Reviewerly/exHarmony huggingface dataset:
On the training of scientific personnel at post-graduate school

The possibilities of modern packages of symbolic mathematics for the scientific work of novice researchers in mathematics are discussed. Counterexamples for Schwartz theorem on the equality of mixed derivatives are considered. It is shown how you can use the graphical and computational capabilities of symbolic mathematics packages for their analysis so that novice researchers with the traditional engineering background in mathematics understand them. The article focuses on the problems of ambiguous perception of the PC user of three-dimensional graphic images on the monitor screen. This is important for operators who make decisions based on information obtained in a mixed virtuality or mixed reality environment. The analysis of counterexamples for the Schwartz theorem is carried out using both classical mathematics and possibilities of symbolic mathematics packages. It is shown that this assertion is not necessary. During the virtual demonstration of three-dimensional graphic objects, recommendations on their perception in terms of information environments are provided.
Modern technical teaching aids as a component of the information and educational environment in teaching a foreign language at a maritime university

One of the main factors in the development and strengthening of the intellectual potential of the state, its self-sufficiency and competitiveness is education. Changes in the information and communication infrastructure have led to the fact that modern society imposes new requirements on the ways of acquiring and transferring knowledge and the role of a person in these processes. Reforming the methods and means of teaching a foreign language in a non-linguistic university is primarily associated with the use of new information technologies lies in the educational process of the concept of information and educational environment. The information and educational environment is considered in close connection with the system of developing learning and represents a set of conditions that not only allow the formation and development of language knowledge, skills, and abilities of a future specialist, but also contribute to the development of his personality, such a system of knowledge that he needs at this stage of solving the tasks of his development, which subsequently provides a real opportunity for mastering new tasks, increasing the level of complexity. The construction of a language IEE is based on principles that reflect the specifics of the studied subject and the learning environment itself, namely: openness, integrativity, systematic and consistent organization of educational material and its use in the educational process, interactivity, visual presentation of the material, multidimensionality, and redundancy of all components of the environment. The information and educational environment can be used as a resource for the formation of various competencies, the development of creative thinking and, most importantly, the striving for continuous improvement. The article examines in detail the resource and content component (RCC) of the information and educational environment modern integrated material and technical resources that contribute to the formation of the necessary competencies of graduates in the study of a professionally oriented foreign language.
The project approach specifics in relation to the student self-organisation development

The study is based on the concept of shaping students' self-organisation in the digital learning environment of a technical university. The recommendations for creating an effective learning system can be used in the creation of effective education systems. They will be created through the use of digital technologies in the educational process of the university to create a culture of self-organization of students. According to these provisions, the author of the work should identify the characteristics of educational activities and ensure their effective implementation during the formation of the culture of students of a technical university. Scientific research on students' self-organization culture formation will be carried out considering the specifics of students' interaction and information environment of the university. The direction of the project approach in pedagogy is "pedagogical planning". The authors have identified the stages of pedagogical planning necessary for successful implementation of the project methodology and the problem under the study. They are necessary in the formation process of university students' self-organization culture. To conduct the research, it is necessary to determine the way in which the ability to create a culture of self-organization in the digital educational environment of a technical university can be developed. The authors have set a goal to provide students with the opportunity to realize their creative potential, to form self-organization. To enable students to design projects independently, the authors suggest that they show their autonomy in designing. They plan means to organize an effective learning process and ensure its effectiveness, which in modern conditions is possible through the introduction of digital technologies in the educational process of the university to create self-organization of students. The article describes technological methods of students' self-organization culture formation in the conditions of digital educational environment of a technical university, namely, project technologies, which are based on the relationship of a teacher and a group.
Issues of forming professional self-efficiency in conditions of personalized education

The paper outlines the modern contexts of the system for training specialists, the tasks and guidelines for its development. The authors describe the characteristics of professional self-efficacy and reveal the essence of individualization and personification. The work also considers problems that require changes in the system of vocational education in terms of approaches, technologies, forms and methods of personalization or individualization, personification and personalization of learning. The purpose of the study is to study the potential of the model for training a specialist, which allows one to successfully compete in the labor market from the standpoint of one’s professional self-efficacy. As a methodological technique, a conceptual analysis of a critical nature is used, in the discourse of which the valorization of a person's professional self-efficacy in the conditions of professional education is realized, triadically revealed in personalization or individualization, personification, personalization. The personalization of education is implemented with the help of personalized technologies that contribute to the disclosure of the actual completeness of the content of the triadic model of professional self-efficacy of the individual. Personalized technologies for self-study of students in the system of higher education based on a specialized methodological content favor independent learning, which forms a modern pedagogical matrix of thinking in the context of the valorization of the educational activity of the student, increasing the maximum value and usefulness of educational programs, knowledge, etc.
An Efficient and Secure Method of Plaintext-Based Image Encryption Using Fibonacci and Tribonacci Transformations

In this article, we have proposed an efficient and secure method of image encryption.This image encryption method is new, where the plain image is confused using Fibonacci Transformation, and Tribonacci Transformation modifies the pixel values.The Fibonacci numbers and Tribonacci numbers of the above transformations are determined using the hash value of the plain image.To our knowledge, this is the first time a Tribonacci Transformation has been used in image encryption.The performance of the present method is evaluated using some standard test images and some special images.The present method has high key space and is sensitive to the plain image.The proposed method is also robust against attacks like brute force attacks, statistical attacks, differential attacks, noise attacks, cropping attacks, and known/chosenplaintext attacks.The performance of the proposed method is equally efficient as the state-of-the-art methods, which establishes the applicability of the present method.
Genesis and evolution of scientific and pedagogical knowledge

From the position of axiological and systemic approaches the paper attempted to reflect the phenomenon of scientific and pedagogical knowledge, which genesis, evolution and formation was carried out in the process of historical development of society, bifurcational socio-cultural changes, and intensive deployment of knowledge in technological paradigms. The consideration of the essence and stages of the formation of knowledge, the main trends in its production and formation through the prism of changing, expanding and improving the terminological apparatus reflects the meanings in the philosophical ego-text of scientists, the development of emerging educational practices, new didactic samples. The authors conducted a typological analysis of the concept of “knowledge”, which makes it possible to state its indisputable philosophical significance, unconditional semantic content, undoubted phylogenetic essence, in accordance with which the paper appeals to cultural and historical paradigms; to education as the most important means of teaching and educating the subject; to the architectonics of the experience of becoming a subject in education by building knowledge potential and developing a thesaurus in a historical aspect, which contributed to the expansion of the sacred landscape of the individual by unfolding his practice of social design. Through the study of analytical differentiation and the identification of the orientation vector of scientific and pedagogical knowledge in the historical aspect, in the modeling of the cognitive picture of knowledge, the paper considers the development of the knowledge paradigm in technological paradigms through the problematic field of their main resource, basic factors, fundamental achievements and sources of narratives, which contributes to the establishment of the social nature of knowledge through the discovery of its main types, character, generation paths, markers of maturity.
Small-Size Target Detection in Remotely Sensed Image Using Improved Multi-Scale Features and Attention Mechanism

Object detection for remote sensing images has problems such as complex backgrounds, multi-scale and difficulty in the detection of small targets. Because of the above problems, an improved object detection algorithm for remote-sensing images is proposed. Firstly, fusing the characteristics of shallow and deep networks, the original feature pyramid structure is reconstructed, the improved adaptive feature fusion structure is introduced before the network prediction, and the location and category information of different feature maps are fused. Secondly, the attention mechanism is introduced into the network to reduce the interference caused by complex backgrounds; Finally, the up-sampling module is improved to expand the receptive field and realize up-sampling based on semantic information. Through experiments, we show that our network achieves 93.9% and 98.5% detection accuracy on Levir and RSOD dataset, respectively, with the detection speed, reaching 98.26fps. Compared with the original network, the accuracy is increasing by 4% and 1.2%, respectively. Experimental results show that the detection accuracy of our algorithm is improved while maintaining the light weights and efficiencies of the original network.
Visibility-Based Fast Collision Detection of a Large Number of Moving Objects on GPU

This paper proposes a simple and efficient collision detection algorithm for a large number of moving objects. The basic idea is to minimise the number of moving objects that go through the complicated the collision checking process, which can improve the overall performance. To this end, we propose a visibility-based culling technique that identifies substantially small or hidden objects that do not cause any visual artifact even if we ignore them. This paper also develops a variable-size Morton codes to speed up the construction time of the Linear Bounding Volume Hierarchy (LBVH), which is used to efficiently check the proximity between objects efficiently. The visibility-based culling technique is based on the so called visibility map on the top of the g-Buffer technique. This map is a texture that contains the ID of the moving object in the screen space. The number of fragments of each object on the map is then counted in parallel manner on the GPU. If the number of fragments is less than a predefined threshold value, the algorithm does not include the object in the LBVH constructions step. Although the performance depends on the camera view point, we have verified through several experiments that the proposed algorithm improves the overall performance at least 70% even when the number of moving objects is more than 10,000.
GAN Neural Networks Architectures for Testing Process Control Industrial Network Against Cyber-Attacks

Protection of computer systems and networks against malicious attacks is particularly important in industrial networked control systems. A successful cyber-attack may cause significant economic losses or even destruction of controlled processes. Therefore, it is necessary to test the vulnerability of process control industrial networks against possible cyber-attacks. Three approaches employing Generative Adversarial Networks (GANs) to generate fake Modbus frames have been proposed in this work, tested for an industrial process control network and compared with the classical approach known from the literature. In the first approach, one GAN generates one byte of a message frame. In the next two approaches, expert knowledge about frame structure is used to generate a part of a message frame, while the remaining parts are generated using single or multiple GANs. The classical single-GAN approach is the worst one. The proposed one-GAN-per-byte approach generates significantly more correct message frames than the classical method. Moreover, all the generated fake frames have been correct in two of the proposed approaches, i.e., single GAN for selected bytes and multiple GANs for selected bytes methods. Finally, we describe the effect of cyber-attacks on the operation of the controlled process.
Predicting Visit Cost of Obstructive Sleep Apnea Using Electronic Healthcare Records With Transformer

Obstructive sleep apnea (OSA) is growing increasingly prevalent in many countries as obesity rises. Sufficient, effective treatment of OSA entails high social and financial costs for healthcare.For treatment purposes, predicting OSA patients' visit expenses for the coming year is crucial. Reliable estimates enable healthcare decision-makers to perform careful fiscal management and budget well for effective distribution of resources to hospitals. The challenges created by scarcity of high-quality patient data are exacerbated by the fact that just a third of those data from OSA patients can be used to train analytics models: only OSA patients with more than 365 days of follow-up are relevant for predicting a year's expenditures.The authors propose a translational engineering method applying two Transformer models, one for augmenting the input via data from shorter visit histories and the other predicting the costs by considering both the material thus enriched and cases with more than a year's follow-up. This method effectively adapts state-of-the-art Transformer models to create practical cost prediction solutions that can be implemented in OSA management, potentially enhancing patient care and resource allocation.The two-model solution permits putting the limited body of OSA patient data to productive use. Relative to a single-Transformer solution using only a third of the high-quality patient data, the solution with two models improved the prediction performance's [Formula: see text] from 88.8% to 97.5%. Even using baseline models with the model-augmented data improved the [Formula: see text] considerably, from 61.6% to 81.9%.The proposed method makes prediction with the most of the available high-quality data by carefully exploiting details, which are not directly relevant for answering the question of the next year's likely expenditure. Clinical and Translational Impact Statement: Public Health- Lack of high-quality source data hinders data-driven analytics-based research in healthcare. The paper presents a method that couples data augmentation and prediction in cases of scant healthcare data.
An Improved Kuhn Munkres Algorithm for Ship Matching in Optical Satellite Images

Ship matching based on multiple satellite images can effectively grasp the trajectories of the ship with long time intervals, providing technical support for marine regulation and surveillance. Ship detection and ship association are two steps for ship matching based on satellite images. However, traditional unsupervised moving object detection methods only apply to the scenes where the camera is stationary. In addition, it cannot segment the ships from the background, interfering with the data association step. Most data association algorithms rely on the target's location information and are not suitable for ship matching based on satellite remote sensing images with long time intervals, because the ship's position has significantly changed. Therefore, an unsupervised automatic ship matching method is proposed in this article. In the ship detection part, the ship is segmented based on the proposed preprocessing algorithm and the GrabCut algorithm to remove the interference caused by background movement; in the data association part, the Kuhn Munkres algorithm is improved by similarity comparison and iteration to find the optimal matching of ships. Compared with other matching methods, the algorithm proposed in this article is not affected by the moving background, which performs a better ship matching accuracy with a lower time cost.
Deep Transfer Learning for Model-Driven Signal Detection in MIMO-NOMA Systems

In this article, a odel-driven signal detection method with deep transfer learning (DTL) is proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Specifically, we first introduce some learnable parameters to an unfolded iterative algorithm for MIMO detection and improve it through a preconditioned process to speed up its convergence. Then we combine this modified algorithm with the successive interference cancellation (SIC) structure in NOMA detection to propose our learned preconditioned conjugate gradient descent network with SIC (LPCG-SIC). The proposed network has fewer parameters and lower computational complexity in which only few trainable parameters need to be optimized, and there are only addition and multiplication operations in online detection. Furthermore, to overcome the drawback that most of the existing deep learning-based detectors only focus on signal detection in a given environment and cannot adapt to changes in feature spaces or distributions, we propose a DTL-based detection algorithm and three model-driven transfer strategies for our LPCG-SIC detector to improve the reusability of the trained network. Simulation results show that our proposed method can perform signal detection under multi-user interference and outperforms conventional detectors. The transfer strategies can obtain significant performance gain with lower training cost compared to no-transfer methods.
Long-Tailed Visual Recognition via Improved Cross-Window Self-Attention and TrivialAugment

In the real world, large-scale image data sets usually present long-tailed distribution. When traditional visual recognition methods are applied to long-tail image data sets, problems such as model failure and sudden decline in recognition accuracy occur. While, when deep learning models encounter long-tailed datasets, they tend to perform poorly. In order to mitigate the impact of these problems, we propose CWTA (Long-tailed Visual Recognition via improved Cross-Window Self-Attention and TrivialAugment). CWTA uses CNN to better capture the local features of the image, uses the Cross-Window Self-Attention mechanism to dynamically adjust the perception domain to better deal with image noise, and uses TrivialAugment to enhance the diversity of a few types of data samples, thus improving the recognition accuracy of long-tailed distributed images. The experimental results show that the proposed CWTA performs best in the classification accuracy of different categories on different long-tailed datasets. We also compared CWTA with other long-tailed recognition algorithms (such as OLTR, LWS, ResLT, PaCo, and BALLAD), and the CWTA is the best when ResNet-50 as the Backbone. On the CIFAR100-LT, ImageNet-LT, and Places-LT datasets, the acc of all categories of CWTA is 12.9%, 0.4%, and 1.3% higher than that of BALLAD, respectively. For F1-Score on CIFAR100-LT, ImageNet-LT, and Places-LT datasets, CWTA is 6.6%, 2.2%, and 1.5% higher than BALLAD, respectively.
Design of an Emergency System Based on Wireless Sensor Networks through Cloud Computing/A Proposed Model at the University of Mosul

Wireless sensor networks are frequently used during evacuations (WSN). Improved evacuation procedures can be achieved using modern means of communication, including the Internet, wireless networks, mobile phones, and more. To aid evacuees in avoiding danger, WSN enables target monitoring, hazard sensing, data collecting, and sharing. This research aims to find ways to reduce road congestion during evacuations. Its purpose is to evenly disperse persons who must evacuate across the available exits in a congested region (i.e., building, campus, zoo, etc.). The concept of wireless sensor networks is central to our strategy. Evacuation routes are expected to be adaptive, dynamic, and traffic-free.
A microworld simulation of dynamic cognition as a test of executive function

The lack of consensus regarding the nature or composition of executive functioning (EF) has led to a proliferation of executive tasks to assess the concept. Many do agree however that the theoretical concept of EF is a holistic one, leading us to consider whether it would be beneficial to assess EF in a more holistic manner. We explore how well a computerized simulation of dynamic cognition - that reproduces the context of real-world complex decision-making - can predict performance on nine classical neuropsychological tasks of EF.A sample of 121 participants completed all tasks, and canonical correlations were used to assess the nine tasks as predictors of the three simulation performance metrics to evaluate the multivariate-shared relationship between the two variable sets: executive functions and dynamic cognition.Results show that a substantial amount of variance in two indices of dynamic cognition can be explained by a linear combination of three key types of neuropsychological tasks (planning, inhibition, working memory), with a larger contribution from the planning tasks.Our findings suggest that tasks of dynamic cognition could augment traditional, separate tests of EF, offering benefits in terms of parsimony, ecological validity, sensitivity, and computerized delivery.