modules allowing continuous data collection, information extraction, and a web-based dashboard visualization to investigate the spatial and temporal distribution of data science-related jobs and skills. The result of this work shows important skills for the main branches of data science jobs and attempts to provide a skill-based definition of these data science branches. The current version of this application is deployed on the web and allows individuals and institutes to investigate skills required for data science positions through the industry lens. (Version 1.1 of this work is available at https://dsi-usa2.herokuapp.com/ . The code is shared at https://github.com/mojtaba-Hsafa/data-science-jobs-app .)
Understanding Language Model from Questions in Social Studies for Students

Artificial intelligence, especially artificial intelligence based on deep neural networks, has improved significantly. In particular, great progress has been made in natural language processing and image recognition. In the natural language processing field, many techniques and methods, such as transformer, attention, and self-attention, have been proposed and have improved this field. Recently, BERT and RoBERTa are expected to be some of the most promising natural processing technologies. Through deep learning has achieved high accuracy in various fields such as natural language processing, it has been pointed out that it lacks interpretability and explainability for decision. For addressing this issue, providing interpretability and explainable AI have been studied. For BERT and RoBERTa, discussions on understanding what language models know about language have been studied. In this paper, we discuss what the pre-trained Japanese BERT and RoBERTa language models know. We solved masked questions for Japanese students using the deep learning artificial intelligence using these language models and investigated their knowledge and the dependence of their accuracies on the domain.
Illumination-Invariant Feature Detection based on Shallow Hybrid Network Model

Feature detection is the fundamental problem of computer vision, and it is the foundation of vision tasks such as image processing, image analysis, and image understanding. It is widely used in research fields such as object detection and classification, target tracking, and motion estimation. The research and development of feature detectors with illumination robustness have very important theoretical and application value. The traditional feature-based detection method represented by SIFT solves most geometric transformation problems, while the learning-based detection method represented by TILDE had created a precedent for data-driven feature detection, and has partial illumination robustness. In this paper, we will propose an illumination-robust feature point detection method based on convolutional neural network. This method is based on the Key.Net network and has been further enhanced in terms of illumination robustness.
Techniques for Evaluating the Robustness of Deep Learning Systems: A Preliminary Review

Machine Learning algorithms are currently being applied to a huge diversity of systems in various domains, including control systems in the industry, medical instruments, and autonomous vehicles, just to name a few. Systems based on deep learning models have become extremely popular in this context, and, like regular machine learning algorithms, are susceptible to errors caused by noisy data, outliers, or adversarial attacks. An error of a deep learning model in a safety-critical context can lead to a system failure, which can have disastrous consequences, including safety violations. In this paper we review the state of the art in techniques for evaluating the reliability (in lato sensu) of deep learning models, identify the main characteristics of the methods used and discuss research trends and open challenges.
Linguistic Annotation of Translated Chinese Texts: Coordinating Theory, Algorithms and Data

Abstract The article tackles the problems of linguistic annotation in the Chinese texts presented in the Ruzhcorp – Russian-Chinese Parallel Corpus of RNC, and the ways to solve them. Particular attention is paid to the processing of Russian loanwords. On the one hand, we present the theoretical comparison of the widespread standards of Chinese text processing. On the other hand, we describe our experiments in three fields: word segmentation, grapheme-to-phoneme conversion, and PoS-tagging, on the specific corpus data that contains many transliterations and loanwords. As a result, we propose the preprocessing pipeline of the Chinese texts, that will be implemented in Ruzhcorp.
Estimating Most Influencing Parameter in TIG Welding of SS304 using Exploratory Data Analysis

Welding is the process of joining two metal pieces. There are various parameters influencing the quality of the weld. It is very important to understand which parameter has a greater influence on the joining process. Understanding the data from a statistical viewpoint will help to understand the hidden facts in the data. Exploratory Data Analysis is an approach to estimate the main characters in a data set. There are lots of tools and technologies available in the EDA which help to get into a final solution. In this study, we are using python to evaluate the main influencing parameter in the TIG Welding in the joining of Stainless Steel SS304.
Key Enforced Access Control and Performance Analysis of DES and RSA Cryptography in Cloud Computing

Cloud computing has been one of the most popular technologies in Information and Communication Technology the last few years. One of the main obstacles for its adoption is the sensitivity of insecurity and privacy violation. Protecting sensitive and high-value data within a cloud system necessitates the use of cryptographic techniques and cryptographic keys. Management of these keys is especially challenging in cloud environments due to the expanded exposure to various insider and outsider threat agents. However, it makes it difficult for data owners to resort to the cloud provider for updating the access control policy when the cooperative relationship changes. Key-enforced cloud access control guarantees the cloud users will outsource their data without outsourcing the control, since the user possesses the key rather than the cloud provider. In this manuscript we analyze the encryption and decryption time for DES, 2DES, RSA &amp; Modified RSA (MDRSA) algorithms.
Weakly-Supervised Semantic Segmentation Based on Improved CAM

When image-level labels are used for weakly-supervised semantic segmentation, there is a problem of missing target position and shape. Using classification networks to generate class activation maps has become a common method. Since the location map generated by Class Activation Mapping (CAM) tends to focus only on a small part of the significant area of the target, the generated class activation map is sparse and rough. To solve this common problem, an improved method based on CAM is proposed, which suppresses the classifier’s attention to salient regions, guides and spreads to adjacent non-salient regions, and generates denser segmentation labels. The generated segmentation label is enhanced by an improved VGG-16 network, and the high-quality pseudo-segment label will be used as the supervision information to supervise the training of the segmentation network. The comparison of the results on the PASCAL VOC 2012 proves the effectiveness of the method and excellent segmentation performance.
Explanation in medical decision support systems

This chapter focuses on locally explainable machine learning (ML) solutions, which in many ways are one of the main problems of ML systems in medicine. We will focus on posterior, model-independent, and local interpretation methods. First, we look at modern systems that explain the ML solution, representing the parameters that led to this decision. Then we propose an explanation of the local solution of the machine based on the comparison of the case under study with the case “nearby” it when the machine makes an alternative solution. Various options for the definition of “nearby” are considered. Finally, the chapter ends with describing an intelligent mediator between a doctor and an ML system, which can be especially relevant when uncertainty in the data is possible.
Governance

Higher Education AbstractsVolume 56, Issue 4 p. 343-343 GOVERNANCEToken Access Governance First published: 08 January 2022 https://doi.org/10.1111/hea.23_12178Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume56, Issue4October 2021Pages 343-343 RelatedInformation
Heuristic Sampling for Fast Plausible Playouts

This paper proposes Heuristic Sampling (HS) for generating self-play trials for games with a defined state evaluation function, with speeds comparable to random playouts but game length estimates comparable to those produced by intelligent AI agents. HS produces plausible results up to thousands of times faster than more rigorous methods.
The Value-Semantic Basis of Pedagogical Activity: a Comparative Analysis of the Position of Teachers and Pupils of Pedagogical Classes

The relevance of the research is justified by the tendency to implement the axiological approach in education in the context of the need to construct a single space for the training of pedagogical personnel, therefore the question of the value-semantic expectations from the teaching profession is important. The purpose of the work is to determine the teachers and pupil’s value-semantic foundations of pedagogical activity. Sample of the study: 61 pupils of teacher classes, 425 teachers. Methods: operationalization of existing standards (educational and professional) and conceptual content analysis, which made it possible to develop an author’s methodology of values, laid down by educational and professional standards and adapt the MUST-test (for diagnosing values for purposes). Results: pupils are focused mainly on themselves, and teachers are focused on creating conditions for their activities. The system-forming values of the first — the ability to implement an individual approach to the child and knowledge of developing technologies, and the second-knowledge of the characteristics and technologies for creating a safe and comfortable educational environment. Diagnosed the tendency for teachers to replace the actual values with the declared. Teachers and pupils have approximately equal values-relationships aimed at the educational environment and professional activity, but values whose goal is the child occupy lower places in the rating. In both groups all values are expressed approximately equally, but they put different personal meaning into them.
Gradient-wise search strategy for blind image deblurring

Blind image deblurring is a long-standing challenging problem to improve the sharpness of an image as a prerequisite step. Many iterative methods are widely used for the deblurring image, but care must be taken to ensure that the methods have fast convergence and accuracy solutions. To address these problems, we propose a gradient-wise step size search strategy for iterative methods to achieve robustness and accelerate the deblurring process. We further modify the conjugate gradient method with the proposed strategy to solve the bling image deblurring problem. The gradient-wise step size aims to update gradient for each pixel individually, instead of updating step size by the fixed factor. The modified conjugate gradient method improves the convergence performance computation speed with a gradient-wise step size. Experimental results show that our method effectively estimates the sharp image for both motion blur images and defocused images. The results of synthetic datasets and natural images are better than what is achieved with other state-of-the-art blind image deblurring methods.
Algorithm of Automated Construction and Search for the Best Layouts of Distribution Devices of 35 kV Substations and Above

This work is devoted to the creation of an automated design system for designing open distribution devices. The main task that is solved by this system is the search for the best layout option of the scheme from the point of view of saving the area of the territory. The paper proposes an approach to form distribution device circuit breakers and the effectiveness of the occupied area. When searching for solutions, the efficiency coefficient of occupied area is used, which makes it possible to get away from the cost indicators. An example of calculation and obtained during the study of the efficiency coefficients for various layouts of schemes with tires are given. The work is made in the original software product of the wrench CAD.
A Survey on Privacy Preserving Dynamic Data Publishing

Many organizations, especially small and medium business (SMB) enterprises require the collection and sharing of data containing personal information. The privacy of this data must be preserved before outsourcing to the commercial public. Privacy preserving data publishing PPDP refers to the process of publishing useful information while preserving data privacy. A variety of approaches have been proposed to ensure privacy by applying traditional anonymization models which focused only on the single publication of datasets. In practical applications, data publishing is more complicated where the organizations publish multiple times for different recipients or after modifications to provide up-to-date data. Privacy preserving dynamic data publication PPDDP is a new process in privacy preservation which addresses the anonymization of the data for different purposes. In this survey, the author will systematically evaluate and summarize different studies to PPDDP, clarify the differences and requirements between the scenarios that can exist, and propose future research directions.
Machine Learning for Human Activity Detection Using Wearable Healthcare Device

In the COVID-19 pandemic, adults are sitting long periods for the job or with their tutors. These prolonged sitting periods constitute metabolic syndrome and raise the risk of health problems. The Internet of Things plays a prominent role in identifying human activity accurately using a wide variety of sensors. In this work, to sense human activities, a smart belt with in-built accelerometer and gyroscope sensors have been utilized. The readings from these sensors expose triaxial acceleration and gyroscope features with its time-domain feature. Different classification techniques are used to review human activity and to detect the overall sitting time from the feature extracted sensor data. Based on our experimental results, the logistic regression-based classifier has shown high accuracy compared to other classification methods. This logistic regression is used to detect the sitting time and to care for humans based on our healthcare application. The data acquired from smart belts are more effective in detecting human activity than the other wearable devices.
Machine Translation Quality in Mobile Apps for Text-based Image Translation

With the advancement of mobile applications, now it is possible to perform instant text translation using a smartphone’s camera. Because text translation within images is still a relatively new field of research, it is not surprising that the translation quality of these mobile applications is under-researched. This study aims to determine the image-to-text translation quality in the English to Lithuanian language direction using popular machine translation apps. To classify errors and evaluate the quality of translation, the present study adopts and customizes the Multidimensional Quality Metrics (MQM) framework (Lommel 2014). The obtained results indicate that image-to-text machine translation apps produce exceptionally low-quality translations for the English-Lithuanian language pair. Therefore, the quality of machine translation for low-resource languages such as Lithuanian remains an issue.
Smart Customized Charging of Portable Devices Through an Authorized App

The proposed research work aims to develop not only portable devices but also huge items that may require charging at times when there is an immediate shortage of power supply. It is anticipated that, every portable device will be IP-based via the DHCP protocol so that it may be accessed via the app, in which users must previously register their usable gadgets. When the user wants to obtain power charging from a remote partner, they may do it by entering their phone number into a phonepe or any other similar app. The user who intends to transfer power in an eco-friendly manner must make a request to the person on their contact list, wherein the state of charging in the percentage form is immediately updated. When the recipient accepted the request with mutual permission, he or she was given the option of selecting the percentage of charging that they were willing to provide to the sender using QR-codes that held the amount of charging. The sender will be notified of the static QR-code via the app. When the sender scans the receiver's QR-code, their charge status is changed to the new level. This procedure comprises technical upgrades as well as enabling plug-ins to be added to the device in order to create an internet-based gadget, as well as supporting long-term batteries with quick charging and fast discharging capabilities.
Interactive Theorem Proving

Traditional analysis techniques cannot ascertain an accurate analysis for the safety- and mission-critical applications of smart grids due to their inability to exhaustively specify and verify the systems. To overcome these issues, we propose the theorem proving based methodology for an accurate analysis of smart grid problems which can be modeled using stability, microeconomics and algorithm design theories. We choose the HOL Light theorem prover to develop the proposed formalizations due to the availability of foundational theories, such as set, differential, real and complex, in the HOL Light library. In this chapter, we present a brief overview of the HOL Light theorem prover and commonly used formalizations from the HOL Light library to facilitate the understanding of the proposed formalizations which are presented, in the rest of the book.
A Study on Bitcoin Price Volatility Based on the SVAR Model and Impulse Response Analysis

Based on the monetary property and finance property of Bitcoin, this paper analyzed the influence factors of Bitcoin price volatility from the perspectives of Bitcoin’s own and external factors. The structural vector autoregression (SVAR) model is applied to investigating the direction and strength of the effects of influence factors on Bitcoin prices and the variance decomposition approach is used to compare the contributions of these factors. The results show that the Bitcoin’s own factors play fundamental roles in Bitcoin price volatility, and the speculation factors have significant impacts on Bitcoin price volatility, which reflects that the financial property of Bitcoin becomes increasingly important. Governments and investors should pay close attention to the financial property of Bitcoin.
Development of an Approach to Building Representation Metaphors for Visualizing Graph Models (by the Example of Cognitive Maps)

The paper presents development of the authors’ approach to visualization of graph models of various types based on the use of visualization metaphors and aimed at increasing cognitive clarity of these models. One of the key problems of this approach is investigated – namely, formalization of the process of constructing representation metaphors for graph models. Features of graph models that allow formalizing the process of their visualization are considered, the necessary terminology is introduced. A number of principles have been formulated that must be considered when forming metaphors for representing graph models. On the basis of the introduced principles, a general approach to the construction of representation metaphors for visualization of arbitrary graph models is proposed. The main ideas for applying the proposed approach are demonstrated by the example of a fuzzy cognitive map when constructing a metaphor for representing the results of its structure and target analysis. The directions of advanced research have been determined, within the framework of which it is planned to further formalize the approach in order to ensure the possibility of its software implementation. In the future, the presented approach can become an important component of an integrated approach to building a visualization mechanism for an arbitrary graph model, which provides support for efficient visual analysis throughout all stages of modeling.