Reviewerly/exHarmony huggingface dataset:
The capabilities of mathematical modeling tools for the system of assessing teacher shortages

Introduction. The article provides an overview and examines the problem of reliability and validity of models and tools for assessing teacher shortages. The purpose of the article is to determine the capabilities of mathematical modeling tools for the system of assessing teacher shortages. Materials and Methods. The research is based on measuring students’ intentions to enter the teaching profession using mathematical modeling tools. The main methodological principle of the research is a systematic approach. Based on the data obtained from a survey of 1,797 undergraduate and postgraduate students at Krasnoyarsk State Pedagogical University named after V. P. Astafiev, the method of multiple regression analysis is used. Results. The main results are that the authors investigated the capabilities of using mathematical modeling tools to assess teacher shortages (multiple regression analysis). It was found that the decision of teacher education students to enter the teaching profession does not depend on their socio-demographic characteristics (gender, year of study, place of residence, field of study, etc.) and is directly related to the characteristics of students’ attitudes to the University, their chosen profession, and professional development. There is a direct dependence of plans to start a teaching career on the self-assessment of their choice of the university. The probability of choosing a teaching career is determined by the ideas of professional development and is directly related to ‘improving skills in the subject area’; ‘mastering meta-competencies: analyzing and reflecting on their activities, evaluating and presenting results, etc.’, ‘mastering new techniques’, ‘transferring knowledge and skills to others, sharing experience’. The authors identified the capacity of using multiple regression analysis for assessing teacher shortages. The authors found that in solving this type of problems, it is reasonable to use empirical mathematical models, which include multiple regression analysis. Conclusions. The article concludes that the mathematical model of multiple regression analysis is a tool for comprehensive assessment. The authors note that multiple regression analysis, as a method of mathematical modeling, allows evaluating the activity in the unity of all characteristics and can be used in the assessment of teacher shortages. The data obtained serve as basic indicators and are used to establish links and relationships between choosing a teacher education degree programme and the intention to pursue a teaching career. Keywords Assessment systems; Assessment tools; Mathematical modeling; Teachers shortages; Students' intentions
Dynamic Bayesian Networks Application for Economy Competitiveness Situational Modelling

In the research, a dynamic BN (DBN) was designed to assess general trends in the level of regional competitiveness depending on economic detectors. This dynamic model is built on the basis of a trained, already verified static Bayesian network for assessing the country’s competitiveness. In contradistinction to an approach based on entrance from a numerical model as an entrance to a static network to foretell the meanings ​​of key detectors, the presented DBN uses the observed meanings ​​of key detectors to foretell future meanings ​​of these detectors taking into account the time component. Sensitivity analysis of BN is carried out, as well as a comparative analysis of “what-if” taking into account time steps. During the development of this DBN, it was found that the more evidence there is, the higher the accuracy of the designed network. The study identified baseline conditions, under which the competition detector at subsequent time steps will tend to increase. It is shown that to achieve the maximum level of competitiveness, we need to ensure the pursuit of maximum investment and innovation performance and improve the overall economic situation of the country.
How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention

Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism.As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive.In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations.We further study their corresponding functions through analytical study.In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation

Multi-source neural machine translation aims to translate from parallel sources of information (e.g.languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems.Despite the remarkable success of existing models, they usually neglect the fact that multiple source inputs may have inconsistencies.Such differences might bring noise to the task and limit the performance of existing multi-source NMT approaches due to their indiscriminate usage of input sources for target word predictions.In this paper, we attempt to leverage the potential complementary information among distinct sources and alleviate the occasional conflicts of them.To accomplish that, we propose a source invariance network to learn the invariant information of parallel sources.Such network can be easily integrated with multi-encoder based multi-source NMT methods (e.g.multi-encoder RNN and transformer) to enhance the translation results.Extensive experiments on two multi-source translation tasks demonstrate that the proposed approach not only achieves clear gains in translation quality but also captures implicit invariance between different sources.
E-biomedical: a positive prospect to monitor human healthcare system using blockchain technology

Purpose The purpose of this research is to eliminate the problems like modification of data related to patient without patient’s consent, management of patient's profile and any type of miscommunication between the specialist. Design/methodology/approach A novel approach is designed to overcome the problem of disease overlapping. To this end, Blockchain technology is utilized due to its numerous benefits. Findings The simulation of the model is done in R-studio, and the results are determined for the difficulty level in terms of time taken to create specific block. The outcome showed the effectiveness of the proposed Blockchain technology. Originality/value The approach discussed in this article is a novel approach that shows the effectiveness of Blockchain technology with modification of patients’ data without his/her will, management of patient profile with latest data, and elimination of any type of miscommunication between the specialists.
Impact of Distance Measures on the Performance of AIS Data Clustering

Automatic Identification System (AIS) data stream analysis is based on the AIS data of different vessel's behaviours, including the vessels' routes.When the AIS data consists of outliers, noises, or are incomplete, then the analysis of the vessel's behaviours is not possible or is limited.When the data consists of outliers, it is not possible to automatically assign the AIS data to a particular vessel.In this paper, a clustering method is proposed to support the AIS data analysis, to qualify noises and outliers with respect to their suitability, and finally to aid the reconstruction of the vessel's trajectory.In this paper, clustering results have been obtained using selected algorithms, including k-means, k-medoids, and fuzzy c-means.Based on the clustering results, it is possible to decide on the qualification of data with outliers and on their usefulness in the reconstruction of the vessel trajectory.The main aim of this paper is to answer how different distance measures during a clustering process can influence AIS data clustering quality.The main core question is whether or not they have an impact on the process of reconstruction of the vessel trajectories when the data are damaged.The research question during the computational experiments asked whether or not distance measure influence AIS data clustering quality.The computational experiments have been carried out using original AIS data.In general, the experiment and the results confirm the usefulness of the cluster-based analysis when the data include outliers that are derived from the natural environment.It is also possible to monitor and to analyse AIS data using clustering when the data include outliers.The computational experiment results confirm that the k-means with Euclidean distance has the best performance.
Handwritten Digit Recognition Using Deep CNN

This study demonstrates how the deep Convolutional Neural Network (CNN) can be effectively employed to enhance the performance of isolated digit recognition systems. The global and local features are extracted automatically by CNN from the normalized image. Classification is carried out using Convolutional Neural Network while the experimental study is conducted on the standard CVL single digit database. A series of evaluations using different configurations of CNNs realized high recognition rates which are compared with the state-of-the-art methods on this subject.
EL-BERT at SemEval-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media

In visual media, text emphasis is the strengthening of words in a text to convey the intent of the author.Text emphasis in visual media is generally done by using different colors, backgrounds, or font for the text; it helps in conveying the actual meaning of the message to the readers.Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with written text.If we consider only the text and do not know the intent, then there can be multiple valid emphasis selections.We propose the use of ensembles for emphasis selection to improve over single emphasis selection models.We show that the use of multi-embedding helps in enhancing the results for base models.To show the efficacy of proposed approach we have also done a comparison of our results with state-of-the-art models.
Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

Abstract Objective image quality assessment (IQA) plays an important role in various visual communication systems, which can automatically and efficiently predict the perceived quality of images. The human eye is the ultimate evaluator for visual experience, thus the modeling of human visual system (HVS) is a core issue for objective IQA and visual experience optimization. The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively, while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity. For bridging the gap between signal distortion and visual experience, in this paper, we propose a novel perceptual no-reference (NR) IQA algorithm based on structural computational modeling of HVS. According to the mechanism of the human brain, we divide the visual signal processing into a low-level visual layer, a middle-level visual layer and a high-level visual layer, which conduct pixel information processing, primitive information processing and global image information processing, respectively. The natural scene statistics (NSS) based features, deep features and free-energy based features are extracted from these three layers. The support vector regression (SVR) is employed to aggregate features to the final quality prediction. Extensive experimental comparisons on three widely used benchmark IQA databases (LIVE, CSIQ and TID2013) demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.
Drivers for Development in Regional Higher Education

Recently, higher education in Russia and abroad has been transformed significantly. Both universities and the state have to tackle challenging and multidirectional tasks. Analysis of theoretical research on the types of higher education institutions and the determining factors provides insights into drivers for development. Simultaneously, multivariate methodology for assessing the state of higher education allows identifying the factors that most affect its transformation. Th e stud y demonstrate s that these factors depend on the development strategy and economic model of a university as well as government programmes for the development of education and science. One of the major factors affecting the development of regional higher education is the presence of a leading university in the region. This situation is economically justified due to the strong dependence of universities on the federal budget. Moreover, a leading university creates an educational environment for academic staff and students. Their participation in scientifi c researc h an d academi c activitie s positivel y influen ce t he developme nt of regional systems. The growing gap between the leading university and other education institutions leads to monopolisation, decrease in competition, and, consequently, decline in performance. Thus, there is a need for structural changes. The particular factors affecting the development of the higher education are the indicators of academic activity and student performance. The conducted analysis suggests the intensification of the trends, indicating further differentiation of regional higher education system with a simultaneous improvement of individual universities in research and development. Additionally, it is necessary to examine the feasibility of structural adjustment within regional systems due to the growing imbalances in development.
Optimized reversible quantum circuits for $${\mathbb {F}}_{2^8}$$ multiplication

Quantum computers represent a serious threat to the safety of modern encryption standards. Within symmetric cryptography, Advanced Encryption Standard (AES) is believed to be quantum resistant if the key sizes are large enough. Arithmetic operations in AES are performed over the binary field $${\mathbb {F}}_{2^m}$$ generated by an irreducible pentanomial of degree $$m = 8$$ using polynomial basis (PB) representation. Multiplication over $${\mathbb {F}}_{2^m}$$ is the most complex and important arithmetic operation, so efficient implementations are highly desired. A number of quantum circuits realizing $${\mathbb {F}}_{2^m}$$ multiplication have been proposed, where the number of qubits, the number of quantum gates and the depth of the circuit are mainly considered as optimization objectives. In this work, optimized reversible quantum circuits for $${\mathbb {F}}_{2^8}$$ multiplication using PB generated by two irreducible pentanomials are presented. The proposed reversible multipliers require the minimum number of qubits and CNOT gates, and the minimum depth among similar $${\mathbb {F}}_{2^8}$$ multipliers found in the literature.
Average coherence with respect to complementary measurements

Abstract We investigate the average coherence with respect to a complete set of complementary measurements. By using a Wigner–Yanase skew information-based coherence measure introduced in Luo and Sun (2017 Phys. Rev. A 96 , 022130), we evaluate the average coherence of a state with respect to any complete set of mutually unbiased measurements and general symmetric informationally complete measurements, respectively. We also establish analytically the relations among these average coherences.
Cyber Security Crimes, Ethics and a Suggested Algorithm to Overcome Cyber-Physical Systems Problems (CybSec1)

Digital systems have changed our world and will continue to change it.Supportive government policy, a strong research base and history of industrial success place the benefits of an emerging digital society.Protecting benefits and minimizing risks requires reliable and robust cyber security, backed by a robust research and translation system.Trust is essential for growth and maintenance of participation in the digital community.Organizations gain trust by acting in a trustworthy way leading to building reliable and secure systems, treating people, their privacy and their data with respect, and providing reliable and understandable information to help people understand how safe they are.Research and revolution in industry and academia will continue to make important contributions to create flexible and reliable digital environment.Cyber Security has a main role in the field of information technology because securing information has become one of the greatest challenges today.When we think about the cyber security, the first thing that comes to our mind is "cyber crimes" which are increasing exponentially day by day.Many governments and firms are taking many measures to prevent these cybercrimes.Besides the various measures, cyber security remains a major concern.This paper intended to give a deep overview of the concepts and principles of cyber security that affect the safety and security in an international context.It mainly focuses on challenges faced by cyber security on the latest technologies and focuses also on introducing security types, cyber security techniques, cyber security ethics, trends that change the face of cyber security and finally attempting to solve one of the most serious cyber security crimes of violating privacy on the internet by improving the security of sensitive personal information (SPI) in Cyber-physical systems using a selected proposed algorithm that analyzes the user's information resources and determines the valid data to be encrypted, then uses adaptive acquisition me-How to cite this paper:
Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection

This paper examines the impact of different box merging strategies for sampling-based uncertainty estimation methods in object detection. Also, a comparison between the almost exclusively used softmax confidence scores and the predicted variances on the quality of the final predictions estimates is presented. The results suggest that estimated variances are a stronger predictor for the detection quality. However, variance-based merging strategies do not improve significantly over the confidence-based alternative for the given setup. In contrast, we show that different methods to estimate the uncertainty of the predictions have a significant influence on the quality of the ensembling outcome. Since mAP does not reward uncertainty estimates, such improvements were only noticeable on the resulting PDQ scores.
Histology Classifier App: Remote Laboratory Sessions Using Artificial Neural Networks

The Histology Classifier app was developed as a strategy to recreate laboratory sessions using digital platforms. This mobile app uses convolutional neural networks (CNN) to help students verify their identification of histological basic tissues. Further work will include measuring any educational benefits of this resource across the medical student cohort.
Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed.This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method.Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time.And then, a threshold value is set to complete the human behavior identification.Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified.Experimental results show that this algorithm is more advantageous for jogging behavior recognition.
Publishing in small European countries

This special issue of Knygotyra is a result of long-lasting collaboration between researchers of few small language countries. We hope this issue will contribute to better understanding the peculiarities of publishing on such markets.
Causal Screening to Interpret Graph Neural Networks

With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. However, current works on feature attribution, which frame explanation generation as attributing a prediction to the graph features, mostly focus on the statistical interpretability. They may struggle to distinguish causal and noncausal effects of features, and quantify redundancy among features, thus resulting in unsatisfactory explanations. In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect. It incrementally selects a graph feature (i.e., edge) with large causal attribution, which is formulated as the individual causal effect on the model outcome. As a model-agnostic tool, Causal Screening can be used to generate faithful and concise explanations for any GNN model. Further, by conducting extensive experiments on three graph classiﬁcation datasets, we observe that Causal Screening achieves signiﬁcant improvements over state-of-the-art approaches w.r.t. two quantitative metrics: predictive accuracy, contrastivity, and safely passes sanity checks.
Wide-view and accurate deformation measurement at microscales by phase extraction of scanning moiré pattern with a spatial phase-shifting technique

A scanning-based second-order moiré method is proposed for high-accuracy deformation measurement in a large field of view (FOV) by analyzing the phase distribution of a single-shot scanning moiré fringe image using a spatial phase-shifting technique. In this method, the grating pitch can be as small as around one pixel in the scanning moiré image to ensure a wide FOV, while high-precision phase measurement is achievable. The strain measurement accuracy has been verified from simulations at different grating pitches, applied strains, and noise levels. The simulation results show that the closer the grating pitch is to the scanning pitch, the smaller the strain measurement error, and the recommended pitch ratio is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0.9</mml:mn> <mml:mo>∼<!-- ∼ --></mml:mo> <mml:mn>1.1</mml:mn> </mml:math> . Furthermore, the feasibility of this method has been verified from a tensile experiment on an aluminum specimen under a laser scanning microscope with scanning moiré images recorded. The microscale strains of aluminum measured at different tensile loads agree well with the strain gauge results. As an integration of the scanning and sampling moiré methods, this method has the advantages of a large FOV, high accuracy, strong noise immunity, and visualization of magnified deformation. Compared with the traditional phase-shifting scanning moiré method, this method only needs to record a single scanning moiré image and is suitable for dynamic deformation analysis.