Reviewerly/exHarmony huggingface dataset:
Artistic and aesthetic innovations of art education used while training future teachers of Music and Choreography

The article substantiates the conditionality of the quality of education regarding innovations that are caused by changes in the sociocultural space and the reform of education, which actualizes its prognostic and innovative character. The purpose of the article is to highlight the results of scientific research on the problems of artistic and aesthetic innovations in the art education and the development of the artistic and innovative competence of future teachers of Musical Arts and Choreography. The author has presented course and results of a comprehensive study which was carried out in the framework of the inter-department scientific theme “Methodology and methods of professional training aimed at future teachers of Musical Arts and Choreography in the context of artistic and aesthetic innovations in art education”. Theoretical and empirical methods, a generalization of the analysis of pedagogical experience and the results of experimental studies were applied. A classification of artistic and aesthetic innovations in art education has been proposed. The essence of artistic and aesthetic innovations has been explained; the essence and structure of the artistic and innovative competence of future teachers of Musical Arts and Choreography have been determined. The criteria and indicators for assessing this professional quality have been developed. The complex nature of the scientific topic made it possible to attract a number of dissertation studies to the solution of the set tasks in the framework of which certain positions of the system as well as qualities, knowledge, skills and experience relevant to the artistic and innovative competence were checked. Summarizing the results of the study made it possible to establish the conformity of its organizational, methodological and content-centred components with the relevant qualification clusters: knowledge, skills, communications, autonomy and responsibility. The obtained results are confirmed by experimental verification and an increase in the quantity and quality of the undergraduate and postgraduate students’ creative achievements in competitions, contests and art festivals. Keywords: quality of education, artistic and aesthetic innovations, future teachers of Musical Arts and Choreography, the artistic and innovative competence.
Entropic uncertainty in the background of expanding de Sitter space-time

We study the dynamics of quantum-memory-assisted entropic uncertainty for a hybrid qutrit–qubit system interacting with fluctuating quantum scalar field in the background of expanding de Sitter space. We firstly derive the master equation that the system evolution obeys. As evolution time goes by, for different initial states, entropic uncertainty develops to different fixed values for different parameter values, whereas entanglement always decays to zero, and there exist monotonous relations between entropic uncertainty, entanglement and various parameters for a fixed initial state, but mixedness behaves differently with entropic uncertainty and entanglement. Further it is found that the entropic uncertainty closely associated with the entanglement and mixedness. In addition, it is shown that the entropic uncertainty can be manipulated effectively via the weak measurement reversal. Our study would give some useful insights about the behavior characteristics of high-dimensional quantum system in expanding de Sitter space-time and may be useful to the tasks of quantum information processing of curved space-time since the uncertainty principle plays vital role in quantum information science and technology.
Second-order Anisotropic Diffusion-based Technique for Poisson Noise Removal

A partial differential equation (PDE) - based technique for filtering the Poisson noise from digital images is proposed in this work. It is based on a nonlinear second-order anisotropic diffusion-based model that is adapted for the Poisson distribution. The considered PDE model is well-posed and its unique and weak solution is computed using a finite difference-based numerical approximation scheme that is consistent to the proposed model. The proposed approach provides an effective feature-preserving Poisson denoising. Some results of our filtering simulations are also described in this paper.
A Study on Translation of Hydroelectric Text from the Perspective of "The Belt and Road Initiative"

This paper focuses on translation of hydroelectric text, deeply analyzes the language features and translation difficulties, as well as further puts forward specific translation methods. The English text of hydroelectric has its own distinctive features in vocabulary, syntax and language expression. Therefore, this paper makes a detailed study on these three aspects. Then, through a large number of examples about translation of hydroelectric English text, I summarize the translation strategies which have been used in the process of translation, such as addition, deletion, literal translation, free translation, comprehensive translation and so on.
An Inverse Mapping with Manifold Alignment for Zero-Shot Learning

Zero-shot learning aims to recognize objects from unseen classes, where samples are not available at the training stage, by transferring knowledge from seen classes, where labeled samples are provided. It bridges seen and unseen classes via a shared semantic space such as class attribute space or class prototype space. While previous approaches have tried to learning a mapping function from the visual space to the semantic space with different objective functions, we take a different approach and try to map from the semantic space to the visual space. The inverse mapping predicts the visual feature prototype of each unseen class via the semantic vector for image classification. We also propose a heuristic algorithm to select a high density set from data of each seen class. The visual feature prototypes from the high density sets are more discriminative, which is benefit to the classification. Our approach is evaluated for zero-shot recognition on four benchmark data sets and significantly outperforms the state-of-the-art methods on AWA, SUN, APY.
On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding

Whilst real-time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles) and road sign-age within the scene. By contrast, for an autonomous vehicle to be truly able to interact with occupants and other road users using a common semantic understanding of the environment it is traversing it requires a considerably extended scene understanding capability. In this work, we consider the performance of extended "long-list" object detection, via an extended end-to-end Region-based Convolutional Neural Network (R-CNN) architecture, over a large-scale 31 class detection problem of urban scene objects with integrated object attribute estimation for appropriate colour and primary orientation. We examine the extended performance of this multiple class object detection and attribute estimation task operating in real-time with on-vehicle processing at 10 fps. Our work is evaluated under a range of real-world automotive conditions across multiple complex and cluttered urban environments.
The Model of E-Higher Education

The obligatory component and precondition for society development is improvement of higher education, which can ensure its mass accessible nature and decrease in education cost. The article shows the development of this field that fosters technological progress, which on the current stage can lead to a new way of education, i.e. the model of e-higher education. The principle feature of e-education is the absence of the university as a center training organization. University authorities in the field of training are delegated, first of all to the state foundation of e-training courses of higher education and to the methodological center that develops the procedure of obtaining different forms of higher education on the basis of mastering the prescribed programs of training courses by the student. The author came to the conclusion that passing over to the non-university model of education is connected with new risks both in the field of higher education organization and related spheres, as it could affect employment of university employees. Labour market tension is increasing due to the trend of combining educational and labour processes by today’s youth. Lower costs and higher productivity can win on the market, thus cheaper for society model of higher education would have higher chances of gaining a certain niche of education service market.
Self-Driving Technology Representations in Art [Tech Travel]

Reports on the self-driving technology and discusses its prospects for the future.
Sports Field Localization using Memory Networks

Sports analytics derived automatically from broadcast footage is agrowing interest because it provides advantageous data to teamswithout the need for specialized equipment or trained staff. A fundamental step in automating sports video analytics extraction is registering the playing surface and transforming the broadcast footageto a top-down view. In this paper, a novel method is presentedthat performs automatic top-down registration of sports fields using temporal information. Using richer input data will increase the performance of the network and will not require an additional correctionnetwork.
Chasing Undetected Fraud Processes with Deep Probabilistic Networks

Hidden networks and weak signals from frauds activities are sometimes statistically undetectable in the endogenous data respective to processes. Human intelligence is able to reverse-engineer new fraud scenarios without statistically significant characteristics. However, machine learning usually needs to be taught for new scenarios or fails to this task. A wave-based alternative in deep learning for social networks analysis constitutes a multidisciplinary attempt in probabilistic machine learning. Inspired from waves temporal reversal in finite space, it is introduced for big data offline analysis and hidden data mining in this chapter, proposing a theoretical alternative to artificial neural networks for deep learning. Experimentation and outcomes related to fraudulent processes generating data statistically similar to legal endogenous data make appear unexpected results. The model seems to be closer to intuitive processes than to perceptive ones and might introduce an alternative in artificial intelligence for a new generation of applications.
FORMATION OF JUNIOR SECONDARY SCHOOL PUPIL’S INFORMATION CULTURE

The article discusses the possibility of using teaching methods, means and techniques promoting the formation of the junior secondary school pupil’s information culture. It is shown that this objective necessarily presupposes the formation of junior pupils’ information competence including information search and processing skills, ability to perceive, understand and apply the acquired information. Educational tasks aimed to form the components of the junior pupil’s information culture are described.
Influence, Information and Team Outcomes in Large Scale Software Development

It is widely perceived that the egalitarian ecosystems of large scale open source software development foster effective team outcomes. In this study, we question this conventional wisdom by examining whether and how the centralization of information and influence in a software development team relate to the quality of the team's work products. Analyzing data from more than a hundred real world projects that include development activities over close to a decade, involving 2000+ developers, who collectively resolve more than two hundred thousand defects through discussions covering more than six hundred thousand comments, we arrive at statistically significant evidence indicating that concentration of information and influence in the developer communication networks of the projects are associated with the quality of a team's work products, even after controlling for various factors related to levels of developer engagement. Our results suggest that merely facilitating easy interaction between team members may not be sufficient to enhance team outcomes. The design of efficient collaborative development environments, and devising tools and processes for team assembly and governance can be informed by our results.
Fast and Exact Algorithms for Some NP-Hard 2-Clustering Problems in the One-Dimensional Case

We consider several well-known optimization 2-clustering (2-partitioning) problems of a finite set of points in Euclidean space. All clustering problems considered are induced by applied problems in Data analysis, Data cleaning, and Data mining. In the general case, all these optimization problems are strongly NP-hard. In the paper, we present a brief overview of the results on the problems computational complexity and on their solvability in the one-dimensional case. We present and propose well-known and new, simple and fast exact algorithms with $$\mathcal {O}(N\log N)$$ and $$\mathcal {O}(N)$$ running times for the one-dimensional case of these problems.
Знаходкі ў кнігах з фондаў Фундаментальнай бібліятэкі БДУ : перспектывы даследавання

The article describes the findings in books from the collections of the Fundamental Library of the BSU. Classification of the findings and a description of some of them are given. The prospects for research are noted and the principles of fixing the findings are proposed. For the first time two pages from the library collection are published.
What Is Causation? – A Solution from Ontology Engineering

From the standpoint of Applied Ontology, we have challenged the problem of causality on the premise that it is real. As a result, it was shown in the former paper the following three items: (1) causation can be mapped to function, and hence it is possible to talk about causation in terms of function, (2) Cause can be divided into four subfunctions such as Achieve, Prevent, Allow and Disallow by introducing two dimensions of direct/indirect and positive/negative, and (3) using Achieve the other three subfunctions are defined and the essence of causality lies in Achieve. However, elucidation of Achieve itself was left incomplete. In this paper, we first restructure the results obtained in the former paper and summarize it so that the results will become clearer than the original paper. Then, the nature of Achieve is revealed to give a world first solution to the problem of what causation is.
An Overview of Adopting Cloud Computing in E-Learning Systems

Education institutions continue to hunt for chances of enhancing the methods of operating and managing their resources. Cloud computing is a modern technology which promises to present opportunities for delivering a wild range of computing services such as software, platform, and infrastructure; in a way that has not been practiced before. Integrating cloud computing into e-learning systems has several advantages and challenges. In this study, we will analyze the effect of cloud computing on e-learning and explore the main factors that made cloud computing to be an attractive method to e-learning systems.
Design and Analysis of IoT-Based System for Crowd Density Estimation Techniques

In this paper, we present an IoT-based solution that can reduce the complexity of crowd estimation. About the human crowd estimation many techniques are in existence but nowadays more work is going on in this field because this is era of IoT and most of the organization is shifted toward IoT-based system. So in our proposed system we are using the Raspberry Pi-3 which are having quad-core processor that can be very useful and gives better result and accurate number even when the humans are very close to each other. This IoT-based model can easily be implemented in crowded areas and monitor the same. The camera module in this model also helps to differentiate between human and other bodies. As this is a mobile model, it can be easily fixed on the walls of street light and in the time of darkness or in night the camera captures clear images for process in the presence of street light. So that this model gives better result almost 70% better result in compare to exiting approaches.
USING COMPUTER TECHNOLOGIES TO FORM JUNIOR PUPILS’ GEOMETRIC CONCEPTIONS

The article shows the potential of computer technologies when familiarizing junior pupils with planar and three-dimensional geometric figures, developed views and projections of a cube, developed views and sections of geometric bodies. Information and communication tools emphasize visualization techniques, make it easier to provide complete and accurate information about the geometric body under study, allow a wider range of methodological techniques when introducing educational material.
A Cross‐Dimension Annotations Method for 3D Structural Facial Landmark Extraction

Abstract Recent methods for 2D facial landmark localization perform well on close‐to‐frontal faces, but 2D landmarks are insufficient to represent 3D structure of a facial shape. For applications that require better accuracy, such as facial motion capture and 3D shape recovery, 3DA‐2D (2D Projections of 3D Facial Annotations) is preferred. Inferring the 3D structure from a single image is an ill‐posed problem whose accuracy and robustness are not always guaranteed. This paper aims to solve accurate 2D facial landmark localization and the transformation between 2D and 3DA‐2D landmarks. One way to increase the accuracy is to input more precisely annotated facial images. The traditional cascaded regressions cannot effectively handle large or noisy training data sets. In this paper, we propose a Mini‐Batch Cascaded Regressions (MBCR) method that can iteratively train a robust model from a large data set. Benefiting from the incremental learning strategy and a small learning rate, MBCR is robust to noise in training data. We also propose a new Cross‐Dimension Annotations Conversion (CDAC) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa. The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization. Moreover, CDAC can run efficiently at up to 110 fps on a 3.4 GHz‐CPU workstation. Thus, CDAC provides a solution to transform existing 2D alignment methods into 3DA‐2D ones without slowing down the speed. Training and testing code as well as the data set can be downloaded from https://github.com/SWJTU‐3DVision/CDAC.
Density Clustering Algorithm Based on the Dynamic Selection of Cluster Center

For the existing clustering algorithms, there are some problems such as the unreasonable selection of cluster center and parameters sensitive. A density clustering algorithm based on the dynamic selection of cluster center (CCDS) is proposed. CCDS proposes a new local density metric based on the cutoff distance adaptive computing mechanism and the K-nearest neighbor idea. Then, the true cluster centers are selected according to the dynamic selection mechanism of cluster center. Finally, the remaining objects are assigned into corresponding clusters according to the minimum distance from the high-density objects. The experimental results on synthetic datasets and UCI datasets verify the effectiveness of CCDS.CCDS not only recognizes arbitrary shapes clusters, but also has high clustering accuracy on multi-density and high-dimensional large datasets.
Bayesian Estimation of Deer Population Dynamics Using Hamiltonian Monte Carlo Algorithm

For the estimation of deer population dynamics, Bayesian method using Gibbs sampler which is one of the Markov Chain Monte Carlo (MCMC) method has been adopted in the previous studies although the method takes much time for calculation. In this study, we introduce Hamiltonian Monte Carlo (HMC) method as a new approach which is more efficient than Gibbs sampling. To verify our proposal method contributes to deer population managements, we modified the model of a previous study to apply Hamiltonian dynamics and conducted numerical experiments by comparing the results which are estimated in two methods with using simulation based data. As a result, we confirmed HMC method is more time-efficient than Gibbs sampler because the number of samples abandoned for after procedures in HMC method was much less than in Gibbs sampler. Therefore, we conclude that the estimation using HMC is valuable for the estimation of deer population dynamics.
Information Visualization and Usability: Tools for Human Comprehension

This chapter makes a review on the progress of two growing areas in the field of communication: information visualization and usability. Information visualization has experimented a lot of changes during the last few years, especially with its arrival to the Internet and the development of different narrative forms by taking advantage of the main characteristics of the new environment. Something similar has occurred with the concept of usability. The following sections explore the significance of this young idea whose spread took place especially with the start of the Internet. With the objective of completing this theoretical framework, a small usability test was conducted with five renowned visualizations. The results of this test show that even the most salient journals do not fulfill many of the main usability recommendations. Hence, this area still has a considerable way to go regarding its appliance to information visualization.
Deformed Phase Prediction Using SVM for Structured Light Depth Generation

In phase-based structured light, absolute phase unwrapping, which is a cumbersome step, is often considered necessary before calculating depth. In this paper, we notice that depth is only related to the deformed phase but not the absolute unwrapped phase. Furthermore, the deformed phase is highly related to the changes of the wrapped reference and captured phases. Based on these findings, we propose a classification-based scheme that can directly report deformed phase. To be specific, we cast the problem of inferring fringe order difference as a multi-class classification task, where phase samples within half a period are fed to the classifier and the fringe oder difference is the class. Besides, we use a radial basis function support vector machine as the classifier. In such a manner, for every pixel, the deformed phase can be obtained directly without knowing the absolute unwrapped phase. Moreover, the proposed method only needs phase from a single frequency and is pixel-independent, so it is free from troubles such as poor real-time performance in temporal unwrapping or error accumulation in spatial unwrapping. Experiments on 3dsmax data and real-captured data prove that the proposed method can produce high quality depth maps.
Göttingen State and University Library (Germany) as a Model for Successful Symbiosis of Municipal and University Library

Objective.The article informs about Göttingen State and University Library (Germany) as about a place, where specific features of public and scientific library are taken into account. Methods. The article is based on personal impressions of a visit to the library of one of the authors. Results. The library keeps its centuries-old traditions and is modern space for individual and group work. Conclusions. The article was an attempt to describe the library's digital and paper resources as an accumulation of experience that could be embodied in the university libraries of Ukraine.