Reviewerly/exHarmony huggingface dataset:
Development and Implementation of the Module “Engineering, Education and Pedagogy in Industry 4.0” in the Structure of the Curriculum “Innovative Pedagogy for Teachers of Engineering Universities” (iPET)

In the framework of educational program EC «ERASMUS + Capacity Building in the Field of Higher Education 2018, PROJECT №1: EngineeriNg educoTors pEdagogical tRaining (ENTER) – Pedagogical Training of Higher Engineering School`s Educators” the results of the development of the module are presented. The module “Engineering, Education and Pedagogy in Industry 4.0″ of the multi-level modular curriculum “Innovative Pedagogy for Teachers of Engineering Universities” (iPET) to improve the qualifications of “educators of the future” is implemented in this educational program [1–3]. The module consists of three parts: 1) Engineering in Industry 4.0; 2) Engineering Education in the Context of Industry 4.0; 3) Engineering Pedagogy in Industry 4.0. This module was implemented in January 2021 for students of the advanced training of educators (all of them are from Kazan National Research Technological University).
Consistency Regularization on Clean Samples for Learning with Noisy Labels

In the recent years, deep learning has achieved significant results in various areas of machine learning. Deep learning requires a huge amount of data to train a model, and data collection techniques such as web crawling have been developed. However, there is a risk that these data collection techniques may generate incorrect labels. If a deep learning model for image classification is trained on a dataset with noisy labels, the generalization performance significantly decreases. This problem is called Learning with Noisy Labels (LNL). One of the recent researches on LNL, called DivideMix [1], has successfully divided the dataset into samples with clean labels and ones with noisy labels by modeling loss distribution of all training samples with a two-component Mixture Gaussian model (GMM). Then it treats the divided dataset as labeled and unlabeled samples and trains the classification model in a semi-supervised manner. Since the selected samples have lower loss values and are easy to classify, training models are in a risk of overfitting to the simple pattern during training. To train the classification model without overfitting to the simple patterns, we propose to introduce consistency regularization on the selected samples by GMM. The consistency regularization perturbs input images and encourages model to outputs the same value to the perturbed images and the original images. The classification model simultaneously receives the samples selected as clean and their perturbed ones, and it achieves higher generalization performance with less overfitting to the selected samples. We evaluated our method with synthetically generated noisy labels on CIFAR-10 and CIFAR-100 and obtained results that are comparable or better than the state-of-the-art method.
Improved Attentive Pairwise Interaction (API-Net) for Fine-Grained Image Classification

Fine-grained classification is a challenging problem as one has to deal with a similar class of objects but with various types of variations. For more elaboration, they are almost similar and have subtle differences, and are confusing. In this study, aircraft will be the fine-grained object to be focused on. Aircraft which has almost similar shapes and patterns can be hardly recognized even for humans, especially those who haven not gone through any training. In recent years, a lot of proposed methods addressed to solve the difficulties in fine-grained problems by learning contrastive clues from an image. This study aims to increase the accuracy of the Attentive Pairwise Interaction Network (API-Net) by introducing data augmentation into the network structure. Some of the previous studies proved that data augmentation does help improve a network. So, this study is going to modify the API-Net with different data augmentation settings. In this study, various settings have been introduced to the API-Net. Several experiments had been done with a simple modification where a portion of the train dataset’s images will randomly convert into greyscale images. These settings are, only brightness & contrast 0.2, only grayscale 0.3, only grayscale 0.5, brightness & contrast 0.2 with grayscale 0.3, and brightness & contrast 0.2 with grayscale 0.5. As a result, the proposed modification achieved with 92.74% with brightness & contrast 0.2, 92.80% on brightness & contrast 0.2 with grayscale 0.5, and 92.86% on brightness & contrast 0.2 with grayscale 0.3. While grayscale 0.3 alone achieve 93.25% and grayscale 0.5 alone achieve 93.46% compared with the original results which reached 92.77%.
Feature Selection for Bayesian Inference Network in Radar Jamming Effect Analysis

Bayesian inference network (BIN) is one of the most popular methods for radar jamming effect analysis (RJEA) and its success stems from its intelligibility and direction-free knowledge inference. However, its training efficiency is always limited by its size. When numerous features are exploited for BIN in RJEA, this issue will become more serious. To tackle this hurdle, we put forward a feature selection method in the premise of keeping or even improving its inference precision. To do so, we develop manifold learning to implement feature selection, where the intrinsic subspace of original features is first estimated by the modified locally linear embedding algorithm and then the features are evaluated according to their contributions to preserving original spatial structure. The results in numerical experiments can illustrate the feasibility of the proposed method.
Development of Engineering Education in Russia: A Historical Perspective

Presently, more and more scholars agree that it is high time the engineering education system was reformed. In this articles we have made an attempt to look at the future prospects of engineering education development through the prism of the historical experience of training engineers in Russia; we have also endeavoured to match the periods of technical breakthroughs with the respective states of engineering education. In Russia has witnessed several paradigm shifts regarding engineering education which included different answers to the following dilemmas: Which part of each dilemma to choose was largely determined at each historical stage by advances in science and technology as well as by socio-economic and political processes in Russia. Future tendencies of engineering education system development will be to a large degree determined by the results of the Fourth industrial revolution (with its accent on IT-related skills and soft skills) as well as by the societal, political processes in the country and globally (globalization, crisis, pandemies etc.)
Bibliometric Analysis of References Selection that Influence Citations among Articles of Thai Multidisciplinary Journals

The purpose of this study is to examine association between reference list factors and citation count among articles published in Thai multidisciplinary journals based on the Scopus database. Spearman’s rank correlation and Negative binomial regression models were used for univariate and multivariate analysis, respectively. The results from 900 articles revealed that many reference list factors such as number of references, proportion of past 10 years references, source items references and impact of references had significantly positive correlation with citation counts both in univariate and multivariate analysis. The number of references is still a good quantitative basic indicator, which is easy to access information. For impact of references list, this study measured by average citation and h-Index of references list. Despite being a factor that reflects the quality of the research as well but for the evaluation of the unpublished research articles in the submission process, the indicators still have a long memory retention period. Such indicators require a long time to collect data and are not flexible.
Compositional noninterference on hardware weak memory models

Weak memory models are employed by all modern multicore processors to improve their performance. For most code, the effects of such memory models can be largely ignored by the programmer. However, for low-level operating system or library code which can include data races for efficiency, these effects may lead to information leaks which cannot be detected without taking the specific memory model into account. While there have been some efforts to develop information flow logics which can detect such leaks, the existing approaches are either not compositional, hindering scalability, or support only a limited form of compositionality, reducing applicability to programs with only simple interactions between threads. This paper is the first to provide a compositional logic for enforcing noninterference properties on more complex concurrent algorithms, while taking into account the underlying hardware memory model. It uses rely/guarantee reasoning to establish how security classifications may be modified by concurrent threads, and considers effects of out-of-order execution allowed on modern multicore processors. The results have been formalised and proved sound in the Isabelle/HOL theorem prover, and automated in a prototype symbolic execution tool.
Optimizing the Performance of Solar Photovoltaic Panels using Machine Learning

Even after decades of developments, the overall system efficiency for Solar Photovoltaics (PV) still gets majorly effected by the energy transition efficiency. Improving the solar panel performance certainly can lead to reduction in per unit cost of the energy generated As PV panels are tending to operate in open environmental conditions, they are subjected to many adverse meteorological phenomena. Photo-conversion efficiency (PCE) out of the PV panels significantly get deteriorated, if the operation of the PVs, such as its position, is not properly optimized. With unoptimized solar tracking methods, direct sun facing can leads to increase in the temperature and can lead to decrease in the performance and life span of the panel and would lead to increase in overall cost of the system. The present work tends to improve the overall output from the PV panels through optimizing their position using the Machine learning process. The machine learning models are written in Jupyter notebook environment. For data collection, the hardware with various sensors are implemented with PV panel. The set-up is designed in-house only. The results have projected that the machine learning has a good potential to improve the overall performance of the PV based systems significantly.
Distance learning development in COVID-19 context

The purpose of the article is to evaluate the practice and experience in the development of distance learning forms. Methodology. This article used secondary data from the UK Student Survey (Office of National Statistic COVID-19 Insights Survey (SCIS) during May 4-12 and November 19-29, 2021, to assess trends in the impact of the pandemic on their success. Results. The study found that limitations in higher education due to the spread of the coronavirus have driven the need for online education. In the UK, the most common education form was self-study or distance learning with a lecturer. Its prevalence has decreased as restrictions continue due to the online learning forms development (from 14% to 55%). With the developments, the number of students wishing to continue learning may be related to the adaptation to the new educational environment. In the UK, the combination of different distance learning methods is also changing, and the availability of online materials is increasing. The reduction in the amount of students' group work, the number of pre-recorded lectures or content, and the number of scheduled online classes from 76% to 40% remains negative.
The Impact of Technical Factors on User Experience in Augmented Reality Sports Spectating

The maturity of augmented reality (AR) technology and research paves the way for practical dissemination outside of laboratories right into the field. However, it is still under-explored which factors are influencing the user experience of an AR application. We are investigating factors in the application field of on-site sports spectating, requiring a careful design with trade-offs to be made between competing technical factors affecting the AR user experience. We present a study that analyzes the influence of latency, registration accuracy, and jitter as major factors. We developed a mobile AR prototype application and 20 participants evaluated an indirect AR stadium experience of scenes of an in-stadium, pre-recorded rugby football game. Earlier research has found that indirect AR is as effective as video see-through AR for such kind of evaluation and that rugby is not only a popular sport but also representative for a host of stadium sports.We found that latency has the highest disruptive impact on the usersâ€™ experience and was the most noticeable with registration accuracy following closely behind. Hence, both factors need to be attended to when designing AR sports spectator experiences. In addition, we found that, when combining noticeable levels of latency, registration accuracy, and jitter, the user experience was negatively affected in a non-linear, combinatorial way. This indicates that considering factors in isolation is necessary, but not sufficient for successful user experiences. Our findings are also relevant for other scenarios where users experience AR in large-scale environments and where the tracking of individual moving objects, e.g. players, vehicles, or animals, is of importance.
Feminization of Engineering in the Situation of Modern Technological Revolutions

The paper is relevant due to the importance of feminization of engineering in the situation of modern technological revolutions. The paper focuses on the root causes of the drastic changes in the content and structure of professional activity associated with the increasing dominance of the feminine factor. The authors have identified the changes in the nature and content of engineering activities in the context of modern industrial revolutions with a wide influx of women into these disciplines for professional and career self-realization. The authors have shown the most significant reasons that determine feminization of engineering activities in the contemporary context.
Towards Knowledge-aware Few-shot Learning with Ontology-based n-ball Concept Embeddings

We propose a novel framework named ViOCE that integrates ontology-based background knowledge in the form of n-ball concept embeddings into a neural network based vision architecture. The approach consists of two main components: (1) converting symbolic knowledge of an ontology into continuous space by learning n-ball embeddings that capture properties of subsumption and disjointness, (2) guiding the training and inference of a vision model using the learnt embeddings. We propose techniques to measure the quality of n-ball embeddings and evaluate ViOCE using the task of few-shot image classification, where it demonstrates superior performance in two standard benchmarks. We further introduce a metric to use background knowledge to measure the degree of incorrect predictions.
Construction of Winter Olympic Games Emergency Medical Security Ontology

The Winter Olympic Games proposes to establish an emergency medical security system for the scientific and technological Winter Olympic Games, and puts forward new requirements for emergency medical security knowledge and information. In order to prepare for the construction of a knowledge graph based on the Winter Olympics Games emergency medical security, the existing ontology construction methods and four-dimensional common diseases ontology model are introduced, analyzed and improved, and an ontology model for the application scenarios of the Winter Olympics Games emergency medical security support is proposed. And taking 15 kinds of diseases such as frostbite as examples, the knowledge graph is used for visual display.
A Survey of Application of Artificial Intelligence Method in Text Emotion Analysis

Human emotion analysis is a very extensive research field, which can be realized by a variety of applications. To engage in any project related to emotion detection, you must collect available models, data sets, and performance statistics related to it. The purpose of this review analysis is to integrate a unique and advanced set of methods proposed and/or implemented by everyone in the field. The research work of this paper mainly focuses on two aspects of classification, one is the classification based on research objects, the other is the classification based on research methods. The research object is mainly the difference between short text and normal text. The research methods mainly focus on machine learning, such as deep belief networks (DBN) and recurrent neural network (RNN). It can be seen from the comparison results that depth learning training is the best at a specific number of layers. If the number of layers is too many or too few, it will lead to insufficient accuracy or a waste of training time. Finally, this paper also puts forward feasible future research directions to provide readers with reference.
A Comprehensive Study on Scope and Challenges in Digital Inheritance

We are living our life online more and more nowadays. We access internet banking, investments online and into communication with our family and friends through various social media and other means over the internet. In this context it is becoming more and more important that what happens to our digital existence after our death. In the process of digital inheritance, digital media is handed over to the beneficiaries in the form of rights and digital assets understanding the rights that exist with the beneficiaries after a person's death. As the digital inheritance concept is quite fresh, substantial legal systems are yet to be established to deal with matters after our death. It is for that reason the websites who govern the act of the users. It is clear that a comprehensive legal arrangement is necessary to address this important issue of digital inheritance and hopefully this will be dealt with by the various Governments across the world in due course. This paper explains the concept of Digital Inheritance and explores the various options social media services provide for managing a deceased user’s data.[1] An effort is also made to suggest that Digital Will may be a choice to address this problem.
Performance Measurement of Reed-Solomon Codes through AWGN Channel under Various Modulation Schemes

The main objective of this paper is to analyze the performance of Reed-Solomon Code (RS) for different digital modulation schemes over an Additive White Gaussian Noise (AWGN) channel. The simulation model provides error detection and correction using Reed-Solomon (RS) with erasure, puncture and shortening. The original message is encoded and decoded using RS code. MATLAB Simulink model is selected, as the investigation tool. Bit Error Rate(BER) Versus Signal to Noise Ration (SNR) plot results are plotted under various modulation schemes like DPSK, QAM & FSK.
Assessment of Temperature Trends in Different Bioclimatic Zones of India

Trends in maximum, minimum and mean temperatures in seven bioclimatic zones have been studied at seasonal and annual scales with the help of data from 57 stations all over India for the period from 1901 to 2015. Linear regression model is used to determine the rate of change of temperature and student t-test is applied for significance testing. Results are cross-checked with those obtained with the Mann-Kandall test. The study reveals that in all zones maximum temperature is increasing at higher rates than minimum temperature except the sub-humid moist zone and typic arid zone during the summer season. The investigation also reveals that the increasing trend in maximum temperature is higher in moist areas than in arid areas, while the increasing trend in minimum temperature is greater in arid areas than in moist areas.
Efficient solution algorithms for the Bounded Acceleration Shortest Path problem

The purpose of this work is to introduce and characterize the Bounded Acceleration Shortest Path (BASP) problem, a generalization of the Shortest Path (SP) problem. This problem is associated to a graph: nodes represent positions of a mobile vehicle and arcs are associated to pre-assigned geometric paths that connect these positions. BASP consists in finding the minimum-time path between two nodes. Differently from SP, we require that the vehicle satisfy bounds on maximum and minimum acceleration and speed, that depend on the vehicle position on the currently traveled arc. We propose solution algorithms that achieves polynomial time-complexity under some additional hypotheses on problem data.
Automatic Model Association Method of Three-Dimensional Model and Monitoring,Control and Management Model of Electric Equipments

To realize data display in the three-dimensional visualization system of electric equipment, it is necessary to build a three-dimensional data model and interact with the data source. The accurate association of models is the basis of information exchange between different systems. The paper proposes a fast and automatic correlation modeling method for the 3D visualization model and monitoring management model of electric equipment. Firstly, the conceptual model of monitoring management and three-dimensional visualized knowledge graph is constructed of the electric equipment. Secondly, supplement the knowledge graph data based on the data of actual equipment and application system. In association modeling, knowledge search based on ontology features is used to realize automatic association of model information. An example is illustrated with oil transformer equipment, a knowledge graph based on transformer and its components is constructed as the basis for the correlation of transformer equipment models between different systems.
Analysis of Stackelberg Game in Central bank Digital Currencies and Digital Payment Platform - Case Study of Chinese Digital Currency

The issuance and promotion of central bank digital currency make it feasible to replace the digital payment channels dominated by private companies, which are currently dominant in the mobile payment market. The two payment methods are bound to compete fiercely in the future payment market. Based on the antecedents and unknowns of digital RMB issuance, this study discusses the game competition between digital RMB and third party payment and researches the trend in the development of the mobile payment market in the future and the best coping strategies for both sides. Based on Metcalfe’s law and CAPM model, this study analyzes the game strategy and equilibrium result between digital RMB and third party payment by establishing the Stackelberg master-slave game model from the point of view of both sides occupying the market to obtain maximum income. Moreover, use Matlab2018a to simulate the profit difference when two sides have a different number of users. The study proves that the digital RMB can not dominate the mobile payment market competing with the third party payment unless the government vigorously promotes the digital RMB in the short-run game. The Nash equilibrium in the game is cooperation. Furthermore, the profits are restricted by the size of the user for both sides.