Reviewerly/exHarmony huggingface dataset:
Development of digital literacy based on innovative approaches to design and research activities in a modern university

The article examines the problem of the development of digital literacy in today's subjects of the pedagogical process, reflects the authors' opinion that there is a correlation between the main determinants of digital literacy and the possibilities of design and research activities, in which it is necessary to involve not only students, but also schoolchildren. The experience of organizing design and research activities in different universities, in cooperation with schools, is disclosed. The results of a survey of university professors and schoolteachers are presented, confirming the effectiveness of project activities in the development of digital literacy of students and schoolchildren.
METHODOLOGY OF DETECTION OF COMPUTER CRIME OFFENSES

Computer crime is characterized by an exceptional phenomenological diversity that complicates its unique and precise conceptual definition, and the permanent intensive development of computer technologies contributes to the constant expansion of its forms and poses increasing challenges to the authorities responsible for combating it. After a brief review of the concept and importance of criminology as a science, and criminological methodology as a branch of criminology, and the conceptual definition and basic characteristics of crime in general, and computer crime, the paper analyzes the most important aspects of crime detection methodology in this area. The conclusion is that the basic characteristics of computer crime, difficult detection, complicated proving and ineffective prevention certainly encourage potential perpetrators to make a decision on taking criminal acts in this area, which again indicates in which direction to move in order to eliminate difficulties in the methodology of detection and collection of evidence in criminal offenses of computer crime.
Detection Of Defective Videosurveillance Camera In Train Stations

In the past decade, imaging sensors have known noteworthy improvements and a growing interest from both academics and industries. In particular, video surveillance cameras have widely spread across the world, and many systems depends on their inputs. Either to provide visual information to human operators or to give input data to Artificial Intelligence algorithms, it is important to be able to assess the proper operation of sensors and the quality of images and videos they record. However, existing methods are either very specific (detect only limited kinds of default) or perform poorly in unpredictable environment. Artificial Intelligence algorithms have proven to be useful to detect anomalies and could be used to highlight a wide range of defaults, including noise, obstruction and intentional tampering such as knocking the camera out of alignment. In this work, the authors present a comparative study for defective camera detection, in the context of video surveillance.
Quantifying image naturalness using differential curvelet features

Distinguishing computer-generated (CG) images from natural images is an effortless job for the human eye but not for a machine. Artificial or CG image processing services are growing rapidly due to popular smartphone applications and filters in social media applications. The traditional image quality assessment (IQA) metrics are mostly defined for real-world images in terms of attributes describing noises and distortions. In contrast with traditional natural image content, artificial or CG image content has special characteristics that differentiate them from natural images. This difference opens new opportunities of research towards designing metrics that define &#x2018;naturalness&#x2019; in terms of image attributes. In this paper, we investigate how curvelet features of a natural image can represent naturalness of the image. By training various classifiers using differential curvelet features of artwork-like images, we report a novel approach to estimate image naturalness and its performance for various levels of naturalness. The reported results are promising and show potential of improvement using detailed feature engineering.
Control of Spherical Robots on Uneven Terrains

Hybrid robots incorporate the advantages of both aerial-only and terrestrial-only vehicles to achieve enhanced mobility and better energy efficiency. Among hybrid vehicles, spherical robots offer the best maneuverability. While operating on uneven surfaces is one of the main benefits of spherical robots, the current literature only covers control of these robots on flat surfaces. This work presents two control algorithms to track a desired trajectory and angular velocity of spherical robots on uneven terrains. The proposed control algorithms can be used when the terrain is known analytically or empirically (i.e., point cloud). By allowing the controller to use empirical information about the terrain profile, this work broadens the implementation of spherical robots in real applications.
University Students’ Inclusive Literacy: General Characteristics, Ways of Formation, Influence in the Context of Professional Development

The paper aims to ground the need to develop undergraduate students’ inclusive literacy which directly influences the process of projecting individual paths of university students’ professional development. The article describes inclusive literacy as a qualitative term to characterize modern university students; analyzes the practice of organizing students’ initiative project activity which is considered a resource for formation of undergraduate students’ inclusive literacy; ascertains the connection between the level of students’ inclusive literacy and the dynamics of their professional intentions and interests which influence the process of projecting individual paths of professional development. The scientific originality of the research lies in the fact that it proves that inclusive literacy is a factor which influences the actualization of individual paths of students’ professional development in the context of specification of its leading vectors towards specific professional direction, initiative project socially-oriented and scientific-educational activity. As a result, the data on the practical approbation of complementing the models of learning process organization that aim to form students’ inclusive literacy which is considered a resource for initiative project activity have been summarized; the conceptual principles of the socially-oriented scientific-educational metaproject “University of inclusive literacy” have been presented; the main effects of inclusive literacy in the context of projecting individual paths of students’ professional development have been identified.
Greatly Accelerated Scaling of Streaming Problems with A Migrating Thread Architecture

Applications where continuous streams of data are passed through large data structures are becoming of increasing importance. However, their execution on conventional architectures, especially when parallelism is desired to boost performance, is highly inefficient. The primary issue is often with the need to stream large numbers of disparate data items through the equivalent of very large hash tables distributed across many nodes. This paper builds on some prior work on the Firehose streaming benchmark where an emerging architecture using threads that can migrate through memory has shown to be much more efficient at such problems. This paper extends that work to use a second generation system to not only show that same improved efficiency (10X) for larger core counts, but even significantly higher raw performance (with FPGA-based cores running at 1/10th the clock of conventional systems). Further, this additional data yields insight into what resources represent the bottlenecks to even more performance, and make a reasonable projection that implementation of such an architecture with current technology would lead to 10X performance gain on an apples-to-apples basis with conventional systems.
A Novel Approach for the Recruitment Process in Human Resources Management

Discovering and using valuable and meaningful data which is hidden in large databases can have strategic importance in the managerial decision-making process for organizations to gain competitive advantage. With the increasing data flow, it has become more difficult for organizations to store this data and gain useful knowledge to manage their business operations and functions. A knowledge discovery process that is based on data mining methods has widely been used in business operations and management functions. This paper investigates formal concept analysis which is a powerful tool in knowledge representation and discovery and explains association rule mining based on formal concept analysis. An experimental study is given for employee selection function of HRM by using formal concept analysis method to model the qualifications of candidates which are needed for the job position. The qualifications of the candidates are modelled with concept lattices, and the qualifications of the candidates are matched with the ones determined in the job specification.
Re-Attention Is All You Need: Memory-Efficient Scene Text Detection via Re-Attention on Uncertain Regions

Scene text detection plays an important role on vision-based robot navigation to many potential landmarks such as nameplates, information signs, floor button in the elevators. Recently, scene text detection with segmentation-based methods has been receiving more and more attention. The segmentation results can be used to efficiently predict scene text of various shapes, such as irregular text in most scene text images. However, two kinds of texts remain unsolved: 1) tiny and 2) blurry instances. Moreover, the annotations for tiny/blurry texts are usually ignored during training, while tiny/blurry texts can still offer visual auxiliaries for robots to understand the world. Therefore, in this paper, we propose a new approach to effectively detect both clear and blurry texts. Specifically, we propose a re-attention module without increasing the learnable parameters, which first predicts the region of texts as the candidate region and leverages the same network to detect the candidate region again for reducing the required memory. Moreover, to avoid the errors from the first detection propagating to the re-attended area, we propose a new fusion module that learns to integrate the results of the re-attended regions and the first prediction. Experimental results manifest that the proposed method outperforms state-of-the-art methods on four challenging datasets.
Development of Open-Source Codec Compliant with HTJ2K Standard

The High Throughput JPEG 2000 (HTJ2K) is Part 15 of the JPEG 2000 family of standards, and it is published as ITU-T T.814 | ISO/IEC 15444-15. HTJ2K brings significant improvement of coding throughput to image compression pipelines with JPEG 2000 technology. In this paper, an open-source C++ implementation of HTJ2K is proposed. The principles of the implementation are "easy to understand what is done inside HTJ2K codec" and "easy to use." Unlike the existing open-source implementation, our decoder is fully compliant with the conformance testing defined in the new edition of JPEG 2000 Part 4. The experimental results demonstrate the proposed codec performs well in terms of encoding/decoding speed.
Benchmarking privacy in text classification

In most Machine Learning models, the data used for training or testing is public, available to anyone who wishes to see it. New research has improved these models, by adding privacy and distributing the processing load on multiple workers in the cloud. The aim of the paper is to perform an analysis between a classical approach (in which we have access to all the data) and one in which the privacy is preserved (Federated Learning) to explore the cases when a private model can be suitable in real-life scenarios.
From Semantic Metrics to Semantic Fields

A new approach to build semantic fields based on the metric method to calculate semantic distances between lexical units is put forward. The general case of different weights of semes contributing to the word meaning is considered. It is assumed that the word semes are fed from their definitions or explanations available in a sufficiently large and comprehensive dictionary. We repeatedly apply the formula for semantic representation of a given word treating its semes as new words having their own semes, thus forming a lexicographic semantic tree. Due to hypercycles rising in real dictionaries, the number of iterations is finite which allows one to exactly calculate the weights of semes and, henceforth, to build semantic fields. It is demonstrated that the comprehensive componential analysis of the word should be based on the semantic fields. The described approach may be useful in a variety of Natural Language Processing tasks based on supervised learning that require semantic information: computer lexicography, semantic analysis, information search and retrieval, Machine Translation, etc.
An Inverse Tone Mapping Algorithm Based on Multi-scale Dual-branch Network

Inverse tone mapping has drawn more attention recently because it can convert a large number of existing standard dynamic range (SDR) images into high dynamic range (HDR) images. In this paper, we proposed an inverse tone mapping algorithm based on a multi-scale dual-branch network which can restore the original information lost in under-/over-exposed areas. A multi-scale structure and masking mechanism are used to guide the reconstruction of image texture and structure. In order to enhance the robustness of the model for dealing with extremely exposed images, we apply a preprocessing method of exposure adjustment which improves the quality of the generated images. With quantitative and visual inspection experiments, we prove that the proposed algorithm has better performance than most state-of-the-art algorithms.
Automatic detection of Kikuchi bands based on Radon transform and PPHT

The information of crystal structure and orientation can be provided by analysing the EBSD (electron backscatter diffraction) patterns which are obtained with the EBSD devices. The reliability and accuracy of the information relies on the location of bands and intersections of the EBSD patterns. In this study, a method is proposed to automatically obtain the locations and intersections of the EBSD patterns, that is, Kikuchi bands. The proposed method uses Radon transform and progressive probabilistic Hough transform to detect straight lines and line segments of the Kikuchi band edges, respectively. Then, Kikuchi bands can be presented by fitting the hyperbolas with the endpoints of line segments. The results can numerically describe the information of Kikuchi bands. Experimental results show that the method is robust and can detect more accurate Kikuchi bands and intersections.In this paper, a novel method is proposed to detect the electron backscatter diffraction patterns. Electron backscatter diffraction patterns are a class images consisting of multiple parallel lines of light and dark pairs. The bands on the image can reflect the information of crystal structure and orientation. Most existing methods are complex to implement and computationally intensive in detecting edges and intersections of bands. Therefore, we designed a fast and easy-to-implement detection method with relatively good accuracy to overcome the drawbacks of existing methods. Our method is based on straight line detection and line segment detection. After matching the straight line detection results and the line segment detection results, the edges are obtained by fitting the line segment endpoints using a hyperbola, and the intersections are obtained by using centerline positioning. Experiments have shown that our method has good accuracy and can detect less perfect patterns . In addition, our method is easy to implement and and is valuable for computationally constrained cases.
Deep Bidirectional LSTM Network Learning-Aided OFDMA Downlink and SC-FDMA Uplink

In this paper, deep learning (DL)-aided signal detection is proposed for the orthogonal frequency division multiple access (OFDMA) in the downlink and for the single carrier frequency division multiple access (SC-FDMA) in the uplink. A deep bidirectional long short-term memory (D-BiLSTM) is considered for training OFDMA/SC-FDMA symbols. The traditional orthogonal frequency division multiplexing (OFDM) receiver explicitly estimates the channel state information (CSI) and recovers the transmitted symbol with the aid of the estimated CSI. By contrast, the proposed scheme implicitly estimates the CSI, and the receiver directly detects the transmitted symbols. Our simulations results show that the DL-based system is capable of addressing the channel distortion and can recover the transmitted data. A DL model is trained offline in order to mitigate channel distortion. Following the training, the model is tested to recover the transmitted online data symbol directly. Our simulations demonstrate that BiLSTM based DL may be effectively employed to detect signals in the OFDMA downlink and the SC-FDMA uplink scenarios.
The structure of professional readiness of future lawyers

The purpose of the article is to substantiate the structure of the professional readiness of future lawyers to work in law enforcement agencies, as well as to substantiate the conditions for its formation in the educational environment of the university: the social and personal significance of legal education and its relevance; students' awareness of legal education as a necessary component of their professional competency; integration, unity and complementarity of state and regional interests in the content and structure of legal education; educational and methodological support of the educational process in relation to the spheres of professional activity of subjects of legal practice; monitoring, which allows diagnosing the formation of general cultural and professional competencies of students, modelling and predicting the pedagogic process, making managerial decisions on its correction based on the obtained objective information. The structure of professional readiness of future lawyers is presented as a multifunctional system education, which includes motivational and value, cognitive, operational-activity, evaluative-reflexive components. Our research complements research on this issue, defines the essence of professional readiness and its structure. The study expands the range of ideas about the professional training of students at university as a whole; complements the theoretical understanding of training of future lawyers for law enforcement.
Grover on Simplified AES

NIST(National Institute of Standards and Technology) estimates the Grover key search cost required for symmetric key cryptography as the postquantum security strength. The Grover search algorithm is applicable to key recovery in symmetric key cryptography, and for this, the target encryption process must be implemented as a quantum circuit. In this paper, we present an optimized implementation of Simplified AES as a quantum circuit. Substitution, Mix Columns, and Key Expansion are implemented efficiently, and as a result of resource analysis, qubits, quantum gates, and circuit depth are significantly reduced compared to the previous result.
Feature selection algorithm with feature space separability estimation using discriminant analysis

this paper seeks to address a range of feature selection methods used in intelligence analysis tasks. The methods usually help to find a set of features that optimize classification procedure, enhance machine learning algorithms accuracy or reduce dimensionality. Generally speaking, data is comprised of a vast number of features, a lot of which are irrelevant for the task of interest. Application of some specific features may improve both accuracy and performance of algorithms. Such features are usually named as relevant features. This paper proposes a new feature selection algorithm based on estimation of feature space separability. Separability criteria is calculated by using discriminant analysis. In the paper an opportunity to select relevant features faster than existing methods with the proposed algorithm has been empirically shown. Experiment shows that a set of relevant features collected by the means of the proposed algorithm, in contrast to previous feature selection algorithms, allows to build more accurate classification model.
A drawing robot pipeline with artist-inspired execution

This paper presents a novel pipeline for vision-based robot drawing, which mimics the artists&#x0027; execution process. The pipeline takes an image as an input and outputs the result of the drawing process, or optionally, the position and velocity needed for each motor of a planar robotic arm to execute the aforementioned drawing.
GEODYNAMICS

In order to study the deep structure of the southwestern Ukrainian Carpathians, where the Carpathian conductivity anomaly is located, in 2015 and 2020, modern synchronous magnetotelluric studies were carried out on the profiles of Mukachevo-Skole, Seredne-Borynya and Karpatsky at twenty-three points and the spatiotemporal distribution and the electric field on the Earth's surface, which can be used to assess the conductivity and geoelectrical structure of the region, was obtained. Processing of experimental data was performed using the software PRC_MTMV, which provides a common noise-canceling impedance estimation for synchronous magnetotellurical recordings. Curves of apparent electrical resistivity (amplitude values and phases of impedance) from 10 to 10000 s were obtained reliably. A joint analysis of the apparent resistivity and impedance phases and the formal interpretation of the deep magnetotellurical sounding curves using the Niblett transformation indicate the presence of the spatially inhomogeneous conductor both in the earth's crust and in the upper part of the upper mantle. The chain of local conductive sections in the earth's crust coincides with the axial part of the Carpathian conductivity anomaly. High conductivity of the upper mantle was recorded in the Ukrainian Carpathians from the Transcarpathian Depression to the Skiba cover. It is shown that it is not a homogeneous layer, there is a general deepening of the upper edge to the northeast from 40-60 km (Transcarpathian depression) to 90-100 km (Krosno cover). Sharp deepening along the Porkulets and Dukla covers is revealed. Information about the existence of a deep conductor and its parameters should be the basis for quantitative interpretation and construction of the 3D deep geoelectrical model.